CLOSUP Student Working Paper Series Number 27 April 2018
Changing Winds of Community Attitudes Towards Wind Energy Development: an Analysis of Huron County, Michigan Alexander H. Wood, University of Michigan
This paper is available online at http://closup.umich.edu Papers in the CLOSUP Student Working Paper Series are written by students at the University of Michigan. This paper was submitted as part of the Winter 2018 course PubPol 495 Energy and Environmental Policy Research, that is part of the CLOSUP in the Classroom Initiative. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the Center for Local, State, and Urban Policy or any sponsoring agency
Center for Local, State, and Urban Policy Gerald R. Ford School of Public Policy University of Michigan
CHANGING WINDS OF COMMUNITY ATTITUDES TOWARDS WIND ENERGY DEVELOPMENT an Analysis of Huron County, Michigan
Alexander Holland Wood Gerald R. Ford School of Public Policy, University of Michigan
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Abstract Transitioning our energy supply to renewable sources has become an objective for policymakers, worldwide, and wind energy has emerged as a leading alternative energy source. In the US, much of the wind energy growth has occurred in rural communities where sentiment varies dramatically. Huron County, Michigan, has seen significant wind development over the past decade and demonstrates the inconsistency of community perceptions towards wind energy. In 2010, residents voted to support a wind development proposal but in 2017, voted overwhelmingly to oppose additional developments. This study assesses how the township-level 2010 proposal voting results compare to those of 2017, and how they may correlate with other township characteristics. For each township, this study inventories (1) whether the proposed projects were located within the township, (2) prior turbine presence, (3) the percentage of housing units occupied by renters, (4) the percentage of vacant housing units, and (5) the percentage of the township workforce in agriculture. This study found only the prior presence of turbines to be consistent with the relevant hypothesis that townships with more turbines would show higher support. This study highlights the importance of county and township zoning procedures in determining the success of proposed wind developments and provides further evidence of the volatility of community attitudes towards wind development.
Introduction The US National Aeronautics and Space Administration (NASA) reports that 97% of climate scientists agree that the warming of the earth’s atmosphere in recent decades is largely attributed to human activities (NASA, 2017). The dominant contributor to anthropogenic climate has been the release of greenhouse gases (GHG) from the consumption of fossil fuel resources. Furthermore, energy production is a primary reason for the burning of fossil fuels and
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subsequently, energy consumption derived from fossil fuel production is a main driver of GHG emissions. Scientists overwhelmingly agree that climate change will result in significant ecological and economic consequences if efforts are not taken to drastically reduce the quantity of GHG emitted into the atmosphere (NASA, 2017). Thus, to mitigate the effects of anthropogenic climate change, efforts are being made to transition the world’s energy production away from fossil fuel resources towards less carbon intensive, renewable energy sources. In recent years, wind energy has emerged as a leading alternative energy resource. The 2016 Global Wind Energy Outlook estimated that by 2030, wind energy could supply up to 19% of global electricity production, potentially reaching 30% of the global electricity supply by 2050 (Global Wind Energy Council, 2016). In the US, wind energy made up 5.6% of electricity production in 2017, doubling since 2010, and makes up to 20% in some states (McKenna, 2017). Much of the growth in US wind energy developments has taken place in rural communities, where community sentiment towards wind energy varies dramatically (Ailworth, 2017). Community acceptance of wind energy developments will therefore play a significant role in dictating the progress of wind energy in the US. Huron County, located in the ‘thumb’ region of Michigan, has undergone significant wind energy development over the past decade and is a prime illustration of the variance in community perceptions towards wind energy. In a 2010 ballot proposal, residents of Huron County voted in support of a proposed wind energy district. In 2017, however, residents of Huron County voted overwhelmingly in opposition to two additional proposed wind projects. This study examines the variance in support between townships in Huron County by comparing the township-level results of the 2010 and 2017 ballot measures, in addition to evaluating how
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township vote counts correlate with both the presence of wind turbines in the township and the characteristics of each townships’ residents.
Literature Review Extensive research has been conducted to better understand the relevant factors that determine the level of community acceptance for wind energy developments. Support for wind energy is generally high; however, support for wind energy developments in specific communities varies greatly. Much research has assessed the application of the Not In My BackYard (NIMBYism) framework, which suggests that individuals support wind energy development overall, but are more likely to oppose wind energy implementation within close proximity to their own communities. However, further research has largely discredited this theory and determined that community acceptance of wind energy implementation is more nuanced, as attitudes are subject to influence from many factors beyond residents’ proximity to wind turbines. A research synthesis on wind energy acceptance in North America by Rand and Hoen (2017) found several overarching themes related to acceptance of wind energy developments within communities. First, the study concluded that the NIMBYism framework is unsupported. The study also found that socioeconomic impacts of wind energy developments (e.g. property values, tax revenue, royalties, etc.) have a major influence on community acceptance, annoyance from the sound and visual impacts of turbines are strongly correlated to opposition, and proximity to turbines influences other confounding variables, although its overall effect is uncertain. Additionally, the study found that acceptance is influenced by concerns over fairness, participation, and trust during the development process.
