WIND OF CHANGE: OFFSHORE WIND FARMS, CONTESTED VALUES AND
ECOSYSTEM SERVICES
by
Sarah Catherine Klain
B.A., Reed College, 2003
M.Sc. The University of British Columbia, 2010
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
in
THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES
(Resource Management and Environmental Studies)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
October 2016
© Sarah Catherine Klain, 2016
Abstract
Increasing reliance on renewable energy promises to reduce carbon emissions. Although
national-scale polls demonstrate high levels of public support for developing renewable energy,
local opposition has led to cancelations of renewable energy projects globally. This dissertation
empirically investigates barriers to the siting of offshore wind farms in reference to their
perceived risks and benefits; people’s willingness to pay to mitigate environmental risks; values
that influence these choices and attitudes; and public deliberation processes used to engage local
citizens in decisions about local siting and alternative energy options.
The first study investigates perceptions of offshore wind farm impacts and why risks to some
ecosystem services (ES, i.e., benefits from nature to people) may induce greater concern than
others. These differences are attributed to the affective ways in which people perceive risk.
Affectively-loaded impacts (e.g., harm to charismatic wildlife, visual intrusion) were assigned
greater weight than more easily quantifiable impacts (e.g., displacement of fishing, impact to
tourism). This study suggests that government authorities and developers can anticipate and
more explicitly address affective dimensions of renewable energy proposals.
The second study quantifies stated preferences for specific attributes of wind farms: effect on
marine life, type of ownership, distance from shore, and cost. The strongest preference was for
farms that greatly increased biodiversity via artificial reefs at an additional cost of ~$3442/month. This research demonstrates substantial willingness to pay for ecologically
regenerative renewable energy development.
ii
The third study pilots methods on ‘relational values,’ which link people to ecosystems and
include associated principles, notions of a good life and virtues. Preliminary results suggest that
relational values are distinct from standard methods of measuring ecological worldview and
predictive of attitudes towards offshore wind farms.
The fourth study assesses attributes of effective public engagement processes to site renewable
energy projects as they played out in three island communities. Amongst the array of criteria for
robust analytic deliberative processes, good public engagement may be condensable to two
themes: enabling bidirectional deliberative learning and providing community benefit. Attending
to these themes may improve relationships among communities, government authorities and
developers when deciding if and where to site renewable energy infrastructure.
iii
Preface
Chapters 2, 3, 4 and 5 of this dissertation are distinct manuscripts written with the goal of
publication in academic journals. These chapters are meant to stand alone, which results in some
repetition across chapters regarding descriptions of the broader research context and methods.
I was responsible for the idea of exploring perceptions of a hypothetical wind farm, creating an
animated visualization for the project, as well as the analysis and writing of Chapter 2. I
collected data via interviews with the help of a local research assistant. Although I was the lead
author of this chapter, my adviser Kai Chan and committee member Terre Satterfield helped me
develop the theoretical framing, several research questions and analytical tools to address the
questions. I also collaborated with Jim Sinner and Joanne Ellis from Cawthron Institute, who
hosted me and others from my UBC lab group in New Zealand. They also provided important
background information for my study and valuable feedback on drafts of my interview protocol,
data analysis and the resulting manuscript. UBC’s Behavioural Research Ethics Board approved
this project (certificate number H14-00842).
I am the lead author of Chapter 3 having conceived of the research questions, selected the
methods, conducted the analysis and written the manuscript. Kai Chan contributed with feedback
that improved the design, interpretation, analysis and results of this study. I benefited from
discussions with Gunilla Oberg about regenerative design. Robin Naidoo and Noah Enelow
provided some technical guidance when I built my choice experiment models. Chapter 3 and 4
were approved by UBC’s Behavioural Research Ethics Board (certificate number H15-01325).
iv
Paige Olmsted and I share the role of first author on Chapter 4. Kai Chan, Terre Satterfield,
Paige Olmsted and I collaborated to develop and refine survey questions that Paige Olmsted and
I tested with different populations. Kai Chan and Terre Satterfield recommended statistical
approaches, which Paige and I conducted. Paige and I equally shared the writing of the
manuscript, which also benefited tremendously from Kai Chan’s and Terre Satterfield’s input.
Chapter 5 was supported by UBC’s Public Scholars Initiative, which seeks to re-imagine the
PhD process via expanding the types of contributions that are recognized as legitimate
components of a PhD and dissertation. I oriented this chapter to bring academic literature to bear
upon practitioner’s challenges with local rejection of renewable energy systems. As such, this
contribution differs from how it would have been structured it if it had been purely an academic
exercise (e.g., we selected our study sites based on the partner organization’s experience working
with these communities, rather than a more academically rigorous method of selecting sites).
This study was conducted in collaboration with a non-profit organization, Island Institute. Two
of my co-authors, Suzanne MacDonald and Nicholas Battista, are staff at this organization. We
worked together to identify the main thrust of this project: reflecting on lessons learned from
engaging New England island communities with offshore wind. We drafted and distributed a
report for public audiences based on our findings [Klain, S., MacDonald, S., & Battista, N.
(2015). Engaging Communities in Offshore Wind (pp. 1–44). Island Institute], which is freely
available on Island Institute’s website. I led the analysis and drafting of this manuscript with
input from all co-authors. Terre Satterfield and Kai Chan provided critical feedback and
guidance on several drafts to help me improve the structure of the manuscript as well as the
figures and better relate these lessons learned to academic theories.
v
Table of Contents
Abstract.......................................................................................................................................... ii
Preface ........................................................................................................................................... iv
Table of Contents ......................................................................................................................... vi
List of Tables ............................................................................................................................... xii
List of Figures............................................................................................................................. xiii
List of Abbreviations ...................................................................................................................xv
Acknowledgements .................................................................................................................... xvi
Dedication ................................................................................................................................. xviii
Chapter 1: Introduction ................................................................................................................1
1.1
Dissertation goals ............................................................................................................... 2
1.2
Theoretical underpinnings ................................................................................................. 3
1.2.1
Social studies of risk ................................................................................................... 4
1.2.2
Ecosystem services ..................................................................................................... 7
1.2.2.1
Cultural ecosystem services ............................................................................... 10
1.2.3
Environmental and relational values ......................................................................... 11
1.2.4
Analytic-deliberative processes ................................................................................ 14
1.3
Chapter overviews ........................................................................................................... 16
1.4
Summary .......................................................................................................................... 20
Chapter 2: Bird killer, industrial intruder or clean energy? Perceiving the risks of offshore
wind farms ....................................................................................................................................21
2.1
Introduction ...................................................................................................................... 21
vi
2.2
Methods............................................................................................................................ 26
2.2.1
Study area.................................................................................................................. 26
2.2.2
Interview sample ....................................................................................................... 29
2.2.3
Interview design ........................................................................................................ 30
2.2.4
Weighting of concerns .............................................................................................. 31
2.2.5
Risk factor scoring using risk perception theory ...................................................... 33
2.2.6
From scoring risk associated with wind farms to analysis ....................................... 34
2.2.7
Weighting of benefits................................................................................................ 37
2.3
Results .............................................................................................................................. 37
2.3.1
Concerns ................................................................................................................... 37
2.3.1.1
Narrative expressions of concern ....................................................................... 37
2.3.1.2
Weights assigned to concerns ............................................................................ 39
2.3.2
Benefits ..................................................................................................................... 41
2.3.2.1
Narrative expressions of benefits and trade-offs ............................................... 41
2.3.2.2
Weights assigned to benefits .............................................................................. 42
2.4
Discussion ........................................................................................................................ 43
2.5
Conclusion ....................................................................................................................... 47
Chapter 3: Rethinking renewable energy: high willingness to pay for ecologically
regenerative offshore wind farms ...............................................................................................49
3.1
Introduction ...................................................................................................................... 49
3.2
Methods............................................................................................................................ 54
3.2.1
Study location ........................................................................................................... 54
3.2.2
Sample characteristics ............................................................................................... 55
vii
3.2.3
Choice experiment design ......................................................................................... 57
3.2.4
Econometric analysis of choice experiment data ...................................................... 61
3.3
Results .............................................................................................................................. 63
3.3.1
Model results: strong preference for biodiversity benefits ....................................... 63
3.3.2
Estimates of willingness to pay for offshore wind farm characteristics ................... 65
3.4
Discussion ........................................................................................................................ 67
3.4.1
3.5
Policy implications.................................................................................................... 70
Conclusion ....................................................................................................................... 71
Chapter 4: Relational values resonate broadly and differently than intrinsic or
instrumental values, or the New Ecological Paradigm .............................................................72
4.1
Introduction ...................................................................................................................... 72
4.2
Methods............................................................................................................................ 77
4.2.1
4.2.1.1
Online survey ..................................................................................................... 80
4.2.1.2
Paper-based survey ............................................................................................ 81
4.2.1.3
Sampled population characteristics.................................................................... 82
4.2.2
4.3
Survey value statements and sample......................................................................... 78
Statistical analysis ..................................................................................................... 83
4.2.2.1
Eigenvalues and scree test ................................................................................. 83
4.2.2.2
Factor analysis ................................................................................................... 84
4.2.2.3
Principal components analysis ........................................................................... 85
4.2.2.4
Consistency measure: Cronbach’s alpha ........................................................... 85
4.2.2.5
Correlation testing of environmental values and wind farm attitudes ............... 85
Results .............................................................................................................................. 86
viii
4.3.1
Two distinct factors based on eigenvalues and scree test ......................................... 86
4.3.2
Factor analysis results: NEP is distinct from relational value .................................. 87
4.3.3
Principal components analysis: NEP is distinct from relational values .................... 88
4.3.4
High levels of agreement and consistency with types of environmental value
statements.............................................................................................................................. 89
4.3.5
Majority of M-Turk sample have positive attitudes towards wind farms ................. 93
4.3.6
Significant correlations between wind farm attitudes and environmental values..... 94
4.3.7
Environmental values influence wind farm attitudes at national and state level ...... 96
4.4
Discussion ........................................................................................................................ 98
4.4.1
Diverse populations tend to agree with strong relational value statements .............. 98
4.4.2
Relational value responses are distinct from NEP .................................................. 101
4.4.3 Relational statements can be a single construct and have potential as new index.. 102
4.4.4
Theory implications ................................................................................................ 104
4.4.5
Policy and practical implications ............................................................................ 107
4.4.6
Proposed paths forward........................................................................................... 110
4.5
Conclusion ..................................................................................................................... 112
Chapter 5: Will communities “open-up” to offshore wind? Lessons learned from New
England islands ..........................................................................................................................113
5.1
Introduction .................................................................................................................... 113
5.1.1
5.2
Theorizing public engagement processes ............................................................... 117
Methods.......................................................................................................................... 121
5.2.1
Context of study: collaboration with community-based organization .................... 121
5.2.2
Data collection and analysis.................................................................................... 123
ix
5.3
Results and discussion ................................................................................................... 124
5.3.1
Focal island communities and wind farm engagement experiences ....................... 125
5.3.1.1
Block Island: the ocean state’s offshore wind farm pioneers .......................... 126
5.3.1.2
Martha’s Vineyard: moving forward with a cooperative approach ................. 128
5.3.1.3
Monhegan: confronting deep water and community challenges ..................... 130
5.3.2
Bi-directional deliberative learning and community benefits as key to good
engagement ......................................................................................................................... 133
5.3.2.1
5.3.2.1.1
Readily available and accessible information ........................................... 136
5.3.2.1.2
Trusted messenger .................................................................................... 137
5.3.2.1.3
Bridging organizations .............................................................................. 138
5.3.2.1.4
Timing: substantial iterative public engagement before site selection ..... 140
5.3.2.2
Provision of community benefits ..................................................................... 143
5.3.2.2.1
Deliberation to determine community benefits......................................... 145
5.3.2.2.2
Flexible models for custom tailored benefits ............................................ 148
5.3.2.3
5.4
Defining bi-directional deliberative learning ................................................... 135
Relevance to components of public participation in deliberation .................... 149
Conclusion ..................................................................................................................... 151
Chapter 6: Conclusion ...............................................................................................................153
6.1
Realization of renewable energy research goals and research implications .................. 154
6.2
Limitations ..................................................................................................................... 156
6.3
Future research directions .............................................................................................. 159
6.4
Towards ecologically and socially sustainable energy .................................................. 160
References ...................................................................................................................................162
x
Appendices ..................................................................................................................................178
Appendix A Golden Bay interview consent form................................................................... 178
Appendix B Golden Bay Interview request letter ................................................................... 180
Appendix C Golden Bay interview protocol .......................................................................... 182
Appendix D Full table of risk components ............................................................................. 193
Appendix E Choice experiment consent form ........................................................................ 196
Appendix F Choice experiment Mechanical Turk request description .................................. 198
Appendix G Choice experiment survey .................................................................................. 199
Appendix H Variables in choice experiment .......................................................................... 210
Appendix I Factor Analysis by population ............................................................................. 211
Appendix J Scree plot ............................................................................................................. 213
Appendix K Graphical PCA results ........................................................................................ 214
Appendix L M-Turk Cronbach’s alphas ................................................................................. 215
Appendix M Variables on wind farm attitudes and indices of environmental value.............. 216
Appendix N Wind farm attitudes ............................................................................................ 217
Appendix O Distribution of responses to value prompts ........................................................ 218
Appendix P Detailed site descriptions .................................................................................... 219
xi
List of Tables
Table 2.1. Common concerns associated with offshore wind farms. ........................................... 33
Table 2.2. Psychometric Risk Characteristics (P. Slovic, 1987). ................................................. 35
Table 2.3. Explanation of composite risk factor scoring. ............................................................. 36
Table 3.1. Survey respondents demographic characteristics compared to census data. ............... 57
Table 3.2. Description of attributes and levels used in the choice experiment. ............................ 59
Table 3.3. Choice experiment conditional and mixed logit models with WTP estimates (N=400).
....................................................................................................................................................... 64
Table 4.1. Value statements used in surveys. ............................................................................... 79
Table 4.2. Demographic characteristics of our three samples. ..................................................... 83
Table 4.3. Factor Weights ............................................................................................................. 87
Table 4.4. PCA loadings based on correlation matrix. ................................................................. 89
Table 4.5. Cronbach’s alpha, mean response and standard deviation of responses across value
statements...................................................................................................................................... 90
Table 4.6. Top six mean responses to environmental value statements across three populations.93
Table 4.7 Linear model results for fixed effects on attitudes towards wind power as anticipated
by responses to environmental value statements and demographic characteristics. ..................... 97
Table 5.1. Key differences between New England island study sites and mainland communities
relevant to engagement on energy issues. ................................................................................... 126
Table 5.2. Summary of good practices and challenges related to community engagement. ...... 134
xii
List of Figures
Figure 1.1. Conceptual framework of barriers to scaling up renewable energy. .......................... 17
Figure 2.1. New Zealand electricity generation by source in 2014 (MBIE 2015)........................ 27
Figure 2.2. Study Site: Golden Bay, New Zealand. ...................................................................... 29
Figure 2.3. A still image from the animated seascape visualization of an offshore wind farm in
Golden Bay, New Zealand using Google Earth. ........................................................................... 31
Figure 2.4. Relative weighting of offshore wind farm concerns with standard error. .................. 39
Figure 2.5. Average level of concern according to stakeholders for risks to ES plotted against
psychological dimensions of each risk with standard error bars. ................................................. 40
Figure 2.6. Perception of relative value of benefits from an offshore wind farm......................... 43
Figure 3.1. Wind resource potential for states in study. ............................................................... 55
Figure 3.2. Example of choice scenario. ....................................................................................... 60
Figure 3.3. Willingness to pay (WTP) for offshore wind farm attributes..................................... 67
Figure 4.1. Graphical results of Factor Analysis. ......................................................................... 88
Figure 4.2. Mean and distribution of responses to relational value prompts and New Ecological
Paradigm Statements..................................................................................................................... 91
Figure 4.3. Mean response with standard errors to value prompts across three populations. ...... 92
Figure 4.4. Attitude toward wind at the national (left) and state level (right). ............................. 93
Figure 4.5. Expected impact of an offshore wind on going to the coast for recreation. ............... 94
Figure 4.6 Correlation matrix of attitudes towards wind farms and environmental values. ......... 95
Figure 4.7. Value-belief norm model (green) with our proposed ways in which relational
framings (purple) could influence steps of this pathway (black dashes). ................................... 106
xiii
Figure 5.1. Normative theory of public participation in decision-making, adapted from Abelson
et al. (2003). ................................................................................................................................ 118
Figure 5.2. Map of focal islands . ............................................................................................... 123
Figure 5.3. A robust approach to developing community benefits. ............................................ 147
Figure 5.4. Design and evaluation principles for public participation processes with community
benefit outcomes. ........................................................................................................................ 150
xiv
List of Abbreviations
ES
Ecosystem services
OWF
Offshore wind farm
WTP
Willingness to pay
xv
Acknowledgements
I am grateful for Kai Chan’s unwavering confidence in my ability to learn, work through
challenges and improve myself as a scientist and citizen. I admire his commitment to his students
and unwavering motivation to conduct research that is both academically robust and applied to
finding solutions to sustainability issues. His mentorship has profoundly shaped and enhanced
my professional trajectory. Also, his Connecting Human and Natural Systems (CHAN’s) lab
group provided an intellectual and personal safety net as well as springboard during my
academic journey.
