WIND FARM DEVELOPMENT: AN ANALYSIS OF FACTORS INFLUENCING
RESIDENTS‘ PERCEPTIONS OF WIND TURBINES
By

Theresa M. Groth

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Community, Agriculture, Recreation and Resource Studies
2011

ABSTRACT
WIND FARM DEVELOPMENT: AN ANALYSIS OF FACTORS INFLUENCING
RESIDENTS‘ PERCEPTIONS OF WIND TURBINES
By
Theresa M. Groth
Assisting communities adjust to change is crucial in ensuring that successful, vibrant
communities continue to flourish. Recent studies have suggested that a lack of community
involvement in planning stages, uncertainty regarding proposals, place attachment and NIMBY
(Not-In-My-Backyard) are some factors used to explain resistance to wind development (Jones
& Eiser, 2009; 2010; Swofford & Slattery, 2010). This research studies perceptions and support
levels held by residents in four townships of Huron County, MI of wind farm development. The
aim of this study was to determine whether proximity to a wind turbine, ownership of land, and
knowledge of wind energy influenced perceptions of and support of wind farm development.
Social exchange theory aided in the interpretation of the research results. Social, economic and
environmental belief factors were tested to evaluate their role in shaping perceptions and support.
Data were collected using the mixed methods of interviews of stakeholders and mail surveys to
landowners (n=497 respondents, 50% response rate). The results suggested proximity, amount
of land owned, and self-rated knowledge of wind energy were not related to perceptions of or
support for wind farm development. Social beliefs about positive outcomes were strongly
related to perceptions of wind farm development, followed by economic beliefs about outcomes
of wind farm development. Respondents who were neutral in their opinions before development
tended to perceive wind farms negatively after construction. Social and environmental beliefs
about positive outcomes of wind turbines were strongly related to support for wind farm
development, yet concerns regarding impact on the environment, economy and people existed.

For everyone who believed in me.

iii

ACKNOWLEDGEMENTS
I am grateful to my advisor and committee chair, Dr. Christine Vogt for her guidance,
patience and advice throughout this project. I would like to thank the other members of my
committee, Dr. Dan McCole and Dr. Zenia Kotval, for their suggestions and insight to improve
my thesis and broaden my thinking.
I would also like to acknowledge the residents of Huron County, Michigan for their
willingness to be a part of my study.
I am thankful to my family and friends who grounded me when I needed to be brought
back down to earth, who always saw the light at the end of my tunnel even when I couldn‘t, and
most importantly who encouraged me to see this thesis to the end. I am more grateful than you
know.

iv

TABLE OF CONTENTS
LIST OF TABLES ....................................................................................................................... vii
LIST OF FIGURES ........................................................................................................................x
Chapter 1 ..........................................................................................................................................1
Introduction ................................................................................................................................. 1
Statement of the Problem ........................................................................................................ 3
Purpose Statement ................................................................................................................... 4
Significance of the Study......................................................................................................... 4
Delimitations ........................................................................................................................... 5
Limitations ............................................................................................................................... 6
Hypotheses............................................................................................................................... 7
Definition of Terms ................................................................................................................. 7
Chapter 2 ......................................................................................................................................... 9
Literature Review ........................................................................................................................ 9
Sustainable Development ........................................................................................................ 9
Public Perceptions ................................................................................................................. 11
Trust ....................................................................................................................................... 13
Proximity to Wind Turbines .................................................................................................. 15
Community Engagement ....................................................................................................... 17
Theoretical Basis ................................................................................................................... 21
Summary................................................................................................................................ 24
Chapter 3 ....................................................................................................................................... 25
Methodology ............................................................................................................................. 25
Arrangements for Conducting the Study ............................................................................... 25
Development of Interview Instrument ................................................................................... 26
Selection of Interview Subjects ............................................................................................. 26
Procedures for Gathering and Analyzing Interview Data ..................................................... 27
Development of Survey Instrument ....................................................................................... 28
Township Description............................................................................................................ 30
Selection of Survey Subjects ................................................................................................. 32
Administration of the Survey Instrument .............................................................................. 34
Treatment of the Data ............................................................................................................ 34
Chapter 4 ....................................................................................................................................... 37
Analysis of Data ........................................................................................................................ 37
Interview Results ................................................................................................................... 37
Data-Gathering Instrument Distribution ................................................................................ 42
Descriptive Findings .............................................................................................................. 42
Additional Comments on Survey .......................................................................................... 47
Hypothesis Testing ................................................................................................................ 47
Chapter 5 ....................................................................................................................................... 66
Summary, Conclusions, Discussion of Findings and Recommendations ................................. 66
v

Summary................................................................................................................................ 66
Findings ................................................................................................................................. 66
Discussion of Findings .......................................................................................................... 67
Limitations ............................................................................................................................. 74
Conclusions ........................................................................................................................... 74
Recommendations ................................................................................................................. 75
APPENDICES ............................................................................................................................ 78
A. Participant Consent Form .................................................................................................80
B. Interview Guide ............................................................................................................... 82
C. Comparison of Survey Respondent Responses by Township by Question ......................85
D. Additional Comments from Survey ................................................................................ 94
E. Survey Response Rate Graph ..........................................................................................115
References .................................................................................................................................117

vi

LIST OF TABLES
Table 1.

Township Categorization ..........................................................................................33

Table 2.

Data Analysis Steps ................................................................................................. .35

Table 3.

Interviewee Results – Land Ownership and Wind Farm Perception ....................... .38

Table 4.

Interview Emergent Themes ......................................................................................39

Table 5.

Mail Survey Statistics ................................................................................................42

Table 6.

Gender by Township ..................................................................................................43

Table 7.

Age and Mean Length of Residency..........................................................................45

Table 8.

Survey Response Rates ..............................................................................................44

Table 9.

Registered Voters and Voting Behavior ....................................................................45

Table 10. Comparison of Voters & Survey Respondent on Support of Development ..............46
Table 11. Gender & Age Comparison of Voters & Survey Respondents .................................46
Table 12. Independent samples t-test measuring proximity w/in 1 mile of wind turbine vs.
support of wind farms w/in Huron County ................................................................52
Table 13. Independent samples t-test measuring proximity w/in 1 mile of wind turbine vs. the
BEFORE and NOW perception of wind farms..........................................................52
Table 14. Perceptions of turbines ‗Before‘ wind farm development and ‗Now‘ that wind
turbines exist ..............................................................................................................53
Table 15. Number of Individuals per Township whose perceptions did not change after the
wind farms were built ..............................................................................................53
Table 16. One-Way ANOVA examining differences in knowledge and meeting attendance ..54
Table 17. Correlation between knowledge of wind energy and the before and now perception of
wind farms ...............................................................................................................55
Table 18. Correlation between knowledge of wind energy and level of support of wind farms
within Huron County ...............................................................................................56
Table 19. Independent Samples T-Test between attendance of meetings and before and now
perception of wind farms .........................................................................................57
Table 20. Independent Samples T-Test between attendance of meetings and support of wind
farms in Huron County ..............................................................................................57
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Table 21. Chi-Square – Residency Location and Acres Owned ..............................................58
Table 22. Independent Samples T-Test between residency location and BEFORE and NOW
perceptions of wind farms ........................................................................................59
Table 23. Independent Samples T-Test between residency location and support of wind farms in
Huron County ...........................................................................................................60
Table 24. ANOVA – Acres owned and BEFORE and NOW perception of wind farms ..........61
Table 25. ANOVA – Acres owned and support of wind farms w/in Huron County ...............62
Table 26. Survey Belief Outcomes Regarding Wind Energy ....................................................63
Table 27. Regression analysis for the prediction of support for wind farms from economic,
environmental and social beliefs related to wind farms ...........................................64
Table 28. Regression analysis for the prediction of perception of wind farms BEFORE
development from economic, environmental and social beliefs related to wind farms
....................................................................................................................................64
Table 29. Regression analysis for the prediction of perception of wind farms NOW that
development has occurred from economic, environmental and social beliefs related to
wind farms .................................................................................................................65
Table 30. Property Values of Non-respondents across townships .............................................72
Table 31. Count of respondents under wind developer contract and have turbines on property
......................................................................................................................................................74
Table 1a. Location of residency .................................................................................................85
Table 2a. Land ownership, wind contract and wind turbines on property ................................85
Table 3a. Number of miles, as the crow flies, respondent lives from the nearest wind turbine .86
Table 4a. How knowledgeable do you consider yourself about…[the following] .....................86
Table 5a. Perception ‗Before‘ and ‗Now‘ that wind turbines exist ............................................87
Table 6a. Number of public meetings regarding wind energy respondents attended in the last
five years ....................................................................................................................87
Table 7a. Location and frequency of seeing wind turbines .......................................................88
Table 8a. Level of agreement that wind energy creates outcomes ............................................88
Table 9a. Level of agreement on whether wind turbines symbolize a sign of progress in modern
energy crisis .................................................................................................................................90
Table 10a. Wind energy is beneficial to have in Huron County .................................................90
viii

Table 11a. Location resident would be willing to support wind farms ......................................90
Table 12a. Support of wind farm development at any location in your township ......................91
Table 13a. Age of Survey Respondent .......................................................................................92
Table 14a. Gender of Survey Respondent ..................................................................................92
Table 15a. Length of time over lifetime spent living in Huron County .....................................92
Table 16a. Open-Ended Comments ............................................................................................94

ix

LIST OF FIGURES

Figure 1. Surveys Received by Township ....................................................................................44
Figure 2. Survey Response Rate Graph ......................................................................................116

x

Chapter 1
Introduction

A global effort to combat climate change is underway. The United States government is
advocating for more renewable energy to replace the reliance on foreign oil. In an effort to
reduce the amount of ozone degrading pollutants expelled into the air, the newly created
American Recovery and Reinvestment Act of 2009 set a standard to double the renewable energy
production in the United States by 2012. The state of Michigan enforces Public Act 295, better
known as The Clean, Renewable and Efficient Energy Act, requiring Michigan electric providers
to derive at least ten percent of retail supply from renewable energy sources by 2015
(Commission, 2005). As a result, energy providers are securing leases from private landowners
to erect wind turbines in order to meet their renewable energy goals. Wind developers are
increasingly focused on rural areas for future and continued development as land is less
expensive compared to more developed regions. Rural communities in Michigan have been
struggling economically, and have the opportunity to reposition themselves with this new
development that uses minimal land areas and utilizes air-based resources. The erection of a
wind turbine on agricultural land allows a farmer to continue farming around the turbine base
while the income from housing a turbine exceeds the amount of revenue lost from decreased
crop production (Development, 2007).
To facilitate wind developments, The Clean, Renewable and Efficient Energy Act (PA
295) of 2008 created a Wind Energy Resource Zone board to identify regions in the state with
the highest wind potential. Referencing wind energy maps created in 2004, the board identified
the Thumb region of Michigan as having the highest potential, and it was declared the state‘s
primary wind resource zone (Birkholz et al., 2008). Wind developers initially contacted only
landowners with large tracts of land to secure leases to build turbines upon. Those who agreed to
1

participate, signed a non-disclosure agreement prohibiting landowners from discussing their deal,
received a signing bonus and were promised additional income if a turbine was built upon their
property in the future. Some residents were shocked to learn that wind farms were being
introduced into their area and they had no idea anything was being planned. Two wind farms
were consequently constructed. A 46 turbine, 69 megawatt wind farm near Ubly (Bingham
Township) and a 32 turbine, 52.8 megawatt wind farm near Elkton/Pigeon (Oliver Township) in
Michigan both of which went online in 2008 (Nordman, 2009a). In the years since, ―lawsuits
were filed, heated discussions swirled in packed public hearings and work on future
developments continued, as did dissent between wind energy proponents and opponents‖
(Hessling, 2010). In 2010, two new wind districts were proposed in Huron County and a petition
was filed to put this decision on a ballot to confirm the creation of the districts. Proponents said
―future wind developments will bring jobs, increase local tax revenue and help the environment‖
while opponents declared that ―the proposals will open the door to thousands [of] turbines in the
Thumb, and future developments will cause health problems, increase utility prices, lower
property values and harm aesthetic value of Huron County‖ (Hessling, 2010). Those voters
residing in townships under County zoning (50% or 14 out of the 28 townships in the County)
were allowed to vote on the proposals and each proposal passed by more than 600 votes across
the voting townships.
Huron County is recognized for agriculture and with nearly 1,200 farms with 430,000
acres of land being devoted to farming, wind energy development was suited for Huron
County (Commission, 2005). A review of the Huron County Master Plan Amendment
(Commission, 2005), might suggest that the authors may have overlooked some factors that are
important in successful construction and operation of wind farms. This document was adopted by
the Huron County Board of Commissioners in July 2005. The list of ‗limiting factors‘ in the
2

Agricultural Preservation/Alternate Energy Resource Overlay strategy included tree cover areas,
wetland areas, shoreline areas and proximity to airports. The limiting factors were physical in
nature and did not list any ‗human‘ or social exclusions (i.e. schools, hospitals or churches). The
public‘s perception or general attitude/acceptance toward wind energy generation was not listed
as a potential limiting factor. It would seem as if the Planning Commissioners presumed that the
residents in Huron County would just follow along with their recommendation to approve wind
energy generation in an effort to slow development and preserve prime agricultural lands. While
benefits exist, this alone does not ensure the success of wind development in rural areas.
Statement of the Problem
The focus of the research was on the factors influencing residents‘ perceptions of wind
energy development. Specifically, the study sought to answer the following research questions:
1.

Does an inverse relationship exist between the proximity of a residence to a wind
farm and positive perceptions of wind development among residents?

2. Does the level of attendance of wind zoning meetings in the planning process of wind
farms influence the support for wind farm development among residents?
3. Does the amount of land owned affect perception and support of wind farm
development among residents?
4. What belief factor outcomes contribute to perceptions of wind farm development
among residents?
The social exchange theory was utilized to frame study and explain results. To answer
these questions, a geographic location was studied that included four townships in a county with
zoning for selected wind turbine siting: two rural townships with wind farms and two rural
townships without wind farms.
3

Purpose Statement

If wind farms are to be a prominent source of renewable energy and economic growth in
Michigan, residents‘ perceptions should be understood to the same extent as turbine siting
setbacks and allowable decibel levels. This research will provide communities with a framework
to acknowledge perceptions that may exist in rural communities and efforts that may aid in
successful incorporation of constructing wind farms.
Significance of the Study

In general, public support for onshore wind development is high (Jones & Eiser, 2009;
2010; Swofford & Slattery, 2010). Researchers, however, are addressing the fact that ―despite
high levels of public support for onshore wind development in principle, specific projects often
experience local opposition‖ (i.e. social gap) (Jones & Eiser, 2009, p. 4610). Research in this
area has found that the over-used term of NIMBY (Not-In-My-Backyard) is generally not
applicable as the dominant and only reason that local opposition exists. NIMBYism alone
cannot describe the general acceptance of wind energy in the face of local opposition. Recent
studies have suggested that lack of community involvement in the planning stages, uncertainty
regarding the proposals, and place attachment are just some factors that continually become
apparent in resistance of wind energy development (Jones & Eiser, 2009; 2010; Swofford &
Slattery, 2010).
With the growing interest and enthusiasm expressed by the state and national government
on renewable energy and the lack of concrete evidence as to what influences residents‘
perception of wind energy development, an in depth analysis of wind farm development was
thought to be necessary. The need for a more thorough understanding of the hierarchy of factors
influencing perception is essential for further successful construction of wind farms. In the
4

present study, an effort was made to make generalizations regarding residents‘ perceptions in
selected townships in Huron County, Michigan in hope that they could assist wind developers
and local decision makers in formulating standards that would consider residents‘ perceptions.
This study will be helpful to the communities directly involved, as well as other rural
communities who are facing a similar decision about wind farm development. In addition, this
research will inform current efforts by community developers to improve the communication of
specific practices of sustainable development to local residents.
Delimitations

The study was delimited to the following:
1. The focus was on small (population < 3,500) communities with heavy reliance on
agriculture production and recent (2008) wind farm development or future wind farm
development, thus this researcher profiles early stages of development.
2. Eleven semi-structured interviews and two scientific studies (Jones & Eiser, 2009;
Swofford & Slattery, 2010) informed the development of the ‗Resident Wind Farm
Perception Survey.‘
3. Interviews were conducted over the summer and fall of 2010 after two years of exposure
to wind turbines.
4. Only those residents who were landowners appeared on the tax assessor‘s list, and
included in the one per household mail survey in the sampling of 1,000 residents of four
townships in Huron County, Michigan. Only permanent or full-time homeowners were
included in this study.
5. The mail survey was distributed at the end of January 2011 and was ‗closed‘ the
beginning of March 2011.
5

6. While there are many forms of engagement, meeting attendance was the only behavioral
item used to measure the level of resident engagement with planning processes.
7. Profiling four townships as the unit of analysis does not infer a representation of all
Huron County homeowners.
Limitations

The study was limited by the following factors:
1. Survey participants represented in the sample of residents in the four selected townships
may or may not represent all residents in that community or other communities; this
research does not estimate non-respondent bias that may exist from those who did not
complete the questionnaire.
2. Data collected represented opinions after the construction of two wind farms, which may
have influenced how some individuals answered certain questions (i.e., perceptions
before wind farms were built).
3. Only those residents who appear on the property tax assessors list were included in this
study; those residents who do not own property (i.e. renters) were not included. This list
did not signify any relationship between residents and wind developers (i.e.
acknowledgement of wind development contract/lease or turbines on property). There
was no way to easily cross-reference the tax assessors list and the database that contained
the information of who currently had wind development contracts or turbines. A
landowner in any of the townships studied who may or may not hold a contract or turbine
on their property, and lived outside one of the four townships were not represented in the
sample.
4. A budget of $2,000 constrained the sampling of residents to only four townships.
6

5. All subjects who received a mail survey were not randomly selected. Two townships
(Oliver and Rubicon) had a proportionately smaller population of land owning residents
than the other two townships (Bingham and Sand Beach) included in this study. All
permanent, land owning residents in Oliver and Rubicon townships were included in the
mail survey.
Hypotheses

The study was designed to test the following hypotheses:
1. Proximity of residence to wind farms negatively influences perceptions and support of
wind farm development.
2. Knowledge (controlling for attendance of meetings) of wind farms positively influences
perception and support of wind energy development.
3. Ownership of land positively influences wind energy perception and support, controlling
for city versus rural residency location. Owners of large tracts of rural land hold more
'use-value' than owners of small tracts of land who focus more on 'existence value'.
4. Economic beliefs are the most significant factor in shaping perceptions and support of
wind farm development compared to social or environmental beliefs.
Definition of Terms

For consistency of interpretation, the following terms are defined:
Community Engagement: Building a shared understanding for the mutually beneficial
exchange of knowledge and resources in a context of partnership and reciprocity (Boyer, 1996;
Rogers, 2005; Ward & Moore, 2010).

7

NIMBY (Not-In-My-Backyard): ―The phenomenon that certain services are in principle
considered as beneficial by the majority of the population, but that proposed facilities to provide
these services are in practice often strongly opposed by local residents‖ (Swofford & Slattery,
2010, p. 2509).
Renewable energy: Energy which comes from renewable resources such as sunlight,
wind, rain, tides and geothermal heat, which are naturally replenished. Fossil fuels, such as coal
and oil, are considered non-renewable resources because they are consumed much faster than
nature can create them (Nordman, 2009b).
Social exchange: The term used to describe social behavior as the process of exchange –
maximizing benefits while minimizing costs in coming to a decision (Chadwick-Jones, 1976;
Cook, 1987; Skidmore, 1975).
Sustainable development: Development that improves the social well-being,
environmental and economic conditions of current and future generations (Audirac, 1997; Fergus
& Rowney, 2005; Gilchrist, 2004; Jepson, 2004; Stimson, Stough, & Roberts, 2006).
Wind lease: An agreement signed by a landowner that grants a developer the right to use
their land for wind development, and in return, provides compensation to the landowner.
Typically, the developer owns any turbines that are put up and does all of the work of developing
the project. Wind leases are binding legal documents that typically cover 30 to 60 years or more.
These agreements can allow turbines to be constructed on privately owned, actively farmed land
(Nordman, 2009b).
Wind turbine: A machine that captures the force of the wind (Nordman, 2009b).

8

Chapter 2

Literature Review

The literature related to residents‘ perception of wind farm development is reported in
this chapter. Literature was reviewed from community development, recreation, land use,
economics, renewable energy and tourism journals. Research on sustainable development in
recent years has placed more emphasis on the importance of interaction between policy makers
and the community. For organizational purposes, the topics covered in this paper include
sustainable development, public perception, trust, proximity to wind turbines, community
engagement and theoretical base. While many other aspects such as poverty, unemployment
rates, and cost of living are important to how communities perceive and adapt sustainable
development innovations, those topics will not be addressed in this thesis.
Sustainable Development
Governments around the world are identifying environmental sustainability as a ―major
driver of technological and economic development—with innovative direction being found at the
interface of our efforts to become more socially and environmentally sustainable‖ (Rogers, 2005,
p. 109). Rural communities, ―faced with the pressures of unprecedented change, have an
opportunity to embrace the principles of sustainable development, to create a new future at the
leading edge of global change—but they need help. They need both knowledge and skills to
enable them to self-evaluate and strategically plan, and they need a highly motivated, creative,
and coherent community to carry it through‖ (Rogers, 2005, p. 109).
Sustainable development is a form of progress that integrates social, economic, and
environmental objectives within a single strategy or program (Fergus & Rowney, 2005;
9

Gilchrist, 2004; Jepson, 2004; Stimson, et al., 2006). While the term ―Sustainable Development‖
was brought to fruition by the World Commission on Environment and Development in 1987,
the importance of rural communities‘ involvement in sustainable development projects in recent
years has begun to make research headlines (Lane, Vanclay, Wills, & Lucas, 2007; Rogers,
2005; Roseland, 2000). Research suggests that the inclusion of the local community aids in the
success of sustainable development projects. For example, Rogers (2005) suggested that the
process of community engagement could be used to draw together the concepts of communitybased indicators of sustainability, triple bottom line performance evaluation (i.e. social cohesion,
ecological foot-printing, and economic activity) and cultural activities as a tool to spur
engagement.
Keller (2001) stated that to ―survive, and even prosper, rural communities will need to
embrace change rather than rail against it, and adopt new forms of innovation and development
which will leave very little unchanged‖ (p. 110). According to Elliott (2000) and Cash and
colleagues (2003), science and technology must ―play a more central role in sustainable
development‖ (p. 8086). Nash, Martin and Krishnan (2007) and Blazevic (2009) suggested that
renewable energy projects such as wind farms have ―no negative impact on tourism‖ and may
actually draw in more tourists (Blazevic, 2009, p. 180). A study conducted of tourists‘ attitudes
toward renewable energy in Australia implied that tourists desire ―environmentally friendly
accommodation[s] and renewable energy supply‖; these tourists also preferred to see wind
turbines onshore as opposed to offshore (Dalton, Lockington, & Baldock, 2008, p. 2174). This
tourist preference could aid communities in further diversifying their economies by expanding
their sources of income, while becoming more sustainable in the process and ultimately gaining
more public support.