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To more specifically examine factors that were found to influence perceptions of wind energy within communities, Baxter, Mozaria, and Hirsch (2013) conducted a case-control study to analyze differences in attitudes towards wind energy in communities with and without wind turbines. This study assessed residents’ attitudes towards wind energy in two nearby communities with similar demographics in Ontario—one with a wind energy development in operation and a control without. The study found significantly higher levels of support for wind energy development in the community with turbines. Sixty-nine percent of survey respondents in the case community responded they would vote in favor of wind energy development in their community, compared to only 25% of respondents in the control community. The study also measured residents’ perception of wind energy and found that residents of the control community were more concerned than the case community about each category of potential impacts: aesthetic, health, animal, economic, and citing process fairness. Baxter, Mozaria, and Hirsch found that attitudes vary significantly between communities with and without implemented wind developments. However, a study done by Mulvaney, Woodson, and Prokopy (2013a) evaluated levels of support for wind energy development in three rural counties in Indiana in which wind turbines had been installed but at varying levels. The study was intended to determine the reasoning for the varied levels of energy development in each of the three counties and identify the key reasons for support and opposition. By conducting a mail survey of residents of each county, the study found high community support for wind turbines in their county. Eighty-eight percent of all respondents in the three counties agreed or strongly agreed that they support wind turbines within their county. Although no factors were statistically significant, reasons for support were primarily economic benefits to the community, environmental benefits, and protection of agricultural lifestyle and landscape. The
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study’s main insight, found through review of local news articles and government documents and interviews with eleven key stakeholders, was that because community support was relatively equal in each county, levels of support for wind energy within local governments was the most influential factor in creating differences in the wind energy capacity between the three counties. To verify the findings of the original study and further understand the reasons for which one of the counties possessed significantly more wind energy developments, Mulvaney, Woodson, and Prokopy (2013b) conducted another study to specifically examine Benton County to assess the reasoning behind Benton County’s high concentration of wind energy development. Community support levels were consistent with the other two counties; however, through document review and stakeholder interviews, the study confirmed that Benton County’s highly supportive local government was the primary reason for the county’s high levels of wind energy installation. As in the previously mentioned studies, much research has been directed toward evaluating perceptions of wind energy in communities where wind energy developments have already been implemented. However, additional research has also sought to understand the changes in community perceptions of wind energy developments pre and post implementation. As part of this literature, a case study by Wilson and Dyke (2016) assessed changes in attitudes towards the social, economic, and environmental effects of wind energy for a community in the United Kingdom where two 70m turbines had been proposed and eventually implemented. Through interviews with residents and review of voting data from the original wind development proposal, the study found that many factors influenced community members’ perception of wind energy and that attitudes changed significantly before and after the turbines were installed. Eighteen of the 52 community members (34%) interviewed objected to the
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original proposal in 2002; however, in 2013, five years after installation, 20% of those interviewed ‘disliked’ the turbines while 80% had neutral or positive perceptions. The study found that noise effects were a concern primarily for those who lived relatively closer to the turbines, and that visual effects were largely insignificant five years after installation. Additionally, 65% of those interviewed said some community benefit would influence their perceptions, although 99% of those interviewed said the existing turbines offered no benefit to the community. Because previous literature, such as the preceding study, found that community attitudes towards wind development can change significantly after turbines are implemented, Fergen and Jacquet (2016) sought to better understand how attitudes change throughout different periods of the development process. In a case study of two counties in South Dakota, Fergen and Jacquet surveyed residents of the two counties before, during, and after the installation process of two wind energy developments within the counties. The study broadly found that there were some positive expectations and that these expectations were largely unmet. However, the results also showed that overall, residents in both counties maintained positive attitudes towards the developments after they had been installed. Because research has found clear evidence that community perceptions of wind developments change after implementation, Groth and Vogt (2014) sought to identify relevant factors to public perceptions of wind energy developments. In a case study specific to Huron County, Groth and Vogt (2014) surveyed residents on factors believed to potentially influence perceptions of wind developments in communities where wind energy projects had been implemented in the recent past. This study surveyed residents of two townships in Huron County where wind energy developments were erected four years prior. The study found that out of the
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four tested independent variables — property ownership, age, gender, and length of time spent living in Huron County — only property ownership made a statistically significant contribution. Those who owned land had significantly more positive perceptions of the developments. This study also found the three most recurring negative attributes of wind energy to be perceived increase in electricity rates (no increase related to the turbines occurred), general uncertainty surrounding the turbines, and concern regarding noise. Although research has been done to understand the dynamics of community attitudes towards wind energy developments, the narrowest scope of research of has been at the county level. Therefore, research on community responses to wind energy developments lacks an understanding of perceptions with an even more local level as the unit of analysis. Additionally, past research has neglected to fully evaluate how attitudes towards wind turbines change over time in communities that have faced continued development. The case of Huron County offers an opportunity to evaluate how local acceptance of wind energy varies at a more local level and how attitudes change upon installation of turbines within the community. Thus, this study will assess how the 2010 township-level voting results compare to those of the 2017 Huron County ballot measure and evaluate how the results correlate with other township characteristics. This will contribute to a better understanding of the dynamics of community acceptance of wind energy developments in the US.