Terre Satterfield generously shared her time, wisdom, encouragement and compassion, which
was invaluable as my dissertation directions evolved. I am indebted to my committee members,
including Terre Satterfield, Hisham Zereffi and Scott Harrison, for their support and remarkable
patience with me.
My research in New Zealand was made possible due to Jim Sinner and Joanne Ellis at Cawthron
Institute. Our collaboration was funded by the Ministry of Business, Innovation and Employment
(MBIE) (contract MAUX1208). Evan Jones provided essential animation assistance. I also thank
the following research assistants for their help in conducting and transcribing interviews:
Ruaridh Davies, Jakob Öberg, Allison Thompson, Calum Watt and Adrian Semmelink.
I also appreciate the financial support from the Vanier Fellowship. UBC’s Public Scholar
Initiative and The Biodiversity Research: Integrative Training & Education (BRITE) Natural
xvi
Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and
Training Experience Program (CREATE) program enabled me to collaborate with the non-profit
organization Island Institute. Suzanne MacDonald, Brooks Winner, Harry Podolsky, Rebecca
Clark Uchenna at Island Institute made me feel welcome and shared their considerable
knowledge and experiences that shaped our work. Additional support for my dissertation came
from the Social Sciences and Humanities Research Council of Canada (SSHRC) grant F1204439 Environmental meanings and ecosystem services: the social risks of ecological change
and the Gordon and Betty Moore Foundation.
I am also grateful to my family and friends who provided emotional support during my PhD
process. I appreciate my mother’s boundless energy and ability to cut to the chase. My father’s
love of the sea, sailing and wind has rubbed off on me. My accomplished sister and her lovely
daughter inspire me to do my best.
xvii
Dedication
To Josephine Ellen Kellner-Klain, with love. You are the future and a considerable part of why I
devote myself to addressing sustainability challenges.
xviii
Chapter 1: Introduction
“When we choose the kind of nature we will live with, we are also choosing the kind of
human beings we will be. We shape the world, and it shapes us in return. We are the
creator and the created, the maker and the made.”
~J.B. MacKinnon
Securing sustainable energy is among humanity’s most urgent problems, particularly in
the context of climate change (Yergin, 2011). Energy choices involve trade-offs replete
with environmental, economic and social consequences. Over 1,300 scientists around the
world have prioritized the following human impacts as key global concerns: climate
disruption, extinctions, loss of diverse ecosystems, pollution, and human population
growth in conjunction with high levels of consumption (Barnosky et al., 2014). These
concerns are directly and indirectly linked to the production and consumption of energy
for human use.
The scientific consensus on the need to decrease greenhouse gas emissions has coalesced.
Part of mitigating climate change involves decarbonization—reducing the carbon
intensity of energy (IPCC, 2014). Rapidly scaling up low carbon electricity production to
replace energy from fossil fuels plays a crucial role in decarbonization goals set by
countries around the world during COP21 (Bagheri and Del Amo, 2016) and can help
achieve the United Nation’s Sustainable Development goals relevant to energy (Angelou
et al., 2013; UN, 2015). Renewable energy development is part of the requisite energy
transition to mitigate climate change.
1
Numerous studies demonstrate broad public support for renewable energy development
in general (Krohn and Damborg, 1999; Krosnick and MacInnis, 2013; Toke, 2002; G.
Walker, 1995; Wüstenhagen et al., 2007). Despite this widespread support at national
levels, when it comes to siting specific new energy technologies, many vociferously
debate what constitutes clean and locally desirable energy systems (J. Barry et al., 2008;
Devine-Wright, 2011; Devine-Wright et al., 2011; Roberts et al., 2013; Warren et al.,
2005). In modern democratic societies, local opposition to particular renewable energy
infrastructure poses a challenge to rapid decarbonization because it can shape if and how
energy infrastructure is built (Ansolabehere and Konisky, 2014; Devine-Wright, 2005;
Devine-Wright et al., 2011; Roberts et al., 2013). Fierce local resistance to proposed
energy infrastructure has stalled or stopped some energy developments, including cases
where federal and regional approval has been granted. For example, the Cape Wind
offshore wind farm in the US instigated vigorous local opposition resulting in multiple
lawsuits against the project proponents despite governmental approval (Firestone and
Kempton, 2007; Shellenberger and T. Nordhaus, 2009). Within this context of global
climate change coupled with significant local opposition to renewable energy projects,
this dissertation seeks to identify, characterize and anticipate perceptions of risks,
benefits and trade-offs associated with the development of offshore wind farms.
1.1
Dissertation goals
The purpose of this dissertation is to provide insight on 1) the source and nature of
resistance to and conflicts surrounding some renewable energy development—
specifically offshore wind farms; and 2) identify novel approaches and opportunities for
2
working through such conflicts. It is my hope that this research will inform options for
transitioning to low carbon electricity sources in a socially and environmentally
responsible manner.
More specifically, I aim to better understand and address how people perceive this
renewable energy technology, what trade-offs they are willing to make in light of its
environmental impacts and costs and how decision processes about this technology can
open up rather than close down public involvement in decisions about this technology.
My research also addresses the critique that energy research has downplayed “the role of
choice and the human dimensions of energy use and environmental change” (Sovacool,
2014, p. 1) and suffered from a lack of “human-centered research methods” (e.g., field
research, interviews, focus groups, surveys) (Sovacool, 2014, p. 2).
1.2
Theoretical underpinnings
Social theories of perceived risk are central to these research goals, as are emerging
characterizations of ecosystem services (ES) and environmental values as pertains to the
environment and energy. Also pertinent to this work are theories of public engagement in
policy decisions, particularly regarding the design of analytic-deliberative processes
involving public groups in decision making and siting. Topically, the focus here on
offshore wind farms is also, by definition, a testing ground for new ways of applying,
hybridizing and contributing to these theories and fields of inquiry.
3
1.2.1
Social studies of risk
Risk perception is central to the choices people make regarding both energy use and their
support or opposition to the development of new sources of energy. Public risk
perception can profoundly push, constrain or impede action to address specific risks
(Leiserowitz, 2006). Risk perceptions are critical elements of the social and political
context in which policy develops and is implemented. Perceptions, rather than technical
knowledge per se, drive human behavior (Bennett, 2016; Leiserowitz, 2006).
Understanding perceptions sheds light on what worries and motivates people.
The field of risk perception has been used broadly to understand why people accept or
reject new technologies, design communication and education efforts and create robust
risk management strategies (Haidt, 2001; Satterfield et al., 2009; P. Slovic, 1987;
Wilsdon and Willis, 2004). The risk perception literature has delved into how people
integrate affective (“system 1”) and deliberative (“system 2”) cognition when forming
risk judgments (Epstein, 1994; Finucane et al., 2000a; Loewenstein et al., 2001; Sloman,
1996; P. Slovic, 2010).
The psychometric paradigm in perceived risk research is foundational to this newer ‘twosystem’ thinking in that it first demonstrated the intuitive nature of risk judgments.
Central to this is empirical work on how perceived risk is both predictable and
quantifiable based on a limited set of often intuitive and affective factors, including how
well a risk is understood, how much it invokes dread, whether or not a risk is seen as
controllable and how many people are thought to be exposed to it. Risk perception
studies have proliferated and now attend to more affective and social considerations,
4
including race, gender, vulnerability, and trust (Bord and O'Connor, 1997; Finucane et
al., 2000b; Irwin and Wynne, 2004; Satterfield et al., 2004). Nonetheless, these
psychometric dimensions continue to explain much of the variance in perceived risk for
both new and familiar technologies (Helgeson et al., 2012; Satterfield et al., 2009).
The psychometric paradigm was a precursor to five categories of influences on the
formation of risk perception at the individual level including cognitive, sub-conscious,
affective, socio-cultural, individual factors (Helgeson et al., 2012). Cognitive factors
include expected utility and rational estimations of likelihood and impact. Subconscious
drivers include cognitive heuristics (rules of thumb) that can lead to substantial and
persistent biases (e.g., misunderstandings of probabilistic processes) (Kahneman, 2011;
Leiserowitz, 2006; Tversky and Kahneman, 1974). Affective factors, including like
(positive valence), dislike (negative valence), fear, anxiety and worry, tend to direct how
we process information and make judgments about risks (Finucane et al., 2000a;
Loewenstein et al., 2001). Socio-cultural factors that influence risk perceptions include
social organization (hierarchical versus egalitarian) and social relations or cohesion (high
conformation to norms versus loose conformation) as well as broadly shared values and
beliefs constituting worldviews (Douglas and Wildavsky, 1983). Risk perception can be
linked to commitments to cultural and political groups, as explained with the cultural
theory of risk (Douglas and Wildavsky, 1983; Kahan, 2015; Kahan et al., 2012). Lastly,
the social amplification of risk framework demonstrates how communications with
different qualities and from different communication sources (media, NGOs, etc.) can
amplify or attenuate risk perception (Kasperson et al., 1988; Pidgeon et al., 2003).
5
Individual factors also play a role in risk perception formation, notably that people with
low levels of self-efficacy, which is an individual’s perception of his/her capacity to
instigate change in his/her life, experience higher levels of perceived personal risk.
Additionally, direct experience of a risk also strongly influences risk perception
(Helgeson et al., 2012).
Risk perception research has thus far largely focused on personal health and safety
concerns, but some opposition to energy infrastructure stems from environmental
considerations (e.g., polluted or destroyed habitat) (Ansolabehere and Konisky, 2014),
particularly the reduction of ecosystem services (ES), defined as benefits from the
environment to people (e.g., fisheries, freshwater) (Entrekin et al., 2011). In contrast to
many of the foci of risk research (e.g., radiation, natural disasters), which have direct
consequences for human health and safety, risks to ES tend to have more indirect impacts
to people.
I apply risk perception theories in a new context: perceptions of the ecological risks
posed by the development of a renewable energy technology. Understanding these
perceptions of risks to ES could help design mitigation strategies for local environmental
impacts and potentially garner greater public support for transitioning away from fossil
fuels. I seek to better understand intuitive risk judgments and perceptions of benefits
associated with renewable energy infrastructure. My research in Chapter 2 tests the extent
to which the psychometric risk paradigm can be extended to and help explain the
magnitude of locally perceived risks to ES.
6
I first address this set of risk perception research challenges in Chapter 2 with the
question: Can the psychometric risk paradigm be extended beyond human health and
safety concerns to less direct risks mediated by the environment—e.g., can it predict
perception of ecological risk associated with new energy infrastructure? How do people
perceive environmental risk associated with a new technology? And do such applications
of the psychometric risk paradigm helps anticipate the salience of ES impacts to
stakeholders in relation to a new renewable energy technology? In sum, the aim of that
chapter is to use an illustrative case study to provide a proof of concept for bringing
together ES and risk perception literature.
1.2.2
Ecosystem services
The concept of ES emerged in the early 1980s to characterize the subset of ecological
functions that are valuable to people but not always captured by conventional cost-benefit
approaches (P. R. Ehrlich and A. H. Ehrlich, 1982; P. R. Ehrlich and Mooney, 1983;
Kremen, 2005). ES became more mainstream after more than 1000 scientists around the
world collaborated to write the Millennium Ecosystem Assessment (MA, 2003), which
launched the concept on a global stage (Abson et al., 2014; Gómez-Baggethun et al.,
2010). ES as a research field seeks to identify, quantify and value the benefits that nature
provides to people (G. C. Daily, 1997; MA, 2003). Considerable effort has been invested
into developing strategies to integrate ES into natural resource decision-making at
multiple scales (G. Daily and Matson, 2008; G. C. Daily et al., 2009). The ES framework
has become a common structure with which to identify and categorize the benefits that
nature provides to people in ways designed to inform decision-making (Guerry et al.,
7
2015; Ruckelshaus et al., 2013; Tallis and Polasky, 2011). ES research often includes the
estimation of trade-offs across multiple ES depending on the location and type of
development (Kareiva et al., 2011). I use ES to categorize environmental impacts because
this field of research highlights the connections between environmental changes and
changes in benefits that people derive from ecosystems.
Risk perception research has not yet been substantially integrated into the ES research
agenda. This integration is important because of increasing recognition that perceptions
of and decisions relevant to ES, similar to risk, are largely about non-material values and
considerations. That is, ES are most salient when there is perception of real or potential
harm or loss rather than just a static provision of a service.
Understanding risk perceptions of new technologies and how they impact ES is crucial
because, as previously noted, perceptions drive human behavior (Bennett, 2016;
Leiserowitz, 2006). When a risk is already controversial, new information about
scientifically assessed risks of a technology does not easily change preconceived
perceptions and biases (Leiserowitz, 2006; Satterfield et al., 2009). If a risk, however, is
not widely known and not (yet) controversial, new information can shift perceptions of
risk (Allum et al., 2008; Satterfield et al., 2012).
ES assessments have the potential to provide new information that can clarify trade-offs
associated with management options and inform decision-making. They generally focus
on the consequences of a natural resource management decision on the benefits that
8
people derive from ecosystems. In practice, these consequences are often only a small
part of what drives stakeholder support, consternation, and/or rejection (Gregory et al.,
2012; Spash, 2008a). Part of my motivation for Chapter 2 is based on the premise that
researchers and people conducting ES assessments and associated decision-making
processes could likely better anticipate (and potentially change) levels of support for a
project or policy if they had a better understanding of some psychological dimensions of
ES perceptions.
This dissertation also explores perceptions of ES change and level of support for
particular changes. Many critique ES valuations for their limited uptake in real-world
contexts (Förster et al., 2015; Honey-Rosés and Pendleton, 2013; Martínez-Harms et al.,
2015), perhaps because they are not sufficiently specific as to what people would pay via
realistic payment vehicles for ES protection or restoration. Chapter 3 aims to estimate
how much people would be willing to pay for a feasible ecologically beneficial artificial
reef in conjunction with an offshore wind farm.
The widespread uptake of the concept of ES in government (SAB, 2009), nongovernmental organizations (Tallis et al., 2010), academia (Seppelt et al., 2011), global
financial institutions (Naber et al., 2009), and to a growing extent corporations (Tercek
and J. S. Adams, 2013), has left many uncomfortable with the way ES assessments tend
to embrace an anthropocentric, often individualistic and consumer-oriented worldview,
replete with the language of markets, producers, consumers and dollar values attached to
nature rather than emphasis on nature’s intrinsic value (W. M. Adams, 2014; Spash,
9
2008b). Research on cultural ecosystem services, defined as “ecosystems' contributions
to the non-material benefits (e.g., capabilities and experiences) that arise from human–
ecosystem relationships” (Chan et al., 2012b), has critiqued this market value orientation
while also attempting to broaden the types of values integrated into ES assessments
(Chan et al., 2012a; Daniel et al., 2012; Klain and Chan, 2012).
1.2.2.1
Cultural ecosystem services
As a field, ES has tended to emphasize the instrumental value of nature — nature is
valuable because it is useful to people. Numerous ES studies have estimated the
instrumental value of provisioning, supporting and regulating ES, but instrumental and
monetized value falls short when identifying, assessing and characterizing cultural ES
(Chan et al., 2012b; Daniel et al., 2012). Instrumental values are substitutable, but
cultural values are often not (Chan et al., 2011; 2012b). Quantified and/or monetized ES
data often omit intangible values, including connectedness and belonging to a community
(both human and non-human), sense of place and other culturally and psychologically
mediated relationships between people and ecosystems (Russell et al., 2013). This led
researchers from diverse fields, not just ecology and economics which dominated earlier
ES studies, to design and test methods aimed at enabling social, cultural and intangible
values to play a more prominent role in ES assessments and decision-making
(Chan et al., 2012b; 2012a; Daniel et al., 2012; Gould et al., 2014; Klain and Chan, 2012;
Martín-López et al., 2009; 2012; Milcu et al., 2013; 2014; Plieninger et al., 2013; Sherren
et al., 2010). One new frontier along this cultural ES research trajectory is testing
10
relational value framing, which may motivate pro-environmental behavior (Chan et al.,
2016).
1.2.3
Environmental and relational values
People concerned about climate change, the biodiversity crisis and other ecologically
detrimental anthropogenic impacts often propose changing human values as a means to
achieving more sustainable behavior and policies (Dietz et al., 2005; Nichols, 2014).
Values can be defined as assigned values (degree of goodness, worth, importance or
meaning that people put on an object) or held values (underlying ideals)(Brown, 1984).
Identifying, characterizing and quantifying the “value” of nature underpins ES research.
The original architects of the field of ES explained their research as an attempt to
highlight the value of nature in ways that were previously overlooked with the
assumption that this information would push decision-making towards more naturefriendly outcomes (G. C. Daily, 1997; MA, 2003; Spash, 2008b). This “value” of nature
in ES literature has often been summed up in monetary value (Costanza et al., 1998; Karp
et al., 2013), which has limitations and, in some contexts, may not benefit biodiversity or
conservation (W. M. Adams, 2014).