10

Public Perceptions

As countries look for more sustainable energy sources, the focus on wind energy
development is becoming increasingly relevant. Wind is an infinite resource that is costless and
available for harvesting. As communities are approached with a proposal for developing a wind
facility, local opposition greatly slows the process of moving forward. Even though wind energy
is generally accepted in principle, it is not uncommon for opposition to exist when facilities are
actually developed. NIMBY (Not-In-My-Backyard) has long been the term that is used to
explain any and all local opposition to any development in a community. However, in recent
years the term NIMBY has come under close scrutiny from researchers looking to explain what
else may be driving local opposition (Jones & Richard Eiser, 2010; Swofford & Slattery, 2010).
In general, public support for wind development is high (Jones & Eiser, 2009; 2010;
Swofford & Slattery, 2010). Researchers, however, are addressing the fact that ―despite high
levels of public support for onshore wind development in principle, specific projects often
experience local opposition‖ (i.e. social gap) (Jones & Eiser, 2009, p. 4610). Research in this
area has found that the over-used term of NIMBY is generally not applicable as the dominant
and only reason that local opposition exists. NIMBYism alone cannot describe the general
acceptance of wind energy in the face of local opposition. Recent studies have suggested that
lack of community involvement in the planning stages, uncertainty regarding the proposals, and
place attachment are just some factors that continually become apparent in resistance of wind
development (Jones & Eiser, 2009; 2010; Swofford & Slattery, 2010).
Scholars suggest that NIMBYism is one of three hypothesized explanations for this
‗social gap‘ (Bell, Gray, & Haggett, 2005; Jones & Eiser, 2009; 2010; Swofford & Slattery,
2010) Researchers hypothesized that ‗democratic deficit‘ and ‗qualified support‘ are other
11

possible reasons to explain this phenomenon. The ‗democratic deficit‘ hypothesis claims that
those who oppose a planning decision hold more sway over decision makers than the silent, but
supportive, majority. This means that ―wind farm proposals can be rejected even in the face of
[the local] majority (but inactive) support‖ (Jones & Eiser, 2009, p. 4604). The ‗qualified
support‘ hypothesis claims that ―whilst people might support wind power in principle, there are
often caveats to this support‖ (Jones & Eiser, 2009, p. 4606). The question stands as to what is
driving support or opposition to planning projects.
Jones and Eiser (2010) and van der Horst (2007) both agreed that NIMBYism alone is
not the only prevailing factor in determining acceptance of a wind energy project. They argue
that there are many other contributing factors. Van der Horst (2007) offered a plethora of
reasons why people may be opposed to development. People may not agree with a part of the
process, the fear of the unknown, and utilitarian value placed on the land are just some of the
reasons van der Horst (2007) offers as other explanations. Jones and Eiser (2010) concurred
with van der Horst (2007) that the value people place on land differs among people and that
could play a part in resistance to change. ‗Existence value‘ is often correlated with emotional
attachment and is commonly associated with retirees who migrate into a rural community to get
away from urban life. They place value on the ‗rurality of their new home‘ and are less likely to
agree with significant change. Conversely, those who focus on ‗use-value‘ (i.e., those
predominately focused on farming) are more open to the idea of mixed land use as they hold less
emotional attachment to the land.
While van der Horst (2007) primarily focused on whether initial opinions (prior to any
local project) were significantly different from post-project opinions, Jones and Eiser (2010)
focused more on how distance from a proposed site affected opinion. Jones and Eiser (2010)
concluded that visibility of a proposed site would attribute to the spoilage of the landscape and
12

would therefore decrease the acceptance of wind development. They also determined that
proximity did not play as big of a role in the Sheffield study as they initially thought it would.
Hills located near participants created a ‗separation‘ or ‗distance‘ that led to more community
acceptance. In the analysis, Van der Horst (2007) did not include visual separation as one of the
determinants of local opposition. In the end, however, both studies determined that a more
integrated, collaborative process was needed between community members and wind developers.
One suggestion was for the developers take a more bottom-up approach and less of a top-down
approach when ‗courting‘ a new community. Researchers suggest that the term NIMBY is
lacking in many areas and needs either to be defined more stringently or cease from being used
all together (Swofford & Slattery, 2010). A single term, like NIMBY, is inadequate for
explaining a complex social situation but there is hope that further research will provide more
explanations. Communities are constantly changing in areas of politics, zoning, development,
etc., and therefore cannot be considered static and isolated. Consequently regular and involved
dialogue between all community stakeholders is one key to success of wind farm development.
Obtaining the trust and support of local residents and knowing their ability to adjust to changes
cannot be disregarded.
Trust

Public support can be gained or lost by the amount of publicly held information regarding
the new technology in question. Mumford and Gray (2010) stated that trust allows for
individuals to save time by assuming that information coming from a trusted
individual/organization is indeed factual and that time does not need to be taken to ensure truth
lies in every statement made. Trust allows people to deal with new situations and new
technology more quickly because they are able to overlay their own thoughts with those of others

13

(Mumford & Gray, 2010). Ricci et al. (2010), however, argued that more is involved in that
process than pure trust.
Ricci et al. (2010) believed that the various roles that a person maintains in everyday life
(consumer, resident, citizen, kin-keeper), condition the way a person views the world. It is
through this lens that decisions are made and not necessarily solely trust. The study conducted
by Ricci et al. (2010) indicated that when issues are relatively unfamiliar to individuals, ―people
are more likely to be swayed by what they think an investigator wants to hear‖ (p. 2635). One
could assume that people also dictate their responses to questions based on that they want to
please an investigator and do not want to ‗lose-face.‘ Ricci et al. (2010) agreed with Mumford
and Gray (2010) in that if a person encounters a new technology and they can equate it with
something that is familiar, they are able to intertwine the pieces of information whether they are
actually similar or not.
Both Mumford and Gray (2010) and Ricci et al. (2010) found that distrust in the source
of information was a contributing factor in whether people accepted new technology. Results
indicated that people trusted governments and for-profit industry less than they did their peers or
non-profit organizations. People viewed private industry as self-serving and were hesitant and
skeptical about believing any information put forth by such organizations. It was also found in
the studies that people often felt let down by local government as they assumed that the local
governing power would have taken a larger role in the regulation of a new industry. More often
than not, governments allowed and encouraged private businesses and the public to interact.
Without the assistance of the local governing body to act as a facilitator, people felt as though
their opinions were not being heard.

14

The conclusion from both Mumford and Gray (2010) and Ricci et al. (2010) is that more
collaboration is needed between all stakeholders involved in a new endeavor. Quality ‗facetime‘ could lead to a more trusting relationship between all parties and a more successful wellrounded future for a community. The more ‗trusted‘ information that a public holds, the more
they will be willing to seek out other alternatives and advocate for options that will keep costs
low, yet lower the overall carbon-footprint. As evidenced in Mumford and Gray (2010) and
Ricci et al. (2010) collaboration is key in moving forward and ensuring that a trusted party exists
in the community to act both as facilitator between stakeholders and to act as a disseminator of
information is crucial to the success of a project. If residents are unable to make an educated
decision on the current status of a situation then the future of a community could be austere.
Proximity to Wind Turbines

As concern for climate change and carbon emissions continues, the focus on renewable
energy, specifically wind energy, and the attitudes held by residents that surround a renewable
energy facility will continue to grow (Jones & Eiser, 2009; 2010; Swofford & Slattery, 2010).
Research suggests new technology has allowed wind energy development to come into
competition with fossil fuels for energy production as the new technology is aligning itself with
the price of fossil fuel extraction and production (Swofford & Slattery, 2010). As research
continues, results indicate that while general acceptance for wind energy development exists,
local opposition often stands in the way of development (Jones & Eiser, 2009; 2010; Swofford &
Slattery, 2010). If the state of Michigan is to meet the renewable energy quota by 2015,
community planners need to understand why residents hold certain perceptions about renewable
energy production.

15

While both Swofford and Slattery (2010) and Jones and Eiser (2009; 2010) examined the
attitudes towards wind energy, the authors did so using different techniques and also sought to
answer slightly different research questions. One of the goals of Swofford and Slattery (2010)
was to determine if proximity to wind farms influenced the attitude of local residents. One wind
farm in northern Texas was chosen as the target research region and the circular areas around the
wind farm were divided up into zones using predetermined measurements: Zone A (0-5 km),
Zone B (5-10 km), and Zone C (10-20 km). This ―allowed survey responses to be separated into
geographic zones in order to examine the effect … proximity had on public attitudes‖ (Swofford
& Slattery, 2010, p. 2512). Jones and Eiser (2009), however, sought to identify more predictors
of attitudes (general and specific attitude towards wind development) than just proximity. Ten
locations in the United Kingdom (5 near wind farms and 5 away from wind farms) were chosen
for a comparison of attitudes to evaluate if proximity played a factor. Results from recent studies
(Jones & Eiser, 2010; Swofford & Slattery, 2010) indicated that an inverse relationship between
proximity and positive attitudes exists; whereby public acceptance of wind development
decreased the closer one located to the wind farm(s).
However, research suggests that while the mandates and deadlines to meet renewable
energy goals vary from country to country, the interplay of proximity to wind farms on public
perceptions do not. More research is needed to further understand the complexities that go into
forming opinions on a relatively new technology. However, Jones and Eiser (2009) and
Swofford and Slattery (2010) are in agreement in how to provide the best opportunity for
development to come to fruition. Community education is needed on the topics of
environmental issues, the different types of renewable energy and what options exist to a
community to allow for a more trusting, connected community that is more likely to accept and
embrace this new technology in the future. If community members are engaged in the process
16

and understand that we are deeply connected with the environment, their involvement in
decision-making may lead to a brighter, more prosperous future.
Community Engagement

There is a general agreement that humans cannot be considered separate from nature
(Berkes, 2004). Modern environmental management literature stresses the need for inclusion of
community in decision making to foster community empowerment and acceptance (Eltham,
Harrison, & Allen, 2008; Fraser, Dougill, Mabee, Reed, & McAlpine, 2006; Wolsink, 2007).
Lane (2007) conducted research in a rural community of Australia where her aim was to study
how an increase in community engagement promoted interest in local environmental issues. The
implication of Lane‘s study was that ―creative expression assists in building capacity for civic
engagement of all kinds and has the potential to extend the set of ideas currently in circulation
about place, community, and environmental change‖ (Lane, et al., 2007, p. 161). If capacity
building is to be achieved, it is important for residents to have a say in the direction of the future
of their community (Devine-Wright, 2005; Jones & Eiser, 2009; 2010; Swofford & Slattery,
2010). Lane (2007) found that the initial approach to program development could significantly
shape the range of issues presented and the nature of ―communicative forms established‖ (p.
159). By including the community from day one, communication keeps the community involved
and vested in the project because their voices are heard and concerns addressed.
Arnstein (1969) argued however, that there are eight different levels of participation
ranging from the lowest level of participation - manipulation (nonparticipation) to the highest citizen control (citizen power). Each level, or rung, offers different benefits and opportunities
and should be utilized for different reasons. Arnstein (1969) stated that the real objective of the
bottom rungs of manipulation and therapy ―is not to enable people to participate in planning or
17

conducting programs, but to enable power holders to ‗educate‘ or ‗cure‘ the participants‖ (p.
242). The author also went on to say ―there is a critical difference between going through the
empty ritual of participation and having the real power needed to affect the outcome of the
process‖ (Arnstein, 1969, p. 217). At higher levels of participation, citizens are able to negotiate
and have the majority say in decision-making. Blair (2004) suggested, people want to be
involved and have a stake in the plans that affect them. Local strategies on how to position
communities for a sustainable future are changing. There is an increase in self-development that
―emphasizes self-sufficiency, local capacity, resources, and sustainability‖ (Blair, 2004, p. 106).
Julian and Reischl (1997) determined that public participation is often limited in the planning
stages of community development. Residents within a community need to have the opportunity
and desire to ‗move up‘ the ladder to have more of an impact on decision-making. While
community engagement alone cannot guarantee a community‘s prosperity, perhaps because there
are different levels of participation, the chances of a community achieving sustainability, selfsufficiency, and a better quality of life significantly improve (Blair, 2004; DeFilippis, 2001).
While some sustainable development studies have been conducted in rural areas
(Joireman, Posey, Truelove, & Parks, 2009; Lane, et al., 2007; Rogers, 2005), these areas are
usually in countries other than the United States (Jones & Eiser, 2009; 2010). The lack of
attention paid to the importance of community engagement in rural sustainable development
results in inadequate knowledge of the relationship between the two. Research suggests that
rural communities can contribute to the success of sustainable development by becoming a part
of the solution (Jones & Eiser, 2009; 2010; Swofford & Slattery, 2010). Conventional top-down
system approaches have failed to realize successful sustainable environmental management in
the past. Thinking holistically and with the notion of systems being interconnected is important
in sustainable development. The objective of sustainability at the local level is to capture as
18

much economic activity as possible in order to maximize multiplier effects to aid in revitalizing
the economy (Day, 1998). Sustainability requires a collective shift in action, empowering
communities to take control of their own destiny while still maintaining their national and
regional ties (Shortall & Shucksmith, 2001) Opponents say that community involvement is a
slippery process; the opportunity exists for citizens to stay in their economic status overtime.
Money is often the main focus in setting agendas. Often times issues related to economics get
the attention of decision-makers. Putnam (2000) contradicted that by saying, ―civic connections
help make us healthy, wealthy, and wise‖ and advocated for more involvement with local
communities (p. 287). This phenomenon is of interest to the wind industry in order to find
explanations as to why people either support or do not support progress, reinvention and
adoption of new economies in their local environment.
European countries are fast approaching deadlines to increase renewable energy
production. Research and the continued slow progress in production has emphasized the need
for public participation in local planning efforts (Higgs, Berry, Kidner, & Langford, 2008;
Rogers, Simmons, Convery, & Weatherall, 2008). There are various schools of thought on the
best way to involve local residents into the success of renewable energy facilities. Higgs et al.
(2008), for example, recommended using software programs such as Geographical Information
Systems (GIS) to take into account the various viewpoints of local residents. Using GIS to
visualize the components of Multi-Criteria Decision Analysis (MCDA) is suggested to increase
community support for renewable energy projects as community members will feel more
engaged in the planning process. Rogers et al. (2008) suggested that decentralizing renewable
energy projects from the commercial to the community level will achieve the same result.
While Rogers et al. (2008) and Higgs et al. (2008) approached community participation
using two different methods, one would be inclined to believe that they would support the claim
19

of the other. Attaining ‗buy-in‘ from the community can be achieved from the use of
visualization in which the community can see beforehand how construction will affect the
community on an environmental and aesthetic level. The level of support will increase as
residents feel like they were a part of the process and feel as if they have a stake in the process.
Using software such as GIS is a way to ‗layer‘ information to see how different decisions will
affect the ‗lay of the land,‘ however this software is constrained to the information readily
available in databases. GIS is not all inclusive of every decision or preference that community
members may consider. This could be a drawback in trying to ‗unite‘ or bring the community to
a consensus on where to site a renewable energy project and to decide upon the scale of the
development.
Rogers et al. (2008) suggested that giving the power to the local community in
determining the size, scale and placement of a renewable energy facility will allow for
community engagement and an increase in the success rate of construction. This success is often
determined on what expectations the community has and what negative outcomes will be
incurred. In the study conducted by Rogers et al. (2008), residents were found to welcome the
idea of a locally run renewable energy project yet lacked the resources or know-how to achieve
this desire. Residents are more apt to favor renewable energy if there are incentives (i.e. warmer
house, saving of money) associated with the local siting of a facility. If the negative (visual
change in the landscape) are determined to outweigh any positive outcomes, the construction of
the facility could be halted in its tracks. The authors both agreed that more research is needed in
all areas of public participation in renewable energy planning.

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Theoretical Basis

Social-exchange theory aides in explaining the relationship between people and the
environment. This theory includes a ―collection of explanations, propositions and hypotheses,
embodying certain assumptions about social behavior‖ (Chadwick-Jones, 1976, p. 1). While this
theory can be helpful, some may view the inadequacies for understanding the full dynamics of
the human dimension as problematic (Emerson, 1987; Johansson & Laike, 2007). Socialexchange theory can explain how residents perceive their personal benefits and how that reflects
in a higher increase in support of sustainable development; community members feel that they
will be getting value from their involvement. The benefits can be of social, economic or
ecologic value. In the case of renewable energy projects, NIMBY has often been used to
describe the resistance in local opposition in the face of general approval. This term has been
challenged as being too simplistic. Social-exchange theory, conversely, can explain how
residents perceive their personal benefits and how that reflects in a higher increase in support of
wind energy. Social exchange theory is immersed with social psychology and social
perspectives that explain social change and stability as a process of negotiated exchanges
between parties (Chadwick-Jones, 1976). For example, all human relationships are formed by
the use of the subjective cost-benefit analysis and the comparison of alternatives. This can help
to explain why some people are open to wind turbines and some are not. To some, the economic
benefits outweigh any environmental and social costs, however additional studies (Johansson &
Laike, 2007) have listed other factors that may influence behavior.
Johansson and Laike (2007) found that ―the perceived unity of the environment became
the only individual significant predictor‖ in identifying the intention to oppose local wind
turbines; unity being defined by the authors as how well the various components in the
environment fit and function together (p. 445). While the results of this study confirmed
21

previous results from other scholars, ―rather few of the investigated perceptual and attitudinal
factors seem to be critical‖ (Johansson & Laike, 2007, p. 447). The most important factor
deemed by Johansson and Laike (2007) for reducing public intention to oppose wind turbine
development seems to be the aesthetic appeal and fit with the surrounding environment. While
the authors of this study utilized the Theory of Planned Behavior, Chadwick-Jones (1976) very
well may have argued that Social Exchange Theory would explain any intention to oppose
development as well. The Theory of Planned Behavior ―deals with the antecedents of attitudes,
subjective norms, and perceived behavioral control, antecedents which in the final analysis
determine intentions and actions,‖ and which also ―postulates that behavior is a function of
salient information…relevant to behavior‖ (Ajzen, 1991, p. 189). While there is plenty of
evidence for significant relations between the acting variables, ―the exact form of these relations
is still uncertain‖ (Ajzen, 1991, p. 206). Social Exchange Theory is ―limited to actions that are
contingent on rewarding actions from others‖ (Emerson, 1976, p. 336). One could say that the
rewards of the development (within a community) outweigh any costs (i.e., spoilage in the
landscape). Wind developers may be able to utilize this information in further wind farm
construction and mitigate any negative responses from the community by presenting positive
information about the construction and choosing aesthetically pleasing locations to minimize the
local impact.
Jolivet and Heiskanen (2010) explored the use of the Actor-Network Theory (ANT) to
shed light on the complexities and intricacies of policy, participation and perception surrounding
wind farm development. ANT draws on a large experience of innovation processes and
management studies and has ―developed concepts to help seize both the local dynamics of
actors‘ resistance in specific project and the close intertwining of the social and material nature
of such processes‖ (Jolivet & Heiskanen, 2010, p. 6747). The concepts of ‗framing,‘ (a process
22

through which actors come together to establish a common world and achieve a collective
scenario of a desired outcome), and ‗overflows,‘ (the repercussions that follow when actors do
not ―conform to expectations, adopt conflicting positions and develop their own interpretations
of the project‖ and in affect ensuring that new frames are enacted), are the crux of this theory.
The authors applied ANT to a wind farm project in the South of France. Framing occurred when
a site was determined to house the five turbine wind farm and the surrounding municipalities, or
actors, were invited into the discussion of planning for the potential to recreate this specific area
as one that is future focused (no longer reliant on coal mining) and which would allow for
economic development through acting as a tourist destination. A large neighboring city, not
included in the planning discussion, felt as if they would be negatively impacted by co-visibility
(i.e. wind turbines were visible outside of the scope of the initial discussion ring). This city
disagreed with the current ‗framing‘ and built their own vision for the wind farm (i.e. overflow).
This vision became an alternative that could not be ignored. Jolivet and Heiskanen (2010)
suggested that ―project managers should be wary of ‗recipes‘ for participation‖ and that a
successful wind farm development is continuously reframed and adapted ―to channel and
stabilize the process of wind farm creation, and gradually make it a shared material reality that
fits its environment‖ (p. 6753).
With all of the theories and models that are available today, why is there still a problem
getting communities or individuals to support sustainable development? One explanation is that
human behavior is persistently multifaceted. Not one individual theory or model can be used to
analyze why people become involved in their community. An interdisciplinary approach is
needed to see how and why different variables affect one another. Looking only from one
perspective, without understanding how the different theories interact, would make it next to

23

impossible to suggest a viable solution for improvement and success. More research is needed
on the interplay of humans and the environment.
Summary

The importance placed on perception and community engagement regarding sustainable
development is about a decade old. Research suggests that the theory of NIMBY does not
adequately portray local opposition to a project. Recent literature suggests that NIMBY is only
part of the explanation and that further research is needed to fully understand what influences a
persons‘ perception on renewable energy. Evaluating residents‘ perceptions of wind farm
development is important as an emphasis is being placed on renewable resources as energy
sources (Swofford & Slattery, 2010). While wind farm development can help to diversify the
economy, there is a still a difference between the general acceptance of such proposals and local
oppositon that needs to be addressed. This literature review indicated that there are still many
areas that need further research. If responsibly planned with local residents, wind farms can be a
viable option for many rural communities and can lead to more positive perceptions, potential
growth in the economy and an overall increased quality of life.