Methods: Inventory How do the 2010 township-level voting results for the 2010 wind energy development proposal in Huron County, MI compare to the township-level results of the 2017 proposal, and how do these results correlate with other township characteristics?
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Of the 28 townships in Huron County, MI, 16 are county-zoned, which means these 16 townships do not have township-level zoning ordinances to preempt county-level zoning ordinances, including ordinances related to wind energy zoning. In Michigan, challenges to zoning decisions may be put to a ballot referendum to all of those voters who live within the confines of the zoning jurisdiction. Therefore, voters in all county-zoned townships were eligible to vote on the 2010 and 2017 proposals regardless of whether the proposed wind energy development projects were located in their township. Dozens of wind energy developments were approved by planners between 2010 and 2017; however, the only two that were challenged, were those that spurred the 2010 and 2017 ballot proposals. In 2010, voters in the 16 county-zoned townships voted in support of a proposed wind development, but in 2017, voted overwhelmingly against two additional proposed wind developments. County-wide results have been published, but there has been no analysis to evaluate whether there are more localized differences at the township level. Huron County, therefore, offers a unique opportunity for analysis of community responses to wind developments at a more local level than what has previously been studied. To assess which aspects of Huron County townships’ relation to wind energy may have influenced the changes in results of the 2010 and 2017 proposals, the scope of this study will be limited to the 16 Huron County townships that were eligible to vote on each proposal. (Note: Brookfield and Sebewaing township were township-zoned at the time of the 2010 proposal and were, therefore, ineligible to vote. They became county-zoned prior to the 2017 proposal and were, therefore, eligible to vote on the 2017 proposal.) Both 2010 and 2017 ballot proposals asked voters to vote “yes “or “no” to allow the proposals to move forward towards implementation. To determine how differences between townships may have influenced the
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outcome of both proposals, the following variables for each township that participated in the 2010 and 2017 ballot proposals are inventoried in this study.
For the 2010 proposal 1. Was the proposed wind project located in the township? 2. Voting results for the ballot proposal: total votes, percent yes, and percent no. 3. Were turbines already present in the township? If yes, how many were installed and in what years? For the 2017 proposals 1. Were the proposed wind projects located in the township? 2. Voting results for the ballot proposal: total votes, percent yes, and percent no. 3. Were turbines already present in the township? If yes, how many were installed and in what years?
In addition to inventorying variables relevant to each township’s wind development, this study also inventories characteristics of township populations that have been suggested to possibly affect attitudes towards wind development in rural communities. These variables have yet to be evaluated by other research as possible influencing factors. Five-year estimates from 2012 - 2016 data from the US Census Bureau American Community Survey are used to evaluate these township population characteristics. The first township characteristic inventoried is the percentage of housing units occupied by renters. One commonly voiced concern in communities where wind developments are proposed is that the presence of wind turbines will lower nearby property values (Clemente,
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2015). Thus, considering this township characteristic allows this study to evaluate the assertion that communities with a higher percentage of renters will have higher support for wind energy development because renters are not concerned with the change in property values that wind turbines may be perceived to cause. The second characteristic of interest evaluated in this study is the percentage of housing units in the township that are vacant. Because Huron County is surrounded by Lake Huron shoreline, we assume some residents of shoreline townships intentionally live near the shoreline because they value the scenery surrounding their homes more than residents of inland townships. Furthermore, lakeshore townships are expected to have more houses that are not the owners’ primary residence because they are occupied seasonally as summer homes. Therefore, we would expect lakeshore townships to have a higher percentage of vacant housing units. Thus, because we predict higher vacancy rates to be a proxy for residents’ higher valuation of scenery, this study evaluates the assertion that townships with a higher vacancy rates will exhibit lower support for wind energy development. While this idea has been commonly considered with respect to off-shore wind (for example, the Cape Wind project off Cape Cod), it has not been considered for on-shore wind installations (Eckhouse and Ryan, 2017). The third township characteristic evaluated in this study is the percentage of the employed population age sixteen years or over that work in agriculture. A study by Slattery et al. (2012) surveyed residents of two communities in Iowa and West Texas where wind developments had been implemented. Respondents to their study noted that wind turbines were compatible with other forms of land use such as farming. Furthermore, respondents noted that turbines allowed landowners to continue farming and ranching because “there was enough confidence in the residual income generated by the wind farms.” This finding may suggest that