One of the perils of ES approaches that emphasize monetary valuation is that money and
appeals to financial benefit and self-interest reinforce extrinsic values, which are
associated with the pursuit of prestige, power, image and status. Psychological research
has shown that reinforcement of extrinsic values can suppress intrinsic values, which are
11
linked to concern for others and the environment, kindness, understanding, appreciation,
tolerance and protection of people and nature (Blackmore et al., 2013). Intrinsic
motivations for conservation—protecting nature for its own sake—has driven many
conservation biologists and conservation efforts, but such environmental value framing is
critiqued as being overly narrow (Marvier and Wong, 2012), lacking appeal to diverse
audiences and deaf to the needs of many people, particularly poor people (Kareiva et al.,
2012).
Relational-value framing might be more broadly appealing and motivating to proenvironmental behavior than instrumental and intrinsic value framing. Relational values
include “eudaimonic” values, defined as those related to living a good life, justice,
reciprocity, care and virtue (Jax et al., 2013; Muraca, 2011; Ryan and Deci, 2001; Ryff
and Singer, 2008). Interactions with and responsibilities to humans, non-humans,
landscapes and ecosystems give rise to relational values (Chan et al., 2016). Research on
relational values in the context of social-ecological interactions has been lacking. Chapter
5 uses quantitative methods to test the application of social-ecological relational
statements as preliminary steps towards further testing if such value framing can enhance
connection to the natural world and pro-environmental behavior and policies.
Diverse and often conflicting environmental values come into play when considering if
and where to build renewable energy infrastructure. The “green-on-green” debate about
wind farms characterizes conflict related to the extent to which environmentally minded
stakeholders prioritize global environmental concern (i.e., climate change) versus local
12
environmental concern (e.g., bird strikes from wind turbines, aesthetic degradation of
landscape) (Warren et al., 2005).
More explicit consideration of relational values, broadly conceived, may be key to
addressing renewable energy and other sustainability issues. Activating relational values
focused on concern for and protection of people and the environment could help change
individual and collective behavior, policies and ultimately society’s relationship to
nature. The types of relationships with ecosystems that we choose may “change
everything,” in the words of Klein (2014), who advocates transitioning from extractive to
regenerative systems:
Extractivism is a nonreciprocal, dominance-based relationship with the earth, one
purely of taking. It is the opposite of stewardship, which involves taking but also
taking care that regeneration and future life continue. Extractivism is the mentality of
the mountaintop remover and the old-growth clear-cutter. It is the reduction of life
into objects for the use of others, giving them no integrity or value of their own—
turning living complex ecosystems into “natural resources,”... In an extractivist
economy, the interconnections among these various objectified components of life are
ignored; the consequences of severing them are of no concern (Klein, 2014, p. 169).
In a regenerative system, links between components of ecosystems are recognized.
Regenerative systems increase diversity, require little external inputs and produce
virtually no waste. Such systems promise restoration, renewal and revitalization (Lyle,
1996; McDonough and Braungart, 2002). Regenerative technology is increasingly
common in medical sciences but not yet prominent in conservation efforts. Regenerative
design concepts, which can enhance biodiversity while providing for human needs, have
13
not yet been applied to offshore wind farms. Chapter 3 assesses the extent to which
ecologically regenerative wind farm characteristics might affect preferences and
willingness to pay for this technology.
Relational values in the context of energy transitions raise many questions addressed in
Chapter 5, such as what improves or erodes the quality of the relationships between wind
farm developers, government authorities and local communities? What role should
community benefits from developers play in the decision process? What environmental
mitigation efforts should be taken to offset the local environmental impacts? Such
relational value questions could prime analytic-deliberative processes to increase the
likelihood of reaching legitimate outcomes when it comes to considering and siting
renewable energy infrastructure.
1.2.4
Analytic-deliberative processes
Robust public engagement strategies may help to assuage renewable energy
controversies. Accordingly, this dissertation draws upon literatures focused on analyticdeliberative processes of engagement that have the potential, in the words of Stirling
(2008) to “open-up” rather than “close down” discussions about new technologies and
innovations.
Abelson et al. (2003) and Ryfe (2005) review the normative theory of public participation
in decision-making. Abelson et al. (2003) operationalizes this theory into pragmatic
evaluation principles with explicit recognition of the role of power in deliberative
14
processes. These reviews emphasize how there is no simple formula for an optimal public
engagement process, but four key issues deserve attention: 1) representation; 2)
procedural rules; 3) information employed in the process and 4) the outcomes including
decisions resulting from the process. Representation determines who represents the
“public, ” which poses challenges. Namely, legitimate and fair processes provide
meaningful opportunities for learning and recognize diverse perspectives, so
consequently tend to be time-intensive and relatively exclusive processes in which it is
only feasible to involve a small number of people. Also, citizens are more likely to get
involved if they fear losing something they value, which further complicates fair
representation (Abelson et al., 2003). Situations can arise when a majority of people
support or are neutral towards a proposal, but they are a “silent majority” because they
opt not to get involved with the decision process (Stephenson and Lawson, 2013).
Abelson et al. (2003) documents how procedural rules can help manage this potential
self-selection of who gets involved. Choices about information are crucial, specifically
what information is selected then how it is presented and interpreted. Finally, not just the
process leading to the decision, but also the outcome (the decision) needs to be associated
with legitimacy and accountability (Abelson et al., 2003).
These evaluation criteria were developed in the heath sector, but much of them apply to a
wide array of contexts, including decisions involving communities about renewable
energy. The length and complexity of analytic-deliberative process features deemed
important to reach legitimate conclusions is likely overwhelming to practitioners. Based
on field work in three island communities that have considered offshore wind farm
15
development, I derived a shorter, more practitioner-friendly list of key design features of
both the decision process and an outcome, specifically bi-directional deliberative learning
and the provision of community benefits.
1.3
Chapter overviews
Broadly stated and referenced above, I situate my dissertation within a broader context of
the major barriers to scaling up renewable energy. Figure 1 below depicts my conceptual
framework of barriers to scaling up renewable energy, including national and regionalscale obstacles to the rapid expansion of renewables. I do not, however, incorporate these
national and regional barriers pertaining to finance and policy in this dissertation. Instead,
I focus on facets of public opposition, which tend to operate at local and regional scales.
The following chapter overviews focus on different sources of public opposition to
offshore wind farm development.
16
Na9onal & Regional
Financial
Structure of
electricity markets
à dominant players
suppress
newcomers
High expense to
create new grid
infrastructure where
renewable resource
is most abundant
Regional & Local
Government
& Policy
Insufficient long-term
planning to implement
renewable energy targets
à Uncertainty
Ineffec9ve
communica9on between
government and
regulatory bodies
à Confusion for
developers and delays
Shortage of experienced
staff in government and
regulatory agencies
à Delays and
uncertainty
Public Opposi9on
Concerns
about
consequences
Local
environment
Provisioning
ecosystem
services
Cultural
ecosystem
services
Value
orienta9ons
Flawed
engagement
processes
Social
Financial
Biodiversity
Community
benefits do not
jus9fy burden
Lack of
realized
WTP
Figure 1.1. Conceptual framework of barriers to scaling up renewable energy.
National and regional financial, governance and policy issues impede the proliferation of renewable energy development globally (WEF, 2011). Public
opposition to renewables, the main topic of this dissertation, operates at local and regional scales. Each dissertation chapter focuses on different elements of
public opposition (topics in blue boxes).
Source for gray box: Economic and Government & Policy: WEF. (2011). Scaling up renewables (pp. 1–48). World Economic Forum. Geneva.
17
Chapter 2, Bird Killer, Industrial Intruder or Clean Energy? Perceiving the Risks of
Offshore Wind Farms brings together ES research with risk perception theory,
specifically the psychometric risk paradigm. This research touches upon concerns about
consequences particularly as related to the local environment as well as value orientations
(see Figure 1.1). The study context is a hypothetical wind farm in a location with
excellent wind quality near an area of high bird diversity and abundance. This research,
which uses an animated wind farm seascape visualization, addresses the questions: when
considering offshore wind farms, what risks and benefits do people perceive? What is the
relative magnitude of how people perceive risks to ES? It tests the hypothesis that
features of the psychometric risk paradigm predict relative levels of risk associated with
impacts from an offshore wind farm. The results suggest that attributes of this risk
paradigm do indeed apply to concerns about ES. Also, this kind of anticipation of risk
perceptions can contribute to technology designs that better reflect citizens’ risk
perceptions.
Technology design plays a crucial role in Chapter 3, Rethinking renewable energy: High
willingness to pay for ecologically regenerative offshore wind farms. This chapter delves
into concern about consequences, the local environment and lack of realized willingness
to pay within the conceptual framework. This choice experiment addresses: Is there latent
willingness to pay for ecologically regenerative renewable energy infrastructure? More
specifically, what if offshore wind farms provide high quality marine habitat via artificial
reefs? I test the hypothesis that people are willing to pay more than current utility rates
for an offshore wind farm that provides marine biodiversity benefits. This study also
18
assesses willingness to pay to reduce the visual impact of an offshore wind farm (i.e.,
increase the distance from shore) and preferences for ownership type (i.e., state,
municipal, private or cooperative).
Chapter 3 focuses on assessing preferences and willingness to pay while Chapter 4,
Relational Values Resonate Broadly and Differently than Intrinsic or Instrumental
Values, or the New Ecological Paradigm (NEP), explores the extent to which relational
value statements resonate across three distinct populations. This exploratory research
characterizes the extent to which relational values resonate differently than purely
instrumental or intrinsic values. Also, it tests for correlation between the strength of
environmental values measured and attitudes towards wind farms. It explores value
orientations associated with opposition and support of wind power as well as concerns
about consequences to the local environment as depicted in the conceptual framework
(Figure 1.1).
Chapters 2 through 4 used a hypothetical wind farm. In contrast, Chapter 5, Will
communities “open-up” to offshore wind? Lessons learned from New England Islands
focuses on three New England islands near proposed wind farms. This chapter addresses
public opposition arising from concerns about consequences, value orientations, and
flawed engagement processes. All of the topics linked to public opposition in the
conceptual diagram in Figure 1.1 connect to Chapter 5. This chapter streamlines best
practices and design principles for analytic-deliberative processes that can improve the
quality of the relationships between wind farm developers, government authorities and
19
local communities. It explores features of decision processes that built or eroded trust,
including explicit consideration of community benefits. This has implications for a range
of development proposals where one scale or group of interest imposes on another.
1.4
Summary
In the aggregate, this dissertation explores theory and empirical data relevant to meeting
the challenge of climate change while promoting rigorous decision processes,
encouraging reflection on social-ecological relational values and protecting biodiversity.
This background of climate change and energy infrastructure controversy in general and
the offshore renewable energy frontier in particular provides a novel context for the
application of and contribution to the fields of social studies of risk, ES, environmental
and relational values and analytic-deliberative process design. The conclusion highlights
the main findings of my research, recommendations for future studies and implications
for practitioners. This research helps reconcile renewable energy development and
biodiversity conservation. It aims to clarify psychological attributes that influence
perceptions of ES change, assess support for ecologically regenerative renewable energy,
explore relational values and contributes to improving public participation in decisions
about renewable energy. Together these insights provide collaborative and proactive
approaches to creating new energy systems that are more conducive to long term
prosperity for human and non-human life.
20
Chapter 2: Bird killer, industrial intruder or clean energy? Perceiving
the risks of offshore wind farms
Sarah C. Klain, Terre Satterfield, Jim Sinner, Joanne I. Ellis, Kai M.A. Chan
2.1
Introduction
A central strategy for climate change mitigation entails the replacement of existing
sources of energy with low carbon renewable energy (Hoffert, 2002; IPCC, 2011). The
speed and scale at which renewables are deployed and fossil fuels phased out will have
significant consequences on the world’s climate trajectory (Moss et al., 2010; W. D.
Nordhaus, 2013). Local opposition to renewable energy development is a major
challenge to transitioning to low carbon technologies since it can shape if and how energy
infrastructure is built (Ansolabehere and Konisky, 2014; Devine-Wright, 2005; Roberts et
al., 2013). Such opposition can be a function of numerous factors, including but not
limited to actual and perceived economic costs, inequitable distribution of costs and
benefits, unfair siting processes and unacceptable risks associated with the development,
such as the risk of environmental impacts (Bell et al., 2005; Devine-Wright, 2005;
Roberts et al., 2013; Wolsink, 2000).
While recognizing the numerous facets of the social acceptance of new technologies, we
focus here on risk perception, which has been widely used to understand some
predictable patterns, logics and mental models that underpin evaluations of new
technologies (P. Slovic, 1999). In particular, this literature has documented the role of
21
what is known as dual processing theories of cognition: how people integrate affective
(“risk as feelings”) and deliberative (“risk as analysis”) cognition when forming risk
judgments (Finucane et al., 2000a; Loewenstein et al., 2001; P. Slovic, 2010; P. Slovic
and Peters, 2006).
Qualitative understandings—meanings—influence people’s perceptions of risk, in
addition to, and perhaps even more than, quantitative information (P. Slovic, 2010). In
this sense, studies of risk perceptions have demonstrated how perceived risk is both
predictable and quantifiable based on a limited set of often intuitive and affective factors,
including the extent to which a risk is understood, who is exposed, and whether or not the
object in question invokes dread, which can be defined as extreme fear or anxiety
regarding future events (P. Slovic, 1987)(for a full list of factors, see Table 2.1). This
research, typically conducted with expressed preference surveys, has sought to explain
why and how people evaluate a hazard according to various psychometric rating scales
(e.g., severity of consequences, novelty). Risk research has evolved to focus more on
affective responses (Loewenstein et al., 2001; P. Slovic, 2010; S. Slovic and P. Slovic,
2010), but we use the psychometric risk paradigm because it helps explain why people
have affective responses to particular risks. The psychometric risk paradigm theorizes
that perceived risk is both predictable and quantifiable based on the extent to which the
risk is known to science and dreaded/affectively loaded (Slovic, 2000).
Risk perception studies have also generally focused on risks of direct harm to personal
health with less attention paid to environmental risks. We see an opportunity to integrate
22
ecosystem services approaches into the risk literature. Scientists and practitioners have
used the ecosystem services (ES) framework to identify, quantify and often estimate a
monetary value for the human consequences of environmental impacts. However, ES as a
field has focused primarily on impacts as quantified biophysically and often translated
into monetary terms to highlight benefits from nature that could be lost depending on
development choices (G. C. Daily, 1997; Kareiva et al., 2011; Nelson et al., 2009) (e.g., a
specified tract of forest in a watershed provides x amount of clean water worth $y). There
has been little attention to understanding how some services and benefits at risk from
infrastructure development might be cause for greater public concern than others based
on the affective and intuitive ways by which people perceive risk.
Thus far, risk perception theory has been tested primarily in the context of direct risks to
human health and safety, rather than risks to one’s broader sense of well-being as
experienced via loss or degradation of ES. This paper addresses the broad question: do
the same logics by which some personal risks loom larger than others also apply to the
context of perceiving risks to ES?
Our research applies risk theory and methods in a new context: perceptions of the risks
posed by the development of an offshore wind farm as mediated by the environment.
That is, people remain those judging the risks, but instead of evaluating risk to human
health or even environmental health (e.g., air quality), we instead attempt to measure the
relative level of concern associated with risk to various ESs.
23
For instance, we assess the relative magnitude of concern associated with the risk that an
offshore wind farm would pose to birds, which tends to be a prominent concern based on
public surveys (Firestone et al., 2009; Warren et al., 2005), as compared to other
ecosystem services (ES). We hypothesize that the relative weighting of various risks to
ES follows the logic of the psychometric theories of risk, which posits that the relative
weight of risks will follow the degree to which an impact is affectively loaded and/or
dreaded and unknown to science (Slovic, 2000).
Results from early studies based on the psychometric paradigm are now interpreted as
derivative of the affect heuristic (P. Slovic et al., 2007). The affect heuristic explains how
feelings or emotions often precede and drive judgments of risk and benefit. Instead of
judging potential outcomes impartially, people tend to judge risks based on immediate
emotional reactions. Non-experts generally perceive an inverse relationship between risk
and benefit; high-risk activities or technologies are associated with low benefits and vice
versa. If people like or, in other words, attach positive affect to an activity or technology,
they tend to see associated risks as low and benefits as high. If they dislike it, they will
associate it with high risk and low benefits (Finucane et al., 2000a). Feelings of dread are
now seen as predictors of a high level of perceived risk because dread is an affectively
loaded quality.
Such affective aspects of risk perception are likely key for understanding why some
proposed energy projects elicit highly charged resistance. Understanding these risk
perceptions and what drives them is particularly important because renewable energy
24
infrastructure and risks associated with them are likely to be increasingly salient to
people as such technologies become more widely known and prominent in inhabited
landscapes.
