Wind Value: Report on Community Risk Aversion Measures from China, France, Ireland and USA

End-of-Life Decisions for Wind Farms: An Opportunity for Climate Action and for Energy Communities Report: Community Risk Aversion Measures from China, France, Ireland and USA Work Package 7.3 Month 42/48, Aug 2025 Authors: Marceau Delnatte and Peter Deeney Wind Value Funding Ref: IRC*21/PATH-A/9348 Peter Deeney SFI-IRC Pathway Prog October 6, 2025 Contents 1 Introduction 3 2 Method 3 2.1 Background....................................... 3 2.2 TheQuestionnaire ................................... 3 2.3 ResearchQuestions................................... 3 2.4 ScoringMethod..................................... 4 2.5 DataCleaning ..................................... 4 3 Results 5 3.1 Self-Assessed Risk Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 DemographicStudy .................................. 6 3.2.1 Analysis of the risk-aversion score . . . . . . . . . . . . . . . . . . . . . . 7 3.2.2 Analysis of the self-assessed risk-loving score . . . . . . . . . . . . . . . . 9 3.2.3 Analysis of the error score . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2.4 Conclusion on the demographic RA and SARL analysis . . . . . . . . . . 11 3.3 Demographic and Gender Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3.1 General gender analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.3.2 SARL and error scores among gender . . . . . . . . . . . . . . . . . . . . 13 3.3.3 Results in regard of demography . . . . . . . . . . . . . . . . . . . . . . . 15 3.3.4 Conclusion on the gendered demographic analysis . . . . . . . . . . . . . . 15 3.4 Links with Wind Value Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Conclusions 17 1 The Wind Value project is based in the Sustainability Institute of University College Cork (UCC), Ireland with assistance from Cork University Business School, UCC. The output from the Wind Value project is in here at Zenodo. The PI, Peter Deeney, may be found at the Cleaner Production Promotion Unit, G0.3, Environmental Research Institute, Ellen Hutchins Building, Lee Road, Cork T23 X10, Ireland. The Research Team comprises: Luca Bernardi, Kevin Campbell, Peter Deeney, Marceau Delnatte, Claire Ducourtieux, Niall Dunphy, Fabian Gogolin, Paul Leahy, Dorcas Allan Mikindani, John O’Brien and Rebecca Windemer. The authors of this report are Marceau Delnatte and Peter Deeney. The work here extends earlier work led by Kevin Campbell in Baxter et al. (2023) and Qianhui Chen in Chen & Deeney (2024). Marceau Delnatte wrote this report during an internship on Wind Value project during the summer of 2025. He is studying as a engineering student in the Ecole des Mines de Saint-Etienne in France. Peter Deeney, who edited the report, is the project’s principal investigator. Suggested Citation: Delnatte, M. & Deeney, P. (2025) Report: Community Risk Aversion Measures from China, France, Ireland and USA. Zenodo, https://zenodo.org/records/13995914, Accessed (date). DOI Executive Summary The Wind Value research project seeks to estimate a financial valuation for onshore wind farms in Ireland. It will develop decision support tools which will assist wind farm managers in deciding between decommissioning, re-powering, and life-extension for the end-of-life of a wind farm. This research will also assist local communities who may be interested in buying part or all of their local wind farm. In this report the risk appetites of participants in China, France, Ireland and USA are compared. We find that the lowest risk appetite is displayed by French participants, and that Chinese women have the largest difference between their self reportetd risk appetite (very low) and their measured risk appetite (low). This publication has emanated from research conducted with the financial support of Taighde Éireann – Research Ireland under Grant number IRC*21/PATH-A/9348. 2 1 Introduction The Wind Value research project aims to assist communities who wish to invest in wind energy. Part of this requires an understanding of the risk appetite and risk aversion of those who may wish to invest. In this report the risk aversion and risk appetite from four groups of participants in different countries is compared. It is important to understand this topic as any investment vehicle aimed at non-professional investors, must offer as low-risk a product as possible, and must understand the risk appetite of investors. This report does not claim to survey the risk appetites of four countries, but to give an insight into the methods which can be used even at a small scale. 2 Method 2.1 Background This research builds on the studies of Annarita Colasante and Luca Riccetti 1 which used a questionnaire to assess the risk aversion of participants. This method was used in earlier Wind Value reports by Baxter et al. (2023) and Chen & Deeney (2024) to assess risk aversion for small samples of Irish and Chinese participants respectively. Here the sample was extended to include additional Irish participants (collected by Dorcas Mikindani), and new participants from France (collected by Marceau Delnatte) and USA (collected by Peter Deeney). 