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communities where agriculture is of more importance will exhibit greater support for wind energy development. This study considers the portion of townships’ workforce in agriculture to be a proxy for the importance of agriculture in each township. Thus, this study evaluates whether townships with a higher percentage of the employed population age sixteen years or over working in agriculture to exhibit higher support for wind energy development. [Note: the American Community Survey tracks all jobs in agriculture, forestry, fishing and hunting, and mining in the same category. However, Huron County produces the most total value of agricultural products sold of all Michigan counties (Slattery et al., 2012). Therefore, this study assumes nearly all jobs in this category are in agriculture.] As determined by Rand and Hoen (2017) and previously discussed in the literature review, the NIMBYism framework has been widely applied to explain the reluctance of community members to support wind development within their own communities. Although it has been accepted that the NIMBYism framework is generally unsupported, no studies have evaluated the NIMBYism framework with townships as the unit of analysis. Thus, comparing the township-level voting results between townships where the wind developments were proposed and those where they were not, may offer insight into possible nuances to the NIMBYism framework applied at a more local level. Secondly, as discussed in the literature review, additional research by Mulvaney, Woodson, and Prokopy (2013) and by Baxter, Mozaria, and Hirsch (2013) found that communities where wind developments currently existed demonstrated better perceptions and higher levels of support for wind development relative to communities where wind turbines were not present. Thus, by evaluating the voting results for the 2017 proposals for townships with varying amounts of turbines installed prior the 2017 proposal, this study will also provide insight
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into how support for wind development differs in nearby communities with varying amounts of wind turbines previously installed. Lastly, as discussed in the literature review, research conducted by Wilson and Dyke (2016) and by Fergen and Jacquet (2016) demonstrated that community perceptions towards wind energy development after turbines had been installed in the community changed significantly from before turbines had been installed. Therefore, by evaluating the changes in voting results for the 2010 and 2017 proposals in townships where turbines were installed between the two proposals will contribute to further understanding how attitudes towards wind energy change after turbines are installed at a more local level. By evaluating these variables for each township in Huron County that voted on the 2010 and 2017 proposals, this study will offer a unique perspective on the forces that shape community perceptions of wind energy at the township level.
Results The difference in voting results between the 2010 and 2017 proposals was drastic. A significant decline in approval occurred in nearly every township eligible to vote on both proposals. There were no obvious trends in support level or change in support for variables collected from each township. The only finding that can initially be positively identified is that support for further wind energy developments in the Huron County townships declined substantially between 2010 and 2017. Results of the inventory with townships listed in alphabetical order is presented in appendix A.
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2010 Proposal Results Votes were cast for the 2010 proposal across the 14 townships eligible to vote. The 2010 proposal excluded Brookfield and Sebewaing townships which were still under township-zoning and were therefore ineligible to vote in 2010. Of the 3237 votes cast for the 2010 proposal, 60.1% of votes approved the proposal, which passed in 13 of the 14 townships. (Figure 1) In Sigel township, where the proposal narrowly failed, 49.56% of voters approved the proposal. The highest approval level occurred in Winsor Township, at 84.02%.
Figure 1: 2010 Ballot Proposal Results 90 80 70 60
% Yes
50 40 30 20 10 0
Township (n=14)
2017 Proposal Results In 2017, only 37.98% of the 3054 votes cast across the 16 eligible counties approved the proposal. (Figure 2) The proposal was only approved in Bloomfield and McKinley townships, with 54.17% and 50.44% majorities, respectively. The lowest approval level occurred in Grant Township, at only 25.54%. The average difference in approval levels between the 2010 and 2017
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(excluding Brookfield and Sebewaing townships who were ineligible for the 2010 proposal) was dramatic, with an average decline of 22.39% in the 14 townships eligible to vote on both proposals. The largest decline in approval—34.63%—occurred in Winsor township where 84.02% of voters approved the proposal in 2010 and 49.39% of voters approved the proposal in 2017. The smallest decline—7.29%—occurred in Sigel Township where 49.56% of voters approved the proposal in 2010 and 42.27% of voters approved the proposal in 2017.
Figure 2: 2017 Ballot Proposal Results 60 50
% Yes
40 30 20 10 0
Township (n=16)
Analysis Each variable collected in the inventory is analyzed to assess whether the findings are consistent with the proposed hypothesis for the given variable.
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NIMBYism Under the NIMBYism framework, which has been widely researched and largely dismissed at levels beyond the township level, we would expect approval levels to be lower in the townships where the proposed developments were located relative to townships where the developments were located outside of the township, for both the 2010 and 2017 proposals. However, the results do not support his hypothesis.