24

Chapter 3
Methodology
The focus of the research was on the factors influencing wind farm perceptions held by
residents in four townships in Huron County, Michigan with current or future wind farm
development. This study utilized both qualitative and quantitative research methods.
Specifically, the study sought to answer four research questions. The first was ―Does an inverse
relationship exist between the proximity of a residence to a wind farm and positive perceptions
of wind development among residents?‖ The second question was, ―Does the level of attendance
of wind zoning meetings in the planning process of wind farms influence the support for wind
farm development among residents?‖ The third question asked, ―Does the amount of land
owned affect perception and support of wind farm development among residents?‖ The final
question was, ―What belief factor outcomes contribute to perceptions of wind farm development
among residents?‖ The conduct of the study included the following organizational steps: (a)
arrangements for conducting the study, (b) development of interview instrument, (c) selection of
interview subjects, (d) procedures for gathering and analyzing interview data, (e) development of
survey instrument, (f) selection of survey subjects, (g) administration of the survey instrument,
(h) treatment of the data, (i) interpretation of findings, and (j) policy implications.
Arrangements for Conducting the Study

The study was conducted in Huron County, Michigan upon receipt of a human subjects
safeguard clearance regarding the participant consent (see Appendix A) and interview guide (see
Appendix B) from the Institutional Review Board from Michigan State University. The study
format comprised of interviews of a small sample of local stakeholders (n=11) followed by a
mail survey to a larger sample (n=1,000) of residents.
25

Development of Interview Instrument

The interview instrument was developed based on issues that were reoccurring in the
local newspaper, issues that emerged in talking with residents in the county, issues that were
raised during public comment sessions at monthly Huron County Board of Commissioners
meetings and issues this researcher thought were important and meaningful in studying
perception. The interview instrument was divided into five overall categories: demographics,
economic development, wind farms, sustainable development and community development.
Selection of Interview Subjects

The first stage of this research was interviewing a subset of different stakeholders
regarding wind farm development within Huron County. The five stakeholder groups included
landowners with contracts/turbines on their property, landowners without contracts, rural
homeowners (those outside of the city/village limits), village residents (those living within
village/city limits) and community decision makers. This researcher conducted two to three
audio-recorded interviews per stakeholder group in summer and fall 2010. The interviews
allowed the formation of major themes to be developed. This preliminary research also allowed
for the formation of hypotheses. Rubin and Rubin (1995) stated, ―Design in qualitative
interviewing is iterative. That means that each time you repeat the basic process of gathering
information, analyzing it, winnowing it, and testing it, you come closer to a clear and convincing
model of the phenomenon you are studying‖ (pp. 46 - 47). This seemed to be the case in
interviewing a total of eleven local residents in Huron County using a semi-structured, twentyseven question interview guide. Interviewing is a complex and delicate procedure that requires
advanced planning and forward thinking. Like most research methods, this is not something that
one conceives and completes without mindful consideration of the root of the issue. The
26

researcher must have a clear direction in what he or she expects to gain from the interviewee in
order to keep the interview going in the right direction. Babbie (1998) noted that interviewing is
much like a normal conversation, however one must be mindful ―that you are not having a
normal conversation‖ (p. 292). Two individuals (a rural resident and a community decision
maker) were initially chosen as interviewees. The remaining nine interviewees were determined
by utilizing the snowball sampling technique. Kumar (1996) stated that ―snowball sampling is
the process of selecting a sample using networks;‖ interviewees suggest others to interview (p.
162).
Procedures for Gathering and Analyzing Interview Data

Once the amount of detail and depth was determined in interview transcription, the
dialogue was transcribed into detailed notes including some quotations. Following this step, the
coding process began. There is a prodigious amount of ways to code data (Babbie, 1998;
Bernard, 2010; Boyatzis, 1998; Glesne, 2011; Saldana, 2009). In thematic analysis, the
researcher organizes the coded data and identifies patterns or themes. Glesne (2011) stated that
―making comparisons is an analytical step in identifying patterns within some theme‖ (p. 188).
Thematic analysis is a way to encode qualitative information.
There are three distinct stages of thematic analysis and they are: (a) deciding on sampling
and design issues, (b) developing themes and a code, and (c) validating and using the code. In
the first stage, a researcher must determine what and how many of a population will be sampled
and design their research instrument accordingly. Within the second stage of developing themes
and a code ―there are three different ways to develop a thematic code: (a) theory driven, (b) prior
data or prior research driven, and (c) inductive or data driven‖ (Boyatzis, 1998, p. 29). Theory
driven code development is the most common used approach in social science research and one
27

that this researcher employed. The researcher begins with a theory and then ―formulates the
signals, or indicators, of evidence that would support this theory‖ (Boyatzis, 1998, p. 33).
Theory driven codes are subject to the researchers‘ beliefs and assumptions and this may prove
difficult to determine what the interview data may be truly saying. In the third stage of
‗validation and using the code,‘ the researcher double checks to ensure that consistent coding is
taking place and that codes are clearly defined.
Key concepts from the interviews were placed in a large matrix with the interview
questions in a row along the top and the interviewees in a column along the left hand side. This
researcher formulated eight major themes. These themes helped to forecast the answer to the
research question, ―What belief factors contribute to a positive, negative or neutral perception of
wind farm development.‖ In an effort to be transparent in motives and procedures utilized,
researchers should be clear in their relationships to their subjects or the research topic in general
so that others may determine if there are any biases in the research or analysis. This researcher
was originally from Huron County and the interest to study wind farms was developed from
wind farms constructed there. In addition, the first two interviewees this researcher had met
once before which implies there was some sort of standing relationship before the interview
started. This researcher however did not feel that impeded the results; the interviewees felt more
comfortable sharing information. As was indicated earlier, the interviews helped to inform a
survey, which was distributed in early 2011 to further analyze factors influencing perception of
wind farm development.
Development of Survey Instrument
The survey instrument, ‗Resident Wind Farm Perception Survey,‘ was developed based
on the themes that emerged from the interviews and also from studying two similar instruments
28

developed by researchers who had implemented surveys on wind farm perceptions elsewhere one in Texas (Swofford & Slattery, 2010) and one in the United Kingdom (Jones & Eiser, 2009).
The Texas study utilized a mail survey to ―identify the physical and environmental
characteristics that are linked to both negative and positive perceptions of wind farms‖ of 1,500
randomly selected households within a 20 km radius of a wind farm (200 were returned)
(Swofford & Slattery, 2010, p. 2512). Frequencies were used in data analysis. The UK study
utilized a door-to-door survey of 1,200 households (600 to target towns within one mile of a
turbine and 600 to comparison towns) (843 surveys were returned). Chi-square tests,
independent samples t-tests, along with simple and multiple regression, were utilized in analyses.
This researcher contacted the authors of these studies and obtained a copy of the survey
instrument they had disseminated in their study. Some survey questions were identical in nature
to those distributed by Jones and Eiser (2009) and Swofford and Slattery (2010); this has aided in
drawing conclusions and implications for further research. Dillman‘s (2000) mail protocol was
also consulted to determine the best layout design, paper color, and spacing timeframe for
reminder postcards and a follow-up survey.
The survey contained yes/no, open ended and Likert-type questions with nominal, ordinal
and interval categories. The survey was broken into the following four sections: primary
residence, perceptions of wind turbines and wind energy, level of support for wind energy and
general demographics. Four questions regarding perceptions of wind energy were interval in
nature while two questions regarding level of support included both interval and nominal
categories. Two questions regarding perceptions held ‗before‘ and after (‗now‘) wind farm
development were used in testing hypotheses about perceptions. The two questions asked about
the general perception about wind energy both before and after development of wind farms in
Huron County using a five-point Likert scale; very negative to very positive including the option
29

of ‗no opinion.‘ One question (level of support for wind farms within Huron County) was used
in hypothesis testing of level of support of wind farms. This question asked the locations the
respondent would be willing to support wind farms using a five-point Likert scale; strongly
disagree to strongly agree. One question of the survey regarding knowledge level of wind
energy was used in hypothesis testing the influence of knowledge on level of support and
perceptions of wind farms.
Township Description
Huron County is located in the ―thumb‖ area of Michigan, which is surrounded on three
sides by water – Saginaw Bay and Lake Huron. The county has a total land area of 824 square
miles, which is 139 square miles greater than the average square miles for the other 82 counties
in the state. Huron County ranks as one of the top agricultural counties in Michigan based on
agricultural farm income. Some of the major crops grown include corn, navy beans, sugar beets,
wheat and alfalfa, while major enterprises include dairy and livestock production. Huron County
is an ideal setting for the tourism industry because of the natural beauty and ideal topography.
Although small industry and tourism is included in the county‘s industry portfolio, agriculture
remains the chief source of income for most residents (Government, 2006). In 2005 landowners
in Huron County were approached by wind developers to lease land in exchange for a monetary
incentive (generally around two dollars per acre). These ‗signing bonuses‘ enticed landowners to
enlist their land, with the promise of a sizeable sum of money ($10,000) to be paid if a turbine
was constructed on their property. Royalties from energy produced would be paid to those
landowners who had leased land to that specific developer. Landowners who did not sign up
land were excluded from any monetary benefit.
The study area is comprised of four rural townships in Huron County; two coastal and
two inland. Bingham Township, population 1,751, is an inland community home to the largest
30

commercial-scale wind farm in Michigan, Michigan Wind 1. The project consists of 46 GE 1.5
megawatt turbines with a total capacity of 69 megawatts – enough to supply 20,000 homes with
electricity. This project is expected to reduce over 124,000 tons of carbon dioxide annually
(Development, 2007). Oliver township, population 1,626, is an inland community with a variety
of entities from many small businesses to agricultural farms growing a variety of crops and
livestock, to a Cooperative Elevator Company, a Tier 1 auto supplier and the second largest wind
farm in the State of Michigan (Oliver Township, 2010). Harvest Wind Farm is located on 3,200
acres consisting of 32 Vestas turbines, each capable of producing 1.65 megawatts of electricity,
for a total project capacity of 52.8 megawatts – enough to supply electricity to more than 15,000
Michigan homes. Harvest Wind Farm was the first utility-scale wind farm operating in
Michigan. A Northern Michigan Company has an agreement to purchase the energy produced,
which is sold to customers throughout Michigan. Ownership of the two wind farms was bought
and sold numerous times by various companies in the last five years. As of April 2011, Exelon
Corporation owns both wind farms in Huron County. As of April 2011 Exelon was not the only
developer in the county - other wind developers were still actively pursuing wind farm projects
within areas of Huron and neighboring counties.
Rubicon, population 778, and Sand Beach, population 3,307, townships are coastal
communities. While neither of these townships had wind farms in 2011, plans for wind farm
construction within Rubicon Township had been progressing towards that avenue. Sand Beach
Township, while not formally being pursued by wind developers in 2011, had numerous acres
enlisted in wind development leases. Sand Beach and Oliver townships do not reside under
Huron County zoning and therefore procedures for any type of development must go through
different channels for approval than Bingham or Rubicon townships.

31

Selection of Survey Subjects
Four townships in a single county (Huron) were purposely selected to study wind energy
perceptions held by local residents. The townships were Bingham, Oliver, Rubicon and Sand
Beach. Each township contained a village or city: Ubly, Elkton, Port Hope and Harbor Beach.
The remainder of this thesis will refer to Bingham township as township #1, Oliver township as
township #2, Rubicon township as township #3 and Sand Beach township as township #4.
Townships #1 and #2 were chosen as they contained the only developed wind farms in Huron
County. Townships #3 and #4 were chosen as comparison townships as they were neighboring,
but non-adjacent, townships whose potential for wind development in the future is very good. In
addition, township #4 is the hometown of the researcher. Two of the townships (#1 and #2) are
inland and two are coastal (#3 and #4). Townships #1 and #3 had the opportunity to vote in
November 2010 on two ballot measures to allow future wind farm development within Huron
County. Townships #2 and #4 did not have the opportunity to vote on this issue as these
townships do not reside under County zoning.
The Huron County Tax Assessors Office was contacted to acquire a list of names,
addresses and parcel descriptions from the four townships in this study. A total of 10,390
addresses were obtained. In an effort to study primary residency only those entries in which the
owner and property addresses matched were selected and placed into a separate database. All
business, commercial, exempt, industrial and entries in which the owner and property addresses
did not match were excluded. The townships had varying amounts of entries in which the
primary and owner addresses matched: township #1 (including the village of Ubly) - 475,
township #2 (including the village of Elkton) – 246, township #3 (including the village of Port
Hope) – 177, and township #4 (including the city of Harbor Beach) – 771. Only 17% of the
addresses acquired from the Tax Assessors Office were primary addresses. Since townships #2
32

and #3 had a proportionately smaller land owning resident population, it was deemed to be in the
best interest to include all of these entries in the mail survey. The remaining 577 addresses were
proportionately selected from townships #1 and #4.
The cumulative population of the two townships (#1 and #4) total primary addresses was
1,246. The method probability proportional to size was implemented to determine the number of
addresses that should be selected from the remaining townships (Ott & Longnecker, 2010).
Township #1 (475) was 38% of the total 1,246 with township #4 (771) being 62% of the total
addresses. Each townships proportion were then multiplied by the remaining 577 addresses
needed. To reach the goal of 1,000 addresses across four townships the following were the total
addresses selected from each township: township #1 – 220, township #2 – 246, township #3 –
177 and township #4 – 357. Every other entry within townships #1 and #4 were selected with 18
additional random entries deselected from township #1 and 28 random entries deselected from
township #4 to reach the desired address amounts (See Table 1). In an effort to reach female
landowners, every other female name in the tax assessors list was chosen as this list
predominately lists male names first.
Table 1. Township Categorization
Township

Location

Wind Farms
in 2010

Voted on
Future Wind
Development

Total
Primary
Addresses

Township #1
Township #2
Township #3
Township #4
Total

Inland
Inland
Coastal
Coastal

Yes
Yes
No
No

Yes
No
Yes
No

475
246
177
771
1,699

33

Number of
Addresses
Selected for
Study
220
246
177
357
1,000

Administration of the Survey Instrument

The initial round of surveys were distributed on January 21, 2011. The 1,000 survey
addresses were printed on address labels and a large postage stamp was affixed on each
envelope. A cover letter and business reply envelope were included with the survey. Each
survey was numbered one through 1,000 and entered into the address database to track those who
had and had not responded. As surveys were returned, they were keyed into SPSS and marked
as returned in the address database. Reminder postcards were sent out one week later on
January 31, 2011. On February 11, 2011 a final mailing of cover letters and surveys were
distributed to those who had not yet returned their survey. These surveys were assigned a new
number prefixed with the number ‗2‘ to further track who returned surveys. These assigned
numbers were also imported into the address database.

The data collection period closed on

March 4, 2011 at which time a table of random numbers was used to determine who would
receive each of four $25 Visa gift cards. The first four numbers of each randomly selected entry
in the table of random digits was used to determine the winners. A cover letter accompanied the
Visa gift cards in the mail.
Treatment of the Data

The interviews were audio recorded. The audio recordings were transcribed into detailed
note form after each interview had taken place. Interview data analysis consisted of reading
through all notes and coding the data. Open coding was used to break the data into larger
categories and axial coding was used to create new categories by identifying relationships
between initial categories. Each interview was coded and then transferred onto a large matrix.
This matrix allowed the researcher to easily see patterns or anomalies between interviewees
(Glesne, 2011).
34

Survey data were analyzed using frequencies, contingency tables, chi-square tests,
correlations, ANOVAs and the Scheffe‘s Test along with linear regression utilizing an alpha
level of p<.05. The Scheffe‘s Test was chosen because this can be used ―to make all possible
comparisons among the t population means, it is more conservative‖ and less sensitive than
other procedures ―for detecting significant differences among pairs of population means‖ (Ott &
Longnecker, 2010, p. 476). There may be other procedures that are more powerful, but the
Scheffe‘s Test still ―finds the critical difference between any two sample means that is necessary
to reject the null hypothesis that their corresponding population means are equal‖ (Sirkin, 1995,
p. 303). The IBM SPSS Statistics, version 19 was used to analyze the data. Any incomplete
questions were treated as missing values; they were not included in the analysis. Total columns
are never presented in data analysis tables in this study. An overview of the steps taken to
analyze survey data follows in Table 2.
Table 2. Data Analysis Steps
Hypothesis
H1

Steps Taken in Analysis
1. Chi-square testing for two variables with nominal, ordinal data and
ANOVAs with interval, ratio data for each survey question
2. Recoding townships within SPSS into two categories – those living
within 1 mile or greater than 1 mile from a turbine
3. Independent Samples t-testing of proximity and support; proximity and
‗before‘ and ‗now‘ perceptions
4. Compute new variable to measure the change in perception from ‗before‘
to ‗now‘
5. Crosstabs: the number of survey respondents whose perceptions did not
change over time (from ‗before‘ development to ‗now‘ – after
development)

35

Table 2 (cont‘d). Data Analysis Steps
H2

H3

H4

1. ANOVA testing: independent variable- meeting attendance; dependent
variable – knowledge of renewable energy, wind energy and sustainable
development
2. Bi-variate and Partial (controlling for meeting attendance) Correlation:
independent variable- knowledge of wind energy; dependent variable –
perception ‗before‘ and ‗now‘
3. Bi-variate and Partial (controlling for meeting attendance) Correlation:
independent variable- knowledge of wind energy; dependent variable –
level of support of wind farms within Huron County
4. Independent samples t-testing: independent variable – meeting
attendance; dependent variables – perceptions, ‗before‘ and ‗now‘
5. Independent samples t-testing: independent variable – meeting
attendance; dependent variable – level of support of wind farms within
Huron County
1. Compute a new variable – rural or village resident from responses to
survey question 1 – residency location
2. Compute a new variable – creation of 4 categories of acres owned based
on responses to survey question 2
3. Chi-Square testing: independent variables – residency location (rural or
village); dependent variables – 4 categories of acres owned
4. Independent samples t-testing: independent variable – residency location;
dependent variables – perceptions, ‗before‘ and ‗now‘
5. Independent samples t-testing: independent variable – residency location;
dependent variables – level of support of wind farms within Huron
County
6. ANOVA testing: independent variables – 4 categories of acres owned;
dependent variable – perception, ‗before‘ and ‗now‘
7. ANOVA testing: independent variables – 4 categories of acres owned;
dependent variable – level of support of wind farms within Huron County
1. Compute new variables (economic, environmental, social) based on
categorization of statements in question 8 regarding wind energy
2. Compute new variables: economic average, environmental average,
social average
3. Reliability Analysis – internal consistency confirmed
4. Simple Linear Regression Analysis: independent variables – economic,
environmental and social averages; dependent variable – level of support
of wind farms within Huron County
5. Simple Linear Regression Analysis: independent variables – economic,
environmental and social averages; dependent variable – perception
‗before‘
6. Simple Linear Regression Analysis: independent variables – economic,
environmental and social averages; dependent variable – perception
‗now‘

36

Chapter 4
Analysis of Data

The problem of the study was to determine what factors influence perception of and
support for wind farm development. Included in the study was an attempt to prioritize which
belief factors have influence over residents‘ perceptions. The analysis of the data is presented in
this chapter according to the following topics: 1) interview results, 2) data-gathering instrument
distribution, 3) descriptive finding, and 4) hypothesis testing.
Interview Results

Overall, this researcher interviewed 11 residents of Huron County. Research question 1,
‗Does an inverse relationship exist between the proximity of a residence to a wind farm and
positive perceptions of wind development among residents?‘ and research question 2, ‗Does the
level of attendance of wind zoning meetings in the planning process of wind farms influence the
support for wind farm development among residents?‘ were unable to be supported or rejected
from the responses to the interviews. Interviewees did not directly address these questions as the
interviews were meant to create an understanding of the differing perceptions held by residents
and to help inform the development of the survey instrument.
Importing interview data into a large matrix allowed for themes and patterns to emerge.
Analyzing the matrix provided evidence for some support for the hypothesis that ownership of
land influenced wind farm perception. Nine out of the eleven interviewees held either a positive
or a neutral perception of the local wind farms (see Table 2). Those interviewees who own very
little land are hold neutral perceptions, while those who own many acres hold positive
perceptions of wind farms. Two of the interviewees were anomalies – they owned property yet
held negative views of the wind farms. The level of uncertainty and fear of change may have
37

Table 3. Interviewee Results - Land Ownership and Wind Farm Perception
Interviewee #
#1
#2
#3
#4
#5
#6
#7
#8
#9
#10
#11

Acres of Land Owned
City Lot
City Lot
City Lot
Rural Lot
0 acres = Rural Renter
85 acres
120 acres
140 acres
560 acres
700 acres
N/A

Wind Farm Perception
Neutral
Neutral
Neutral
Neutral
(+)
(-)
(-)
(+)
(+)
(+)
(+)

been stronger in those two individuals than the rest of the interviewees. Uncertainty was one of
the concepts that was determined to be a factor influencing the overall view of perception.
While uncertainty was expressed by most of the interviewees, the majority still viewed wind
farms in a neutral or positive manner. This preliminary finding would support hypothesis 4,
‗Economic beliefs are the driving factor in shaping perceptions and support of wind farm
development as opposed to social or environmental beliefs.‘
Implementing thematic analysis allowed eight major themes to emerge. These themes
helped to inform the research question, ―What belief factor outcomes contribute to perceptions of
wind farm development among residents?‖ The interview data indicated that the factors that
were seen to be ‗limiting development‘ (i.e. lack of highway, improved/widening of M-53), the
‗potential for monetary gain‘, the ‗potential for employment‘ and the ‗potential for further
increased interest in development‘ (by other companies) are some of the positive themes
associated with favorability or a positive perception of wind farms. The environmental impact
considerations constituted a danger to wildlife in bird migration and deer feeding grounds. The
social themes including ‗information source‘ and ‗uncertainty‘ seem to lead to a more negative
view of wind farms in the area. The last theme of ‗perception‘ is segmented into both positive
38

and negative with the list of negatives (i.e., large body of misinformation, minds are made up and
not easily changed, uncertain health effects on humans) far outweighing the common threads
through the positive associations (i.e., additional source of revenue) (see Table 3). One
Table 4. Interview Emergent Themes
Environmental
 Environmental Impacts
Social
 Information Source
 Perceptions
 Uncertainty