In this article, we thus test theories of risk as applicable to the changes in ES potentially
introduced by an offshore wind farm. Our investigation focuses on ES concerns
associated with both tangible (e.g., commercial fisheries) and intangible services (e.g.,
aesthetic value as assessed by perception of negative visual impact). Our illustrative case
study provides a proof of concept for integrating risk perception and ES literatures. We
seek to advance the integration of risk perception theory and method into ES assessment
and research agendas and inform mitigation strategies for local environmental and social
impacts of renewable energy. Another aim is to contribute to understanding of public
support or rejection for energy transition options. In so doing, we address three research
questions:
1. On a relative scale, what are study participants most concerned about when it
comes to the development of an offshore wind farm?
2. Do psychometric risk dimensions and the associated affect heuristic predict how
study participants weight potential consequences of the risk from wind farms to
the provision of ES?
3. On a relative scale, what do study participants perceive as important benefits
associated with an offshore wind farm?
25
2.2
Methods
We used semi-structured interviews to ask two overarching questions: What risks
associated with a hypothetical offshore wind farm are most salient to people who live
near potential wind farm sites? What benefits are most salient?
The hypothetical wind farm site is physically well suited for the technology, but no wind
farm proposal currently exists for the site. Participants’ perceptions were not influenced
by local campaigns for or against an offshore wind farms since such campaigns were
nonexistent. The interviewer provided brief background materials using neutral language
about energy, renewable energy, and offshore wind farms, followed by a visualization of
an offshore wind farm in a location familiar to participants. Participants were asked about
their perceived impacts to ES and opinions on offshore wind farms and then asked to
assign weights to a variety of risks from the hypothetical wind farm development. The
risk weighting scores from participants were then compared to (correlated with) a set of
coded risk attributes based on how interviewees responded to open-ended questions. The
topics of these coded risk attributes were derived from the psychometric risk paradigm.
The following subsections explain the study context, sample, interviews, weighting of
risk and risk factor calculation methods in more detail.
2.2.1
Study area
New Zealand relies heavily on renewable energy for electricity. As show in Figure 2.1,
hydroelectric dams generate a majority of the electricity (57%), followed by geothermal
(16%) then gas plants(12%) (MBIE, 2015). Energy demand continues to increase, as
26
evidenced by consumer energy demand increasing by 4.3% in 2014 (MBIE, 2015). It is
possible that expanding electricity production from wind could replace some reliance on
fossil fuels, especially if electric cars become more widespread. New Zealand, at latitudes
in the “roaring 40s,” has exceptional wind resources (Fortuin et al., 2009), much of which
remains untapped. Terrestrial-based turbines in New Zealand generate twice the
international average for power generation per turbine (Fortuin et al., 2009). Despite New
Zealand’s abundance of wind, the wind energy sector has been slow to develop (M. Barry
Annual Electricity Genera/on
(GWh)
and Chapman, 2009).
25,000
20,000
15,000
10,000
5,000
-
Figure 2.1. New Zealand electricity generation by source in 2014 (MBIE 2015).
Purple denotes renewable energy sources and gray denotes fossil fuels.
Due to several factors including lawsuits, costs, environmental concerns and public
opposition, New Zealand power companies in recent decades have canceled several
proposed hydroelectric projects such as the Mokihinui dam (RNZ, 2012a) and wind
farms such as Project Hayes in the Lammermoor Range (RNZ, 2012b). Meanwhile,
various people and organizations are contesting investments in fossil fuel extraction, as
indicated, for example by protesting offshore drilling (NZME, 2015).
27
In July 2015, the government of New Zealand nonetheless set a national target to “reduce
greenhouse gas emissions to 30% below 2005 levels by 2030” (Ministry for the
Environment, 2015). Achieving this goal will requite additional development of low
carbon energy. We explore perceptions of concerns and benefits that could be associated
with the further expansion of renewable energy infrastructure.
To explore these perceptions, we selected the coastal communities in Golden and Tasman
Bay, New Zealand (see Figure 2.2) in collaboration with Cawthron Institute, an
independent New Zealand science organization that conducted a marine ES assessment
for this region. Given its relatively shallow water and strong, consistent wind, parts of
Golden Bay, New Zealand are physically well-suited for an offshore wind farm (Fortuin
et al., 2009). Suitable locations based on wind strength and water depths are within 20km
of Farewell Spit, a 26km long sand bar that is protected as a “Wetland of International
Importance” by New Zealand’s Department of Conservation (Davidson et al., 2011).
The site of the hypothetical farm is physically well suited for the technology, but there
are no proposals for wind farms on the site. Despite this, interviewees may have been
aware of the controversy leading to the cancelation of a proposed wind farm called
Lammermoor in Central Otago (RNZ, 2012b) and the Makara wind farm near
Wellington, which was built at a smaller scale than originally proposed (O'Neil, 2015).
28
Figure 2.2. Study Site: Golden Bay, New Zealand.
Google Earth image of New Zealand. Public domain. Inset image of Farewell Spit from NASA.
2.2.2
Interview sample
We interviewed people with professions and/or livelihoods linked to the marine
environment or energy sector who therefore have a vested stake in marine and energyrelated decision-making. Local staff at a research institute (Cawthron Institute) that
specializes in marine, coastal and water resources, recommended opinion leaders,
business owners, managers and engaged citizens in the region for interviews. We used
non-proportional quota sampling (Tashakkori and Teddlie, 2003) to solicit a range of
attitudes and opinions with those who have knowledge of marine ecosystems, energy
systems, and/or environmental planning. Interviewees worked in the following sectors:
fisheries, aquaculture, ecotourism, community planning, environmental consulting, town
council, Department of Conservation (government), energy and Maori resource
management. Maori are New Zealand’s indigenous people who, based on statute, have
29
environmental interests that must be taken into account by those making decisions about
environmental management. A total of 27 people were interviewed, including 18 men and
9 women. A total of 25 were Caucasian and two Maori.
2.2.3
Interview design
The semi-structured interview was designed to identify and weigh the perceived risks and
benefits associated with an offshore wind farm. Our methods probed people’s perceptions
of the ways in which this hypothetical wind farm might alter the provision of ES.
Interviews began with a warm-up consisting of questions related to occupation and town
of residence. Basic information about New Zealand’s current sources of electricity was
provided including tables about consumer energy demand by sector. The interviews
included the following statement about the context for a hypothetical offshore wind farm:
“electrifying the transportation sector could reduce carbon emissions. This would entail
developing additional sources of low carbon electricity.” We then asked questions about
perceptions of energy security, attitudes towards existing and proposed renewable
electricity sources, and perceptions of risk to energy infrastructure associated with an
earthquake, a salient concern in this region. We asked if respondents had heard about
offshore wind farms as well as about their concerns and potential benefits of this
technology. We also asked if and how people felt attached to Golden Bay. See Appendix
A, B and C for the interview consent form, request letter and protocol, respectively.
The interviewer then showed a three minute animated seascape visualization of an
offshore wind farm in Golden Bay created with Google Earth and SketchUp (See
30
Appendix E and https://youtu.be/w_JYLRHi_Bc). Animated visualizations often engage
more complex dimensions of perception and aesthetic preference than photographs and
text (Sheppard and Cizek, 2009).
Figure 2.3. A still image from the animated seascape visualization of an offshore wind farm in
Golden Bay, New Zealand using Google Earth.
After showing the visualization, we provided open-ended opportunities for interviewees
to consider potential impacts to ES. We asked: “If you think about the ways in which
nature and this place is important to you, what do you think could be lost if this project
was developed?” then “What do you think could be gained if it went through?” These
results were coded as explained below in section 2.3.2.
2.2.4
Weighting of concerns
Next, interviewees were asked to distribute 20 tokens representing concern across 16
possible topics derived from the literature on public acceptance and rejection of offshore
wind farms (Devine-Wright, 2005; Firestone and Kempton, 2007; Gee and Burkhard,
2010; Wolsink, 2010) as well as feedback from local environmental planning
31
practitioners on early drafts of the interview protocol. This list included an “other,
specify” category for interviewees to add additional concerns (Table 2.1). We recognize
that a few of these topics are interrelated, to the extent that the source of the problem
might be one and the same. But all are discrete in relation to the endpoint concern. For
example, concerns about property values and tourism are likely related to visual impact.
However, we wanted to know how people assigned weights to and thought about these as
parsed topics. Hence, we have a relatively wide range of specific concerns. The list of
concerns included multiple impacts to (and/or concerns about) the provision of ES likely
to be affected by our proposed wind farm. The ES concerns were not differentiated from
the human safety and economic concerns when presented to participants.
32
Table 2.1. Common concerns associated with offshore wind farms.
They were derived from literature and early tests of the interview protocol. Interviewees allocated 20
tokens representing relative level of concern across these topics. The ecosystem service concerns are
the dependent variables in the proceeding analysis.
Ecosystem Service Concerns
Human Safety & Economic Concerns
farms to ES provision
with a wind farm
_______________________________________________________________________________________
_______________________________________________________________________________________
Negative impact on birds
Negative impact on marine mammals
Displacement of commercial fishing
Negative impact on tourism
Displacement of recreational fishing
Displacement of recreational boating
Negative visual impact
Negative impact on other species
(Specify)
Navigational safety issue
Cost of construction
Cost of compliance with regulations
Cost of maintenance
Increased cost of electricity
Decreased property values
Insufficient local benefit
Other (specify)
Potential consequences of risk from wind
2.2.5
Potential costs and hazards associated
Risk factor scoring using risk perception theory
We investigated the level of concern that people have regarding the potential
consequences of an offshore wind farm based on a set of attributes identified in the
psychometric risk paradigm. We used the coding scheme in Figure 2.3 to evaluate
interview content and literature on offshore wind. An enduring finding in the risk
literature is that two fundamental factors drive perceived risk. These are referred to as
“dread risks” and “unknown risks” (P. Slovic, 2010; 1987). Each of these factors
generally comprises several qualities, defined in Table 2.2. For example, dread risk is a
summative label for whether or not people perceive a risk object as relatively dreaded,
controllable, equitable, and/or reversible (see Table 2.2). Instead of using conventional
risk research methods of asking people to rate factors such as controllability, we used an
open interview design to avoid pre-assigning any logics to how people explained their
perceptions of impacts and benefits. We then assigned a risk factor score to each of those
33
concerns. In this sense, we inferred a risk factor score when coding qualitative responses
from open-ended questions in the interviews. If attitudes towards a particular risk
dimension did not frequently arise in our semi-structured interviews, we relied on
academic literature on perceptions of risk related to offshore wind. For each component
of the psychometric risk paradigm, we assigned a -1 or +1 as shown in Table 3 and
Appendix D. For example, we assigned +1 to the dread impact to seabirds because people
articulated negative emotions and/or dread associated with potential harm to birds. As an
example, a man in his late 70s who volunteers for forest and bird conservation efforts
said the case study region is a destination for migratory birds. He said, “I would feel
dreadful if we suddenly developed [a wind farm resulting in] carnage of those birds....
They do arrive in thousands, tens of thousands."
We scored some categories based on our interpretations of how offshore wind planning
processes have unfolded in Northern European countries and the U.S. because the topic
did not arise in our interviews about Golden Bay. For example, we scored displacement
of commercial fishing as -1, which denotes that it is “controllable” because various
stakeholders generally have opportunities to play a role in ocean planning processes and
may influence the location and size of a wind farm (Nutters and Pinto da Silva, 2012);
people tend to have some control in relation to displacement of fishing.
2.2.6
From scoring risk associated with wind farms to analysis
We then analyzed the assigned risk scores to determine if they correlated with the
weights that interviewees assigned to each concern. That is, the composite risk factor
34
score was the explanatory variable and the mean token allocation to the concerns in
Table 2.1 was the dependent variable. This partially tests the extent to which perceived
intensity of risks to ES provision is predictable based on attributes of the psychometric
risk paradigm.
Table 2.2. Psychometric Risk Characteristics (P. Slovic, 1987).
The left component of each pair reduces risk perception while the right increases it. Subcomponents
in gray italics varied little across the ES concerns reported by interviewees so were not included in
the analysis. See Appendix D for full explanation.
Factor 1 Dread Risks
Controllable
Uncontrollable
Not dread
Dread
Consequences not fatal Consequences fatal
Equitable
Not equitable
Easily reduced
Not easily reduced
Not globally catastrophic Globally catastrophic
Low risk to future
High risk to future
genera7ons
genera7ons
Risk decreasing over 7me Risk increasing over 7me
Voluntary exposure
Involuntary exposure
Factor 2 Unknown Risks
Risks known to science Risks unknown to science
Observable
Not observable
Known to those exposed Unknown to those exposed
Effect immediate
Effect delayed
Old risk
New risk
35
Table 2.3. Explanation of composite risk factor scoring.
Some risk characteristics are associated with consequences to ES from a wind farm. The psychometric risk paradigm inspired our risk
characteristics (components of Factor 1 and 2). Our scores in gray are based on data from our interviews and publications from the social and
ecological sciences on offshore wind farms. We removed several factor 1 and 2 characteristics (e.g., globally catastrophic, risk to future generations)
because that appeared to vary little across the ES concerns. WF is wind farm. A score of 1 means this increases perceived risk while a -1 means it
diminishes perceived risk according to the risk perception literature. See Appendix D for further explanation of omitted risk characteristics.
Factor 1 Dread
Risk factor
Can the person who suffers
Does potential consequence
negative consequences control the
evoke a feeling of dread?
severity of the consequences?
Diminishes risk
Controllable (-)
perception (-)
Can precautions be easily taken to reduce the negative impact?
Consequences not fatal (Easily reduced (-)
)
Not dread (-)
Example
Car: driver can drive cautiously to
reduce severity of potential
Bicycle, car
accident
Medical x-ray
Medical x-ray: wear a lead apron, bicycle: wear a helmet
Increases risk
perception (+)
Uncontrollable (+)
Consequences Fatal (+)
Not easily reduced (+)
Example
Airplane: passengers relinquish
Terrorism, shark attack, nuclear
control to pilot, passengers do not
Nuclear meltdown
meltdown
control severity of accident
Displacement of
recreational
fishing
-1
Dread (+)
-1
Stakeholders generally have
Area displaced tends to be
opportunities to influence location
relatively small in comparison
and size of wind farm; they tend
to the much larger extent of
to have some control in relation to
fishing grounds, this tends not
displacement and consequently
to be not a dreaded concern
impact on fishing
Displacement of
commercial
fishing
Displacement of
recreational
boating
Potential Ecosystem Service Consequence
Is a particular
consequence fatal?
Negative impact
on tourism
Negative visual
impact
-1
Not fatal
As long as area of wind farm is not prime or irreplaceable fishing
grounds, impact can be reduced by moving fishing effort
elsewhere
-1
-1
-1
Same as above in relation to
commercial fishing
Area displaced is small relative
to size of bay, this is not a
dreaded concern
Not fatal
Impact easily reduced by moving commercial fishing effort
elsewhere
-1
-1
-1
-1
Same as above in relation to
impact on fishing
No expressions of dread found
in literature in relation to
displacement of recreational
boating
Not fatal
Impact easily reduced by recreational boating elsewhere
-1
-1
-1
1
Results are inconclusive
regarding if wind farms
negatively impact tourism. It is a
common concern, but tour
operators control what they
advertise and show so they could
capitalize on the green tech aspect
of farm. Many tourists may want
tours of the farm (Lilley, 2010).
No expressions of "dread" per
se found in literature in relation
to negative impact on tourism
nor in interviews. People are
concerned, but we did not find
documentation of widespread
anxiety or fear (aka dread).
Not fatal
Not easily reduced: tourism operations would likely need to
change their operations that currently focus on wildness of land
and seascape
1
-1
-1
1
Not fatal
Placing the turbines further offshore to reduce visual impact is not
feasible with existing technology given water depths at distances
at which farm would not be visible from land
1
1
1
People tend not to control bird
behavior. Perception of high
likelihood of collisions
Impact on
marine
mammals
-1
-1
Dread or fear does not
The negative affective reaction to
characterize most people's
visual impact is subjective. We attitudes to a WF. Many dislike
interpret it as uncontrollable.
and don't want it but it's not a
source of dread.
Impact on
seabirds
Ocean acidification
1
1
People strongly value region's
high density of nesting sea
Some bird mortalities are
birds. There is widespread fear
associated with wind
that development could harm
turbine collisions
bird populations.
1
People dread potential harm to
whales as evidenced by strong
Can not control marine mammal affective response in interviews
behavior with regards to wind
and to whale strandings and
turbines, collision is a common deployment of volunteer time
concern
and resources to reduce
fatalities of common whale
strandings in bay
1
Extensive studies on bird migrations have been conducted to
inform siting of WFs. Once constructed, few options currently
exist to reduce risk of bird collisions with commercial scale
modern turbines
1
Perception of fatal
collisions (although none
have been documented in
WF studies); perception Interviewees do not know of technologies to safely keep whales
that electromagnetic fields
away from turbines
from underwater cables
could effect whale
strandings
36
2.2.7
Weighting of benefits
We also asked participants to weight the benefits associated with a potential offshore wind farm
by allocating 20 tokens across 16 potential benefits, including an “other, specify” category.
These benefits, which accrue at different scales (local, regional, national, global), were based on
renewable energy literature (Dincer, 2000; Snyder and Kaiser, 2009).