2010 Proposal. The 2010 ballot proposal included proposed developments in four townships: Bloomfield, McKinley, Rubicon, and Sigel. The average support level in these four townships was 59.36%. Results for the four townships are presented in the table below. Ten townships where the proposed developments were located outside the township were eligible to vote on the 2010 proposal. The average approval level was 60.39% in these 10 townships with the highest approval level of 84.02% occurring in Winsor Township and the lowest approval level of 51.32% occurring in Lincoln Township. In summary, the average approval level in the 4 townships where proposed developments were located within the township was 59.36%, compared to 60.39% in townships where the proposed developments were located elsewhere. These averages do not contrast enough to be consistent with the hypothesis supported by the NIMBYism framework.
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Figure 3: Relevance of NIMBYism, 2010 Proposal 90 80 70
% Yes
60 50 40 30 20 10 0
Township (n=14)
2017 Proposal. The 2017 ballot proposal included proposed developments in four townships: Dwight, Lincoln, Sherman, and Sigel. For the 2017 proposal, the average support level in these four townships was 35.13%, with an average difference in approval level between the 2010 and 2017 proposals of 19.22%. The largest difference in these four townships occurred in Dwight township where 26.29% less voters approved the 2017 proposal. Twelve townships where the proposed developments were located outside the township were eligible to vote on the 2017 proposal. The average 2017 approval level for these twelve townships was 38.94% and the average difference in approval level between the 2010 and 2017 proposals was 23.65%. For these townships, the largest decline was 34.63% in Winsor Township, and the smallest difference was 10.37% in Bingham. In summary, the average approval level in the 4 townships where proposed developments were located within the township for the 2017 proposal was 35.13% compared to 38.94% in the 12 townships where the proposed developments were located
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elsewhere. The average difference in approval level between the 2010 and 2017 proposals for the four townships where the proposed developments on the 2017 ballot were located within was 19.22% compared to 23.65% for the 10 townships where the 2017 proposed developments were located elsewhere.
Figure 4: Relevance of NIMBYism, 2017 Proposal
60 % Yes Difference % Yes
50 40 30 20 10 0
The NIMBYism framework would suggest that both the 2017 average approval levels and the average difference in approval levels between the 2010 and 2017 proposals would be greater in the four townships where the 2017 proposed developments were located. The analysis in Figure 5 shows that the average approval level in 2017 was slightly lower in the townships where the 2017 proposed projects were located. In contrast, the average difference in approval levels between the 2010 and 2017 proposals was actually smaller in the townships where the 2017 proposed projects were located. These findings suggest there is weak evidence to support NIMBYism. As discussed in the literature review, Rand and Hoen (2017) found, through their
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research synthesis, that the NIMBYism framework is largely unsupported. However, they did not analyze research which assessed the NIMBYism framework with the unit of analysis smaller than at the county level. The findings from this study provide limited support for the NIMBYism framework but do not provide clear evidence that the NIMBYism framework is applicable at the township level. However, this study is limited, and future research should further evaluate the NIMBYism framework at a more micro level.
Figure 5: Relevance of NIMBYism, Average Approval Level for Townships with/without Proposed Developments on Ballot
70 60
% Yes
50 40 30 20 10 0 2010 Proposal
2017 Proposal
Difference (2010 - 2017)
Townships With Proposed Development on Ballot Townships Without Proposed Development on Ballot
Prior Turbine Installation As discussed in the literature review, past research has found that community attitudes towards wind turbines are likely to improve after turbines have been installed (Baxter, Mozaria and Hirsch, 2013). In the inventory, the effects of turbine installation can be tested two ways: by presence/absence of turbines prior to the vote and by the number of turbines installed at the time
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of the vote. If the hypothesis that with familiarity comes acceptance is true, then we'd expect to see higher approval levels in townships where turbines had been installed prior to the vote, as well as higher support among townships with more turbines. Furthermore, we would expect to see lower differences in approval levels between the 2010 and 2017 proposals among townships where turbines were present, as well as lesser differences in townships with more turbines, relative to those with less. Data are presented in Table 1.