Emergent Themes
Economic
 Monetary Incentives
 Employment Options
 Potential Development
 Limiting Development

additional common thread through the majority of the interviews was the escalating hardship in
farming. This was not in itself made into a theme as this researcher saw this topic as more of a
supporting issue than a main content issue. This appears to be a motivating factor in acceptance
of wind farms in the face of a long list of uncertainties and seems to contribute to why economic
incentives may override all negative aspects associated with wind farm development. This
researcher would classify farming hardship as a subset of economic incentives.
The eleven interviewees felt strongly about a number of topics asked in each interview.
The following statements from some of the interviews revealed that there were many factors
influencing perceptions. Interviewee nine thought that jealously may have played a part in the
surrounding negativism around the wind farms, but that the community ―didn‘t go into this
totally blind‖ as two charter buses filled with residents and community decision makers visited a
wind farm in Fenner, New York before the wind farm construction began. This interviewee
went on to say ―the turbines in the area are purely economic – it‘s not because we want to be
green.‖ When asked what were some of the keys to success for the area this interviewee said,
―there isn‘t a whole lot of anything that is considering coming into the area. Not that I‘m locked
39

on green again, but solar panels could be something or electric cars. Nothing else is coming into
Huron County – turbines are all we have.‖
Interviewee four sounded a little more pessimistic regarding wind farms. This
interviewee focused on the amount of uncertainty regarding wind turbines, the developers, and
the science behind wind energy generation. This interviewee admitted that most of the
information obtained was through the newspaper or ‗through the grapevine,‘ and had come to the
conclusion that the wind developers ‗tell you so much until they get them up and then things
change. That is the scary part – once they get a toe-hold on the land where will they stop?‖ This
interviewee thought that the construction was rushed and that not enough education was supplied
to the residents, which only adds to the uncertainty in their eyes. ―People are jumping towards
the money and aren‘t thinking about the long-term effects.‖ Interviewee six agreed and added
that,
―less tax money is coming in and they [county officials] are looking for a lifeline
– if that is paid by wind farms they [county officials] are going to grab it – bar any
consequences. It is a way to keep a guaranteed stream coming into the county.
Usually the dollar sign drives what happens – unfortunately that is what is driving
their [county officials] desire to have 1,000 [turbines].‖

Interviewee five spoke about the different perceptions surrounding the two wind farms
and how the developer‘s process of approaching the community led to the perceptions residents
hold today. In township #2 the wind developer, John Deere, ―came and laid out everything in
front of the people. Noble (the wind developer in township #1) signed up people before they
ever went public…they approached it wrong – they bowled themselves into the community.‖
This interviewee went on to say that ―if you want to put rumors to rest – tell the truth.
Misinformation is the biggest problem.‖ This interviewee stressed the importance that
misinformation played in shaping the perceptions of wind energy and wind farms. ―We are all
40

ignorant until we are taught.‖ People will rarely deviate from the status quo unless they are
presented with new information; ―put figures in front of people – it would change minds or put
efficiencies [hard numbers] in front of people.‖ This interviewee also emphasized that ―people
look year to year and not decade to decade…we want what we want now …this leads to big
issues down the road.‖
Interviewee seven agreed that more information needs to be made available to the public
and felt like the first round of development (in townships #1 and #2) was too rushed. Initially,
the wind developers contacted only those landowners surrounding this interviewee who owned
large tracts of land and did not approach this interviewee who owned only 120 acres. When the
wind developers needed this interviewees‘ land to act as a buffer zone to make the siting and
construction of a wind turbine possible this interviewee said, ―We were not good enough in the
beginning to be involved…why do you want me now?‖
Interviewee eleven provided some clarification on the big picture of wind farm
development and said,
―the goal of the [Huron County] Master Plan is to retain the 400,000+ acres of
farmland and limit where new residential and industrial developments can occur.
That is the reason we…developed the wind overlay concept maps [to] encourage
wind development in prime ag land…this becomes a catalyst for ag preservation
[and] discourage non-farm use in farm areas.‖

This interviewee stressed that the Master Plan was not eliminating, but intended to, discourage
an isolated acre or two from residential development. Overall, the list of economic belief factors
out-numbered the voiced environmental or social belief factors. After reviewing the initial
conclusion of research question 3 (acres owned) and data from research question 4 (belief
factors), this led to the conclusion that economic incentives have a stronger influence on
perception than social or environmental beliefs.
41

Data-Gathering Instrument Distribution

The sample of survey recipients included 1,000 residents from four different Huron
County townships. Each household was asked to return the survey in the postage paid envelope
to the investigator. The results of the mailings are presented in Table 5.
Table 5. Mail Survey Statistics

Surveys Sent
Number of Addresses Undeliverable
Surveys Returned
Percent
Number Declined to Participate

First Mailing
1,000
5
354
35%
6

Second Mailing
646
2
143
22%
0

Total
1,000
7
497
50%
6

The initial mailing yielded a return of 354 completed surveys (35 percent), 6 residents who
declined to participate (6 percent) and 5 envelopes that were returned undeliverable (5 percent).
A second mailing of 646 surveys yielded a return of 143 completed surveys (22 percent) and 2
envelopes that were returned undeliverable (2 percent). A total of 503 (50 percent) residents
failed to complete and return the surveys by the data collection closing date.
Descriptive Findings

The age of all survey participants ranged from 23 to 94 years of age with the average age of
respondents being 58 years old. An even distribution (50/50) of male (n= 247) and female
(n=242) respondents completed the survey with eight participants omitting their gender. A chisquare test revealed that all four townships were similar with respect to the number of male and
female respondents (p>.05) (see Table 6). The range of number of years living in Huron County
over the participants lifetime ranged from 1 to 94 years with 44 years being the average length of
time spent living in Huron County (see Table 7).

42

Table 6. Gender by Township
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
Male
48.2%
51.4%
55.1%
49.4%
a
Gender
Female
51.8%
48.6%
44.9%
50.6%
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
No significant differences were revealed.
*p<.05, **p<.01, ***p<.001

ChiSquare
1.083

Table 7. Age and Mean Length of Residency

Mean Age

With Wind Farms
Twp #1
Twp #2
54.3 yrs
59.4 yrs

Without Wind Farms
Twp #3
Twp #4
59.7 yrs
57.6 yrs

F
3.283*

Mean length of
43.6 yrs
49.1 yrs
38.4 yrs
43.1 yrs
5.452**
Residency
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
No significant mean group difference.
*p<.05, **p<.01, *** p<.001

Group
Comparison
a

n.s.
2>1

The following include the number of respondents of the four townships. Fifty-two
surveys were returned from residents living within the village of Ubly with an additional 62
surveys being returned from residents residing outside the village limits but still within township
#1 (n=114). Sixty surveys were returned from residents living within the village of Elkton with
an additional 52 surveys being returned from residents residing outside the village limits but still
within township #2 (n=112). Twenty-five surveys were returned from residents living within the
village of Port Hope with an additional 66 surveys being returned from residents residing outside
the village limits but still within township #3 (n=91). Ninety-two surveys were returned from
residents living within the city of Harbor Beach with an additional 85 surveys being returned
from residents residing outside the city limits but still within township #4 (n=177) (see Figure
1).

43

FIGURE 1. Surveys Received by Township
Surveys Received by Township
200
150

92

100

52

60

25

62

52

66

85

Bingham Township

Oliver Township

Rubicon Township

Sand Beach
Township

50
0

Outside Village/City Limits

W/in Village/City Limits

Three surveys had the written identification number colored over by black ink making it
impossible to determine who had returned those specific surveys or what township they lived in.
The survey response rates ranged from 46% in township #2 to 51% in townships 1 and 3 (see
Table 8) ending with an overall response rate of 50%.
Table 8. Survey Response Rates by Township
Township

Location

Total
Primary
Addresses

Number of
Addresses
Selected for
Study
Township #1
Inland
475
220
Township #2
Inland
246
246
Township #3
Coastal
177
177
Township #4
Coastal
771
357
Total
1,699
1,000
* Three surveys had the identification number marked out.

Total
Number of
Surveys
Returned
113
112
91
178
494*

Response
Rate

51%
46%
51%
50%
50%

Respondents in two of the four studied townships had the opportunity to vote on allowing future
development of wind farms in Huron County in November 2010. Only the residents under
County Zoning (townships #1 and #3) living outside of village/city limits were allowed to vote
on Proposal 10-03 and 10-04 on November 2, 2010. Proposal 10-03 regarded allowing
44

construction of wind farms in Bloomfield, Rubicon and Sigel Townships. Proposal 10-04
regarded allowing construction of wind farms in McKinley Township. Townships #1 and #3 had
over a 42% voting turnout with both proposal 3 and 4 passing by majority vote (see Table 9).
Table 9. Registered Voters and Voting Behavior
With Wind Farms
Twp #1
Twp #2
a

Without Wind Farms
Twp #3
Twp #4

Registered Voters
1256
1088
562
Voter Turnout
602
460
341
Percentage
47.9%
42.3%
60.7%
In Favor
54.4%
-52.6%
b
Oppose
40.1%
-45.5%
Proposal 3
n
329
-211
In Favor
52.9%
-52.1%
b
Oppose
41.2%
-43.1%
Proposal 4
n
329
-211
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach

961
493
51.3%
-------

a

Includes all registered voters within the designated townships

b

Only the residents under County Zoning (Twp #1 & Twp #3) living outside of village/city limits
were allowed to vote on Proposal 10-03 and 10-04 on November 2, 2010. Percentages not
equaling 100% take into account those individuals who did not vote on the specific proposals.

Townships #2 and #4 were allowed to vote on all other agenda items. Countywide, the proposals
passed with a 2/3 majority vote. When comparing actual voting results to survey results, survey
responses indicated a greater percentage of opposition than the actual proposal voting results (see
Table 10). The question on the survey asked if the respondent would support wind farm
development at any location (public land, farm land, etc) within Huron County. Only those
respondents from townships #1 and #3 who lived outside of village limits were included in the
comparison table 10. Fifty-nine out of the 62 respondents in township #1 and 62 out of the 66
respondents in township #3 responded to this question.

45

Table 10. Comparison of Voters and Survey Respondents on Support of Development
Dependent Variable

With Wind Farms
Without Wind Farms
Twp #1
Twp #3
a
a
Survey
Survey
Proposals
Proposals
In Favor
49.2%
53.7%
50.0%
52.4%
Support of
Oppose
50.8%
40.7%
50.0%
44.3%
development
n
59
329
62
211
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a

Includes all registered voters within the designated township voting on Proposals 10-03 and
10-04. Only the residents under County Zoning (Twp #1 & Twp #3) living outside of village/city
limits were allowed to vote on Proposal 10-03 and 10-04 on November 2, 2010. Percentages not
equaling 100% take into account those individuals who did not vote on the specific proposals.
This table reflects a comparison of survey respondents outside of city limits and those who were
allowed to vote in the selected townships.

Survey participants and November 2010 registered voters are similar in regards to gender
composition and age distribution (see Table 11).
Table 11. Gender and Age Comparison of Registered Voters and Survey Respondents
With Wind Farms
Twp #1

Twp #2

Voted on 11/2/10
a
(n=602)

Survey
Respondent
(n=112)

47.9%
52.1%

55.0%
45.0%

Voted on 11/2/10 Survey Respondent
a
(n=460)
(n=111)

Gender
Male
Female

47.5%
52.5%

47.1%
52.9%

Age
18-21
1.3%
0.0%
1.1%
22-30
7.5%
5.4%
7.6%
31-40
11.1%
10.7%
11.9%
41-50
21.4%
24.0%
16.3%
51-59
18.9%
29.4%
21.5%
60+
39.8%
30.5%
41.6%
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a

0.0%
2.7%
10.8%
15.3%
13.8%
57.4%

Reflects those who voted in the November 2, 2010 election – regardless if they were allowed to
vote on the proposals or not.

46

Table 11 (cont‘d). Gender and Age Comparison of Registered Voters and Survey Respondents

a

Without Wind Farms
Twp #3

Twp #4

Voted on 11/2/10
a
(n=341)

Survey
Respondent
(n=89)

51.3%
48.7%

50.8%
49.2%

Voted on 11/2/10 Survey Respondent
a
(n=493)
(n=176)

Gender
Male
Female

47.5%
52.5%

54.8%
45.2%

Age
18-21
1.2%
0.0%
1.1%
22-30
4.8%
1.1%
5.6%
31-40
6.8%
8.8%
9.6%
41-50
12.8%
20.2%
13.6%
51-59
17.2%
15.3%
20.0%
60+
57.2%
54.6%
50.1%
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach

0.0%
3.6%
10.0%
13.9%
37.1%
35.4%

a

Reflects those who voted in the November 2, 2010 election – regardless if they were allowed to
vote on the proposals or not.
Additional Comments on Survey
Space was intentionally left at the end of the survey which asked for and allowed survey
participants to write any additional comments they would like to share. A total of 241
respondents, or 48.5% of all respondents, provided comments. These additional comments were
not analyzed in this study but are inserted in Appendix D for reference.
Hypothesis Testing
The following pages will entail a detailed look at data analysis results.
Research Question 1
The first research question asked about the relationship between proximity of a residence
from a wind farm and perceptions and support of wind farm development. Three questions in
the survey related to distance from wind turbines; (q1, q3 and q7), four questions addressed
perception (q5, q9, q10 and q11) while two questions related to support (q12 and q13).
47

An initial analysis utilized both one-way ANOVA and chi-square test of each individual
question to identify significant associations in responses between township samples and
geography measures (see Appendix C – Table 1a). The first survey question asked about the
location of the survey participant‘s residency. A chi-square test revealed that survey respondents
in township #2 were more likely to live within village limits than townships #1, #2 or #4 (p<.01).
Survey respondents in township #1 were more likely to live outside of village limits in the
country than townships #2, #3 or #4 (p<.001), while those survey respondents living in township
#3 were most likely to live outside of village limits along or near the Lake Huron coastline
(p<.001).
A question asked about the distance in miles that the respondent lived to the nearest wind
turbine. A one-way ANOVA was used to determine that there was a significant difference
between those who lived in townships with wind farms and those who did not (see Table 3a).
Respondents in township #3 lived the furthest away (mean miles of 20.38) and those who lived
in township #1 lived the closest to wind turbines (mean miles of .82), p<.001. In validating
participants‘ responses, the survey responses were split up into two categories: those that lived
in a township adjacent/containing turbines and those that lived in nonadjacent/comparison
townships. Each township is a six mile by six mile section respectively. Using a Huron County
Township map, the farthest a resident in township #3 is from a turbine in township #2 is
approximately 26 miles while that resident would be approximately 14 miles from a turbine in
township #1. The farthest a resident living in township #4 would be from a turbine in township
#2 is approximately 30 miles while that same resident would only approximately 13 miles from a
turbine in township #1. Each survey respondent within township #1 and township #2 should
have listed a maximum of 6 miles away for a turbine since they are within a six by six mile

48

section. Correspondingly, residents living within township #3 and township #4 should have
written that they lived 6 miles or greater to the nearest turbine.
A cross tab of distance across townships and the information provided above indicate that
the division between those who would be considered living ‗adjacent‘ or ‗not adjacent‘ to
turbines would occur between the 6 and 7 mile marker. Of the 467 survey respondents who
answered this question (30 didn‘t answer), 210 surveys out of the 225 surveys returned from
township #1 and township #2 indicated that they lived six miles or closer to a turbine while 261
surveys out of the 269 surveys returned from township #3 and township #4 indicated that they
lived greater than 6 miles from the closest turbine. The following indicate the responses that are
highly unlikely: one survey from township #2 indicated that the participant lived 10 miles from
the closest turbine, one survey from township #3 indicated that they lived .5 miles away, one
survey from township #3 said 4 miles, one survey from township #4 indicated that they lived one
mile away, two surveys from township #4 indicated that they lived 3 miles away, two surveys
from township #4 indicated that they lived 5 miles away and one survey from township #4
indicated that they lived 6 miles from the nearest turbine. Overall, a small percentage of
respondents misrepresented the distance they lived from a turbine.
Next, respondents were asked about the frequency and location of seeing wind turbines
using a five point Likert-type scale (1 = never, to 5 = everyday). In all cases, the respondents
who reside within a township with wind farms (township #1 or #2) see wind turbines more
frequently when at home, when driving, when in the countryside, when in town and when along
the coastline than those who do not live within a township with wind farms, p<.001(Table 7a).
Analyzing question 1, 3 and 7 collectively, the two townships with wind farms (township #1 and
#2) and the two townships without wind farms (township #3 and #4) were deemed sufficiently
similar to each other to be compared within subsequent analyses.
49

Four questions in the survey related to wind farm perception; question 5, 9, 10 and 11.
Question 5 and 9 asked about perceptions held about wind farms both ‗before‘ the wind farms
were constructed and ‗now‘ that the wind farms exist (Table 5a). There was not a significant
difference in perception across all four townships ‗before‘ the wind farms were developed.
There was, however a significant difference between townships #1 & #2 and townships #2 & #3
‗now‘ that the wind farms have been constructed, p<.01. Survey respondents in township #2
were significantly more positive in their perceptions than respondents in township #1 or #3.
There was a significant difference in the change in perception from ‗before‘ to ‗now‘ between
townships #1 & #2 and townships #2 & #3, p<.001. On average, survey respondents‘
perceptions in three out of the four townships (township #1, #3 and #4) became more negative
over time, while the perceptions of survey respondents in township #2 became more positive
over time (see Appendix C, Table 5a).
A question asked about level of agreement of whether wind turbines symbolize a sign of
progress in the modern energy crisis (Table 9a). A significant difference was found in the means
between those respondents in townships #2 and #3. Township #2 held stronger beliefs that wind
turbines symbolize progress than all other townships. Question 11 asked about the level of
agreement of whether wind energy is beneficial to have in Huron County (Table 10a). A
significant difference was found to exist between townships #1 & #2 and townships #2 & #3,
p<.001. Survey respondents in township #2 held the highest level of agreement amongst all four
townships.
Two questions within the survey related to level of support of wind farms; question 12
and question 13. Question 12 asked about the level of support for development of wind farms on
different locations within Michigan (Table 11a). ANOVA determined that there was not a
significant difference in level of support on the sections ‗don‘t support‘ (p>.05), ‗on public land‘
50

(p>.05), ‗on the coast of Huron County‘ (p>.05), ‗off the coast of Huron County‘ (p>.05) and
‗within Michigan‘ (p<.05 –however there was no significant mean group difference). On three
of the items there was a significant difference in responses between townships #1 and #2; ‗on my
property‘ (p<.05), ‗on farmland‘ (p<.01) and ‗within Huron County‘ (p<.01). There was a
greater number of significant differences in the section asking about level of support ‗within
sight of my property‘ (p<.001) where survey respondents in township #2 were significantly more
favorable to support turbines within sight of their property than respondents in townships #1, #3
or #4. In analysis of question 13, a chi-square test revealed that respondents in township #2 were
more supportive of wind farm development than respondents in all three other townships (Table
12a).
Next, an independent samples t-test analysis was utilized to further test the relationship
between proximity and support of wind farms within Huron County. Each survey respondent
was coded into either living within one mile from a wind turbine or greater one mile. The
independent samples t-test revealed that there was no significant difference between
respondents‘ support in townships #1, t = .341, p>.05 and township #2, t = -.832, p>.05 and the
proximity to a wind turbine (Table 12).
Next, an independent samples t-test was utilized to further test proximity (living within
one mile of a wind turbine or greater than one mile) and perception of wind farms. The analysis
revealed no significant differences: township #1 ‗Before‘ perception, t =1.824, p>.05; township
#1 ‗Now‘ perception, t = 1.235, p>.05; township #2 ‗Before‘ perception, t = -.467, p>.05; and
township #2 ‗Now‘ perception, t = .361, p>.05 (Table 13).

51

Table 12. Independent samples t-test measuring proximity w/in 1 mile of wind turbine vs.
support of wind farms w/in Huron County
Independent Variable

With Wind Farms

Proximity

N

W/in 1 mile
Over 1 mile

89
18
b

t
Twp #1 = Bingham, Twp #2 = Oliver

Twp #1
a
Mean
.33
.44
.34

SD

N

1.36
1.29

49
50

Twp #2
a
Mean
1.0
.84
-.83

SD
1.04
.87

a

Five point scale from (-2) strongly disagree, to (2) strongly agree.

b

No significant differences were revealed.
*p<.05, ** p< .01, ***p<.001
Table 13. Independent samples t-test measuring proximity w/in 1 mile of wind turbine vs. the
BEFORE and NOW perception of wind farms
Dependent
Variable
Proximity
W/in 1
mile
Over 1
mile

With Wind Farms
Twp #1
N
85
16

Twp #2

Before
Now
a SD
N Meana SD
Mean
.46
1.24 90
.19
1.50
1.06

1.06 18

b
1.82
t
Twp #1 = Bingham, Twp #2 = Oliver

.67

1.50

45

Before
a
Mean
.71

41

.61

N

1.24

SD
.92

Now
a SD
Mean
50
.82
1.08
N

1.09 49

.90

-.47

.70

a

Five point scale from (-2) very negative, to (2) very positive.

b

No significant differences were revealed.
*p<.05, ** p< .01, ***p<.001
Table 14 indicates the amount of change and the directionality of the change in mean
perceptions over time. Township #2 was the only township in which the respondents‘
perceptions became more positive.

52

1.07

Table 14. Perceptions of turbines ‗Before‘ wind farm development and ‗Now‘ that wind turbines
exist
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
Dependent
a
a
a
a
Mean
Mean
Mean
Mean
Variable
F Test
Before
3.55
3.63
3.57
3.72
.64
Now
3.31
3.84
3.19
3.58
5.13**
Difference
-.20
.20
-.43
-.14
5.73***
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scales from (1) very negative, to (5) very positive.
*p<.05, ** p< .01, ***p<.001

Group
Comparison
n.s.
2>1, 3
2>1, 3

The majority of respondents who did not change perceptions regarding wind farms over
time held a range of mostly neutral, positive or very positive perception (Table 15).

Table 15. Number of Individuals per Township whose perceptions did not change after the wind
farms were built
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
Very Negative
7
3
3
8
Negative
2
3
1
3
Neutral
8
7
8
17
Positive
13
21
13
27
Very Positive
23
13
8
27
Total
53
47
33
82
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach

In sum, the hypothesis, ―Proximity of residence to wind farms does influence perceptions
and support of wind farm development‖ is partly accepted. Residents of township #2 held the
most positive perceptions and the greatest level of support of wind farm development than the
other three townships. Respondents in townships #3 and #4 (without wind farms) were similar to
each other in their perceptions and support of wind farm development. Residents of townships
#1 and #2 (with wind farms) differed on their perceptions and support of wind farm
development.

53

Research Question 2

The second research question asked if the level of attendance of wind zoning meetings in
the planning process of wind farms influenced the perception and support for wind farm
development. Independent samples t-tests revealed that those who attended meetings considered
themselves to be more knowledgeable about renewable energy, wind energy and sustainable
development than those who did not attend meetings (Table 16).
To further test the hypothesis of any association between the variables knowledge of
wind energy, perceptions of wind farms ‗before‘ development and perception ‗now‘ (after
development), each variable was analyzed utilizing correlation (Table 17), as well as conducting
analysis on the relationship between knowledge of wind energy and level of support within
Huron County (Table 18). No significant findings were observed in any of the results.
Table 16. One-Way ANOVA for examining differences in knowledge and meeting attendance
Dependent
Independent
Variable
Variable
How knowledgeable Have you
a
attended
are you about…
meetings?