2.3
Results
Our results suggest that particular risk dimensions from psychometric risk theory are positively
correlated with the mean level of concern to risk items that our study participants assigned.
Results include narrative expressions of concerns as well as benefits and an exploratory
quantitative analysis of our data on wind farm concerns.
2.3.1
2.3.1.1
Concerns
Narrative expressions of concern
Interviewees expressed “place-protective” concern about development of any kind in this area,
similar to concerns expressed in Devine-Wright (2009). For example, one explained that “our
marine environment is not a built environment and you are extending the built environment…
beyond the land. Personally in an ideal world I wouldn’t want to see the built environment
extend around the coasts...extending the built environment into the marine area. That is
impacting on the… the wilderness, intrinsic values… And it is Farewell Spit.”
37
Interviewees expected the magnitude of impact to sea birds and visual impact and to be large.
One interviewee said, “I think the bird kill from those wind farms is massive isn’t it?”
Interviewees had contrasting affective reactions to the visual impact. One said, “Something like
that would completely alter the view.” An interviewee involved in local tourism and government
complained that the visualization “makes me feel sick…. Something like that would completely
alter the view…half the population [of Golden Bay] would think it would be a really good idea
and the other half would think it is a bloody disaster.”
A local government consultant said wind turbines “look stunning…they are quite a striking
feature… It’s easy to look at a wind farm in someone else’s back yard and say it looks stunning
and that it is a great place for it but if there was a proposal for a wind farm out there [near
Farewell Spit], no I don’t think I would support that. I would rather see one somewhere up on the
hills on the back here.”
A mid 60-year old female policy planner said “the negative impact is more than visual and
ecological. There is a component to landscape that is to do with a sense of place, a sense of
associations and meanings. So it’s the cultural, it’s how it is interpreted through art and
aesthetics. So it’s more than visual. Visual you might just be looking at it purely in terms of
aesthetics but it is the meaning that people hold.”
38
2.3.1.2
Weights assigned to concerns
The data on concerns address the first research question. As shown in Figure 2.4, interviewees
assigned the highest level of concern to the potential impact of the wind farm on birds, followed
by negative visual impact and impact on marine mammals.
Impact on birds*
NegaGve visual impact*
Impact on marine mammals*
Cost of construcGon
Insufficient local benefit
Displacement of commercial fishing*
NegaGve impact on tourism*
NavigaGonal safety issue
Cost of compliance with regulaGons
Cost of maintenance
Displacement of recreaGonal fishing*
Increased cost of electricity
Other:Specify
Displacement of recreaGonal boaGng*
NegaGve impact on other species*
Decreased property values
0
1
2
3
4
5
Mean of Rela/ve Concern
Figure 2.4. Relative weighting of offshore wind farm concerns with standard error.
Participants distributed 20 tokens representing the weight of their concern across 16 topics. The concerns
denoted with a “*” can be understood as potential consequences of the risk from wind farms to the provision
of ES. The relatively low assignment of concern to the “other” category indicates that our specified categories
captured the vast majority of what people worry about in relation to this hypothetical context.
We ran a correlation to explore the second research question on the extent to which psychometric
risk dimensions and the associated affect heuristic predict how study participants weight various
ES concerns. The psychometric risk paradigm predicts that some risks are perceived as higher
than others based on a relatively small subset of risk dimensions (e.g., perceived dread,
controllability, fatality of consequences, reducibility of consequences). We used these
39
dimensions of perceived risk from the literature as discrete predictor ‘psychological’ variables.
The dependent variable is the weight assigned to relative concern for specific ecosystem services
(e.g., concern about tourism, visual impact, sea birds). As show in Figure 2.5, the composite risk
factor score positively correlates with the mean level of concern that interviewees expressed
Mean Level of Concern for Ecosystem Services
when they assigned tokens to various ES impacts (R2 = 0.67).
5
4.5
4
Seabirds
3.5
R² = 0.67007
Visual Impact
3
2.5
Commercial Fishing
2
1.5
Tourism
Rec Fishing
Marine Mammals
1
0.5
Rec BoaGng
0
-5
-4
-3
-2
-1
0
1
2
3
4
5
Composite Risk Factor Score: Perceived Dread, Controlability, Consequences,
Reducability
Figure 2.5. Average level of concern according to stakeholders for risks to ES plotted against psychological
dimensions of each risk with standard error bars.
The level of stakeholder concern was quantified as the mean number of tokens representing amount of
concern that interviewees assigned to potential consequences of an offshore wind farm to the provision of
various ES. The composite risk factor score expresses the nature of the risk, not its magnitude, via a set
of risk dimensions. These risk dimensions were determined based on published literature and qualitative
responses during the interviews on if each ecosystem service impact is generally perceived as uncontrollable,
dreaded, has fatal consequences and is not easily reduced. See Table 2.3 for more explanation on the scoring.
The distribution of tokens allocated to different concerns can be interpreted as allocations
proportional to the magnitude of expected impacts (see Discussion). Given the small footprint of
the wind farm relative to the bay, the expected magnitude of the impacts are small to ES that
may be displaced, such as fishing and boating. People expressed considerable uncertainty about
40
the impact of the farm to marine mammals. A study participant who was a technical adviser for
marine resource management said “when it comes to marine mammals, I’ve got no idea what
that [wind farm] means for them… Will those towers be perceived as a threat? Will they see
them as a curiosity? Will they attract more [whales]…Or it could be a negative…. I’ve got no
idea.”
2.3.2
2.3.2.1
Benefits
Narrative expressions of benefits and trade-offs
Some interviewees emphasized positive rather than negative aspects of visual impact. A male
environmental planner in his late 40s said, “I don’t see them as threatening, I just see them as an
opportunity… that you need to work through and figure out how the community is going to react
to it, how much they appreciate and understand local [electricity] supply requirements, whether
they are willing to accept their own footprint in their own backyard…. I like people to see where
it’s [electricity is] coming from and having the effect localized.” He assumed that people would
be more responsible energy consumers if they lived proximate to their sources of electricity and
saw the environmental impacts of their personal electricity consumption regularly.
In stark contrast to the more common negative perception of visual impact, a female
environmental planner in her early 50s said offshore wind turbines “are amazing. I don't find
them offensive at all. I think they are quite beautiful… they're almost like a sculpture… in the
right context they're quite neat.”
Two interviewees responded positively because an offshore wind farm aligned with their work to
41
create more marine reserves. A mid-40 year old tourism company owner who has been actively
engaged in marine conservation issues referred to the potential displacement of commercial
fishing by a wind farm in a positive way. He said marine reserves “work so well and there’s so
little of them. I know everyone’s got to eat and people have got to make money but I think it’d
be pretty awesome to see a cluster of windmills there to protect that environment under it.”
A female in her late 70s who was an active volunteer for an environmental advocacy group
expressed a willingness to accept a view with anthropogenic structures as long as it contributed
to reducing reliance on fossil fuels: “Your example of Farewell Spit, that’s something that is very
precious to me and I would be prepared for there to be a windmill there if it is as you say one of
the best places in NZ for [offshore] wind. So it’s iconic to me but I could still accept a windmill
for the sake of not having to use petrol.”
2.3.2.2
Weights assigned to benefits
We addressed the third research question using benefit-weighting data to determine the relative
importance assigned to benefits associated with an offshore wind farm. The most heavily
weighted benefit associated with this hypothetical wind farm was increased regional selfsufficiency, followed by increased diversity of New Zealand’s energy portfolio, then the
contribution to New Zealand’s energy independence. See Figure 2.6.
42
Increased regional energy self-sufficiency
Increase diversity of New Zealand’s energy poraolio
Contribute to New Zealand energy independence
ReducGon in carbon emissions associated with
electricity generaGon
Source of new jobs in Golden Bay
A source of local pride in energy innovaGon
Benefit to recreaGonal fishing because fish will
aggregate near structures
Electricity without natural resource depleGon
Increased local control of energy producGon
Taps into abundant local resource (wind)
Electricity without impact on air quality
Increase marine species abundance from arGficial
reef effect
This could start a new industry in New Zealand
PosiGve visual impact
PosiGve impact on tourism
Other: specify __________
0
1
2
3
4
5
Mean of Rela/ve Benefits
Figure 2.6. Perception of relative value of benefits from an offshore wind farm.
The mean number of tokens assigned to each benefit is presented with the standard error.
2.4
Discussion
Participants assigned higher levels of concern to affectively loaded topics, including visual
impacts and impacts to iconic wildlife, than the topics typically included in cost benefit analyses
(see Figure 2.4). We found a large positive correlation between the elements of the psychometric
risk paradigm, which we incorporated as composite risk factor scores, and how interviewees
assigned tokens representing relative concern for different risks to ES (see Figure 2.5, R2 = 0.67).
43
Based on this correlation, our analysis supports an expansion of the predictive power of the
psychometric risk paradigm beyond its original focus on risks to human health and safety. Our
findings suggest attributes from the psychometric risk paradigm, specifically notions of control,
dread, associated fatalities of animals and the reducibility of a risk, can help predict relative
levels of concern associated with the consequences of renewable energy, in this case an offshore
wind farm. Local residents tended to express greater relative concern regarding potential losses
of ES that tend to be uncontrollable, dreaded, associated with animal fatalities and irreducible.
This means, for example, that high concern for birds could have been anticipated based on the
dimensions of the psychometric risk paradigm. It would thus appear that theories of risk that
have been powerful in explaining variation in perceptions of personal harm also apply to indirect
risks experienced via ES (e.g., bird strikes).
Our results have implications for wind farm developers, wind farm regulators and, more
generally, for people conducting assessments relevant to a proposed change in ES provision. We
recommend that developers, regulators and ES assessors pay attention to concerns voiced by
stakeholders characterized by attributes of the psychometric risk paradigm about potential
changes to the delivery of ESs. In our case study, potential impacts of offshore wind farms on
birds and marine mammals and negative visual impacts emerged as the top concerns. These top
concerns are most closely linked to attributes long associated with high levels of perceived risk.
Addressing such concerns could entail acquiring additional scientific information, e.g.,
conducting environmental impact analyses of wind farms on birds and marine mammals (a
requirement in most developed countries), a trade-off analysis for reducing visual impact (i.e.,
44
develop scenarios for siting farms at different distances from shore while recognizing the higher
costs further from shore), and developing mitigation strategies for these potential impacts (e.g.,
funding bird habitat restoration elsewhere). Conducting such analyses, however, is not sufficient
for addressing local concerns. The scientific results and mitigation plans ought to be
communicated in publically accessible ways (see Klain et al., 2015) and incorporated into a
deliberative decision process. Such a process would include negotiations about relevant facts and
values, particularly those strongly associated with attributes of the psychometric risk paradigm.
Our results also demonstrate that, among our sample of people with natural resource and energy
related livelihoods, environmental concerns tend to be weighted more heavily than economic
costs as shown in Figure 2.4. For example, people on average expressed a higher level of
concern about impacts on birds and marine mammals than the costs of construction, compliance
with regulations, and maintenance as well as the increased cost of electricity. We acknowledge
that this project was hypothetical with no real costs to be borne by participants. The results,
however, do align with findings from Ansolabehere and Konishky (2014), who conducted
surveys of U.S. citizens demonstrating that people want clean and cheap energy, but the foremost
concerns driving energy preferences are minimizing environmental harms, then economic costs.
We recognize limitations to our study. We do not compare our results to objective metrics
associated with risk to ES. Instead, we used our understanding of pertinent literatures to derive
composite risk factor scores. We used our coded risk factor statements and scores (independent
variable) to understand the logics behind the weighting of concerns associated with a potential
wind farm (dependent variable). One limitation with our method is that it is possible that
45
variation in the perceived absolute magnitude of the risk contributed to the observed relationship
between the coded risk factor scores and relative levels of concern.
Future research could address similar questions using survey methods rather than interviews to
obtain a larger and representative sample. Nonetheless, the qualitative data from these interviews
illustrates the diversity of values and risk perceptions associated with this novel technology.
Our case study demonstrates how offshore wind can be an ambiguous risk, which means there
are various legitimate perspectives on the extent to which the technology may result in adverse
impacts, largely due to scientific uncertainties. Also, ambiguity arises when there is no consensus
as to whether potential impacts are acceptable, tolerable or intolerable (Renn et al., 2011).
Another contributor to ambiguity is that people respond to risks based on their particular riskrelated images and constructs (Keeney, 2004). If a wind farm was proposed in our case study
area, we would expect numerous legitimate interpretations of results from any formal risk and/or
ES assessment given the ambiguities associated with risks related to offshore wind.
Participants in our research, as demonstrated in the narrative results, voiced concern for
biodiversity in relation to the hypothetical wind farm. This concern points to a need for future
research and potentially scenario-based ecosystem service assessments. This work could
investigate the extent to which designing renewable energy infrastructure that also provides
natural habitat (e.g. an offshore wind farm built with excellent artificial reef habitat) can elicit
positive affective responses that could neutralize negative affective responses to this technology.
46
2.5
Conclusion
The affectively-loaded language and risk ratings evident across stakeholders’ evaluations of the
benefits and concerns about an offshore wind farm have implications for energy transitions.
Traditional risk assessments quantified biophysical and economic risks, which tended to be
reduced to estimates of probability and severity. They overlooked critical psychological
dimensions of risk (NRC, 1996). Affectively charged dimensions of risk can profoundly impact
the uptake of new technology, how ES valuations are interpreted and consequently how society
deals with climate change.
Our results have implications for communications used to introduce proposed wind farms,
particularly those directed to communities near a proposed development site. We recommend
that such communications anticipate and be sensitive to perceptions of control, dread, associated
fatalities of animals and the reducibility of risks associated with a proposed development.
Identifying and disseminating mitigation measures for concerns associated with these qualities
could help garner greater public support for renewable energy developments.
Based on our results and the social science literature on risk, we recommend that energy
infrastructure proponents invest considerable effort into interdisciplinary and deliberative risk
estimations that support mutual learning among diverse constituents, which is necessary for
managing uncertain, complex and/or ambiguous risks. Deliberative, participatory processes can
account for a diversity of causal beliefs embedded in different worldviews in relation to risks. In
the words of Renn (2011, p. 240), “what is safe enough implies a moral judgment about
47
acceptability of risk and the tolerable burden that risk producers can impose on others.” Our
research extends these “others” to include non-human species.
Our research also has implications for initiating broader discussions on re-imagining our energy
systems to transition towards reliance on low carbon renewable energy rather than fossil fuels.
We recommend that planners and proponents of renewable energy technology pay particular
attention to the ways in which the consequences of our current energy systems and proposed
renewable energy developments can be interpreted, amplified or played down by the public and
stakeholders in relation to the attributes of the psychometric risk paradigm.
48
Chapter 3: Rethinking renewable energy: high willingness to pay for
ecologically regenerative offshore wind farms
Sarah C. Klain, Terre Satterfield, Kai M.A. Chan
3.1
Introduction
The ongoing quest to define and secure sustainable energy is one of humanity’s most pressing
challenges, particularly in the context of climate change (Yergin, 2011). The United Nations set
ambitious sustainable development goals, including universal access to affordable, reliable,
sustainable and modern energy (UN, 2015) while the World Bank and International Energy
Agency’s Sustainable Energy For All initiative calls for the doubling of renewable energy in the
global energy mix and tracking this energy transition (Angelou et al., 2013). One approach for
mitigating climate change involves rapidly scaling up low-carbon energy production to replace
energy from fossil fuels. Various pathways have been proposed to transition away from fossil
fuels and towards renewable energy, with offshore wind playing a substantial role in proposed
pathways for several countries with coastlines (Foxon et al., 2010; Green and Vasilakos, 2011;
Jacobson and Delucchi, 2011). Along the US Eastern seaboard, Kempton (2005) argues that
offshore wind is the only spatially proximate utility-scale renewable energy source that could
displace significant carbon emissions in the near term.
And yet, negotiating what constitutes both clean (i.e., no greenhouse gas emissions) and locally
desirable energy systems is an ongoing debate at local and regional scales with global
ramifications (Devine-Wright et al., 2011; Roberts et al., 2013), despite the fact that scientific
49
consensus on the need to decrease greenhouse gas emissions has coalesced. Also, environmental
and human safety risks associated with large scale offshore wind farms (OWFs) over their life
cycles are relatively benign as compared to risks associated with other energy sources, including
fossil fuels and nuclear (Ram, 2011). For example, OWFs, in contrast to coal plants or nuclear
reactors, do not pose any catastrophic risks that could result in human deaths or property damage
in excess of $1 million (Ram, 2011).
Despite this relatively low level of risk, developing offshore wind farms has been controversial
for various economic, social and environmental reasons. In particular, wind farm debates have
focused on their relatively high levelized costs compared to fossil fuels; dissatisfaction with who
owns and operates these utilities; visual/aesthetic impact of the farms; and impacts on species
and habitat (Firestone and Kempton, 2007; Firestone et al., 2012; Pasqualetti, 2011; Wiersma
and Devine-Wright, 2014). In light of these concerns, our research estimates how specific wind
farm features augment or erode public support for developing this technology where each feature
is also linked to a cost, measured as willingness to pay.