Table 1: Township Rank by Number of Turbines Installed During 2017 Proposal
Township
2010 Proposal: % yes
2017 Proposal: % yes
Difference % Yes
Number of Turbines Installed in Township (2017)
Gore Hume Sherman Lincoln Grant Fairhaven Sheridan Sebewaing Rubicon McKinley Brookfield Sigel Dwight Bingham Bloomfield Winsor
63.92 57.68 52.55 51.32 51.82 63.39 58.05 NA 53.62 68.65 NA 49.56 63.95 57.19 65.61 84.02
40.38 30.25 33.33 27.24 25.54 30.18 34.72 46.2 25.55 50.44 33.61 42.27 37.66 46.82 54.17 49.39
-23.54 -27.43 -19.22 -24.08 -26.28 -33.21 -23.33 NA -28.07 -18.21 NA -7.29 -26.29 -10.37 -11.44 -34.63
0 0 0 1 2 4 5 8 10 15 17 24 35 41 41 51
For the 2010 ballot proposal, only two townships already had turbines present: Sheridan and Bigham. Turbines were installed in both townships in 2008 with 5 in Sheridan and 41 in
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Bingham. As discussed previously, we would expect these two townships to have greater approval levels in 2010 compared to townships that had zero turbines installed as of the 2010 ballot proposal vote. However, the average approval level in 2010 between Sheridan and Bigham townships was 57.62%, compared to 60.51% for the twelve townships who voted on the 2010 ballot proposal that did not have any turbines installed. This does not support the hypothesis. As of the 2017 proposal, at least one turbine had been installed in 13 of the 16 townships assessed in this study. Excluding Sheridan and Bingham townships which had turbines installed in 2008, eleven townships had turbines installed between the 2010 and 2017 ballot proposals. Of the three townships with zero turbines installed prior to the 2017 proposal, the average approval level was 34.65% (Range: 30.25% - 40.38%), and the average difference in approval between the 2010 and 2017 proposals was 23.40% (Range: 19.22% - 27.43%). Of the twelve townships with one or more turbines installed prior to the 2017 proposal, the average approval level was 38.75% (Range: 25.54% - 54.17%), and the average difference in approval between the 2010 and 2017 proposals was 22.11% (Range: 7.29% - 34.63%). Although there is no significant difference between the averages, and given the limited sample, the average 2017 approval level was greater in the 13 townships with 1 or more turbines (38.75%) than in the three townships with zero turbines installed (34.65%), which is consistent with the hypothesis. However, the average difference in approval levels between the 2010 and 2017 proposals for the 13 townships with 1 or more turbines (22.11%) is slightly less with than the average difference in approval levels between the 2010 and 2017 proposals for the three townships with one or less turbines installed (23.40%). These mixed findings provide weak support for the hypothesis.
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Table 1: Average 2017 Approval and Difference for Townships with and Without Turbines
Turbine Presence
N
2017 Average % Yes
No (0) Yes (1-51)
3 13
34.65 38.75
Average Difference % Yes -23.40 -22.11
To further evaluate this hypothesis, it is useful to separate the 16 townships into scaled brackets corresponding to the number of turbines installed in the townships as of the 2017 ballot proposal vote. We would expect those brackets with higher numbers of turbines to have higher average approval levels for the 2017 proposal and smaller average differences in approval levels between the 2010 and 2017 proposals. To better assess the hypothesis, we will further separate the townships into brackets based on the number of installed turbines present. When split into four brackets, we see that the average 2017 approval level consistently rises for each bracket with a greater number of installed turbines. However, the average difference in approval levels between the 2010 and 2017 proposals does not consistently decrease for each bracket with a greater number of installed turbines. The average 2017 approval levels and average differences between the 2010 and 2017 elections are presented in table 2. These results do suggest that, on average, townships with more turbines installed had higher approval levels for the 2017 proposal. The R-squared value of the correlation between number of turbines present in a township and 2017 approval levels is .4762, which is relatively high for social science research. However, the average difference in approval levels between the 2010 and 2017 proposals for the four brackets shows no correlation. These findings are consistent with those of Baxter, Mozaria, and Hirsch (2013), who found higher
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levels of support for wind development in communities where turbines were present, relative to comparable communities with no turbines present.
Table 2: Average 2017 Approval and Difference by Number of Turbines in Township # Turbines Present 0 -1
N 4
2017 Average % Yes 32.80
Average % Yes Difference -23.57
2- 8
4
34.16
-27.61
10 -24
4
37.97
-17.86
35 - 51
4
47.01
-20.68
Percentage of Housing Units Occupied by Renters The following table ranks the 16 townships by percent of housing units occupied by renter. Previous speculation has suggested there may be a positive correlation between higher rates of renter occupied housing and support for wind developments in communities where developments have been proposed. However, as demonstrated by the table, it appears no correlation exists in the 16 Huron County townships analyzed in this study. The R-squared value of the correlation between percentage of housing units occupied by renters and approval level was .1378 for the 2010 proposal and .253 for the 2017 proposal, neither of which are considered significant.