With Wind Farms

No

N
62

Yes

Renewable energy

Twp #1
Mean
2.34

48

3.10

T Test
Wind energy

SD
.77
.79

N
83

Twp #2
Mean
2.23

SD
.85

24

2.79

.88

26.60***

8.08**

No

63

2.33

.78

86

2.33

.82

Yes

48

3.27

.77

24

3.00

.93

T Test
Sustainable
development

39.86***

11.99**

No

2.05

.87

80

1.94

.99

Yes
T Test

61
45

2.56

1.03

23

2.57

.84

7.51**

7.70**

Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scales from (1) not at all knowledgeable, to (5) extremely knowledgeable.
*p<.05, ** p< .01, ***p<.001
54

Table 16 (cont‘d). One-Way ANOVA for examining differences in knowledge and meeting
attendance
Dependent
Independent
Variable
Variable
How knowledgeable Have you
a
attended
are you about…
meetings?

Without Wind Farms

No

N
57

Yes

Renewable energy

Twp #3
Mean
2.40

29

2.83

T Test
Wind energy

SD
.84
.97

N
144

Twp #4
Mean
2.43

SD
.92

29

3.00

.85

4.41*

9.47**

No

58

2.41

.82

143

2.47

.92

Yes

29

2.97

.98

28

3.04

.88

T Test
Sustainable
development

7.69**

9.06**

No

57

1.89

.82

140

1.97

.98

Yes

28

2.75

1.01

28

2.79

.96

T Test

17.64***

16.19***

Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scales from (1) not at all knowledgeable, to (5) extremely knowledgeable.
*p<.05, ** p< .01, ***p<.001

Table 17. Correlation between knowledge of wind energy and the before and now perception of
wind farms
With Wind Farms
Twp #2
Partial
Partial
Perception of
(control for
(control for
Wind Farms
Bi-variate
mtg. att.)
Bi-variate
mtg. att.)
Corr.
Corr.
Corr.
Corr.
a
BEFORE
.07
.12
.09
.10
Wind Energy
NOW
-.07
.23
-.04
-.01
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
No significant differences were revealed.
IV
How knowledgeable are you
about

DV

Twp #1

55

Table 17 (cont‘d). Correlation between knowledge of wind energy and the before and now
perception of wind farms
Without Wind Farms
Twp #3
Twp #4
IV
DV
How knowledgePartial
Partial
able are you
Perception of
(control for
(control for
about
Wind Farms
Bi-variate
mtg. att.)
Bi-variate
mtg. att.)
Corr.
Corr.
Corr.
Corr.
a
BEFORE
-.11
-.14
.10
.10
Wind Energy
NOW
-.21
-.18
-.02
-.10
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
No significant differences were revealed.

Table 18. Correlation between knowledge of wind energy and level of support of wind farms
within Huron County
With Wind Farms
IV
How knowledgeable are you about

DV
Level of
support w/in

Twp #1
Partial
(control for
mtg. att.)
Corr.
Corr.
-.07
-.02
Bi-variate

Bi-variate

Twp #2
Partial
(control for
mtg. att.)
Corr.
.10

Corr.
Huron
.08
Wind Energy
County
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
No significant differences were revealed.
a

Table 18 (cont‘d). Correlation between knowledge of wind energy and level of support of wind
farms within Huron County
Without Wind Farms
IV
How knowledgeable are you about

DV
Level of
support w/in

Twp #3
Partial
(control for
mtg. att.)
Corr.
Corr.
-.21
-.15
Bi-variate

Bi-variate

Twp #4
Partial
(control for
mtg. att.)
Corr.
.03

Corr.
Huron
.03
Wind Energy
County
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
No significant differences were revealed.
a

56

An independent samples t-test between attendance of meetings and perceptions (‗before‘
and ‗now‘) (Table 19) along with attendance of meetings and support of wind farms within
Huron County (Table 20) revealed no significant difference across townships. The independent
samples t-tests and correlations revealed no association existed between knowledge of wind
farms and perception or support of wind farms. In sum, the hypothesis that knowledge of wind
farms is related to perception and support of wind energy development was not supported.
Table 19. Independent Samples T-Test between attendance of meetings and before and now
perception of wind farms
DV
IV
Perception Meeting
of Wind
attendance
Farms

With Wind Farms

Twp #1
a
Mean
58
.62
47
.47
.64

Twp #2
a
Mean
69
.64
22
.59
.19

Twp #3
a
Mean
50
.60
28
.50
.43

Twp #4
a
Mean
130
.77
29
.55
.99

63
49

82
24

56
29

129
29

n

BEFORE

No
Yes
t

NOW

Without Wind Farms

b

No
Yes

.46
.12

n

.93
.54

n

.36
-.14

b
1.18
1.35
1.63
t
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (1) very negative, to (5) very positive.

n

.64
.38
1.02

b

No significant differences were revealed.
Table 20. Independent Samples T-Test between attendance of meetings and support of wind
farms in Huron County

DV

IV
Meeting
attendance

With Wind Farms

Without Wind Farms

Twp #1
Twp #2
Twp #3
Twp #4
a
a
a
a
n
n
n
Mean
Mean
Mean
Mean
63
.49
84
.92
58
.72
144
.56
Support No
w/in
Yes
48
.23
23
.78
30
-.03
29
.48
H.C.
t
1.03
.58
2.82**
.346
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree, to (2) strongly agree.
*p<.05, ** p< .01, ***p<.001
n

57

Research Question 3

The third research question asked whether land ownership affects perception and support
of wind energy development. A number of tests were conducted to test whether there were
relationships between the variables of residency, acres owned, perception (‗before‘ and ‗now‘)
and support of wind farms within Huron County. A Chi-Square test revealed that no significant
difference existed between the variables of respondent residency (village, p>.05 or rural, p>.05)
and acres owned (Table 21).
An independent samples t-test revealed that no significant difference existed between
residents who lived in rural areas or within village limits and their ‗before‘ and ‗now‘
perceptions (Table 22). An independent samples t-test also revealed that no significant
difference existed between residents who lived in rural areas or within village limits and their
support of wind farms within Huron County (Table 23). While not significantly different, this
test indicated that the overall mean of support was higher within respondents of village limits
than rural areas.
Table 21. Chi-Square – Residency Location and Acres Owned
Variable
Residency
a
Location

Variable
Number of
Acres Owned

Less than 1
acre
At least 1 but
Rural Resident less than 3
At least 3 but
less than 20
20+

With Wind Farms
Twp #1
Twp #2
(n=61)
(n=43)

Without Wind Farms
Twp #3
Twp #4
(n=58)
(n=73)

6.6%

4.7%

12.1%

16.4%

27.9%

39.5%

32.8%

Chib
Square

31.5%
10.86

41.1%

23.3%

29.3%

23.3%

24.6%

32.6%

25.9%

28.8%

58

Table 21(cont‘d). Chi-Square – Residency Location and Acres Owned
Twp #1
(n=25)
36.0%

Twp #2
(n=22)
31.8%

Twp #3
(n=13)
23.1%

Twp #4
(n=40)
42.5%

Less than 1
acre
At least 1 but
32.0%
31.8%
30.8%
42.5%
Village
less than 3
Resident
At least 3 but
12.0%
13.6%
15.4%
10.0%
less than 20
20+
20.0%
22.7%
30.8%
5.0%
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
n=335

7.94

b

No significant associations were revealed.
*p<.05, ** p< .01, ***p<.001

Table 22. Independent Samples T-Test between residency location and BEFORE and NOW
perceptions of wind farms
DV

IV

Perception of Wind
a
Farms

Residency
Location

BEFORE

With Wind Farms
N

Rural
Village

SD

N

Twp #2
Mean

SD

60
45
T Test

NOW

Twp #1
Mean
.42
.73
-1.33

1.23
1.18

43
47

.74
.57
.84

.90
1.02

64
48

.16
.52
-1.28

1.56
1.41

49
55

.84
.93
-.44

1.01
1.07

b

Rural
Village
b

T Test
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) very negative to (2) very positive.
b

No significant differences were revealed.
*p<.05, ** p< .01, ***p<.001

59

Table 22 (cont‘d). Independent Samples T-Test between residency location and BEFORE and
NOW perception of wind farms
DV

IV

Perception of
Wind Farma

BEFORE

Without Wind Farms
Twp #3
Twp #4
Residency
Location

Mean

56
21
60
21

Rural
Village
T Test

NOW

N

a

N

.97
1.03

84
76

.57
.88
-1.83

1.24
.89

.08
.50
-1.31

b

SD

.59
.48
.45

b

Rural
Village

a

1.38
1.14

82
78

.41
.73
-1.64

1.34
1.09

Mean

SD

T Test
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) very negative to (2) very positive.
b

No significant differences were revealed.
*p<.05, ** p< .01, ***p<.001
Table 23. Independent Samples T-Test between residency location and support of wind farms in
Huron County
DV

IV
Residency
Location

With Wind Farms
Twp #1

Twp #2

a
SD
N
Mean
Mean
Rural
63
.22
1.39
51
.69
Support
Village
48
.58
1.25
55
1.05
w/in H.C.
T Test b
-1.42
-1.97
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree to (2) strongly agree.

N

a

b

No significant differences were revealed.
*p<.05, ** p< .01, ***p<.001

60

SD
1.01
.91

Table 23 (cont‘d). Independent Samples T-Test between residency location and support of wind
farms in Huron County
DV

IV
Residency
Location

Without Wind Farms
Twp #3
a
Mean
.41
.67
-.87

N

SD

N

Twp #4
a
Mean
.41
.65
-1.43

SD

Rural
63
1.27
86
Support
w/in
Village
21
1.09
89
b
b
H.C.
T Test
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree to (2) strongly agree.

1.23
1.02

b

No significant differences were revealed.
*p<.05, ** p< .01, ***p<.001
A one-way ANOVA revealed that there were no significant differences between the

number of acres owned and the perceptions held by residents (Table 24) or in their level of
support for wind farms within Huron County (Table 25) across the four categories of land
ownership (less than 1 acre, at least 1 but less than 3 acres, at least 3 but less than 20 acres and
more than 20 acres). In sum, the number of acres owned does not appear to be a predictor of
perception or support of wind farms within Huron County.

Table 24. ANOVA – Acres owned and BEFORE and NOW perception of wind farms
Variable
a
Perception

Variable
Number of Acres
Owned

With Wind Farms
Twp #1

Without Wind Farms

Twp #2

Twp #3
N

Less than 1 acre
12
7
8
At least 1 but
25
20
20
less than 3
At least 3 but
25
.60
13
.38
18
.39
less than 20
BEFORE
20+
18
.28
16
.81
18
.78
F
.62
.65
.52
Group
n.s.
n.s.
n.s.
Comparisons
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) very negative to (2) very positive.
*p<.05, ** p< .01, ***p<.001

24
38

Mean
1.21
.79

22

.41

23

.43
2.85*
n.s.

Mean
.33
.72

61

N

Mean
.29
.55

a

Twp #4
a

N

a

N

Mean
.63
.70

a

Table 24 (cont‘d) ANOVA – Acres owned and BEFORE and NOW perception of wind farms
Less than 1 acre
13
.00
9
1.11
9
.78
At least 1 but
25
.48
25
.72
22
.27
less than 3
At least 3 but
28
.25
13
.23
19
-.11
less than 20
NOW
20+
20
-.15
17
.82
20
.20
F
1.66
1.18
.83
Group
n.s.
n.s.
n.s.
Comparisons
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) very negative to (2) very positive.
*p<.05, ** p< .01, ***p<.001

25
37

.96
.59

22

.41

23

.30
1.36
n.s.

Table 25. ANOVA – Acres owned and support of wind farms w/in Huron County
Variable

Variable
Number of
Acres
Owned

With Wind Farms
Twp #1
Twp #2

N

Mean
.15

a

N

Mean
.78

Without Wind Farms
Twp #3
Twp #4
a

N

Mean
1.33

a

Less than 1
13
9
9
acre
Support of
At least 1
24
.42
25
1.04
24
.21
wind farms but less than
w/in Huron 3
County
At least 3
28
.21
12
.08
19
.42
but less than
20
20+
20
.25
19
.79
20
.35
F
.14
2.15
1.86
Group
n.s.
n.s.
n.s.
Comparisons
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree to (2) strongly agree.
*p<.05, ** p< .01, ***p<.001

N
29

Mean
.72

40

.38

22

.32

23

a

.57
.69
n.s.

Research Question 4

Research Question 4 asked about belief outcomes that contribute to perception of wind
farm development. This question tested whether environmental, social or economic components
were stronger or more influential than each other toward perception and support of wind farms
62

using regression analysis. The choices for survey question 8, ―There are many positive, neutral
or negative issues related to wind energy. How much do you agree or disagree or are you neutral
with the following statements?‖ was divided into three categories (economic, environmental and
social) (Table 26).
TABLE 26. Survey Belief Outcomes Regarding Wind Energy
Environmental







Allows land to be
reverted to its natural
state
Allows multiple land
uses
*Has an uncertain or
unproven impact on
the environment
*Is a danger to wildlife
Is a clean energy
source
Is a renewable
resource (limitless)

Social











Economic
*Creates a disturbing
noise from turbines
Is an attractive feature
on the landscape
*Requires too many
number of turbines
Indicates a willingness
of the community to be
part of an innovation
Brings renewed
interest to the area
Is a safe energy source
*Creates a
strobe/flicker affect
from turbine blades
*Brings general
unwanted change to
the community
*Is hazardous to
people‘s health
*Has an uncertain or
unproven impact on
people








Increases property
value
*Is an unreliable
output of electricity
(not always windy)
*Has an uncertain or
unproven impact on
the economy
Brings money and
other economic
benefits to the
communities in which
they are sited
Creates tourism (nonresidents are interested
in seeing

*Items were reverse coded for analysis.
Survey respondents were asked to rate their level of agreement with 21 belief outcomes on a five
point Likert scale from ‗strongly disagree‘ to ‗strongly agree.‘ The belief outcomes per
environmental, social and economic category were averaged in order to use one number in the
linear regression analysis. Linear regression indicated that three (townships #1, #3 and #4) out
of four townships showed that there was a stronger effect of social belief outcomes on support
63

for wind farms – standardized beta (β)=.40 (township #1); (β)=.60 (township #3); (β)=.48
(township #4) – compared to the affects of economic or environmental belief outcomes (Table
27). The findings from all four townships revealed that economic belief outcomes were not as
significant as environmental or social belief outcomes in predicting support for wind farms
within Huron County.
Table 27. Regression analysis for the prediction of support for wind farms from economic,
environmental and social beliefs related to wind farms
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
(n=111)
(n=105)
(n=89)
(n=176)
Belief
Components
β
t
p
β
t
p
β
t
p
β
t
p
Economic
.14 1.02 n.s. .09 .53 n.s. .02 .15 n.s. .03 .35
n.s.
Environmental .28 2.21 <.05 .18 1.12 n.s. .00 -.02 n.s. .29 3.38 <.01
Social
.40 2.51 <.05 .21 1.14 n.s. .60 3.21 <.01 .48 4.21 <.001
F=57.4, p<.001
F=6.6, p<.001
F=17.8, p<.001
F=80.18, p<.001
2
2
2
Adjusted R =.61 Adjusted R =.18 Adjusted R =.36
Adjusted R2 =.58
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
Table 28. Regression analysis for the prediction of perception of wind farms BEFORE
development from economic, environmental and social beliefs related to wind farms
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
(n=105)
(n=90)
(n=79)
(n=161)
Belief
Components
β
t
p
β
t
p
β
t
p
β
t
p
Economic
.38 2.38 <.05 -.01 -.08 n.s. -.14 -.79 n.s. .02 .16
n.s.
Environmental .11 .76 n.s. .38 2.50 <.05 .49 2.96 <.01 .10 .94
n.s.
Social
.24 1.30 n.s. .34 1.91 n.s. .27 1.33 n.s. .61 4.53 <.001
F=32.3, p<.001
F=24.4, p<.001
F=15.2, p<.001
F=51.38, p<.001
Adjusted R2 =.47 Adjusted R2 =.44 Adjusted R2 =.35
Adjusted R2 =.49
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
Linear regression revealed that in two (townships #2 and #3) of the four townships there
was a stronger affect of environmental belief outcomes on the perception held ‗before‘ wind
farms were developed – standardized beta (β)=.38 (township #2); (β)=.49 (township #3) –
compared to the effects of economic or social belief outcomes (Table 28). Linear regression
64

revealed that the four townships showed a stronger affect of social belief outcomes on the
perception held ‗now‘ that wind farms are developed – standardized beta (β)=.52 (township #1);
(β)=.44 (township #2); (β)=.47 (township #3); (β)=.56 (township #4) – compared to the effects
of economic or environmental belief outcomes (Table 29).
Table 29. Regression analysis for the prediction of perception of wind farms NOW that
development has occurred from economic, environmental and social beliefs related to wind
farms
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
(n=112)
(n=103)
(n=86)
(n=161)
Belief
Components
β
t
p
β
t
p
β
t
p
β
t
p
Economic
.31 3.25 <.01 .17 1.37 n.s. .28 2.84 <.01 .32 4.57 <.001
Environmental .11 1.25 n.s.
.25 2.24 <.05 .20 2.25 <.05 .06 1.01 n.s.
Social
.52 4.69 <.001 .44 3.40 <.01 .47 4.10 <.001 .56 6.84 <.001
F=155.7, p<.001
F=60.2, p<.001
F=108.4, p<.001
F=234.4, p<.001
2
2
2
Adjusted R =.81 Adjusted R =.64 Adjusted R =.80
Adjusted R2 =.81
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach

In sum, the hypothesis that economic belief outcomes were the driving force of
perception and support was not supported by the data. Social belief outcomes were the strongest
predictor of support for wind development while both social and environmental belief outcomes
were the strongest predictor of perceptions of wind farms.

65

Chapter 5

Summary, Conclusions, Discussion of Findings and Recommendations
Summary

The focus of the research was to determine the factors influencing perceptions held by
residents in four townships of Huron County, Michigan of wind energy development.
Specifically, the study sought to answer whether proximity of a residence to a turbine, level of
knowledge held about renewable energy, ownership of land or which belief outcome factor
explained the perceptions and level of support held by township residents.
To study these topics and their relationships, a two-stage research design was used. The
subjects of the study were 11 interviewees who were either landowners with a wind development
contract and/or turbine, landowners without a contract, rural residents, village residents and
community decision makers. In addition, 497 (50% response rate) residents from four townships
in Huron County completed and returned a mail-survey. The data were collected during the
summer and fall 2010 and winter 2011.
The interview data were analyzed using thematic coding. Survey data were analyzed
using frequencies, contingency tables, chi-square tests, correlations, ANOVAs and the Scheffe‘s
Test along with linear regression. The IBM SPSS Statistics, version 19, was used to analyze the
data.
Findings
The analysis of the data revealed the following:

66

1.

Some support existed for the hypothesis that proximity to a wind turbine negatively
influenced perception and support for wind farms in Huron County.

2. The number of wind energy meetings attended was positively related to how
knowledgeable meeting attendees felt about the topics of sustainable development,
wind energy and renewable energy. However, no relationship was revealed between
knowledge held by landowners of wind energy and level of perception or support of
wind farms in Huron County.
3. The amount of land owned did not have an influence on level of perception or support
for wind farms in Huron County.
4. Social and environmental belief outcomes were more strongly associated with
perception and support of wind farms in Huron County than economic belief
outcomes.
Discussion of Findings
Approximately six out of every ten survey respondents indicated that they would be
willing to support wind farms at any location within Huron County; less than a quarter of all
respondents indicated that they would not be willing to support wind farms at any location within
Huron County. The majority of respondents indicated that they held a positive or very positive
perception of wind farms before the wind farms were built, while approximately a quarter of all
respondents felt neither negative or positive in their perceptions regarding wind farms before
development. Analysis of perceptions after, or now, that wind farms have been constructed
indicates that the majority of respondents stayed positive or very positive in their perceptions, yet
those who indicated a negative or very negative perception nearly doubled. This suggests that
those individuals who considered themselves as neutral before wind farms were constructed had
formed mostly negative perceptions over the course of wind farm construction.
67

The significance of proximity to wind turbines influencing perception and support of
wind farms is only partly supported. The wind farm boundaries in township #2 are in a complete
square. This would signify that those residents living within the boundary were in agreement
with wind farm development. The boundary lines for the wind farm in township #1 are more
jagged and in the shape of an upside down ‗u.‘ This would signify that less residents are in
agreement of support of wind farm development. If residents were in support, the ‗u‘ would be
more in the shape of a square as in township #2 as landowners would have agreed to lease land
for wind development. This ‗u‘ shaped boundary allows for more residents to be either
positively or negatively impacted by wind farm development as the boundary lines are longer
than in township #2. Township #2 was consistently more favorable and more positive in their
perceptions and support for wind farms. Initial analysis of perceptions and support revealed that
there was a significant difference between respondents in townships #1 and #2. Subsequent
analysis indicated that the perceptions and support of wind farms were not statistically
significantly different when the respondents were separated into two groups – those living within
one mile from a turbine and those living further than one mile. This suggests that a one mile
distinction between groups is not an adequate distance in showing influence on the proximity of
a residence to a wind turbine and perception and support. The differences in support and
perceptions between townships #1 and #2 could be explained on the shape of the wind zone
overlay (wind farm boundaries) itself. Looking within the wind farm boundaries for other
explanations, a distinction in the number and type of wind turbines is apparent. Township #1 is
larger of the two – utilizing 46 GE model 1.5 MW turbines, while Township #2 utilizes 32
Vestas model 1.65 MW turbines. The number of wind farms ‗clustered‘ in one area, along with
the size and model of turbines used may affect perceptions of wind farms.