2.2 The Questionnaire The questionnaire is divided into two parts: the first being the questions on financial risk taken from the Colasante and Riccetti (2020, 2021), and the second part being a mix of Wind Value-specific questions and demographic questions. The risk attitude questionnaire was distributed to American and French students, and Irish and Chinese communities using the collectors’ networks (and translated into the respective nationalities’ languages). This report will detail the analysis of the answers received, their implications, and the correlation between different indicators. For the second part, questions include rating the likelihood of investing in wind farms, the amounts that one would invest, as well as the demographic inquiries. Nationality USA France Ireland China Total Initial Responses 34 34 108 68 240 Final Number 34 34 95 61 220 Questions US-Q FR -Q IE-Q CN-Q Answers US-A FR-A IE-A CN-A Table 1: Number of responses obtained per nationality, and links to the Zenodo pages for copies of the questionnaires and responses. As will be explained later, some responses were ruled out, either on the basis of a low rationality score or because of a lack of answered questions. 2.3 Research Questions • RQ1: Is there a non-zero correlation between risk aversion and self reported risk appetite in the participants? 1 Colasante & Riccetti (2020) Risk aversion, prudence and temperance: It is a matter of gap between moments, Journal of Behavioral and Experimental Finance and Colasante & Riccetti (2021) Financial and non-financial risk attitudes: What does it matter? Journal of Behavioral and Experimental Finance 3 • RQ2: Is there a difference between USA, China, France and Ireland for mean of the risk aversion and risk loving scores? •RQ3: Is there a difference between male and female participants’ mean risk aversion and risk loving scores within USA, China, France and Ireland? • RQ4: Is there a non-zero correlation between risk aversion (tested and self reported) and the likelihood towards wind farm investment? 2.4 Scoring Method Following Colasante & Riccetti (2020, 2021), we calculate a risk-aversion score for each participant. A response to the questionnaire is given 1 point for every answer that is the same as the "Risk averse option" in Table 2 which is taken from Colasante and Riccatti (2020). The questions may be found in Section 4. Question 1 2 3 4 5 6 7 8 9 10 11 12 13 Risk averse option B A B B B B A X B X B B B Alternate propensity option A B A A A A B A X A X A A Table 2: “Financial risk scores”: responses gain 1 point towards their Risk Aversion score. Questions with “X” are not used for the computation of the score. The risk aversion scores are associated with lower second and fourth moments, and a higher third moment. The first moment, being the average gain/loss, was the same for both A/B options every question, except question zero. Additionally, we also have a self-assessed risk-loving score (SARL), where participants ranked themselves from 0 (totally risk-averse) to 10 (totally risk-loving). From that, we can obtain a simple self-assessed risk-aversion score (SARA) and an error value with the following logic: Risk-averse (RA) score x Self-assessed risk-loving (SARL) score y Self-assessed risk-aversion (SARA) score 11 - y Error (SARA score - RA score) (11 - y) - x Table 3: Scoring Method Note that the SARL score is on a scale from 0 to 10 while the risk-averse score is from 0 to 11. We should then have resized the SARL score to this range by multiplying it by a factor of 11/10. However, we have not done this for two reasons:(i) No participant scored 0 on the RA score, essentially making it a 1-11 score, which is the same range as the SARA score; and (ii) Resizing the SARL to 0-11 makes the Error score not an integer, rendering analysis of the values more complicated. We believe that these reasons justify our choice of variables. 2.5 Data Cleaning As shown in Table 1, some answers have not been considered in the final data analysis, for two main reasons : • Some participants didn’t answer enough questions thus making their data irrelevant to analyze; • Some participants scored below 3 out of 5 on the rationality score detailed by Colasante and Riccetti. 4 The rationality score of one’s answers is defined as ranging from 0 to 5 ; it is calculated on a few questions, and if the answers chosen present an irrational behavior. For instance, choosing Option A to Question 0 (participating in a lottery with positive expected gain and no loss) adds 1 point to the rationality score ; choosing Option B gives 0. The details of this score are explained in Colasante and Riccetti (2020). Colasante and Riccetti (2021) argued in their paper that answers scoring below 3 should not be considered, as they don’t showcase a logical enough answer selection to be analyzed. After the screening of such "irrational" responses, and those with too few questions answered, a total of 20 participants were removed. 