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Table 3: Township Rank by Percentage of Housing Units Occupied by Renters Township Grant Rubicon Sigel Brookfield Sheridan Bloomfield Lincoln Sherman Gore Hume Bingham Sebewaing Dwight Fairhaven McKinley Winsor
% Renter 8.0% 9.0% 9.6% 9.7% 10.7% 11.3% 13.0% 14.4% 14.9% 14.9% 16.3% 17.4% 17.8% 19.0% 22.7% 24.7%
2010 % Yes 51.82 53.62 49.56 NA 58.05 65.61 51.32 52.55 63.92 57.68 57.19 NA 63.95 63.39 68.65 84.02
2017 % Yes 25.54 25.55 42.27 33.61 34.72 54.17 27.24 33.33 40.38 30.25 46.82 46.20 37.66 30.18 50.44 49.39
Difference % Yes -26.28 -28.07 -7.29 NA -23.33 -11.44 -24.08 -19.22 -23.54 -27.43 -10.37 NA -26.29 -33.21 -18.21 -34.63
Percentage of Housing Units with Vacant Occupancy Status This study proposed there may be a negative correlation between higher rates of vacancy and support for wind developments in communities. This correlation may be due to the notion that communities with high vacancy rates are communities where many housing units are used as second homes for residents who have their primary addresses in other locations. Because Huron County is surrounded by Lake Huron to the west, north, and east, we assume many of these vacant houses in lakeshore townships are summer homes. Moreover, because owners of these homes likely decided to live along the lakeshore at least partially because of the scenery, we would expect these townships to exhibit lower support for wind energy developments because turbines can disrupt the scenery. Therefore, in this study, we would expect that, on average,
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townships with higher vacancy rates will have lower approval levels in 2010 and 2017 and/or larger differences in approval rates between the 2010 and 2017 proposals. Of the 16 townships studied, the six shoreline townships also have the highest vacancy rates with an average of 42.9% of housing units vacant (range: 26.6% - 64.1%). The average approval rate of these six townships for the 2010 proposal was 59.97% and 35.02% for the 2017 proposal. The average difference in approval rates between the 2010 and 2017 proposals was 24.95% for the six shoreline townships. For the ten inland townships, the average vacancy rate was 13.8%. For the ten inland townships, the average approval rate for the 2010 proposal was 60.19% and 39.76% for the 2017 proposal. The average difference in approval rates between the 2010 and 2017 proposals was for these ten townships was 20.46%. The average 2017 approval rate and the average difference in approval rates between the 2010 and 2017 proposals are both consistent with the hypothesis; however, the differences in approval level between inland and shoreline townships is not large enough to justify strong support for the hypotheses. The 2010 average approval rates were not consistent. The R-squared value of the correlation between the percentage of vacant housing units and approval level was .012 for the 2010 proposal and .0419 for the 2017 proposal. Neither are significant. Township rank by the percentage of vacant housing units is presented in table 4. Shoreline townships are highlighted in blue.
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Table 4: Township Rank by Percentage of Vacant Housing Units Township Winsor Lincoln Grant Sigel Brookfield Sheridan Sebewaing Bloomfield Bingham Dwight McKinley Sherman Rubicon Fairhaven Hume Gore
% Vacant 5.7% 8.9% 9.7% 10.5% 14.0% 15.0% 15.7% 18.8% 19.1% 20.1% 26.6% 29.9% 36.5% 38.6% 61.9% 64.1%
2010 % Yes 84.02 51.32 51.82 49.56 NA 58.05 NA 65.61 57.19 63.95 68.65 52.55 53.62 63.39 57.68 63.92
2017 % Yes 49.39 27.24 25.54 42.27 33.61 34.72 46.20 54.17 46.82 37.66 50.44 33.33 25.55 30.18 30.25 40.38
Difference % Yes -34.63 -24.08 -26.28 -7.29 NA -23.33 NA -11.44 -10.37 -26.29 -18.21 -19.22 -28.07 -33.21 -27.43 -23.54
Percent of Township Workforce (16 years & over) Working in Agriculture As discussed in the methods section, some research has suggested agricultural communities may be more supportive of wind energy developments because of the income they generate for farmers who host turbines on their land (Slattery et al., 2012). Thus, wind developments may be seen by residents of rural communities as a means of preserving agricultural lifestyles. Therefore, in Huron County, we expect townships with a higher portion of the labor force in agriculture to have higher approval levels because the agricultural lifestyle will be of greater importance in these townships. However, when the townships are ranked by the proportion of the workforce in agriculture, there is no correlation with higher approval rates. The R-squared value of the correlation between the percentage of workforce in agriculture and approval levels was .0883 for the 2010 proposal and .0046 for the 2017 proposal; neither are
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significant. For instance, Winsor Township ranked third lowest in the percentage of the workforce in Agriculture but had the highest approval level in 2010 (84.02%) and the third highest in 2017 (49.39%). Conversely, Sherman Township had the third highest percentage of its workforce in agriculture but low approval levels in 2010 (52.55%) and in 2017 (33.33%). Complete results are presented in the table 5.