68

Findings from this study are inconsistent with results from recent studies (Jones & Eiser,
2010; Swofford & Slattery, 2010) which indicated that an inverse relationship between proximity
and positive attitudes exists; whereby public acceptance of wind development decreased the
closer one located to the wind farm(s). Township #2 is in direct conflict from the results of these
previous studies. Township #2 contains wind turbines and is consistently the most positive
township in regards to support and perceptions. This difference between townships may also be
explained through some of the interviews in which interviewees felt as if the wind farm
development from start to finish was more transparent in township #2 than in township #1.
Results from the present study support the view that the traditionally used term of NIMBY does
not adequately explain attitudes of wind farms.
Findings from research question two suggested that the level of knowledge is not
statistically significant in predicting support or perception of wind farms. Meeting attendance is
associated with knowledge level however – greater meeting attendance is associated with a
perceived higher level of knowledge of wind energy. This could be explained if knowledgeable
people in general went to meetings or by going to meetings one feels more informed. Lane
(2007) advocated that the community should be engaged from day one of the planning process,
while this study revealed that the average number of meetings regarding wind energy attended by
respondents never exceeded one meeting. This signifies a disconnect between level of
engagement and support. Arnstein (1969) used the ‗Ladder of Participation‘ to describe how the
bottom rungs of the ladder allowed for education of participants. This study did reveal that those
who attended meetings perceived themselves to be more knowledge than those respondents who
did not attend meetings. Numerous studies (Blair, 2004; Jones & Eiser, 2009; Jones & Richard
Eiser, 2010; Lane, et al., 2007; Rogers, 2005; Swofford & Slattery, 2010) advocated for a
greater, more transparent, relationship between wind developers and the community. The
69

interviews conducted in this study suggest that initially wind developers were not as open in the
process of wind farm development as they are in 2011. The fact that some residents didn‘t know
about the plans for the wind farms until they had been approved by the Huron County Board of
Commissioners could possibly explain the low level of meeting attendance.
One of the research questions examined the affect ownership of land had on wind farm
perception and support. This study found that no significant differences between those who
owned a few or many acres existed in regards to perception or support. This is in contrast to the
initial analysis of interviews which suggested that a relationship existed. Van der Horst (2007)
suggested that there may be a difference in individuals holding either more ‗use-value‘ or
‗existence value‘ and their level of opposition to wind farms. This study did not produce results
that would suggest that this explanation suffices as to why individuals hold different levels of
support or more positive perceptions. Analysis controlling for landowners having contracts was
not conducted to see if results would be consistent with previous findings. This researcher
thought that since monetary incentives were correlated to the amount of acres owned that this
would explain some differences in perception and support. The data from the study did not
support that hypothesis.
While locationality of support differs across townships, the mean response for all four
townships were supportive. The mean of the responses indicated that survey respondents in all
four townships were more supportive of wind farm siting within sight of their property as
opposed to on their property; less than one-quarter of survey respondents (n=84) admitted to
owning over 20 acres of land and slightly over half admitted to owning less than 3 acres of land.
This preference to see an increased level of support of siting wind turbines off as opposed to on a
respondents‘ property becomes less surprising. The highest levels of support were found to be
associated with the statements of siting wind farms within Michigan, followed by within Huron
70

County and then farmland. This suggests NIMBY –like behavior, however one of the studied
townships held an overall positive perception of and support for wind farms and would discredit
the applicability of the term NIMBY. The mean level of support for siting wind turbines off the
coast was higher than siting turbines on the coast. This perhaps can be explained as a
development project on the coast of this magnitude would severely limit any compact future
development that would increase tourist activity in the area.
The last research question tested which belief factor (environmental, social or economic)
held the greatest influence over perception and support of wind farms. This study revealed that
social and environmental belief factors played a larger role in forming perceptions and support of
wind farms than did economic belief factors. This is unexpected. However, this finding could
be explained by the level of uncertainty and lack of trust surrounding wind farms as evidenced in
the interviews and in several studies (Jones & Eiser, 2009; Jones & Richard Eiser, 2010; van der
Horst, 2007). Initial conclusions from the interviews led this researcher to believe that since
more economic belief factors were expressed than environmental or social belief factors,
economic belief factors were driving the positive or neutral perception. This is in contradiction
to what was found in the question eight survey results analysis. Three out of four townships (#1,
#2 and #3) had a higher mean in regards to the level of agreement of concern beliefs (not positive
beliefs) or an uncertain impact on people than on the statement regarding an uncertain impact on
the economy. One township (#4) had equivalent means for both questions. Three out of four
townships (#2, #3 and #4) felt stronger about the statement that wind energy creates uncertain
impact on the environment in comparison with the means of an uncertain impact on the
economy.
About one-third of survey respondents responded that they were neutral or unsure of the
effects wind energy had on the environment, the economy and on people. While a range of 28%
71

to 54% of survey respondents agreed or strongly agreed about their concern for the uncertain
effects from wind energy on the environment, the economy and on people. Perhaps, the level of
uncertainty regarding effects to people and the environment ultimately were the deciding factor
in assigning a level of support or perception. Utilizing Social Exchange Theory, the proverbial
weight of uncertainty may outweigh any perceived economic benefits that would be associated
with wind farms. Most residents are giving up the natural landscape horizon and perceived
health costs with little or no benefit. Providing more benefits to the individual communities
might make wind farm development more acceptable as the perceived benefits outweigh
perceived costs.
The taxable value of property in township #2 was on average less than all three other
townships (see table 30). This distinction in property value difference might help explain why
township #2 was consistently more positive in their perception of and support for wind farms.
Respondents in township #2 may have felt like they had more to gain and less to loose in
comparison to respondents in townships #1, #3 and #4. In all four townships the non-responders,
on average, had a lower state equalized value (SEV – ½ of the property‘s true cash value) and
taxable property value. This suggests that the nonrespondents either own on average less land
or own less valuable land than those who responded.
Table 30. Property Values of Non-respondents across townships
With Wind Farms
Twp #1
Responded
Didn't Respond

Twp #2

SEV
$ 72,618.42

Taxable
$ 57,526.70
(n=114)
$ 56,367.92
$ 43,318.08
(n=106)

72

SEV
$53,054.46
$40,438.93

Taxable
$ 44,346.21
(n=112)
$ 34,959.93
(n=131)

Table 30 (cont‘d). Property Values of Non-respondents across townships
Without Wind Farms
Twp #3
Responded
Didn't Respond

Twp #4

SEV
$ 74,803.33

Taxable
$ 57,320.78
(n=90)
$ 57,245.24
$ 49,146.55
(n=83)

SEV
$66,055.93
$52,324.00

Taxable
$ 57,234.61
(n=177)
$ 45,551.18
(n=175)

Discussion on why survey respondents and voters differed in the percentage of support
needs to be addressed. A couple of factors may have played into the differences in the level of
support. The survey question was phrased differently than the questions regarding support on
the ballot. The ballot would have asked about approving wind farm development within the
specific townships in the County for each respective proposal, while the survey asked if the
respondent would be willing to support wind farm development within the township they reside.
This difference may be attributed to the fact that voting is different than answering a survey.
There were different influences at the time of the November election as there were during the
survey time period. The fact that the questions in the survey informed the respondents of the
issues may have influenced their decision on support of wind farm development. Lastly, the fact
that timing could have been an influence cannot be ruled out. The local newspaper reports
almost on a daily basis on any updates to wind farm development or on any meetings held in
response to wind farm development. Respondents could have easily been influenced by new
information that would have surfaced, or by the fact that they heard that the wind farm proposals
passed and that influenced their inclination to support future development. There may be a host
of other reasons that would help to explain perceptions and level of support of wind farms that
were outside the scope of this research and not addressed in this study.

73

Limitations
There are a number of limitations in the applicability of this research:
1. Respondents were almost exclusively homeowners without turbines (only 2
respondents reported have turbines on their property) (see table 31).
Table 31. Count of respondents under wind developer contract and have turbines on property
Dependent
Variable
Currently under a
wind contract
n
Have a turbine on
property
n

With Wind Farms
Twp #1
Twp #2
(n=86)
(n=71)
14.0%
11.3%

Without Wind Farms
Twp #3
Twp #4
(n=78)
(n=125)
16.7%
6.4%

Total
11.4%

12
1.7%

8
2.2%

13
0%

8
0%

41
.8%

1

1

0

0

2

2. Many ways of learning or gaining knowledge exist. Media sources such as blogs,
social networks, newspaper (editorial or headlines), public meeting minutes as well as
talk within a local restaurant is liable to shape, expand or further solidify what was
already believed.
Conclusions
Within the limitations of the study the following conclusions are warranted:
1. Proximity of a residence to a wind turbine, knowledge held about wind energy and
land ownership were not found to be significant predictors of perception and level of
support for wind farms; a focus on these factors, however, may have masked the
importance of other factors from surfacing.
2. Social and environmental belief factors in the form of concerns were found to be
more influential than economic belief factors in determining support and perceptions
74

of wind farms in Huron County. Social and environmental beliefs about positive
outcomes of wind turbines were strongly related to support for wind farm
development, yet concerns regarding impact on the environment, economy and
people existed. Social beliefs about positive outcomes were strongly related to
perceptions of wind farm development followed by economic beliefs about outcomes
of wind farm development. Respondents who were neutral in their opinions before
development tended to perceive wind farms negatively after wind farm construction.
Recommendations
Much research has focused on factors influencing the level of opposition residents have
toward wind farms (Bell, et al., 2005; Blazevic, 2009; Devine-Wright, 2005; Eltham, et al., 2008;
Johansson & Laike, 2007; Jones & Eiser, 2009; Ricci, et al., 2010; Swofford & Slattery, 2010;
van der Horst, 2007; Wolsink, 2007). While this research is important to the field, research must
now focus in on how the level of engagement with community members influences perception
and support of wind farms. The following are recommendations for future study in the area of
wind farm development. While there are no direct recommendations from this research,
information gleaned from literature, interviews and from personal knowledge suggest some
broader recommendations. The recommendations are organized first on the community level
and then on specific research that should be conducted in the community.
1. The local governmental authority and wind developers must work to alleviate the
uncertainty surrounding this innovation among community members, perhaps by
utilizing outreach to the community, creating stronger relationships between wind
developers, local government and residents in addition to being clear and transparent
in the process and decision making of future wind farm development. In addition,
investing in research that tests the ‗promised‘ outcomes or effects of wind farms in a
75

community against the outcomes that actually precipitated from wind farm
development.
2. The county should seek out other benefits that could be rewarded to the communities
‗hosting‘ wind farms. The communities are giving up something (natural landscape)
and have their horizon changed for a significant amount of time. Developers could
become more involved with the community by volunteering in the community (must
be genuine and not forced), sponsor a county-wide event that would celebrate the
presence of the wind turbines that all residents could take part in or offer sponsorship
to underfunded community programs.
3. The community at large must benefit from a wind farm as a neighbor. Wind
developers could dedicate open spaces, parks or trails to the community members.
Doing so will reach a larger audience and will communicate that values other than
economic are associated with wind farms and in turn wind developers will increase
their perceived value in a community.
4. The community must acknowledge and work with the differing perceptions and
beliefs toward wind farms and allow residents to be a part of the planning process
from the beginning of project development.
5. More quantitative research needs to be performed assessing not just perceptions and
support of wind farms, but also the acceptable levels of wind farm development.
6. A focus group discussing survey analysis results and viable options for future
development should be explored.
7. Further research should be conducted, examining how the flow of money affects the
community at large. How large of an impact does the money flowing into the

76

community from royalties or taxes have on the whole community and not just those
individuals receiving direct economic benefit?
8. Research needs to be performed to see if there are projects that can be developed in
conjunction with wind farms to spur the economy and create a better quality of life
for the residents and also move local residents onto the locally generated wind energy
grid creating energy ‗self-sufficiency‘ for the area.
9. Link how level of individual efforts contribute to a larger goal of cleaner, more
efficient energy globally – particularly in a geographic area so close to a fragile Great
Lakes ecosystem.

77

APPENDICES

78

APPENDIX A
Participant Consent Form

79

Participant Consent Form
Research of perceptions of the stakeholders in wind energy
production in the Thumb of Michigan
Participant Consent Form
You are being asked to participate in a research project designed to better understand the
perceptions and experiences of resident landowners, homeowners and community planners
involved in the planning and development of wind turbines. Research consists of a series of
interviews with people who are currently involved and/or affected by the wind energy proposal.
This first stage will inform the development of a survey.
We expect each interview to take approximately 45-60 minutes. We will ask about perceptions
of wind energy development in the area as well as a more general perception of sustainable
development. Interviews will be audio taped. Through these interviews, we hope to document
perspectives on the current motivators and challenges of developing the proposed wind farms in
your area, which could potentially improve future community development research and policy
changes so that they fit the needs of modern rural communities.
We do not perceive any risk to anyone who chooses to participate in these interviews. You have
the right not to participate, to refuse to answer any questions or to withdraw at any time. Your
name will only be used if you give permission. However, it should be kept in mind when
responding to our questions that sometimes people in positions such as yours are well known for
a particular point of view or position that would lead others to be able to identify you. The
researchers will keep all interview information in their offices and on password-protected
computers. Information you give may be used as data in a public documents (e.g., research
journal articles, outreach bulletins). You must be 18 years or older to participate in this study.
If you have concerns or questions about this study, please contact the researchers:
• Theresa Groth, phone: (805) 450-7801; email: grothth1@msu.edu; or by regular mail: 131
Natural Resources Building, Michigan State University, East Lansing, MI 48824-1222
• Dr. Christine Vogt, phone: (517) 432-0318; email: vogtc@msu.edu; or by regular mail: 131
Natural Resources Building, Michigan State University, East Lansing, MI 48824-1222
If you have questions or concerns about your role and rights as a research participant, would like
to obtain information or offer input, or would like to register a complaint about his study, you
may contact, anonymously if you wish, the Michigan State University‘s Human research
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80

APPENDIX B
Interview Guide

81

Interview Guide
Demographics
1.
2.
3.
4.
5.

6.

7.

8.

Tell me a little bit about how long you have lived and worked in the Thumb area.
Do you currently live and work in the Thumb area year-round or only part of the year?
Are you a landowner? If so how many acres?
Do you farm? How long have you been farming? During that time period have you
noticed a shift in the ability to make a living on those acres?
Are you a business owner, employee, elected official, volunteer, member of civic
organization or something else? (Determine level and type of community activity and
roles)
How would you describe your level of engagement in the Thumb‘s community activities?
(Probe: local government, community service, civic organizations, community
committees, economic development work groups)
How would you describe your level of engagement in the Thumb‘s tourism or economic
development activities? (Probe: what type activities and what type of role – leadership,
volunteer or advocate?)
How would you describe your level of involvement in the Thumb‘s community planning
activities?

Economic Development
9. What type of development should be a high priority for the Thumb area? Low priority?
Why? (Probe: transportation, services, attractions, physical community elements, wind
energy, wind farm development, information/marketing, food and agriculture,
manufacturing).
10. What are the greatest challenges to economic development in the Thumb? Tourism
development?
11. What do you feel are the most important activities to further support or develop economic
development?
12. Were you involved in the planning process of the wind farms? If so what was your role
and when did you become involved?
Wind Farms
13. Were you approached by anyone regarding the proposed wind farm development? If so,
which company were they representing? (Probe: attitude toward wind farm/attitudes of
landowners toward wind farms).
14. What was the process? What did they say/offer?
15. How do you currently view the wind farms? Positive/Negative?
16. What are the primary benefits of the current/proposed wind farms? Drawbacks or costs?
17. How do you view further development? Does anything need to be addressed before
development continues?
82

18. Are landowners with turbines or contracts helping everyone? In your opinion is this an
equitable process? Is this the best course of action for this area?
19. Do you have any personal experiences or consequences from the wind farms?
Sustainable Development
20. Are you familiar with the term sustainability? What does it mean to you?
21. Do you view wind farms as fitting into the realm of sustainable development? Why or
why not?
22. Do you think your community focuses on sustainability as one of its planning goals? If
so, can you provide an example?
Community Development
23. Who has the largest responsibility for community planning? The local government,
business community, local residents, grassroots community organization, or a
combination?
24. The success of community planning is oftentimes dependent on local leadership. Please
describe the relationship between local leadership and planning activities in Huron
County. Do local leaders support wind farm development? Is there disagreement among
leaders regarding planning initiatives, goals, and objectives?
25. Do residents feel as if their voices are heard by local leaders? Why or why not?
26. Do current planning efforts give adequate consideration to social, environmental and
economic issues? Are social, economic, and environmental conditions ―in balance‖ in
Huron County? (Probe: What about future plans working toward more sustainable
community conditions)
27. What do you see as being the key(s) to success in Huron County? Does renewable
energy play a role?
28. Additional Comments:

83

APPENDIX C
Comparison of Survey Respondent Responses by Township by Question

84

Comparison of Survey Respondent Responses by Township by Question
Question 1
Table 1a. Location of residency
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
Dependent
(n=112)
(n=109)
(n=88)
(n=175)
Variable
Chi-Square
W/in city/village
42.9%
52.3%
27.3%
50.9%
16.147**
a
limits
Outside
52.7%
47.7%
28.4%
25.7%
29.359***
city/village
a
limits
Outside
0.9%
0.0%
45.5%,
26.9%,
99.580***
2
2
city/village limits
x =.07
x =.20
along/near Lake
a
Huron coastline
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Each question was posed separately with a two point scale from (0) no to (1) yes.
*p<.05, ** p< .01, ***p<.001
Question 2
Table 2a. Land ownership, wind contract and wind turbines on property
With Wind Farms
Twp #1
Twp #2

Dependent
Variable
a
Own Land
Mean # Acres

81.1%,
n=90

b

Under Contract

Without Wind Farms
Twp #3
Twp #4

a

68.2%,
n=75

87.8%,
n=79

70.8%,
n=121

37.7 acres,
n=86
14.0%,
n=12
1.7%,
n=1

25.3 acres,
n=66
11.3%,
n=8
2.2%,
n=1

35.2 acres,
n=73
16.7%,
n=13
0.0%,
n=0

34.8 acres,
n=114
6.4%,
n=8
0.0%,
n=0

Chi-Square
14.54**
F Value
.23
5.80

Turbines on
2.74
a
Property
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Two point scale from (0) no to (1) yes. Some respondents indicated that they did not own land.
b

This was an open ended question.
*p<.05, ** p< .01, ***p<.001

85

Question 3
Table 3a. Number of miles, as the crow flies, respondent lives from the nearest wind turbine
With Wind Farms
Twp #1
Twp #2
(n=109)
(n=103)
Mean
Mean
.82
1.95

Without Wind Farms
Twp #3
Twp #4
(n=84)
(n=168)
Mean
Mean
20.38
15.76

Dependent
Variable
F
Miles to
428.07***
Nearest
a
Turbine
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
This was an open ended question.
*p<.05, ** p< .01, ***p<.001

Group
Comparison
1<3, 4; 2<3,
4; 3>4

Question 4
Table 4a. How knowledgeable do you consider yourself about…[the following]
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
Dependent
Group
Mean
Mean
Mean
Mean
Variable
F
Comparison
Renewable
2.66,
2.36,
2.53,
2.52,
2.05
n.s.
a
n=111
n=107
n=87
n=176
energy
a
2.72,
2.47,
2.58,
2.56,
Wind energy
1.46
n.s.
n=112
n=110
n=88
n=174
Sustainable
2.25,
2.08,
2.16,
2.10,
.71
n.s.
a
n=107
n=103
n=86
n=171
development
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scales from (1) not at all knowledgeable, to (5) extremely knowledgeable.
p<.05, ** p< .01, ***p<.001

86

Question 5 & 9
Table 5a. Perception ‗Before‘ and ‗Now‘ that wind turbines exist
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
Dependent
Mean
Mean
Mean
Mean
Variable
F
a
3.55,
3.63,
3.57,
3.72,
.64
Before
n=105
n=91
n=79
n=161
a
3.31,
3.84,
3.19,
3.58,
5.13**
Now
n=112
n=106
n=86
n=161
a
-.20,
.20,
-.43,
-.14,
5.73***
Difference
n=105
n=90
n=77
n=153
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (1) very negative, to (5) very positive.
* p<.05, ** p< .01,***p<.001

Group
Comparison
n.s.
1<2, 2>3
1<2, 2>3

Question 6
Table 6a. Number of public meetings regarding wind energy respondents attended in the last five
years
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
Dependent
Mean
Mean
Mean
Mean
Variable
F
# Meetings
1.00,
.42,
.83,
.27,
11.71***
a
n=112
n=111
n=89
n=173
Attended
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Six-point scale from (0) zero, to (5) five or more.
*p<.05, ** p< .01, ***p<.001

87

Group
Comparison
1>2, 4; 3>4

Question 7
Table 7a. Location and frequency of seeing wind turbines

Dependent
Variable
a
At home
a

When driving
In the
a
countryside
In town

a

With Wind Farms
Twp #1
Twp #2
Mean
Mean
4.73,
4.35,
n=113
n=107
4.88,
4.67,
n=112
n=110
4.85,
4.58,
n=112
n=108
4.66,
n=111
1.23,
n=103

4.27,
n=103
1.70,
n=97

Without Wind Farms
Twp #3
Twp #4
Mean
Mean
1.28,
1.38,
n=83
n=159
3.22,
3.43,
n=89
n=172
3.11,
3.27,
n=88
n=171
1.30,
n=83
1.06,
n=84

1.23
n=164
1.13,
n=164

F
336.40***

Group
Comparison
1>3, 4, 2>3, 4

119.85***

1>3, 4, 2>3, 4

119.25***

1>3, 4, 2>3, 4

417.40***

1>2, 3, 4,
2>3, 4
1<2, 2>3, 4

On the
16.13***
a
coastline
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (1) never to (5) everyday.
*p<.05, ** p< .01, ***p<.001
Question 8
Table 8a. Level of agreement that wind energy creates outcomes
With Wind Farms

Without Wind
Farms
Twp #3
Twp #4
a
a
Mean
Mean
-.71,
-.49,
n=90
n=175
.12,
-.14,
n=89
n=175
.38,
.02,
n=88
n=175

Twp #1 Twp #2
Dependent
Group
a
a
Mean
Mean
Variable
F
Comparison
Increases
-.60,
-.13,
5.81**
1<2, 2>3
property value n=113
n=109
Unreliable
.16,
-.36,
5.42**
1>2, 2<3
output
n=112
n=109
Uncertain
.29,
-.03,
3.79*
n.s.
impact on
n=111
n=107
economy
Brings
.19,
.44,
.17,
.28,
1.35
n.s.
economic
n=112
n=108
n=88
n=176
benefit
Creates
.25,
.71,
-.12,
.03,
12.37*** 1<2, 2>3, 4
tourism
n=111
n=108
n=89
n=174
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree to (2) strongly agree.
*p<.05, ** p< .01, ***p<.001
Economic

Belief
Factor

88

Table 8a (cont‘d). Level of agreement that wind energy creates outcomes

With Wind Farms

Social

Environmental

Belief
Factor

Dependent
Variable
Land back in
natural state
Multiple uses
Uncertain
impact on
environment
Danger to
wildlife
Clean energy
source
Renewable
resource
Creates noise

Twp #1
a
Mean
-.18,
n=110
.30,
n=109
.22,
n=110

Twp #2
a
Mean
-.08,
n=106
.54,
n=103
.03,
n=106

-.54,
n=113
1.01,
n=111
.94,
n=110
.16,
n=112
-.21,
n=113
.22,
n=112
.14,
n=111
.29,
n=113
.68,
n=113
.55,
n=112
.16,
n=112
-.31,
n=112

-.91,
n=108
1.07,
n=107
1.00,
n=107
-.51,
n=107
.25,
n=108
-.30,
n=108
.62,
n=107
.79,
n=107
.94,
n=105
-.21,
n=107
-.45,
n=108
-.90,
n=108

Without Wind
Farms
Twp #3
Twp #4
a
a
Mean
Mean
-.38,
-.33,
n=89
n=175
.18,
.22,
n=88
n=176
.51,
.07,
n=88
n=174
-.32,
n=90
.83,
n=89
.69,
n=89
.22,
n=90
-.61,
n=90
.30,
n=89
.26,
n=89
.11,
n=90
.58,
n=90
.26,
n=88
.28,
n=89
-.19,
n=88

-.50,
n=173
1.01,
n=176
.93,
n=176
-.02,
n=175
-.24,
n=176
.02,
n=175
.27,
n=176
.33,
n=175
.76,
n=175
.10,
n=175
-.01,
n=176
-.57,
n=175

F
1.20

Group
Comparison
n.s.