We see the potential for regenerative design (Lyle, 1996) to reduce negative perceptions of some
technologies. Design in this context refers to the confluence of society and technology in the
conception and shaping of systems. Regenerative design refers to planning and implementing
systems that evolve from their initial forms and renew a site, thereby shifting it to a more
ecologically desirable condition via human intervention. Lyle (1996) characterizes regenerative
systems with the following attributes:
50
•
System operations are integrated with natural and social processes
•
Minimal reliance on fossil fuels and synthetic chemicals
•
Minimal use of non-renewable materials
•
Sustainable use of renewable resources for operation
•
Associated waste products are re-assimilated without environmental harm
The materials to build OWFs, particularly the foundations and submarine cables, are energy
intensive and many are non-renewable. Lifecycle analyses, however, demonstrate that, the
“payback” period of properly-sited large-scale wind turbines as related to energy and greenhouse
gas emissions embedded in the materials relative to the electricity they generate, is less than one
year (Wagner et al., 2011). The global warming potential per unit of electrical energy generated
by wind farms is lower than solar photovoltaic, biomass and fossil fuel energy sources (Weisser,
2007).
In the context of wind farms, then, regenerative design might address various ecological risks
and uncertainties, including collision risk and diversion of migration routes for seabirds
(Kuvlesky et al., 2007) and potentially bats (a problem for terrestrial farms, but little
documentation exists for offshore sites) (Arnett et al., 2008). In the construction phase, acoustic
disturbance from pile driving likely has a high impact on marine mammals, fish and benthos
(species inhabiting the seafloor). Moderate to high uncertainty is associated with acoustic
impacts to wildlife during the OWF’s operational phase (Bergström et al., 2014).
Electromagnetic fields are anticipated to have a relatively low impact on marine species, but this
impact remains uncertain (Bergström et al., 2014; Gill, 2005). Uncertainty about ecological
impacts remain given the relative novelty of industrial scale OWFs and how few studies have
51
assessed the cumulative impacts and long-term food web effects associated with them
(Bergström et al., 2014; Goodale and Milman, 2014).
Ideally, offshore wind farms could go beyond mitigating negative impacts to instead benefit or
enhance marine habitats. Human activities have strongly affected approximately 41% of the
ocean on a global scale (Halpern et al., 2008). Dredging, mining and some fishing practices, such
as bottom trawling, have reduced benthic structural diversity, which diminishes habitat
complexity, thus altering species composition and diversity (Auster and Langton, 1999; Watling
and Norse, 1998). Reversing these trajectories has the potential to increase localized biodiversity
while ecologically benefiting the surrounding marine environment through appropriate design,
better siting and management and artificial reefs (Baine, 2001; Bohnsack and Sutherland, 1985).
OWF turbine foundations could act as artificial reefs and fish aggregation devices, both of which
have contributed to restoring degraded marine ecosystems (Boehlert and Gill, 2010; Inger et al.,
2009). Further, an OWF may become a de facto marine reserve with associated conservation
benefits (Bergström et al., 2014; Pelc and Fujita, 2002).
For these reasons, this study examines the degree of public support for wind farm design
expressed as support for particular regenerative effects, and asks whether positive attributes can
outweigh negative impacts (heretofore the primary focus of research). We assess this support by
quantifying the attributes linked to stakeholder preference for one potential wind farm over
another. OWF biodiversity benefits may largely accrue underwater where they are not readily
visible. In contrast, the highly visible turbines may impact some bird species and degrade the
perceived aesthetic quality of a seascape. We operationalize debates about perceptions of wind
52
farms as diminishing the aesthetic quality of a land or seascape, typically referred to as a
negative externality (Devine-Wright, 2005; Ladenburg and Dubgaard, 2007; Warren and
McFadyen, 2010), while noting the rising cost per unit of energy generated as wind farms are
sited further from shore (Snyder and Kaiser, 2009). We further test willingness to pay for more
distant and consequently less visible OWFs (Krueger et al., 2011; Ladenburg and Dubgaard,
2007; Westerberg et al., 2013), and we examine the effect of increased cost as linked to
increased biodiversity benefits.
In addition to distance from shore, ownership can also exert a significant impact on wind farm
preferences. Using choice experiment methods, Ek and Persson (2014) found that Swedish
residents prefer cooperatively or municipally owned wind farms over private and state-owned
farms. OWF ownership preferences have yet to be assessed in our study area of coastal New
England (Figure 3.1), where cooperative ownerships models are common in some sectors of the
economy, e.g., lobster and fisheries cooperatives (Acheson, 2003). An energy cooperative exists
in this region (Vineyard Power) and is partnering with an developer to potentially become a part
owner of an OWF (Nevin, 2010). We thus operationalized ownership model variables as well
(see also Ek and Persson, 2014).
In order to prioritize wind farm characteristics that might make its development more socially
acceptable, we quantify preferences for wind farm attributes using a choice experiment. We
used an online panel of residents from coastal New England states where, as of 2016, North
America’s first OWF is under construction. We estimate how much public support there could be
for ecologically regenerative effects, and whether such positive attributes can outweigh negative
effects. This understanding yields estimates of WTP for an OWF that provides marine
53
biodiversity benefits via habitat provision, as well as quantified public preferences regarding
visual impacts and ownership type. This is the first study to assess these features concurrently
and in this geographic area.
3.2
Methods
Our methods included three major components. First, we recruited respondents using Amazon’s
Mechanical-Turk platform, restricted to residents of our study region. Second, we presented
respondents with a choice experiment offering options of wind farms vs. a default of fossil fuel
electricity generation, complete with visuals. Third, we used standard econometric analysis to
infer from respondent choices the relative preference for different OWF attributes (e.g., near vs.
far from shore; biodiversity losses vs. gains, private vs. cooperative ownership), and the WTP for
levels of those attributes. We explain each of these components in greater detail below.
3.2.1
Study location
New England coastal states have strong and consistent wind resources offshore (see Figure 3.1).
An energy transition towards greater reliance on renewables would likely include hundreds of
OWFs off the coasts of these states (Jacobson et al., 2015a; 2015b). Large-scale OWFs have
been developed in Northern Europe, but only one small farm near Block Island has been built in
North America as of 2016. We chose to assess public preferences related to OWFs based on a
survey of coastal New England residents because of this region’s high wind resource potential
and the fact that several farms are currently under consideration near the coasts of these states.
54
Quebec
New Brunswick
Maine
Vermont
New
York
Wind resource
poten4al
New Hampshire
Poor
Fair
Massachuse1s
Connec4cut
Rhode
Island
N
Good
Excellent
Outstanding
mi
Figure 3.1. Wind resource potential for states in study.
Wind data from NREL (2015).
We tested a pilot of the choice experiment with 20 individuals to ensure that the survey was
clear. We made minor adjustments to clarify the wording of the survey (see Appendix F, G and
H for the survey consent form, the M-Turk request description and the survey respectively).
3.2.2
Sample characteristics
We recruited respondents using Amazon’s Mechanical Turk (M-Turk) system, which has
become a common respondent recruitment method for experimental research (Goodman et al.,
55
2012; Paolacci et al., 2010) with data outputs that are as reliable as those acquired via traditional
recruitment methods (Buhrmester et al., 2011). We attempted to minimize bias in our sample by
describing it on M-Turk’s HIT list (Human Intelligence Tasks) in very general terms (as a survey
about preferences based on different text and image-based descriptions, without using any
language related to renewable energy). The sample was limited to M-Turk workers who have
mailing addresses in coastal New England states (Connecticut, Maine, Massachusetts, New
Hampshire or Rhode Island), where several proposals for OWFs are more advanced than
elsewhere in North America.
Respondents meeting the location requirement were provided with a link to our survey hosted on
the Qualitrics survey platform. We used the Qualitrics software to randomly assign survey-takers
to one of four blocks of choice experiment questions. We collected self-reported demographic
data from the sample so we could compare it with census data to determine the extent to which
this sample is representative of the population of these states. When they completed the survey,
respondents were given a completion code to submit in the M-Turk system for payment.
Respondents were paid $1 to take the 10-15 minute survey.
A total of 412 respondents completed the survey. We excluded data from 12 respondents who
failed two questions we inserted to test if the survey takers were paying attention (see Appendix
I, question 6 and 47). This type of screening based on attention-check questions is recommended
when relying on ‘Mechanical Turk’ workers (Goodman et al., 2012).
56
Our respondent pool is typical of M-Turk workers as described in Paolacci (2010). Our sample
had higher self-reported level of education than the general population, was younger (32 years vs
40 as the mean age in these states), more females (59%) than males (41%), and self-reported
household income lower than the states’ average (Table 3). The white/non-white racial
breakdown of our sample (82.5% white) corresponded closely to census data (82.2% white).
Table 3.1. Survey respondents demographic characteristics compared to census data.
Socioeconomic
Characteristics Description
Bachelor degree or
Education
higher
Age
Years old
Female
Gender
Annual household
Income
income before taxes
State
CT
ME
MA
NH
RI
White
Caucasian race
N
Percentage or
Mean of Sample
Percentage or Mean
from 2014 Census*
400
400
400
66.3%
32
59.0%
37.9%
40
51.3%
400
400
400
400
400
400
400
~$53,000*
18.5%
12.3%
45.5%
9.5%
9.8%**
82.5%
$66,200
25.6%
9.5%
48.0%
9.4%
7.5%
82.2%
We used 2014 Census data from coastal New England states including Maine, Massachusetts, New Hampshire,
Connecticut, Rhode Island. State census data was summed then weighted by the state’s population size.
* This is approximate because survey respondents selected an income category rather than reported a specific
amount. For example, category 5 corresponds to $35k to $49k while category 6 is $50k- 74K. The mean income was
5.4, which we interpret as 40% of the value between the middle of category 5 and 6 at ~$53,000.
** All respondents have a mailing address in a coastal New England state (a requirement for eligibility to take this
survey), but 4.4% of the sample did not self-report a zip code in one of these states.
3.2.3
Choice experiment design
In a choice experiment, respondents are presented with options that include various attributes and
they are asked to select their preferred option. The attribute levels are varied based on
experiment design rules so that researchers can build models for choices based on the attributes
of the option that respondents selected.
57
We developed and implemented an online survey hosted on the survey platform Qualtrics. Each
survey included a university-required research ethics consent form, introductory material on
OWFs, the choice experiment component, then demographic questions and finally questions
about environmental values. The minimum number of attribute level combinations required to
estimate orthogonal main effects for four levels each with four attributes was 32, which we
divided into four blocks of 8 choice sets. We used a fractional factorial design (Louviere et al.,
2000) for our choice experiment component in order to keep the survey short and reduce
cognitive burden for the respondents. We used the choice experiment design tool in the software
package JMP to generate our fractional factorial design.
We used four OWF attributes in the choice experiment: effect on marine life, type of ownership,
distance from shore, and addition to monthly electricity utility bill. Each attribute had four levels
(see Table 3.2 and Appendix I). The increases in species diversity and abundance are based on
literature on artificial reefs, wind farms and environmental impacts, which document high levels
of variability across sites and species (IUCN, 2010; Reubens et al., 2013a; 2013b). Although
60% decrease and 60% increase to diversity and abundance are more extreme than most
anticipated assessments of impact, we contend that such changes are possible, particularly if the
base levels of diversity and abundance are low. Our payment vehicle was a monthly addition to
the electrical utility bill. The levels of the bill were based on Krueger (2007) and Krueger et al.
(2011), who recommended a fee over the lifetime of a project. This study used a range of utility
fees up to $30 a month for three years, but, based on their model outputs, found that many
58
residents were WTP more than this. We used a monthly fee over the lifetime of the project,
which we stated as ~25 years (see Appendix I Choice experiment survey).
Table 3.2. Description of attributes and levels used in the choice experiment.
Attribute
Biodiversity
Description
Percent change in marine
species diversity and
abundance
Ownership
type
Owner of wind farm
Distance
Distance of wind farm
from nearest shore
Bill
Monthly addition to
electricity utility bill to
fund wind farm
development
Levels
• 60% decline
• 30% decline*
• 30% increase
• 60% increase
• State ownership
• Municipal ownership
• Private ownership*
• Cooperative ownership
• 1 mile, highly prominent*
• 4 miles, prominent
• 8 miles, somewhat visible
• > 10 miles from shore, barely visible
• $1
• $5
• $10
• $20
*Denotes base case levels
Each survey respondent was asked to assume that his/her state has committed to increasing
electricity generation by 10%. Then he/she was presented with 8 choice sets. Each choice set had
three options. Similar to Kruger (2011), option A or B were OWFs with different attributes.
Option C, the “opt-out” choice, was for constructing a fossil fuel plant (see Figure 3.2 for a
choice set example). We created visual representations of changes to marine life using vector
images from the IAN image library (IAN, 2015) of species common in the Gulf of Maine. We
used Google Earth, Sketch Up and OWF models (reaching a virtual height of 70m above sea
level) from 3D Warehouse to create OWF visualizations at different distances from shore (see
example in Figure 3.2). Visualizations, including photo simulations, are a common feature in
wind farm preference surveys (Bishop and Miller, 2007; Ek, 2002; Krueger, 2007; Wolk, 2008).
59
Op#on A
Wind Farm
Op#on B
Wind farm
Op#on C
Coal or Gas Plant
No Wind Farm
Effect on
marine life
• Small loss
• 30% decline in diversity and
abundance
• Turbine structures provide
poor habitat for underwater
plants and animals, e.g., an#fouling paint used on tower
• Large gain
• 60% increase in diversity and
abundance
• Turbine structures provide
excellent habitat for
underwater plants and
animals
• More coal or natural gas
used
• No direct impact on marine
ecosystems
• Associated CO2 emissions
contribute to ocean
acidifica#on
Wind farm
Ownership
Coopera#ve
Private
Ownership not specified
Visibility
from shore
Prominent
4 miles from shore
Barely visible
≥10 miles from shore
Built on land
Addi#on to
monthly
electricity
u#lity bill
$5
$20
$0
Figure 3.2. Example of choice scenario.
Images made with graphics from IAN image library (IAN, 2015)
60
3.2.4
Econometric analysis of choice experiment data
The Random utility model (RUM) typically underpins choice experiment data analysis
(McFadden, 2001; Train, 2009). This approach assumes that individuals maximize their utility
(satisfaction) when making discrete choices from a set of alternatives for goods and services. The
attributes of a chosen option are assumed to generate individual utility. A RUM relates observed
or stated choices to this individual utility. The respondent n obtains utility U, which depends on
their choice, from an alternative i out of options j such that 1 < i < j in choice task t. The
indirect utility function of respondent n is denoted as Unit:
Unit = β’nXnit + εnit
Respondent characteristics (e.g., demographic variables) and observable attribute levels of option
j are represented by Xnit. The coefficient vector of these attributes is βn, which may be random or
non-random variables. An unobservable random error term is εnit. As described in Börger et al.
(2015, p. 129) the probability Pnit that respondent n chooses alternative i over all other
alternatives in choice task t is:
Marginal WTP or the implicit price for an attribute as compared to a particular baseline can be
calculated as the ratio between an attribute level’s coefficient and the payment coefficient (e.g.,
distance of 5 miles from shore coefficient divided by the bill coefficient). This can be interpreted
as willingness to make a trade-off between each wind farm attribute (e.g., 5 miles from shore)
61
and a price attribute (e.g., utility bill) as a change from the base level of an attribute (e.g., 1 mile
from shore).
Similar to Krueger (2011), we include an “opt-out” choice in the utility function (a fossil fuel
plant rather than a wind farm with no additional bill). The attribute levels of the opt-out choice
are fixed (e.g., no ownership type specified, no impact on marine biodiversity, no visual impact,
no additional cost to utility bill). The logit models incorporate selections of the “opt-out” choice
when coefficients and WTP amounts are estimated (e.g., if more respondents choose Option C of
a fossil fuel plant, WTP for wind farm attributes decreases).
We used conditional and mixed (also known as random parameter) logit models to infer how
respondents value certain wind farm attributes relative to other attributes. The conditional logit
model assumes that preferences are constant across respondents. It also assumes that εnit has a
type 1 (Gumbel) extreme value distribution. The mixed logit model allows for “random taste
variation, unrestricted substitution patterns, and correlation in unobserved factors over time”
(Train, 2009, p. 134). A mixed logit model consists of fixed as well as random effects. Taste
parameters—respondents’ personal preferences embedded in the utility components denoted as
βn—vary randomly across the sample population in mixed logit models.
We considered ownership type, distance from shore and impact on marine species abundance
and diversity as categorical attributes. Following Louviere et al. (2000), the categorical attributes
were used as effects-coded variables. A categorical variable that has n levels is replaced with n 1 effects-coded variables. We refer to the omitted level as the base case. The significance of
62
coefficients of other levels are relative to the base case levels, which are noted in Table 3.2. We
chose our base case attribute as the shortest distance from shore (1 mile) because people tend to
derive greater utility from less visible wind farms. We also selected small loss to diversity and
abundance as the base case and private ownership, which was arbitrary (see Table 3.2). “Bill”,
the payment mechanism, was used as a continuous variable in the models.