3 Results 3.1 Self-Assessed Risk Behavior The first question we will answer is whether we see a correlation between the risk aversion score as measured by the answers to the questionnaire, and the self-assessed risk-loving score. Figure 1: SARL and RA scores scatter plot As the scatter plot of Figure 1 shows, a trend of correlation seems to exist between the tested score and the risk-loving score obtained from participants themselves. We obtain a correlation value of -0,3842 and a t-value of -6,1309, which gives us a p-value of below 0,000 for both two-tailed and one-tailed test. (Note that the comma indicates a decimal point, following French usage.) A Fisher z-test allows us to test the correlation value for significance. Our null hypothesis and our alternative hypothesis are H0:p= 0,25 and Ha:p > 0,25 using these values, we can find that our correlation obtains a z-value of 2,2039, which validates our alternative hypothesis of a non-zero correlation. 5 Because of the linear nature between the SARL and SARA score, we can conclude that there is a significant, moderate, correlation between the tested (RA) and self-assessed risk aversion (SARA) score. 3.2 Demographic Study The following sections will focus on the risk-aversion questions, their relation to demographics, and comparing our results with the Colasante & Riccetti. The first result we obtain is the total score distribution, ranging from 0 (totally risk-loving) to 11 (totally risk-averse). In our pool of answers, no single participant scored a 0, which is in line with Colasante and Riccetti’s results. 0.42% of their answers obtained a score of 0, which in our case would be 0.924 participants on average. Note, each axis in the following graphs will be from 1 to 11. Figure 2: Score Distribution Bar Graph 6 RA Score # of Responses 13 25 39 410 526 637 748 845 925 10 12 11 4 Table 4: Total Score Distribution Figure 2 and Table 4 showcase the distribution of scores in the answers we obtain from our participants. In Figure 2, we also have the score distribution of Colasante & Riccetti’s study. We can see that our distribution is skewed towards a higher risk-aversion average (towards a score of 11) compared to theirs. As we will see after, it might be a result of our participants’ demographic distribution. As Table 1 shows, participants of this study are not distributed uniformly between the different nationalities studied. 3.2.1 Analysis of the risk-aversion score Figure 3: Score Distribution Graph of each Nationality 7 Score USA France Ireland China 10 0 2 1 20 0 1 4 33 0 4 2 41 0 3 6 57 2 12 5 612 5 10 10 74 4 25 15 85 10 20 10 90 10 9 6 10 2 2 7 1 11 0 1 2 1 Mean 6,1 7,9 6,9 6,3 Std Dev 1,6586 1,4218 2,0421 2,1451 Table 5: Total score distribution per nationality The above tables and graph showcase the score distribution for each nationality, with the values of the graph being proportional to the population size of each nationality. As we can see, the distribution varies between the different nationalities. The first two major observations from the above data are that US and Chinese participants seem to score lower than the other nationalities, while French participants score much higher than the others on risk aversion, with the Irish participants in the middle. In their analysis, Colasante & Riccetti found "European subjects more risk averse in financial choices", and we observe a similar result here, with France and Ireland having the lowest average scores. In our study, more than 57% of participants are European, while only 15% are from North America. Colasante & Riccetti did not specify the number of participants from each nationality in their papers, but if they had a higher proportion of North American answers, it would explain the difference in our distribution compared to theirs. A higher number of US participants would skew our distribution towards lower risk-aversion values and make it look more similar to C&R’s distribution. FRA IRE CN USA 4,7887 2,2295 0,4487 FRA / 3,1308 4,4018 IRE / / 1,7664 (a) t-values of the unpaired t-tests between the means of two nationalities FRA IRE CN USA ≤0,001 0,029 0,655 FRA / 0,002 ≤0,001 IRE / / 0,078 (b) p-values of the unpaired t-tests between the means of two nationalities Table 6: Results of difference of means using unpaired t-tests between the scores of each nationality We have further analyzed the difference between the results of different nationalities by performing an unpaired Student’s t-test between each pair of nationalities. From the results above, we can conclude the following: •The French score distribution is hugely significantly different from all of the others; • The US score distribution is significantly different from the European ones, but not from the Chinese distribution; • The Chinese and Irish distributions exhibits differences, but the null hypothesis can’t be rejected. 