2.91*

n.s.

4.14***

2<3, 3>4

5.48**

2<3, 4

1.56

n.s.

2.06

n.s.

9.08***

1>2, 2<3, 4

Attractive
9.17***
1<2, 2>3, 4
feature
Requires too
6.63***
1>2, 2<3
many
Part of
4.07***
1<2
innovation
Renewed
7.52***
1<2, 2<3, 4
interest
Safe energy
2.48
n.s.
source
Creates
10.42*** 1>2, 4, 2<3
flicker
Unwanted
9.94***
1>2, 2<3, 4
change
Hazardous to
9.85***
1>2, 2<3,
people‘s
3>4
health
Uncertain
.34,
.10,
.44,
.02,
4.60**
3>4
impact on
n=108
n=108
n=89
n=176
people
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree to (2) strongly agree.
*p<.05, ** p< .01, ***p<.001

89

Question 10
Table 9a. Level of agreement on whether wind turbines symbolize a sign of progress in modern
energy crisis
With Wind Farms
Twp #1
Twp #2
(n=112)
(n=109)
Mean
Mean
.55
.94

Without Wind Farms
Twp #3
Twp #4
(n=90)
(n=175)
Mean
Mean
.32
.65

Dependent
Variable
F
Sign of
5.59**
a
progress
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree, to (2) strongly agree.
*p<.05, ** p< .01, ***p<.001

Group
Comparison
2>3

Question 11
Table 10a. Wind energy is beneficial to have in Huron County
With Wind Farms
Twp #1
Twp #2
(n=113)
(n=110)
Mean
Mean
.26
.77

Without Wind Farms
Twp #3
Twp #4
(n=88)
(n=176)
Mean
Mean
.24
.53

Dependent
Variable
F
Beneficial to
4.652***
a
County
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree, to (2) strongly agree.
*p<.05, ** p< .01, ***p<.001

Group
Comparison
1<2, 2>3

Question 12
Table 11a. Location resident would be willing to support wind farms

Dependent
Variable
a
Don‘t support

With Wind Farms
Twp #1
Twp #2
Mean
Mean
-.28,
-.61,
n=109
n=109
-.17,
.35,
n=109
n=103

Without Wind Farms
Twp #3
Twp #4
Mean
Mean
-.16,
-.35,
n=88
n=174
-.06,
-.02,
n=87
n=173

F
2.11

On my
3.02*
a
property
W/in sight of
.09,
.76,
.11,
.19,
6.51***
a
n=109
n=105
n=89
n=175
property
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree, to (2) strongly agree.
b

No significant mean group difference.
*p<.05, ** p< .01, ***p<.001
90

Group
Comparison
n.s.
1<2
1<2, 2>3, 4

Table 11a (cont‘d). Location resident would be willing to support wind farms
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
Dependent
Mean
Mean
Mean
Mean
Variable
F
On farmland
.32,
.81,
.39,
.41,
3.78**
n=109
n=106
n=88
n=175
On public land
.25,
.63,
.21,
.39,
2.28
in H.C.
n=111
n=105
n=89
n=172
On coast of
.01,
.29,
-.10,
.06,
1.53
H.C.
n=111
n=108
n=89
n=175
Off coast of
.15,
.40,
.11,
.14,
1.24
H.C.
n=109
n=107
n=89
n=176
.38,
.89,
.46,
.54,
3.92**
W/in H.C.
n=111
n=107
n=89
n=176
W/in MI
.63,
1.01,
.69,
.71,
2.96*
n=112
n=106
n=88
n=175
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Five-point scale from (-2) strongly disagree, to (2) strongly agree.

Group
Comparison
1<2
n.s.
n.s.
n.s.
1<2
n.s.b

b

No significant mean group difference.
*p<.05, ** p< .01, ***p<.001
Question 13
Table 12a. Support of wind farm development at any location in your township
With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
(n=108) (n=103)
(n=86)
(n=163)
Dependent Variable
Chi-Square
Support of
Oppose
44.4%,
25.2%,
45.3%,
41.7%,
11.44*
a
n=48
n=26
n=39
n=68
development
In Favor
55.6%,
74.8%,
54.7%,
58.3%,
n=60
n=77
n=47
n=95
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Two-point scale from (0) no, to (1) yes.
*p<.05, ** p< .01, ***p<.001

91

Question 14
Table 13a. Age of Survey Respondent
Dependent
Variable

With Wind Farms
Twp #1
Twp #2
(n=113)
(n=110)
Mean
Mean
54.29
59.43

Without Wind Farms
Twp #3
Twp #4
(n=89)
(n=173)
Mean
Mean
59.66
57.55

F

Group
Comparison

a
3.28*
Age
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
This was an open ended question.

n.s.

b

b

No significant mean group difference.
*p<.05, ** p< .01, ***p<.001
Question 15
Table 14a. Gender of Survey Respondent
Dependent Variable

With Wind Farms
Without Wind Farms
Twp #1
Twp #2
Twp #3
Twp #4
(n=112)
(n=111)
(n=89)
(n=176)
Male
48.2%,
51.4%,
55.1%,
49.4%,
n=54
n=57
n=49
n=87
a
Gender
Female
51.8%,
48.6%,
44.9%,
50.6%,
n=58
n=54
n=40
n=89
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
Two-point scale from (1) male, to (2) female.
*p<.05, ** p< .01, ***p<.001

Chi-Square

1.08

Question 16
Table 15a. Length of time over lifetime spent living in Huron County
Dependent
Variable

With Wind Farms
Twp #1
Twp #2
(n=112)
(n=111)
Mean
Mean
43.58
49.13

Without Wind Farms
Twp #3
Twp #4
(n=88)
(n=174)
Mean
Mean
38.39
43.13

F

Group
Comparison

a
5.45**
2>3
Years
Twp #1 = Bingham, Twp #2 = Oliver, Twp #3 = Rubicon, Twp #4 = Sand Beach
a
This was an open ended question.
*p<.05, ** p< .01, ***p<.001

92

APPENDIX D
Additional Comments from Survey

93

Table 16a. Open-Ended Comments
Respondent

Comments

Township

372

Wind turbines need to be set farther apart and farther from homes. Wind
turbines should be set on some state land, state could use the money. I
believe strobing/flickering, noise and vibrations do bother/harm some
people. I feel wind companies are taking advantage of landowners and the
contracts are in their favor.

1

402

Wind turbines need to be classified as industrial in nature just like any other
form of energy producing facility and treated as such. The problem is not
that they exist but WHERE they have been allowed to be built. Site
planning and zoning which not only takes into consideration the economic
benefits but also the social and health impacts are a must.

1

521

Wind turbines have caused families not to talk to each other, I hate looking
at them things every day. I cannot even sit in my sunroom and read without
these things going around and around. They will be useless in about 5 years,
just like other things that were invented and the sad part, will have to look at
these without them being removed.

1

561

Wind is free - coal cost money and health

1

851

Wind farms do not bring adequate economic value but deteriorates real
estate marketable valves and tourism scenery.

1

457

Wind energy is preferable to nuclear or coal or oil plants in H.C., which is a
tourist Mecca!

1

99

Wind energy is as clean and environment friendly as any source of energy at
this time. I live as close to a windmill as anyone and have NEVER had any
problem with noise or vibration. I think it is pure jealousy on the part of
those who don't have a turbine and have to look at them!!!

1

903

We need coal and nuclear power plants to provide continuous, dependable,
cheap electricity along with wind and solar power.

1

445

We had to make a decision to sell our 1.5 acres of beautiful, prime building
site due to wind turbine placement. No one wants a turbine right behind
their $250,000 new home:(

1

446

We are guinea pigs. We have no idea what they will do to us over time,
they're expensive "Gov" supported, inefficient could be obsolete shortly
better alternative. No storage! Our children and grandchildren could be
affected. I saw them in CA in desert, NOT highly populated areas. "They"
have a gag order for contracted people, another red flag?

1

94

Table 16a (cont‘d). Open-Ended Comments
852

Villages cannot vote. All the power goes out of the state. Blades cops up
TV. Had to go to cable - I HATE THEM!

1

323

Ubly is located in a valley. Entering our village from any direction you
would be going downhill. It‘s always been quite beautiful for me. Now the
beauty is gone and the focus is on these monstrous eyesores. Our relatives,
who visited before the windmills, ask how we could have allowed these
people to destroy our community this way! All the money in the world isn't
worth what they did to our landscape.

1

338

Turbines - creates shadows/flickers, noise-pending speed and direction, hard
feelings between neighbors/families. They are not cost efficient on turbine
placement, too close to residents. I am interested to see how much Huron
County benefits. The land owner benefits.

1

759

This is the single most devise issue in the county right now. It is turning
family member & friends against one another. The county should explore
solar as a renewable energy source.

1

633

They do not pay for the energy they produce in their lifetime.

1

647

They build these wind farms in Ubly, MI where I live. Where's the savings
for me?? NONE!! Energy is sent across state. Thanks for that.

1

662

These wind mills in Bingham Township are an eyesore. I'm very unhappy
to see them. People who live by them are angry and have health issues.
One transformer was already vandalized w/a bullet hole. I didn't have a
choice - the people who gained monetary values by having them on their
property made the choice w/out my approval. Some of the time w/there is
no wind, so how can this be energy in the making - Just a thought - who
replaces them after 25-50 years of use?

1

897

These things are Horrid! Noisy, distracting, bright lights on top at night.
Brought NOTHING to our community - not even contract jobs (all come
from outside). Wind energy is NOT good for Michigan. The only benefit is
the rich get richer - and then everyone else gets the raw end of the deal.

1

657

There is a mountain of information out there on the health effects of living
too close to wind turbines. We need to sight them further from our homes as
we are bombarded with pressure changes that keep us awake and is affecting
our sleep. This is not healthy.

1

95

Table 16a (cont‘d). Open-Ended Comments
56

The turbines in Ubly were poorly placed and there are TOO many. They
didn't take the landscape into consideration and due to the valley that Ubly is
in the turbines create a loud thumping noise most days and nights disturbing
sleep and outdoor activities. Noble Wind Energy created controversy and
turmoil between family and friends! The type of turbines in Elkton are
much more quiet than the ones in Ubly! Also, due to living within the
village limits of Ubly we could not vote on the turbines in the last election
but we are still affected by them - very unfair.

1

994

The only thing I question as to benefiting Huron County is that all the
electric goes out of the state & does not benefit Huron County electrical
usage.

1

927

The old timers say it best. When electricity came no one wanted it, it was
the 'evil.' Now you can't live without it. Change is hard for some people.
Think of our children and grand children. RENEWABLE RESOURCES!

1

603

Sorry, this box is way too small for my comments.

1

726

Sorry that I didn't respond to your survey sooner, but I've been disillusioned
with the Wind Farm in Huron County. I went to one meeting. At that time
our commissioners told us that they had to move fast or the wind company
was going to pull out. They told about all the pluses, no negatives as far as
they were concerned. One commissioner said he even drove to a wind
farm, parked his car and slept overnight under it. Never heard a thing. I
wrote to another commissioner and told him my concerns, he never
responded. If I talked to these commissioners today, I would tell them that I
built a new home three years before the windmills came to Ubly.
Everything was fine until the windmill came now I have a list of problems: I
can hear the noise inside my new home, depending on the direction of the
wind, red blinking lights reflect off of my furniture through the windows at
night, I had to put blinds on my windows because of the shadows of the
spinning blades, I believe the value of my home has gone down, who would
want to buy a new home with these concerns...the wind companies? I don't
think they would be interested. Thank you for your time. I wish you would
have collected this information before the wind park came to Ubly.

1

122

Question 12 was poorly written - hard to figure out - confusing.

1

63

NOT COST EFFICIENT - no local jobs.

1

966

My niece and her husband have wind turbines on their farm. The closest
from their home is .25 mile. I've only heard noise from it one time when the
wind was out of a certain direction.

1

96

Table 16a (cont‘d). Open-Ended Comments
981

In question 13, I didn't like the use of the word "any." There needs to be
regulations attached. I wouldn't want to see on in the middle of the
playground.

1

589

If we have to hear and see them everyday which I do, it would at least be
nice to benefit from them. I don't understand why the energy is shipped
across the state. Yes, it's clean energy but it's also an annoying one. The
people who benefit are not blinded by flashing lights in their back yard or
hearing whoosh, whoosh in their deer blind.

1

917

If more wind mills go in Huron Co. put stars on motors & paint propellers
red, white & blue for more view.

1

294

If anyone has a wind turbine contract, they should be obligated to buy
THEIR electricity at the higher 'green' energy price.

1

418

If a majority of people in a gov't agency - village, township, city, etc.
wanted wind turbines on public land - not parks - I would likely support.

1

847

I'm very sensitive to motion and movement, so I personally could not have
one in my constant view. As I leave Ubly I have to put my hand to the side
of my eye & focus ahead until I pass them. I'm happy to have them in our
community. Good luck on your research!

1

644

I would like to have a wind mill in my backyard. I like the idea of being
helpful to preserve our nature.

1

637

I would be happier if the people in Huron County actually benefited from
the energy, received the energy, and lowered my energy costs.

1

137

I was asked some time ago how close would you want to live to a turbine?
(No closer than 1/2 mile) What I don't like is whenever they are shut down.
As long as they are working they have a purpose. I wish that there would be
more economic help from wind farms for the people that live with them.
Also help libraries. Thank you!

1

359

I want everybody to have one in their backyard so they can listen to the God
forsaken noise!!!

1

161

I think we need them.

1

291

I think they are nice.

1

926

I think some of the turbines are too close to residences. I've seen the effects
of flickers in people's homes. What a nuisance!

1

97

Table 16a (cont‘d). Open-Ended Comments
922

I think it is a privilege to have the wind mills in our area - they are so breathtaking and such a proof of progress and the future.

1

167

I think $ brought into the community is short term - during building phase. I
think the townships and county should get some of the profits and not just
the landowners. Some of the landowners don't have to look at them or listen
to them and they make pretty good $ having them on their land.

1

808

I support wind farms, although the # of farms/density is a concern. I would
like to see wind farm development throughout the county sporadically, but
NOT EVERYWHERE. If the area is overrun with windmills then my
opinions would likely change. I hope you plan on publishing the findings of
this survey in the Huron Daily Tribune.

1

504

I support wind farms because it is a clean source of energy not like the oil
spill in our lakes or the smell of the gases on your way going to Bay City.

1

243

I live in Ubly - wind mills are great - thank you.

1

679

I live in Bingham township & I see many windmills every day. I enjoy
watching them. I can't understand why people are negative about them. I
welcome the revenue into our area.

1

602

I live close enough to them to know they are bothersome. When the wind is
from the right (wrong) direction the noise and vibrations accumulate over 45 of them and the waves increase and decrease in volume. I have heard
them be louder a mile away from them then a 1/2 mile from them.
Submitted by Son. Sometimes the noise is amplified by the affects of 2-3
windmills. The volume increases. Humidity affects it also. Humid days are
louder. Turbulence is compounded "Accumulate Turbulence"

1

278

I know people who have health issues from wind farms and nothing is being
done. They have to sue the wind farm to get any kind of recourse. Also
most of the work is done by people out of our area. Another reason one and
a half million dollars, creates one job in the wind industry. I really would
like to see a health study done. People in Huron County are having the same
problems that others living near wind farms have.

1

207

I hope the results of this survey will be shared with participants as well as
county officials and the general public.

1

478

I have family with wind turbines and am a backer of the ones we have. 46
in all we've watched the construction from start to finish

1

98

Table 16a (cont‘d). Open-Ended Comments
569

I have a TV antenna system. Ever since the turbines were turned on the end
of November of 2008 my TV reception is BAD. I still have a TV antenna
system. WHY should I be forced to get CABLE TV - DISH- OR DIRECT
TV BECAUSE OF WIND TURBINES. I was told to complain to the FCC
and I am going TO. JOHN DEER SHOULD PAY FOR MY TV SYSTEM
OF MY CHOICE FOR EVER. THE TURBINES ARE THE CAUSE.

1

664

I feel they should be all at one location instead of being scattered all over the
county.

1

790

I feel the statement that turbines save agriculture is a ploy to allow them
here. I think ALL residents of the county should be allowed to vote on
having or not having turbines (all cities, villages & townships should be able
to vote.)

1

55

I feel anyone with farmland should be able to do with it as they please. If
they want a turbine that is their choice. I wouldn't want anyone telling me
what to do with my property. I live right in Ubly. I have a turbine about 1
mile away from my house and yes when I am OUTSIDE I can sometimes
hear the swish from the turbine, but it is nothing compared to the drag strip
every weekend during the summer all three days long. And in the fall the
dryers from the elevator all night I can hear from inside my house.

1

459

I don't like the looks it does to our vicinity. Sorry not to answer all but I am
not interested in them.

1

825

I don't know a lot about this subject. I do believe if it helps to improve our
world in a positive way, why not? I have friends that have the wind mills
and are happy with them. Then people who don't have them that object. As
for me, I'm more neutral.

1

972

I do not think turbines are the answer. They are only going in because of
govt. mandate. They are a bad investment. Conservation is the key to saving
energy.

1

636

I do not believe in use of taxpayer money to subsidize the erection of wind
towers. I also do not benefit or get a discount of electricity of wind tower in
our area.

1

250

I believe these windmills are an 'eye sore' lowering property value and only
benefiting the property owners.

1

109

I am sorry that I cannot fill out my wind farm survey. I would love to be
honest and answer the questions, but I wouldn't want to risk losing my job.

1

99

Table 16a (cont‘d). Open-Ended Comments
631

Huron Co. is/was an agricultural and tourism area. Since wind turbines,
property values are down. No energy break/lower electric bills for those
living near wind turbines only an increase in our bills. People with wind
turbines on their property are benefiting; getting rich.

1

204

Hard to like or dislike something you do not know the long-term effects of.

1

541

Had plans to build new home on farmland. Now that it is surrounded by
turbines have dropped that plan. When we were approached to lease our
land I said "NO" but neighbors agreed.

1

756

Good luck with your research/survey!

1

102

Go MSU!

1

292

From my home I can see approximately 25 wind turbines I have no problem
with them.

1

147

Farmers and land owners where wind mills are located are very supportive
because they receive payment. The rest of us have to live with wind mills in
sight every day. Farmers signed contracts secretly without public
knowledge. Why didn't we get a chance to vote on such an important issue
BEFORE construction.

1

101

As part of the younger generation that is becoming a major part of not only
our history but also future I feel this type of development is badly needed.
It‘s a chance for us to help insure our existence and states financial stability.
I hope that all residents may realize the need for change and support the
good things that are to come.

1

319

As a citizen of Bingham township, I feel that the wind farms tried to push
the start-up without informing the public enough. At meeting many
questions were left not answered. 20 years from now we will know the
effects, Good or Bad! I don't want to move!

1

943

All I know is from hear say which makes me neutral.

1

261

1st had no say in this matter. It began with personal choice of property
owner (they have that right to choose). 2nd not enough proof to the public
that putting these large turbines up, could pollute our well water (my right)
due to how deep to dig & place cement to hold them up. 3rd locally to my
knowledge the energy does not go to my household & they put so many
regulations into place that if I wanted a small turbine placed on my roof the
cost would not benefit me for 10 years, that's if regulation didn't change over
and over. Utility companies want to make their money just like CA did in
the 70's when president Carter tried to have natural energy.

1

100

Table 16a (cont‘d). Open-Ended Comments
362

#13 specific locations, with known high wind or wind conditions - yes

1

124

Wind turbines need to be regulated as to number per certain area, distance
from residential areas, or individual homes. Not placed on shoreline
property or in recreational areas. Not allowed within city or village limits.

2

125

Wind turbines are an absolute joke - 20 year old concept the taxpayers are
paying for it and Huron Casting and Blue Diamond Casting are getting a
cheap ride almost as bad as welfare. It's a taxpayer/government rip off!

2

273

Wind mills are an eyesore!

2

357

Wind energy is a plus for all. Because it is a natural energy. No fuels
needed.

2

160

Why not use land fill and farms with pits for methane gas would be cheaper
than wind mills. Only a few people get money from wind mills. Most are
not from Huron County that set up the equipment.

2

707

While wind energy is unreliable because it is not always windy, it is a
reliable producer of a portion of our energy needs. Wind will never produce
all our energy requirements, but when combined with other technology will
have a positive impact on our environment. Wind and other green energy
sources will make the USA less dependent on foreign energy sources. Many
people that oppose wind call themselves environmentalist, but only support
alternative energy if it doesn't impact them.

2

383

What happens when the turbines are wore out & junk - who tears them
down? Just junk on the land in my book.

2

760

We should be allowed to put windmills on our property. People with 1 acre
or so are made because they are not benefitting from it, but are owns more
acres and pay taxes on that land so we should be entitled to the windmills.