3.3
Results
Our choice experiment results (Table 3.3) show that the strongest preference for the OWF
qualities that we investigated is the provision of biodiversity benefits via high quality artificial
reef habitat. Respondents also prefer siting OWFs further from shore so they are less visible, and
ownership that is not private.
3.3.1
Model results: strong preference for biodiversity benefits
We provide model coefficients and marginal WTP associated with going from a wind farm
associated with a small biodiversity loss (30%), privately owned and 1 mile from shore to wind
farms with the various characteristics (see Table 3.2 and Variables in choice experiment). The
“opt-out” fossil fuel option (Option C) was chosen 10.5% of the time while the wind farm
options (Option A or B) were chosen 89.5% of the time. We report results from conditional and
mixed logit models in Table 3.3.
63
Table 3.3. Choice experiment conditional and mixed logit models with WTP estimates (N=400).
The base case was small loss of biodiversity, privately owned and 1 mile from shore.
Conditional
Mixed Logit
Logit
Variable
Estimate
Std. Error WTP($) Odds Ratio
Estimate
Std. Error WTP($)
big.loss
-4.153 ***
0.408
-20.29
-1.494 ***
0.096
-21.21
0.224
small.gain
4.338 ***
0.360
21.19
1.556 ***
0.081
22.09
4.741
big.gain
6.981 ***
0.539
34.10
2.416 ***
0.103
34.30
11.198
municipal
1.253 ***
0.226
6.12
0.368 ***
0.086
5.22
1.445
state
1.173 ***
0.227
5.73
0.416 ***
0.085
5.90
1.515
cooperative
1.603 ***
0.354
7.83
0.164 .
0.097
2.33
1.178
mi4
0.981 ***
0.245
4.79
0.334 ***
0.090
4.74
1.396
mi8
1.309
***
0.220
6.39
0.463 ***
0.088
6.57
1.589
mi10
2.095 ***
0.345
10.23
0.968 ***
0.120
13.74
2.633
bill
-0.205 ***
0.019
-0.070 ***
0.006
0.932
Log-Likelihood
-2012
-1492.4
McFadden R^2
AIC
0.33993
4047.9
0.510
3118.8
With demographic variables
Variable
Estimate
big.loss
-1.494 ***
small.gain
1.561 ***
Std. Error WTP($) Odds Ratio
Estimate
Std. Error WTP($)
Odds Ratio
0.016
76.577
1075.466
3.501
3.230
4.968
2.668
3.702
8.123
0.815
Odds Ratio
0.096
0.081
-21.14
22.08
0.224
4.765
-4.168 ***
4.554 ***
0.385
0.347
-20.44
22.33
0.015
94.999
big.gain
municipal
state
2.455 ***
0.371 ***
0.420 ***
0.366
0.086
0.085
34.73
5.24
5.94
11.652
1.449
1.522
8.514 ***
1.088 ***
0.996 ***
1.052
0.235
0.241
41.74
5.34
4.88
4982.165
2.969
2.707
cooperative
mi4
mi8
0.171 .
0.337 ***
0.466 ***
0.098
0.091
0.088
2.41
4.77
6.59
1.186
1.401
1.593
1.530 ***
0.504 *
0.973 ***
0.353
0.240
0.235
7.50
2.47
4.77
4.617
1.656
2.645
0.120
0.006
0.007
0.162
0.210
0.173
0.033
13.84
2.660
0.932
0.985
1.616
1.330
1.255
0.965
1.660 ***
-0.204 ***
-0.044 **
-0.065
-0.053
0.529
-0.002
0.351
0.019
0.016
0.362
0.478
0.363
0.067
8.14
5.261
0.816
0.957
0.937
0.948
1.697
0.998
mi10
bill
big.gain:age
big.gain:female
big.gain:white
big.gain:univ_degr
big.gain:income
Log-Likelihood
McFadden R^2
AIC
0.978
-0.071
-0.015
0.480
0.285
0.227
-0.035
***
***
*
**
2003.9
0.3425
4041.787
-1488.2
0.51175
3120.471
Indication of significance codes: *** 0.001; ** 0.01; * 0.05; . 0.1
64
All models show significant estimates (p < 0.05) for the various wind farm features (impacts to
biodiversity, ownership types and distance from shore), except the conditional logit models that
estimate the cooperative attribute as borderline significant (p-value greater than 0.05 but less
than 0.1). Both mixed and conditional models estimate significant and negative estimates for
60% reduction in biodiversity (big.loss), meaning there is a strong preference not to choose the
wind farm that reduces biodiversity. The largest model estimates are associated with 60%
increase in biodiversity (big.gain). We were most interested in demographic features that may
influence the selection of the 60% increase in biodiversity so we interacted this variable with
demographic variables. The negative estimates associated with the interaction between age and
biodiversity gain (big.gain:age) is statistically significant (p <0.01) but the effect sizes are small
in the models (-0.015 in the conditional model, -0.044 in the mixed model), indicating that older
residents may be slightly less likely to choose and therefore somewhat less willing to pay for
large biodiversity gains. The conditional model, but not the mixed logit model, found that
women were somewhat more likely to choose the farm with large biodiversity gains (the estimate
was 0.480, p < 0.01). We found no evidence that other demographic characteristics influence the
selection of wind farms with 60% biodiversity gains.
3.3.2
Estimates of willingness to pay for offshore wind farm characteristics
Our M-Turk sample is younger, somewhat more female, more educated, and has lower
household income than these states’ populations based on census data. Gender may have a
significant impact on choices made, but this is not clear since only the conditional model shows
gender as significant. Both conditional and mixed logit models suggest that older respondents
may be less willing to pay for large biodiversity gains than younger respondents (big.gain:age
65
estimates are negative). If we assume that the coefficients are not biased by our sample, one way
to account for the demographic differences between the M-Turk sample and census data is to
estimate the WTP for each year of age difference between the samples. The average M-Turk age
is 8 years younger than the census data average age. Based on the mixed logit with demographic
variables model output, reduced WTP per year of age is ~$0.21 (big.gain:age estimate of 0.044
divided by the bill estimate of 0.204 is $0.216), so the 8 year difference could be estimated as
reducing WTP by ~$1.73 ($0.216 multiplied by 8). The other demographic variables were not
significant in the best-fit model so we do not correct for other discrepancies between the M-Turk
sample and census data.
The highest addition to the monthly utility bill offered in the choice experiment ($20/month) was
below the predicted WTP values for wind farm attributes (e.g., $34/month for a big gain to
biodiversity according to the mixed logit model with the lowest AIC). Many of our WTP values
are beyond the range of the offered payment mechanism, i.e., greater than $20, so we have lower
confidence in these estimates. Extrapolating to regional WTP for OWFs with a baseline of small
biodiversity loss to large biodiversity gains yields an estimate of $451 million/month (~$34.10
minus $1.73/month multiplied by coastal New England population of 13,952,200) or $5.42
billion/year ($451 million multiplied by 12 months).
66
Willingness to pay for offshore wind farm attributes
WTP ($/month)
20
0
−20
4 miles
8 miles 10 miles municipal
state
coop
big loss small gain big gain
Offshore Wind Farm Attributes
2
Figure 3.3. Willingness to pay (WTP) for offshore wind farm attributes.
WTP is estimated for how distant a wind farm is from shore, ownership type and impact on biodiversity.
These results are based on the mixed logit model, which had the lowest AIC score.
3.4
Discussion
Our results show widespread support and willingness to pay for ecologically regenerative
renewable energy technology, which offers a more optimistic direction for environmental
research than the predominant environmental discourse which has focused on limits (Meadows et
al., 1972), boundaries (Rockström et al., 2009; Steffen et al., 2015) and declines (MA, 2003).
The consequences of this arguably uninspiring emphasis on scarcity and sacrifice may be
seeding and perpetuating doubt and indifference rather than active engagement when it comes to
addressing environmental challenges (Gifford and Comeau, 2011; Robinson and Cole, 2014;
Shellenberger and T. Nordhaus, 2004). This emphasis on minimizing harm—making things “less
67
bad”—may simply prolong environmental degradation rather than contribute to ecological
regeneration (McDonough and Braungart, 2002; Robinson and Cole, 2014).
This study provides empirical evidence that people value approaches to building renewable
energy infrastructure that generate ecological abundance. The strongest driver of wind farm
preference in our study was biodiversity benefit (see Figure 3.3 and Table 3.3). Our results
demonstrate a significant and substantial WTP for habitat enhancement in conjunction with
OWF development when the environmental gains and losses are visually explicit.
Based on our results, New England residents may be willing to pay $34-42 more for electricity
from OWFs that have marine biodiversity benefits rather than losses (60% gain as compared to a
30% reduction in species abundance and diversity). This is higher than WTP estimates identified
by Börger (2015) who estimated that residents living near the Irish Sea Coast had an annual
WTP of £7 for an OWF that increased the diversity of species by 10 and £15 if species increased
by 30.
It is possible that our high WTP estimates are based on respondents making snap judgments
selecting the option with the graphic of the most diverse reef without fully considering the bill.
Respondents may also have overlooked the “monthly” description of the bill, despite how it was
described as renewable energy fee added each month to the bill (see Appendix I. Choice
experiment survey) and each choice scenario included the descriptor “addition to monthly utility
bill” (see Figure 3.2). Moreover, our WTP estimates may be larger than what people would
actually pay due to “hypothetical bias,” which refers to how people frequently, but not always,
68
respond with lower willingness to pay to real as compared to hypothetical valuation questions
(Carlsson et al., 2005; Cummings and Taylor, 1999; List and Gallet, 2001; Neill et al., 1994). A
meta-analysis by List and Gallet (2001) suggests that respondents overstate their valuation of a
good by a factor of approximately 3 when asked under hypothetical settings. We used choice
based elicitation methods, a simulated voter referendum with consequences to the respondent,
and we had an opt-out option, which are methods that tend to reduce hypothetical bias as
compared to other methods of assessing WTP (Loomis, 2011; Murphy et al., 2005). Some
studies employ “cheap talk,” which involves inserting an explicit description of hypothetical bias
and why it might occur into the survey instrument prior to the WTP questions. “Cheap talk”
appears to reduce or eliminate hypothetical bias (Carlsson et al., 2005; Cummings and Taylor,
1999). Other economists argue against “cheap talk” statements, on the basis that telling
participants that hypothetical estimates are generally overestimates is artificially leading
(Adamowicz and Naidoo, 2016). We did not include “cheap talk” nor did we include a reminder
of household monthly budget constraints. All considered, real WTP is likely lower than our
estimates, and a conservative lower bound might be 1/3 of the WTP that we report.
Similar to the findings of Börger (2015), visibility of turbines had a limited and weaker influence
on wind farm choice than the considerably stronger preference for farms that increase marine
species diversity (see Figure 3.3 and Table 3.3). Our results align with past research
demonstrating WTP to site OWFs further from shore. Studies show that people generally
consider an OWF a visual disamenity (Krueger et al., 2011; Ladenburg and Dubgaard, 2007).
Danish residents’ WTP was ~$58, $121, and $153 per household per year (Euros converted to
2006 USD) for a wind farm sited 12, 18 and 50km, respectively, from the coast as compared to
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8km (Ladenburg and Dubgaard, 2007). Krueger (2007) estimated that inland residents of
Delaware were willing to pay to $9, $13, $16, $17, $19 and $21 per month for three years to site
a wind farm at 3.6, 6, 9, 12, 15 and 20 miles, respectively, away from shore as compared to 0.9
miles. Westerberg (2013), however, revealed that some types of tourists associate amenity value
with an OWF at least 8km from shore, while other types of tourists only associate disamenity
value with an OWF.
Our study suggests that private ownership is not preferred, which is similar to the findings of Ek
and Persson (2014). Our results indicate a small but significant preference for municipally and
state owned OWF rather than privately owned. There is some ambiguity related to cooperative
ownership in our study in contrast to the highly significant ownership preferences in Sweden for
state, municipally or cooperatively owned OWF (Ek and Persson, 2014). The conditional logit
model shows cooperative ownership as non-significant, while the mixed logit results indicate it is
statistically significant (~$7.50 WTP). It seems clear, however, from both models that there is
support for OWFs with some degree of community or public ownership, and a WTP more for
this (see Figure 3.3 and Table 3).
3.4.1
Policy implications
Our research strengthens the case for the development of ecologically regenerative offshore wind
farms. This study reveals latent public support and WTP for such technology. Developing OWFs
with effective artificial reefs and communicating this design feature broadly could improve
public support for this renewable energy technology, which has the potential to facilitate
developers obtaining consent for OWF licensing and initiating planning processes.
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3.5
Conclusion
This study reveals high levels of support for the ecologically regenerative design of a type of
renewable energy infrastructure. This is particularly relevant and timely as the scientific
consensus on climate change has coalesced and the need to shift away from fossil fuels has
become increasingly apparent. Public support for renewable energy infrastructure expansion is
needed to achieve commitments made regarding carbon reduction goals and renewable energy
targets. Our research provides evidence of elevated WTP for ecologically regenerative renewable
energy in the form of artificial reefs associated with OWFs along coastal New England states.
Our study suggests that integrating biodiversity benefits into the design of renewable energy
infrastructure could increase public support for such developments.
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Chapter 4: Relational values resonate broadly and differently than intrinsic
or instrumental values, or the New Ecological Paradigm
Sarah C. Klain*, Paige Olmsted*, Kai M.A. Chan, Terre Satterfield
*Equal lead authorship
4.1
Introduction
Conservation scientists and practitioners have often drawn on ethical constructs to articulate
support for policies aimed at addressing the biodiversity crisis. To those outside the conservation
community, it may come as a surprise that the “Why conserve nature?” value debate about how
to motivate people to achieve conservation outcomes has become increasingly heated and
arguably detrimental to conservation science despite calls for “a unified and diverse conservation
ethic” (Tallis and Lubchenco, 2014, p. 27; Vucetich et al., 2015). “Traditional conservationists”
advocate for focusing on the intrinsic value of nature, protecting nature for its own sake. They
often focus on strategies to minimize human interference with ecological processes and invoke
ethical and moral arguments to support their stance while being skeptical of corporate
involvement in conservation (Soulé, 2013). Such advocates are often pitted against the “new
conservationists,” who champion the instrumental value of nature, justifying and prioritizing
conservation action based on nature’s benefits to people (Kareiva et al., 2012). New
conservationists tend to be more open to using market-based incentives and collaborating with
corporations to protect and enhance the benefits of nature to people (ecosystem services), often
derived from human-dominated landscapes (Kareiva et al., 2012; Tercek and J. S. Adams, 2013).
72
Underpinning the intrinsic vs. instrumental debate is a common objective—to promote and
encourage conservation actions, from the level of the individual to national governments and
international decisions. Marvier (2013) and other new conservationists claim that utilitarian
conservation arguments do not undermine conservation justifications based on nature’s intrinsic
value or an ethical duty to protect biodiversity. Rather, many contend that instrumental
arguments offer additional ethical justifications and so “potentially broaden the tent of
conservation” (Marvier, 2013, p. 1). This argument aside, the instrumental-intrinsic dichotomy
can be constraining or possibly alienating to many who may potentially care more and take
additional action if environmental issues were framed differently (Chan et al., 2016). Reducing
the importance of nature to only intrinsic or instrumental and monetized value is also not
reflective of the largely intuitive ways that people make decisions and understand the world and
decide what’s right (Haidt, 2007; Kahneman, 2011; Levine et al., 2015).
The burgeoning field of ecosystem services (ES)(Costanza and Kubiszewski, 2012), long
associated with a purely instrumental perspective, has recently been broadened to include other
perspectives on value. The ES concept became globally recognized with the Millennium
Ecosystem Assessment (MA, 2003), which emphasized diverse connections between human
well-being and nature, but the category of cultural ES arguably never fit well in the publications
that ensued over the next decade (Chan et al., 2012a; Daniel et al., 2012). The instrumental
orientation of ecosystem services is arguably the cause of the poor fit, in part because
instrumental values are by definition substitutable, whereas cultural values are often not (Chan et
al., 2011; 2012b). Quantified and/or monetized ES data often omit the more intangible values
that “really get at well-being” (Hannah as quoted in Chan et al., 2012a), such as connectedness
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and belonging to a community (both human and non-human), sense of place and other culturally
and psychologically mediated relationships between people and ecosystems (Russell et al.,
2013). Consequently, researchers from a wide range of backgrounds, including anthropology,
political science, economics, and ecology, have begun to develop methods designed to enable
social, cultural and intangible values to play a more prominent role in ES assessments and
decision-making without compromising their distinct nature (Chan et al., 2012b; 2012a; Daniel
et al., 2012; Gould et al., 2014; Klain and Chan, 2012; Martín-López et al., 2012; Plieninger et
al., 2013). As a result of these and related efforts, the ES field is evolving to the point that the
IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services)
conceptual framework has included relational values, which are an additional conception of
values, to its mandate (Diaz et al., 2015).