8 The SARL and error score, unlike the RA score, show a significant difference between men and women. Despite scoring similar results on the test, men in this study have a tendency to judge themselves as more risk-loving than women. The correlation between error and gender indicates that there is somewhat a bias for men to rate themselves as more risk-loving than what they score, while women are less subject to this error. 3.3.3 Results in regard of demography SARL US(M) US(W) FR(M) FR(W) IR(M) IR(W) CH(M) CH(W) Average 5,6190 4,9167 3,9130 3,3636 4,9714 4,7083 5,6765 3,5000 Std. Dev. 1,7314 2,0599 1,7424 1,4938 2,0069 1,7073 1,9811 1,6720 Pop Size 21 12 23 11 35 48 34 22 t-value 0,9970 0,9494 0,6275 4,4197 Table 17: SARL score data per gender and nationality With this data, we notice a very skewed result. Among the US, French and Irish demographic, the difference in the SARL score is not significant (the t-values would have to be ≥ 1 , 7). However, in the Chinese demographic, the difference is significantly prominent, having a p-value ≤ 0 , 001. SARL US(M) US(W) FR(M) FR(W) IR(M) IR(W) CH(M) CH(W) Average -0,619 -0,250 -0,913 -0,091 -0,857 -0,750 -0,706 1,091 Std. Dev. 2,179 2,006 2,255 1,136 2,658 1,695 2,154 2,467 Pop Size 21 12 23 11 35 48 34 22 t-value 0,493 1,413 0,209 2,796 Table 18: Error score data per gender and nationality The Error score data show a very similar result, where the Chinese scores are the only ones presenting a significant difference (here, the p-value is 0,008). It seems that the disparity seen in the overall results are mostly due to the Chinese scores. On these two scores, we can notice several interesting facts: • The French and Chinese women have a very similar average and standard deviation regarding the SARL score. • However, the French men’s SARL score data doesn’t differ as much from their female counterpart as the Chinese men’s data do for their counterpart. • Chinese men have the highest average SARL score of our population while Chinese women have the lowest. • Chinese women rate themselves as way more risk-averse than they really are, showing the highest absolute error score. 3.3.4 Conclusion on the gendered demographic analysis Combining the gender and demographic study paints a better picture on the results we had. The first result we obtained is that there are no real difference in the tested risk-aversion scores between men and women from the same nationality. Meaning the differences between different nationalities in the RA scores are driven by sociocultural elements. Meanwhile, the results of the self-assessed and error scores present a higher disparity between women and men, that we initially studied globally. However, we noticed that the disparities are 15 not equally distributed in our total population. Indeed, While US, French and Irish answers did not present significant differences between men and women of these nationalities, the Chinese presented heavily parted results in their SARL and error scores. 3.4 Links with Wind Value Questions In our version of the questionnaire, several questions regarding wind farms and investment were appended to the risk questionnaire. Among these questions were the following: • Max Inv: If you were to consider investing in a wind farm project with an attractive rate of return, what is the maximum amount you would be willing to invest? • Min ROI: If you were to consider investing in a wind farm project, what is the minimum rate of return you would expect to receive on your investment annually? •Likeliness: Given your knowledge about the benefits of using wind energy, how likely are you to consider investing in a wind farm project? The first two questions were scaled from 0 to 3, with 0 being considered as the most risk-averse answer (no investment no matter what), and 3 being the most risk-loving answer (minimum ROI and maximum investment). The third question was scaled from 0, being "very unlikely to invest" to 4 being "very likely to invest". The answers of these questions were tested for correlation against the RA and SARL scores. RA Score SARL Score Max Inv. Min ROI Likeliness Max Inv. Min ROI Likeliness DoF 213 216 218 208 211 213 Correlation 0,0005 -0,0937 -0,2631 0,1421 0,1098 0,2524 t-value 0,008 1,383 4,027 2,071 1,604 3,808 Table 19: Table of correlation between RA and SARL score to Wind Value related questions In bold we have the correlations where their t-values are above the minimum for a ≤ 0 , 05 p-value. As we can see, the two most significant correlations are both the scores against the likeliness of investment. We have also a significant enough correlation between the SARL score and Maximum Investment, but the correlation value is low and isn’t strong against non null hypothesis. Let’s then focus on the results concerning Likeliness. Figure 9: RA - Likeliness of investment scatter plot Figure 10: SARL - Likeliness of investment scatter plot The p-values of both correlations are ≤ 0 , 001. It may seem evident that this link is somewhat significant, since it is in essence another self-assessed rating of one’s risk-loving behavior. However, this question is specifically related to wind farms and not risk in general. We can further our analysis by looking into the correlation values of each nationality in the same manner. 