2

352

We own a very small amount of land in the county. We have turbines all
around us. We had no input or was asked about them. We heard there was
meetings throughout the year but was never told about them. Since the
turbines the geese and swans have changed their flying patterns. We don't
see them anymore. When there is flickering it lasts for 20 minutes.

2

123

We need to quit depending on oil and find other means of energy. This is
definitely a step in the right direction. I hope they put one on my property.

2

334

We need to look to the future, and try and keep the power produced in our
area.

2

101

Table 16a (cont‘d). Open-Ended Comments
741

We have no negative issues with the wind farm in Oliver Township. I hear
more negative comments on the wind farm in the Ubly area but they are
supposedly constructed with noisy parts that won't be practical on newer
wind farms.

2

742

The wind mills do make noise and wouldn't want one within a half mile of
my house unless it was on my land and I benefited from it being there. Can
be annoying if too close. In my opinion on public land, on or off the
shoreline would be a big no-no to Huron County for too many reasons.
Agriculture land only.

2

257

The wind farm in Huron do not benefit us. The energy goes elsewhere.
Companies are the only one that benefit. People just see $ when agreeing.

2

869

The turbines did bring a lot of tourism at first (the first year and a half), but
that has pretty much dwindled down to very few. I guess what I'm trying to
say, it was very noticeable at first, but not now.

2

84

The questions were hard to answer just using a yes or no answer. I know
people directly affected by having wind energy close to their home and
people who have no problem living by them. In the last election I could not
vote on the wind energy proposal even though they're all around me.

2

288

The promise said our electric bill would be cheaper which it is NOT TRUE,
if any it‘s higher. I certainly would not want them running through my town.

2

38

The people opposed to wind development are not informed of the bigger,
longer term picture. Need to educate.

2

430

Q's could be in the "clear understanding" area of such.

2

355

Put the wind mill on federal and state forest up north

2

394

Please send more information.

2

200

People who have wind turbines on their property and receive money from
them are in favor of them. People who live near turbines and don't own the
property they are on and therefore receive no money from them are more apt
to be negative about them (their noise & shadows).

2

560

Overall agree with wind energy.

2

256

It would be nice to see at least some of the created energy stay in Huron
County creating a decrease in Huron County resident‘s energy costs.

2

102

Table 16a (cont‘d). Open-Ended Comments
155

It would be nice if you live by a wind farm or votes for it, would get to
benefit from it. By lowering my electric bill. Also I believe in clean energy,
helps end oil disputes.

2

79

If I had land I would most certainly have some on my property. Also there
is a difference between air cooled and water cooled turbines.

2

272

If generous setbacks where in place I would be much more in favor of wind
mills.

2

244

I'm totally for these wind turbines, but not in our Great Lakes. There is
enough land for them to be on . The turbines are awesome.

2

192

I would like to be able to put a small wind turbine on my property for my
own electrical usage.

2

89

I was born and raised 2 miles from Elkton.

2

716

I understood when this was first discussed that we would benefit in our own
community, by helping our utility bills to be less. I don't understand who
benefits from these wind turbines?

2

64

I think if the electrical energy produced by the wind mills in Huron County
was utilized in Huron County and that energy benefited all residents in the
county, I would then agree

2

990

I only own 4 acres. My lot is too small for a windmill. I like the way
windmills turn with the wind.

2

902

I have seen that it brings business to area. It seems that it takes so many of
them that it does RUIN the attraction. They do bother people when placed
too close to people. The ones on the west side of county seems to have
different affect on people than the east side. West side seem better.

2

286

I have not looked into most of this to be very knowledgeable on these
questions. Would be willing to be open-minded and learn more.

2

59

I have no opinion regarding this matter. I haven't researched it; listened to;
read or participated in any conversation about it. They are one mile from
my home, but I haven't gathered info to form an opinion

2

608

I have heard on the radio that millions of $ will be given to local schools. I
work for a local school as far as I know we have received $0 from the 68 we
now have.

2

103

Table 16a (cont‘d). Open-Ended Comments
118

I don't think turbines should be placed close to homes and that is why I
wouldn't want them on our land as there are two homes right next to the
property. I also like the fact that the turbines being built there has brought
work to our area which we really need.

2

988

I don't know what is wrong with the people of this county or the rich ones
that live on Sand Point or along the lake. We need progress in this County the wind farms help!

2

781

I do support wind energy, but I do not agree with out sourcing work for
NON-LOCAL workers. If the area workers can't get the contracts, I don't
want the wind turbines.

2

600

I do not think that cost of development and repair are worth it.

2

473

I do believe it‘s a positive step

2

111

I am at a farm that has several wind mills on it and is part of a wind farm. I
am outside within range of them several times a month. I have not seen or
experienced any of the negative affects I have read about. I fully support
wind farms in Huron County.

2

556

I am all for the wind turbines.

2

376

I am a recent resident to Huron County, but I love it here. I plan to raise my
4 year old son here. The wind turbines are innovative and beautiful. We
encourage more wind energy in our area!

2

433

How much of the energy created by the now existing wind turbines goes
toward OUR electric or energy costs? It doesn't seem to be making our (the
consumers) energy bills any less. So where does all the new renewable
energy go?

2

212

Have not seen a reduction in electric bill yet since they been in township

2

522

Every since we have wind turbine my electric has doubled because of
distribution.

2

240

Does not help the taxes in the county as they are exempt for 10 years. They
are not feasible w/out help from the govt.

2

219

Because my farm land is so small I could not allow any apparatus to take up
room thus making my income reduced.

2

466

Would like to know more about the rules and regulations of both wind farms
and personal wind turbine use.

3

104

Table 16a (cont‘d). Open-Ended Comments
136

Without subsidy, wind farms would not be practical. When subsidies
disappears turbines will be abandoned. Yet they will still chop up birds,
pose dangerous unmaintained turning blades, fall off, and remain eyesores.

3

287

Wind mills are progress and a sign of our future energy.

3

931

Wind farms are good in deserts & wilderness but not in farm land and living
amidst them. The light and noise are not tested yet and will harm a lot of
humans and animals. I'm sorry to think that the beautiful sky and county
may be gone.

3

270

Wind energy isn't just a 'symbol' of progress - it is progress but wind energy
has been around a long time. Where have you been? I TOTALLY
BELIEVE IN WIND ENERGY. I DO NOT BELIEVE IN WIND ENERGY
IN SUCH A POPULATED AREA.

3

964

Why can't a person that owns property in a village vote on this?

3

297

We live in the village of Port Hope - small town, 300 people; we own home
for 23 yrs.

3

930

Too many unknowns at present for me to support - expense, longevity,
viability at several levels, etc

3

533

Tip of thumb in Huron County would be perfect for wind mills. Shallow
water and lots of wind.

3

427

The turbines are not in our area yet, but is scheduled to be built in the next
couple years or so.

3

836

The question I have and it‘s not new - why locate wind farms so far from
where the greatest demand is. The added cost of transporting the electricity
a distance doesn't come up in discussions but it is a valid point
economically.

3

792

The general public is not sufficiently informed to make an intelligent
decision. The negative reaction I've heard is from those in other states who
have wind energy.

3

221

The companies lie, and will tell you anything to get you to sign the contract.

3

775

Thanks for letting me take your survey.

3

772

Put them 25 miles EAST of Port Hope.

3

105

Table 16a (cont‘d). Open-Ended Comments
553

My biggest concern is they are putting them too close to residential houses
with no regard to the homeowners - 1300 ft set backs are TOO CLOSE!
Second property values decline no one wants to live by these turbines! The
lack of consideration towards the 'average Joe' some owner is very
disturbing!! There are already many problems in Ubly!!

3

863

Know nothing about wind energy development.

3

584

It is a waste of tax payer‘s money. That could be used for better things they will build bigger better energy plants - that will work good. The towers
will be junked then. Save the money for the future.

3

680

It has not reduced my electric bill.

3

313

Inserted 3 pages of comments - visually unappealing, low frequency noise,
etc.

3

331

If wind energy was a viable solution I would be more in favor of it. The
gov't should not have mandated an unrealistic solution to our energy needs.
With food demands growing turbines should not be put on prime farmland.
If they were viable & cost effective they should go on non-productive land
or water.

3

566

If they are placed along the coast they will lower already decreased property
values. I would try to sell my house and move.

3

861

If MI's elimination of the personal property tax includes wind mills I would
strongly oppose them in every aspect. Huron Co has received
approximately $250,000 in tax from these windmills. While I support the
elimination of this tax, I do not want it to include windmills. I also think
that the only reason that we are even seeing these windmills is because MI is
demanding that a % of energy is "renewable." If windmills were profitable
for these companies, you would have seen them prior to this government
mandate. Hence I feel that they will cost energy prices to rise. Clean coal
and nuclear are better options but Obama and other environmentalist cry too
much about them.

3

447

I would support wind energy IF wind energy could support itself. No
subsidies should be used. I think that wind energy will raise our electricity.
Clean coal or nuclear.

3

801

I would like to see more evidence whether turbines are or are not a health
risk.

3

78

I would like to know any long term affects it might have on wildlife and
humans.

3

106

Table 16a (cont‘d). Open-Ended Comments
7

I would be more positive if the energy made in Huron County would stay in
the county to help reduce the charges for electricity. It is my understanding
that the energy is sent to the 'grid' which services Eastern states.

3

890

I wish they never came up with this wind farm. It makes cows not milk,
makes horses lay down, make chickens not lay eggs, and is very UGLY on
our beautiful land scapes. Who thought this up? CHINA?

3

227

I was one of the first and few in the county to install solar panels on my
home for heat about 20 years ago. So am very supportive of renewable
energy.

3

936

I think wind energy is a plus for Huron County and will bring in money and
cheap energy.

3

140

I think the wind farms are very important and the wind is free!

3

752

I strongly feel the need for wind energy to replace some of fuel brought
from foreign countries - must start to use natural resources – more.

3

528

I have not seen my electric bill go down. I have been told that the power
generated in Huron County is not used in Huron County. The only people
or company that benefit are landowners with wind farms and the power
company who own the turbines - $$$$$$ - money, that is all it's about.

3

629

I have concerns about the effectiveness of the wind turbines on the longrange picture. Once government ceases to subsidize. I also have concerns
about what happens once they age.

3

652

I had 40 acres on Brining Rd, Port Hope, MI. But sold it in 2003. I do
support wind turbines.

3

536

I do not like our night sky - to many towers & red lights, but to move
forward, we must live with them. The towers provide cell phones & paging
services for EMS, windmills provide tax dollars, jobs & clean energy.

3

450

I am highly disappointed in our BOC & Zoning Director (Lundberg). I don't
understand how elected officials can ignore a petition with that many
signatures when this all started. I believe they fell all over themselves to
approve this when they had the opportunity to slow this down and come up
with zoning (set backs) the citizens of Huron County could all approve of.
We all must live with this now!

3

107

Table 16a (cont‘d). Open-Ended Comments
704

Huron County, the Thumb area is such a lovely place, it's the 'forgotten' up
North. We moved here from a big city, with all the distractions. You can
see forever over farm lands, cute villages by the lake, just all green forests.
It seems that it is pure, no obstructions just great nature. Now with the wind
farms it looks like something from the moon has landed here - ugly. We
know all about them and consider them a get rich quick deal.

3

420

GO FOR IT

3

410

Filled out the best I could: Don't know a whole lot about wind energy.

3

802

Don't want them on our coast line or anywhere near water.

3

882

As you can see by my answers I'm not educated on wind energy. I won't be
around much longer! So, I remain neutral on the subject!

3

342

After living near the turbines for 1.5 years, I believe they are detrimental to
people. I don't sleep very well, I get more headaches than before, more
irritable heart flutters. Never had these problems before the turbines.

3

982

Above, "on my property" question - living ON Lake Huron, I can't visualize
a wind "farm" but one maybe. I have seen the trucks hauling parts for wind
turbines and they are HUGE. The wind turbines along I40 are HUGE when
assembled.

3

911

Zoning was rushed through by county commissioners, NOT the public.
Easements need a greater distance. This type of energy source needs to be
developed, however, the public's concern NEED to be addressed. I am all
for it, but I do not want one flickering in MY backyard, hear the hum, or the
blades

4

387

Wind turbines contribute nothing to Huron County. No buildings of
machines. More labor to operate, do not make effective use of land
compared to stationary power plant. Without gov't subsidy would not
succeed. Let‘s hope we can move ahead now we don't have the governor
bimbo!

4

416

Wind mills are good for the county and the environment and I support
further development. At the same time I don't want to look out my window
and see one every day.

4

562

Wind mills are a form of "industrial pollution" of the landscape and the
environment. They benefit the few and damage the aesthetic experience of
the many. They are bad for wildlife, esp. migrating birds. They are
unsightly and unnecessary!

4

108

Table 16a (cont‘d). Open-Ended Comments
329

Wind farms are too expensive for the energy return they get back. I worry
about the monitoring of the wind mills and how long they will last. Nuclear
energy is the way to go.

4

850

Wind farms are a waste of money relative to the amount of Energy they
produce.

4

925

Wind energy is one of many alternative energy sources that allows us
freedom from foreign sources of energy - A step in the right direction.

4

302

Will become increasingly detrimental to the environment and landscaping.
Anyone who thinks that the turbines and upcoming electrical lines will
improve the landscape are 1st class idiots.

4

815

What happens to the birds flying into them? I heard about the blades
coming off already.

4

941

What happens when a tornado hits a wind turbine?

4

945

We need more wind farms in MI.

4

398

We moved to Huron County last year and were in awe the first time we
drove by a wind farm. We have seen them in other countries, but not much
in America. In our opinion, Huron County is a windy place that could use
the economic boost that even more wind farms could create. *However, we
have seen how only land owners are seeing any benefit from wind mills.
The communities around wind farms should see some benefit for all
citizens.

4

67

Use nuclear or solar systems. Nuclear for bulk of energy (proven, reliable,
clean). Solar for home use w/ zoning restrictions.

4

196

Use any energy money to drill for oil and gas and to build nuclear plants.

4

709

Too many greedy people mess a good thing up!

4

873

This is probably not to too useful. My knowledge of wind turbines would fit
on a postage stamp, with room to spare.

4

800

They should be considered light industrial for zoning purposes and then
should comply with zoning requirements. The number should be limited in
any given area. The companies need to be bound to removal terms when
they become obsolete or in disrepair. The problem w/this survey is you
have not given a definition of 'wind farm' and we must guess at what you
mean exactly.

4

109

Table 16a (cont‘d). Open-Ended Comments
974

The wind here on my property is constant, on average out of 30 days, the
wind blows at least 20-25 days of that.

4

621

The cost of electricity in the areas where they have turbines has not
decreased for the residents for the trouble they cause, I do not think they are
worth it.

4

494

The concept of wind energy is useful, however as it has progressed in the
county it has what seems to be nothing but a political gain among some and
to my knowledge no jobs have been created locally and the companies have
made money from the federal government to install those to our demise.

4

813

The best place for wind turbines is in the dead lake of Huron. The lakes‘
fishery is gone and all that dead water might as well be used for something.

4

842

Thanks for asking!

4

267

Thank you for this survey - the questions were excellent! I believe the
thumb of MI is a great place for wind energy simply because of the location.

4

840

Revenue sharing or lack of is a MAJOR issue. Why him & not me if I'm
affected?

4

94

Question 8 - part 15 should be able to bring economic benefits. Question 11
should be beneficial.

4

171

Pro wind energy.

4

506

People need to think outside the box and realize that renewable energy is
where it is at. Get with the program!

4

215

New innovation is good as long as it doesn't cause health problems or take
away from the value of life or effect negatively our environment or the
health of wild life.

4

975

My son built Solar energy on his house. It had a nice heat, very clean. I
liked it very much. Wind or solar - I liked them both. Solar didn't bother
anyone. Would prefer solar.

4

732

My parents have a wind test site on "our" family farm. I now own land
beside it and am positive about the possibility for several reasons.

4

110

Table 16a (cont‘d). Open-Ended Comments
156

Most of the people of Huron County were not given the opportunity to vote
on wind mills. Every nation that has tried to use this technology as a source
of energy has abandoned them - see Spain. Hoping to sell my property
before they put these eyesores around us and decrease my property value.

4

24

More information needs to be known from people who already have them on
their land, I would like to know their feelings about wind energy.

4

475

Many years ago my dad had the vision of wind turbines. He felt and stated
he could see it coming in the future. He died before seeing his vision come
true, so I'm here to carry it on. They're beautiful in the horizon and I love to
see my grandkids faces when they see them. I am and always will be a
supporter. The country needs more Education/Info on them though including myself.

4

769

Love to see the study‘s findings. Thank you for writing this term paper.
Good luck.

4

170

Living in the city makes it difficult to answer questions on your survey.

4

205

Living in Harbor Beach, I would prefer a DTE wind farm to the present
DTE coal-fired power plant in my neighborhood.

4

210

It seems there needs to be more data on health issues to people living close
to turbines. Huron County should not be turned into one giant wind farm.
There needs to be a limit on the number put in the county. Where is the
economic plus to the residents of the county?

4

715

It could help out if that would help us with our electric bills. Bring the cost
down.

4

513

I'm the wife – my husband is deceased.

4

72

I would be more in favor of the wind development if they were co-op owned
by the people surrounding & owning the land versus allowing them to be
bought and sold like a mortgage to the highest bidders with many contracts
given to out of state people for projects with huge tax implications & benefit
for them but little to none for us as landowners. In fact my energy bill is
increasing because of these wind mills in our area and the "cost" of
upgrading the grid.

4

111

Table 16a (cont‘d). Open-Ended Comments
739

I think wind turbines are a good thing for Huron County. It is windy in our
area, so good to create energy naturally. It brings in taxes to the county and
creates jobs to maintain them.

4

12

I think wind farms are much better and less polluting than fossil fuel,
HOWEVER they should not be on productive land. (Poor land, water,
desert, O.K.) we already have a lot of good land covered with concrete.

4

296

I think they are attractive. It's like being in Holland. It would be nice if
MORE PEOPLE got a "PROFIT" on the turbines than making just a few
farmers RICH! These farmers are usually tight with their money and not
sharing easily with public. The more the greedier.

4

367

I think the wind farms are a waste of a bunch of money that mostly comes
from the government. If there is a place for renewable energy it is out in the
lake under water so you don't have to see or hear them and the water is
typically always moving. The wind farms have ruined the natural look of
the land and or change or commercialized it.

4

82

I think that wind farms are wonderful and a positive for Huron County.

4

620

I think that we have enough wind turbines now.

4

361

I think people in Michigan as well as Huron County need to get more
informed about this issue. It could be beneficial for so many people in this
area. Thank you for the survey. We all need to be educated on this issue.

4

977

I support the turbines. I hear the energy from existing turbines goes to
Detroit. We need it here!

4

858

I strongly believe the turbines should not be put into the lakes!

4

713

I have had solar (heat only) on my home since 1977. I never regret the
decision. I believe everyone can benefit from solar, wind or other sources of
renewable/sustainable energy sources.

4

690

I feel they are a clean energy source. Possibly hazardous to people living
nearby them. Basically unsightly addition to our landscape.

4

586

I don't understand nothing about wind energy.

4

722

I don't know for sure if wind energy is safe or not. I think some people find
fault with change - will make something negative out of good. I don't have
any turbines near where I live, so I can't really say if the noise would bother
me or not.

4

112

Table 16a (cont‘d). Open-Ended Comments
855

I don't have any comment. Thank you.

4

728

I don't believe the wind turbines are any more unsightly than the electrical
lines & poles we have now.

4

85

I do not support wind farms due to the high cost of electricity produced.
When the technology is improved to yield a competitive price then I will
support it. No problems with the idea. More research is needed before we
proceed.

4

444

I believe wind turbines are a good idea as long as county land owners were
compensated fairly, also creating more jobs. I do not believe we should
have turbines on our shorelines or in the water

4

678

I believe Wind Energy is needed in Huron County.

4

500

I am not against wind energy. I am against building them to close to houses.
The allowable distances should be pushed back to minimum of 1 mile from
a house. Build the windmills on wasted state land up north, then the state
could put the revenue back into schools. I also do not think windmills are
profitable w/out subsidies.

4

175

I am for clean, renewable energy resource & development. I sympathize
with local land owner's concerns & though I disagree with those who oppose
wind farms, I think their voices need to be heard and issues addressed with
care and integrity of information. I do not feel information on why we
should support wind farm development has been communicated well
enough.

4

530

I am a retired electrician. I have worked on many powerhouses in my day.
(Coal, oil, gas, nuke, etc) Wind is the cleanest I have ever seen. And no
fuel.

4

503

How much money from a turbine actually stays in the county? Also what is
the payback time for a turbine and is the landowner really benefitting
financially from them!

4

116

Driveways need to be placed where they are convenient for farming around
as well as convenient for the turbine companies

4

642

Don't put them near the lake. It's a beautiful site don't ruin it.

4

380

Don't like how they only use farm land instead of State land. The way the
leases are worded they could take all of a person‘s land for their turbines and
leave that farmer with no land to till.

4

341

Designated recipient died in 2007

4
113

Table 16a (cont‘d). Open-Ended Comments
520

Cost too much tax payer money for the little bit of return of energy - need
something high that WORKS!

4

469

Contracts are of great concern to land owners who may end up with liability
issues or be stuck with an eye sore. Landowners cannot trust the wind
companies to be fair with regard to money.

4

472

As long as large utility companies have the right of way they will put wind
turbines anywhere they want.

4

Answered by Landowner - brother to original recipient

4

688

Although wind turbines are rather large, I don't consider them unsightly,
noisy, or a threat to humans or animals. In days of economic hardships,
alternative energy sources should be accepted with open arms.

4

188

10 years from now they will be obsolete. Our government is footing the bill.
Workers are very much over-paid. Advances in technology with make them
useless. Components are sent into this country from overseas and
companies will reap the money at the taxpayers‘ expense. Money down the
DRAIN. Edison can produce electric for a lot less.

4

960

#13 I think you have good guidelines as to where they are set and I agree
with the guidelines as they are. I think wind energy is a great way to go.
Where would we be if people would not have wanted electric poles along
the roads??

4

6

I do not like the flashing red lights on the wind turbines in the night sky.
But I will say in one simple sentence, that I am definitely NOT a fan of
them.

114

APPENDIX E
Survey Response Rate Graph

115

Figure 2. Survey Response Rate Graph

Surveys Returned
90
80
70
60
50
40
30
20

10
0

Surveys Returned

116

References

117

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