Table 5: Township Rank by Percentage of Workforce in Agriculture Township Gore Sebewaing Winsor Brookfield Fairhaven Grant Bingham McKinley Rubicon Hume Dwight Sheridan Lincoln Sherman Bloomfield Sigel
% Agriculture 4.4% 5.9% 6.2% 7.3% 7.4% 8.1% 9.8% 10.9% 11.9% 12.8% 14.2% 15.0% 15.3% 17.0% 19.8% 28.1%
2010 % yes
2017 % yes
Difference
63.92 NA 84.02 NA 63.39 51.82 57.19 68.65 53.62 57.68 63.95 58.05 51.32 52.55 65.61 49.56
40.38 46.2 49.39 33.61 30.18 25.54 46.82 50.44 25.55 30.25 37.66 34.72 27.24 33.33 54.17 42.27
-23.54 NA -34.63 NA -33.21 -26.28 -10.37 -18.21 -28.07 -27.43 -26.29 -23.33 -24.08 -19.22 -11.44 -7.29
Summary of Findings Of all the variables used to assess the corresponding hypotheses, only the presence of turbines in townships was consistent with the prediction that townships that had greater numbers of turbines installed prior to the 2017 ballot proposal would, on average, exhibit higher rates of support for proposed wind developments. As discussed in the analysis section for Prior Turbine
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Installation, when townships were separated into brackets based on the number of turbines present in the township, we found that, on average, townships with more turbines had higher average approval rates for the 2017 ballot proposal. It was also predicted that townships with greater numbers of turbines would have less significant differences in approval rates between the 2010 and 2017 ballot proposals. However, when separated into brackets based on the number of turbines, this hypothesis did not hold true. Furthermore, the proposed hypotheses regarding proposed project location, percentage of housing units occupied by renters, percentage of vacant housing units, and percentage of workforce (16 & over) in agriculture were also unsupported.
Conclusion Limitations and Future Research Given that only 16 townships from one county were analyzed, the scope of this study is its most significant limitation. While we found limited support for most hypotheses, these should not be considered conclusively invalid without additional research. Another serious limitation of this study is that the percentage of voters that approved the ballot proposals was used as an indicator of overall support for wind energy developments in Huron County and within individual townships. However, it must be considered that the drastic difference in approval rates between the 2010 and 2017 proposals could be the result of growing wind development opposition (Storrow & Kusick, 2017). Future research should be conducted in other areas where approval of wind energy developments is determined by voters to analyze relevance of the variables used in this study. Additionally, because this study did not consider the location of turbines relative to population centers within townships, future research at the township level should consider the visibility and proximity of turbines in areas where developments are
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proposed. Local government support for wind energy developments, which previous research has found to play an important role in the presence of wind turbines at the county level, was also not considered in this study. Therefore, future research on Huron County should include interviews with local officials, industry representatives, and local media to better understand whether local leaders’ changing views on wind energy may be a better explanation for the dramatic change in attitudes among voters in Huron County. Stakeholder Implications Wind energy developers and local government officials should consider the findings of this study as evidence of the nuances of community attitudes towards wind energy development. The significant takeaway for wind energy developers in the US should be the importance of the role that county and township zoning procedures play in determining the approval of proposed developments. Because the county planners’ decisions on proposed developments were only challenged by residents for the developments that were on the 2010 and 2017 proposals, it is possible that county planners were approving developments despite growing distaste for the presence of wind turbines in Huron County. This suggests the attitudes of local officials, specifically county and township planners, should be of greater importance to wind energy developers than community attitudes at large. Furthermore, local government officials should not consider approval of individual developments to be indicative of the acceptance of additional proposed developments. Conversely, local government officials should recognize that community acceptance of wind turbines may improve after turbines are installed. In summary, both wind energy developers and local officials should be cognizant of the volatile nature of community attitudes towards wind developments.
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Appendix A
Township
2010 2017 Proposal: Proposal: % yes % yes
Difference % Yes (2017 - 2010)
# Turbines Installed (2017)
% Employed Population in Agriculture
% RenterOccupied Housing Units
9.8%
16.3%
19.1%
19.8% 7.3% 14.2% 7.4% 4.4% 8.1% 12.8%
11.3% 9.7% 17.8% 19.0% 14.9% 8.0% 14.9%
18.8% 14.0% 20.1% 38.6% 64.1% 9.7% 61.9%
15.3% 10.9% 11.9% 5.9% 15.0% 17.0% 28.1%
13.0% 22.7% 9.0% 17.4% 10.7% 14.4% 9.6%
8.9% 26.6% 36.5% 15.7% 15.0% 29.9% 10.5%
6.2%
24.7%
5.7%
Bingham Bloomfield
57.19 65.61
46.82 54.17
-10.37 -11.44
41 41
Brookfield Dwight
NA 63.95
33.61 37.66
NA -26.29
17 35
Fairhaven Gore Grant Hume Lincoln McKinley Rubicon Sebewaing Sheridan Sherman Sigel
63.39 63.92 51.82 57.68 51.32 68.65 53.62 NA 58.05 52.55 49.56
30.18 40.38 25.54 30.25 27.24 50.44 25.55 46.2 34.72 33.33 42.27
-33.21 -23.54 -26.28 -27.43 -24.08 -18.21 -28.07 NA -23.33 -19.22 -7.29
4 0 2 0 1 15 10 8 5 0 24
Winsor
84.02
49.39
-34.63
51
average
60.10
37.98
22.39
% Vacant Housing Units
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