16 Test RA - Likeliness SARL - Likeliness Nationality US FR IR CN US FR IR CN Correlation -0,1563 -0,1052 -0,1570 -0,2819 0,1566 0,0289 0,3209 0,2157 t-Value -0,934 -0,621 -1,569 -2,392 0,936 0,169 3,486 1,767 Table 20: Correlation values of RA and SARL Score to Likeliness of investment, per nationality Table 20 shows another layer to this correlation. We see that the strongest correlations are from China and Ireland, for RA-Likeliness and SARL-Likeliness respectively. China’s correlation coefficient between SARL score and Likeliness of investment is also technically above the threshold for a p-value ≤ 0 , 05 for one-tailed-ness, but not for two-tailed-ness ; while Ireland’s coefficient for RA-Likeliness is just below the one-tailed-ness threshold. We can speculate that these results stem from the cultural and societal aspects regarding wind turbines. Ireland has a strong wind farm program and grid, while China is quickly becoming a wind superpower. 4 Conclusions Through our results, we have been able to draw answers for all of our principal research questions. • RQ1 Is there a non-zero correlation between risk aversion and self reported risk appetite in the participants?. Yes, there is a correlation value of − 0 , 3842 (indicating that people that rate themselves as more risk-loving score lower on the risk-aversion test). The coefficient holds up in a Fisher z-test, indicating a strong correlation. The calculation below Figure 1 showed us that the correlation indicates it is significantly stronger than a value of 0,25. • RQ2 Is there a difference between USA, China, France and Ireland for mean of the risk aversion and risk loving scores? Our result showed strong differences between the four nationalities we have on several indicators. Most notably, France’s distributions in both RA and SARL scores are very different from all of the others, having the highest RA mean score and lowest SARL score. The US seems to exhibit the opposite, having both lowest RA score and highest SARL score, however the Student tests show that both distributions are not significantly different enough from Ireland or China. The error score for all countries present similar-looking distributions as well as close medians, average and variance. • RQ3 Is there a difference between male and female participants’ mean risk aversion and risk loving scores within USA, China, France and Ireland? As previous research suggested, there is no difference between men and women regarding the risk-aversion test scores. Both distributions are very similar, and both Student and correlation test proved no significant distinction between them. However, the SARL and error scores show a different story, both indicating dissimilarities between men and women. When also considering demographics, we have been able to conclude that the disparities are hugely driven by the Chinese answers, as they present a large gap between male and female answers. • RQ4 Is there a non-zero correlation between risk aversion (tested and self reported) and wind farm investment likeliness? There is a correlation between the likeliness of investment in wind farms and both RA and SARL scores. The correlations seem primarily driven by Irish and Chinese answers, 17 which might be due to both countries’ strong wind development and familiarity with the technology. The main results for the Wind Value research project are that people are generally very good at estimating their own risk appetite, and that there is no evidence of a significant difference in risk attitude between women and men. For Wind Value this means that community investment does not need to adjust for error in non-professional risk self-assessment and that there is no need to adjust for gender. References • Baxter, N., Campbell, K., Deeney, P., Mikindani, D., Smith, A. L. (2023) Wind Value Report: Donegal Community Engagement Summer 2023, Zenodo, DOI 10.5281/zenodo.10418174 • Chen, Q. (2024) Wind Value: Community Risk Attitude Answers (China) WP 7.3, Zenodo, DOI 10.5281/zenodo.17281554. • Chen, Q., Campbell, K., Deeney, P. (2024) Wind Value: Community Risk Attitude Questionnaire (China) WP7.3, Zenodo, DOI 10.5281/zenodo.11115765 • Chen, Q., Deeney, P. (2024) Wind Value Report on Risk Attitudes Within Communities WP 7.3, Zenodo, DOI 10.5281/zenodo.11147757 • Colasante, A., Riccetti (2020) Risk Aversion, Prudence and Temperance: It is a Matter of Gap Between Moments, Journal of Behavioral and Experimental Finance, 100262 DOI 10.1016/j.jbef.2019.100262 • Colasante, A., Riccetti (2021) Financial and non-Financial Risk Attitudes: What does it matter?, Journal of Behavioral and Experimental Finance, 100494 DOI 10.1016/j.jbef.2021.100494 • Deeney, P. (2025) Wind Value: Community Risk Attitude Answers (USA) WP7.3, Zenodo, DOI 10.5281/zenodo.17280456 • Deeney, P. (2025) Wind Value: Community Risk Attitude Questionnaire (USA) WP7.3, Zenodo, DOI 10.5281/zenodo.17280381 • Delnatte, M. (2025) Wind Value: Community Risk Attitude Answers (France) WP7.3, Zenodo, DOI 10.5281/zenodo.17280245 • Delnatte, M., Deeney, P. (2025) Wind Value: Community Risk Attitude Questionnaire (France) WP7.3, Zenodo, DOI 10.5281/zenodo.17280176 • Mikindani, D. (2025) Wind Value: Community Risk Attitude Answers (Ireland) WP7.3, Zenodo, DOI 10.5281/zenodo.17280100 • Mikindani, D., Deeney, P. (2025) Wind Value: Community Risk Attitude Questionnaire (Ireland) WP7.3, Zenodo, DOI 10.5281/zenodo.17280004 Copy of Questionnaire Q0 You have the opportunity to participate in a lottery in which you have an equal chance (50%) of winning €100 or winning nothing. There is no fee to take part. Option A: you participate in the lottery 18 OR Option B: you do not participate. Q1 You can choose between two investments: Option A: offers an equal chance (50%) to gain €5 or €15 OR Option B: offers you a certain gain of €10. Q2 Suppose you have been fined and you have the opportunity to choose between two alternatives. Option A: pay a fine of €10 OR Option B: have an equal chance (50%) of paying either €5 or €15. Q3 You may decide to participate to a lottery in which you have an equal chance (50%) to gain or lose €5000. Option A: you will take part in the lottery OR Option B: you do not participate. Q4 You may decide to take part in a lottery in which you have the an equal chance (50%) to gain or lose €5. Option A: you will take part in the lottery OR Option B: you do not participate. Q5 Option A: you have an equal chance (50%) of winning or losing €5,000 OR Option B: you have an equal chance (50%) of winning or losing €5. Q6 Option A: you have an equal chance (50 OR Option B: you a sure gain of €10,000. Q7 Option A: pay a fine of €10,000 OR Option B: have an equal chance (50%) of paying either €5,000 or €15,000. Q8 Option A: you have one chance in a million to win one million euro OR Option B: gain €1 for sure. Q9 Option A: You have a on in one thousand chance (0.1%) of losing €1,000, otherwise you lose nothing (zero) OR Option B: pay €1 for sure. Q10 Option A: You have a one in 100 chance (1%) of winning €10,000 and 99 in 100 (99%) of losing €101 OR Option B: An equal chance (50%) of winning €1000 or losing €1000. Q11 19 Option A: 1% chance to lose €10,000 and 99% chance to win €101 OR Option B: equal chance (50%) to win €1000 or lose €1000. Q12 Option A: You have an equal chance (33%) to gain, €0 or €5000 or €10,000 OR Option B: You have an equal chance (25%) to gain, €0 or €2100 or €7900 or €10,000. Q13 Option A: You have an Equal chance (33%) of, €0 or €5000 or -€5000 (negative number means losing) OR Option B: You have an Equal chance (25%) of, €2900 or €5000 or -€2900 or -€5000. Socio-Demographic Questions Q14 How do you see yourself: are you in general a person who takes risk or do you try to evade risk? Please self-grade your choice between 0 (I do not take risk at all) to 10 (I love to take risks). 1 2 3 4 5 6 7 8 9 10 Q15 If you were to consider investing in a windfarm project, what is the minimum rate of return you would expect to receive on your investment annually? 0%-5% 5%-10% More than 10% The return on investment is irrelevant, I would not invest in a wind farm project. Q16 If you were to consider investing in a windfarm project with an attractive rate of return, what is the maximum amount you would be willing to invest? €1-€20,000 €20,000-€50,000 More than €50,000 Nothing, I would not invest in a wind farm project Q17 Approximately, what is the distance from your home to the nearest wind turbine? Less than 1km 1km-5km 5km-10km More than 10km Q18 What is the main deciding factor in your choice of electricity provider? Price Renewable Energy Combination of Price and Renewable Energy Q19 Given your knowledge about the benefits of using wind energy, how likely are you to consider investing in a wind farm project? Very likely Somewhat likely Neither likely nor unlikely Somewhat unlikely Very unlikely Q20 What option below do you believe most accurately describes the purpose of wind farms? Profitable Business Climate Change Solution Electricity Generation Q21 Are you aware of the EU Targets for Reducing Emissions by 2030? Not Aware Somewhat Aware 20 Very Aware Q22 What is your age? 18 - 24 25 - 34 35 - 44 45 - 54 55 - 64 65 - 74 75 + Q23 What is your gender? Write your answer 26.What is the highest level of education you have completed? Primary Secondary Post Secondary University or College 21