The hope, as argued by Chan et al. (2016), is that a relational-value framing will be more
inclusive and responsive to known aspects of sources of well-being (e.g., connection to others,
place attachment) than instrumental and intrinsic values, particularly when addressing how
people make decisions and what they care about. In this case, we refer to framing as in the
framing effect – deliberate construction of (in this case) a value statement that may influence the
response. The relational “framing” is intended to present value statements such that they
facilitate the connection between humans and the natural world.
Relational values encompass “eudaimonic” values — values associated with living a good life as
well as reflection about how preferences and societal choices relate to notions of justice,
reciprocity, care and virtue (Jax et al., 2013; Muraca, 2011; Ryan and Deci, 2001; Ryff and
74
Singer, 2008). Relational values are derived from interactions with and responsibilities to
humans, non-humans, landscapes and ecosystems (Chan et al., 2016). However, despite these
conceptual advances, empirical investigation has been lacking.
Here we test the application of social-ecological relational statements quantitatively, as a first
step to potentially transcend the limitations of the instrumental-intrinsic dichotomy. We pilot
several types of social-ecological value statements, including instrumental, intrinsic, and
relational value statements as well as value statements that use metaphors to convey a value. We
assess if our set of relational value statements demonstrate internal coherence as a single or
multi-dimensional construct. We compare responses to relational value statements across three
populations with instrumental, intrinsic and metaphorically phrased value statements.
We also address a fundamental question: How do relational values compare to other scales often
used to assess strength of environmental commitment? The New Environmental Paradigm
question set (Dunlap and Van Liere, 1978), subsequently revised as the New Ecological
Paradigm Scale (NEP)(Dunlap et al., 2000), is the most widely used method to measure
ecological beliefs. The NEP aggregates responses to 15 (or as few as 5) statements to assess
ecological attitudes, many of which address which people possess ecocentric as opposed to
anthropocentric beliefs. Social scientists have used the NEP scale with diverse populations and
responses have demonstrated variation along the ecocentric-anthropocentric continuum
(Nordlund and Garvill, 2002).
75
Although global values surveys using NEP show variation, the overwhelming majority of people
are indeed concerned about the natural world and prefer the idea of “co-existing” with nature
rather than dominating it (Nordlund and Garvill, 2002). The NEP largely aligns with an
ecocentric vs. anthropocentric framing, by assessing the extent to which people recognize 1)
ecological limitations to growth; 2) the importance of maintaining a balance of nature; and 3)
rejection of the idea that nature “exists primarily for human use” (Dunlap, 2008, p. 6). Thus, the
question remains: does the addition of relational value items add something to the study of
environmental beliefs or values, perhaps complementing the NEP by offering a different
framing?
An additional question is whether relational values, once tested, are instructive in explaining proenvironmental attitudes when compared to other values. The example used here involves
attitudes toward a renewable energy technology. Specifically, we focus on offshore wind
turbines, which have local environmental impacts and global climate benefits. Diverse and often
conflicting environmental values come into play when considering if and where to build
renewable energy infrastructure. The “green-on-green” debate in wind farm literature is based on
conflict over the extent to which stakeholders prioritize local environmental impacts (e.g., bird
strikes from wind turbines, aesthetic degradation of landscape) as compared to global
environmental concern (i.e., climate change, the need to reduce carbon emissions) (Warren et al.,
2005). We evaluate the relationship between NEP scores as well as responses to relational,
instrumental and intrinsic value prompts with attitudes towards building this technology.
76
This discussion of both value types and their applicability can be summarized as four research
questions underpinning our survey design and stated below:
1. Do various types of relational value statements correlate as a single construct?
2. Do relational value statements (including those strongly stated) resonate with (i.e., elicit
agreement) amongst diverse populations?
3. Do people respond to relational value statements in a consistently different way than the
New Ecological Paradigm (NEP) scale statements?
4. Do relational values and NEP scores help explain attitudes towards wind power?
In the following sections, we outline our approach to data collection and analysis, present our
results, and discuss the implications for environmental research and practice.
4.2
Methods
Our methods comprised three components: diverse sampling, comparing value types, and testing
in reference attitudes towards a renewable energy technology. For our sample, we targeted three
populations: farmers and international tourists in Costa Rica, and residents of U.S. coastal New
England states. Our surveys included value/attitude statements followed by Likert scales to
assess agreement/disagreement. Our analysis included conducting principle components analysis
and factor analysis (for correlation in patterns of responses across questions and groups of
questions), calculating Cronbach’s alpha (for assessing consistency in responses across
questions), and running Pearson correlation tests as well as linear regressions. Each step is
described in more detail below.
77
4.2.1
Survey value statements and sample
We derived a list of value statements related to the environment including NEP, instrumental,
relational, intrinsic, and values conveyed using metaphors. The instrumental value statements
were derived from concepts advanced in overviews of ecosystem services (MA, 2003). The NEP
statements are a selection from the standardized NEP survey instrument to assess ecological
worldview(Dunlap et al., 2000). The intrinsic, relational and metaphorically phrased value
statements are derived from cultural ecosystem services literature (Chan et al., 2012b; Gould et
al., 2014; Klain et al., 2014; Raymond et al., 2013). The metaphor statements are a rewording of
four of the relational value statements. In contrast to the metaphor statements that focus on the
social-ecological relationship itself, the relational value statements express the relationship as a
premise for a value statement (e.g., the kin metaphor statement, kin_m, is “I think about the
forest/ocean and the plants and animals in it like a family of which I am very much a part” vs.
the kin relational statement, kin_r, is “Plants and animals, as part of the interdependent web of
life, are like 'kin' or family to me, so how we treat them matters”).
In all three surveys, the value statements (Table 4.1) were the final section, so as not to prime
responses in other areas of the otherwise different surveys. Survey takers were asked to respond
to the value prompts using a 5 point Likert scale (i.e., highly disagree = 1; highly agree = 5).
78
Table 4.1. Value statements used in surveys.
F = Costa Rican Farmers, T = Tourists at San José airport; MT = Mechanical Turk respondents. Reverse
codes were used when appropriate so high scores mean pro-environmental; y = yes; n = no.
Variable
Category
Statement
Population
Reverse
code
comm
Relational
There are landscapes that say something about who we
are as a community, a people
F, T, MT
n
health
Relational
My health or the health of my family is related one way or
another to the natural environment*
F, T, MT
n
iden
Relational
I have strong feelings about nature (including all plants,
animals, the land, etc.) these views are part of who I am
and how I live my life
F, T, MT
n
kin
Relational
Plants and animals, as part of the interdependent web of
life, are like 'kin' or family to me, so how we treat them
matters
F, T, MT
n
resp
Relational
How I manage the land, both for plants and animals and
for future people, reflects my sense of responsibility to
and so stewardship of the land
F, T
n
wild
Relational
I often think of some wild places whose fate I care about
and strive to protect, even though I may never see them
myself
F, T, MT
n
other
Relational
Humans have a responsibility to account for our own
impacts to the environment because they can harm other
people
F, T, MT
n
abuse
NEP
Humans are severely abusing the environment
F, T, MT
n
bal
NEP
The balance of nature is strong enough to cope with the
impacts of modern industrial nations
F, T, MT
y
bau
NEP
If things continue on their present course, we will soon
experience a major ecological catastrophe
F, T, MT
n
crisis
NEP
The so-called "ecological crisis" facing humankind has
been greatly exaggerated
F, T, MT
y
spaceshi
p
NEP
The earth is like a spaceship with very limited room and
resources
F, T, MT
n
decade
Intrinsic
Humans have the right to use nature to meet our needs,
even if this includes impacts that will take a decade or
more to recover
MT
y
right
Intrinsic
Humans have the right to use nature any way we want
F, T
y
79
Variable
Category
Statement
Population
Reverse
code
I think about the forest/ocean and the plants and animals
in it like: **
iden_m
Metaphor
Something I identify with so strongly that it makes me, me
F, MT
n
kin_m
Metaphor
A family of which I am very much a part
F, MT
n
other_m
Metaphor
A world we must care for so that any damage doesn't also
negatively affect humans who depend on it elsewhere
F, MT
n
resp_m
Metaphor
Beings to which we owe responsible citizenship and care
F, MT
n
extract
Instrumental
(economic)
Natural resource extraction is necessary for countries to
develop
F, T
y
clean
Instrumental
(health)
It is important to protect nature so we have clean air and
water
F, T
n
loss
Instrumental
(use)
We can lose forests and wetlands, as long as we are
keeping enough for the environment to function
F, T
y
* This statement was reversed for the M-Turk sample: “My health, the health of my family and the health of others who I care
about is not necessarily dependent on the natural environment.” We do not recommend reversed coding this prompt because
we later realized it caused confusion.
** The farmer sample responded to metaphorical statements related to forest. The M-Turk sample responded to metaphorical
statements related to ocean. Tourists were not presented metaphorical statements.
Our aim with the different populations and samples is not to suggest they are representative, but
to compare across different populations. We targeted three populations with different methods
including online and paper-based surveys.
4.2.1.1
Online survey
For the online sample, we used Amazon’s Mechanical Turk (M-Turk) system to enlist
respondents, which has become a common recruitment method for experimental research
(Goodman et al., 2012; Paolacci et al., 2010). Data outputs are generally just as reliable as those
acquired with traditional recruitment methods (Buhrmester et al., 2011). We attempted to
minimize selection bias in our sample by describing it on M-Turk’s HIT (Human Intelligence
Tasks) list in general terms as a survey about preferences based on different text and image80
based descriptions, without using any language related to ecosystems. The sample was limited to
M-Turk workers who have mailing addresses in coastal New England states (Connecticut,
Maine, Massachusetts, New Hampshire or Rhode Island). We targeted this geographic area
because this survey also included questions assessing attitudes to a proposed renewable energy
technology suited to this region—offshore wind farms (see Klain et al. in prep). We collected
self-reported demographic data from the sample to later compare it with census data to determine
the extent to which this sample is representative of the population of these states. Upon survey
completion, respondents were given a code to submit to the M-Turk system for payment.
Respondents were paid $1 to take the 10-15 minute survey. Given that the typical M-Turk
worker is willing to complete tasks for ~$1.40/hour (Horton and Chilton, 2010), our payment
was higher than the average reservation wage to expedite participant recruitment. Incomplete
responses were discarded for a total of 400 M-Turk respondents.
4.2.1.2
Paper-based survey
Two paper-based surveys incorporated value statements for two distinct populations in
Guanacaste, Costa Rica. The first (n = 260) were international tourists in Costa Rica, who were
randomly sampled in the Liberia Airport upon departure from the country. This airport primarily
services the coastal tourist destinations and thus all international flights at this time were to the
United States or Canada. All tourists in the departure lounge (i.e. those who arrived just in time
to board did not have time to participate) during the week of May 25, 2015 were asked if they
had travelled in the region, and if so if they were willing to participate in a survey. They were
predominantly tourists from North America (and the U.S. in particular). The second group
81
consisted of farmers in the Nicoya region (n = 253), mostly cattle ranchers, who spend a lot of
time working the landscape, while also deriving their livelihoods directly from the environment.
In sum and across all three samples, we sought this diversity as we expected farmers to have a
different profile with respect to their environmental values than the other two groups; but
expected the international tourists to resemble the M-Turk population more closely, insofar as
they both include substantial representation of middle and upper income Americans. The farmers
were randomly selected from lists provided by the agricultural extension agencies in the region,
and the value statements were included as part of a survey about environmental practices on the
landscape more broadly.
4.2.1.3
Sampled population characteristics
Our M-Turk population was on average younger (32) than the tourist (45) or farmer populations
(58)(Table 4.2). The tourists and M-Turk samples were a majority female while the farmers were
mostly male (88% male)(Table 4.2).
82
Table 4.2. Demographic characteristics of our three samples.
Population
Socioeconomic
Characteristics
Description
Percentage
or Mean of
Sample
M-Turk
(N = 400)
Percentage or Mean
from Reference
Population
2014 US Census
Income
Age
Female
Education
White
Annual household
income before
taxes
Years old
Gender
Bachelor degree
or higher
Caucasian race
~$53,000*
$66,200
32
40
0.59
0.51
0.66
0.38
0.83
0.82
Tourist
(N = 260)
Age
Income before
taxes
Years old
Female
Gender
0.63
0.15
Age
Bachelor degree
or higher
Years old
Female
Gender
0.12
Income
~$75,000
~45
Farmer
(N = 253)
Education
4.2.2
~58
Statistical analysis
We assessed the discrimination or uniqueness of each value category using factor analyses and
principal components analyses. Then we analyzed each using Cronbach’s alpha to test the
internal consistency within value measures.
4.2.2.1
Eigenvalues and scree test
We calculated eigenvalues and created a scree plot to determine how many factors to include in
our factor analysis and PCA. Eigenvalues associated with components or factors are included in
83
descending order in a scree plot. The inflection point, or ‘elbow’ at which point eigenvalues level
off, demarcates components/factors to retain while subsequent components/factors are generally
ignored. A common heuristic is to retain components/factors with eigenvalues > 1, which means
that the component/factor accounts for as much or more variance as a single variable (A. Field et
al., 2012).
4.2.2.2
Factor analysis
Our factor analysis investigated the structure of a set of variables to determine if there are
clusters of correlation coefficients, which indicate latent variables, also called factors. This
method derives a mathematical model from which underlying factors are estimated. Each latent
variable is associated with some amount of the observed variable’s overall variance. Eigenvalues
indicate the evenness in the distribution of the variances in the correlation matrix (A. Field et al.,
2012, p. 713). They measure the amount of the variance of the observed variables that a factor
explains. If a factor has an eigenvalue ≥1, then it explains more variance than a single observed
variable. In general, the factors explaining the least amount of variance are ignored.
In Factor Analysis, the amount of common variance is estimated by calculating communality
values for each variable. This is usually done by calculating the squared multiple correlation of
each variable with the others. Factor analysis is mathematically more complex than Principal
Components Analysis. Guadagnoli and Velicer (1988) conducted an extensive literature review
and found that, in general, results from PCA differ little from Factor Analysis. We conducted an
exploratory factor analysis with the hypothesis that responses to relational value statements
comprise a factor distinct from responses to NEP statements (see Figure 4.1).
84
4.2.2.3
Principal components analysis
We used both Factor Analysis and PCA to determine if factors/components could be identified
within our dataset of responses to value prompts. Principal Components Analysis (PCA) assumes
that the communality of all variables is 1. This assumption transposes the original data into
constituent linear components. PCA identifies linear components in the data and how a specific
variable contributes to the component. Factors (called components in PCA) with large
eigenvalues are retained while those with small eigenvalues are ignored (see Table 4.3).
4.2.2.4
Consistency measure: Cronbach’s alpha
We calculated Cronbach’s alpha for all of our social-ecological statements to determine the
extent to which responses are consistent across NEP statements and relational statements.
Cronbach’s (1951) method is loosely understood as splitting a dataset in two in every possible
way, then computing the correlation coefficient for each split. Cronbach’s alpha (!)—the
arithmetic average of these pairwise correlation coefficients within a group of questions—is the
most common metric of scale reliability (A. Field et al., 2012).
4.2.2.5
Correlation testing of environmental values and wind farm attitudes
We created five indices, one for each value type (NEP, relational, instrumental, intrinsic and
metaphor) for the M-Turk population. We calculated indices based on the average response to
these prompts about a type of environmental value because results from the factor analysis, PCA
and Cronbach alpha (Table 4.5, Figure 4.1) suggested that responses to NEP and relational
statements are consistent and distinct from each other and the metaphor and intrinsic value
85
statement responses also had a high level of consistency (i.e., high Cronbach’s alpha for the MTurk responses to these statements, see Appendix M). Despite the lower consistency in responses
to the instrumental value statement, we included them for exploratory purposes.
We tested the correlation between these indices and responses to questions about attitudes
towards offshore wind farms, which were also on Likert scales (see Appendix N).1 We ran linear
regressions to test if demographic and environmental value responses could predict attitudes
towards wind power.
4.3
Results
Our results suggest that relational value statements show internal coherence as a single
dimensional construct, particularly when compared to responses to NEP prompts. We identified
two factors and components when NEP and relational value statements were pooled and
analyzed from our three populations using eigenvalues, a scree test, factor analysis and PCA.
These two types of value statements showed high levels of internal consistency based on their
high Cronbach’s alpha scores. We also found positive correlations between the M-Turk
population responses to environmental value statements and attitudes towards wind farms.
4.3.1
Two distinct factors based on eigenvalues and scree test
In order to understand distinctiveness in responses to types of environmental values and
determine a reasonable number of factors/components to retain in our factor analysis, we
calculated eigenvalues and conducted a scree test (See Appendix K and Table 4.3) and Principal
1
In the M-Turk sample, the Likert scale used to assess environmental value phrased with metaphors had slightly
different meanings than the scale use for the other environmental values (See Appendix E). In future applications of
the metaphor value prompts, “3” should correspond to a neutral, not somewhat positive attitude.
86
Components Analysis (PCA)(See Table 4.4 and Appendix L). Our scree plot, parallel analysis
and optimal coordinates indicate that two factors ought to be retained for the factor analysis. The
acceleration factor identifies where the slope of the curve changes most abruptly, which in our
data, is directly after the first factor (see Appendix K).
4.3.2
Factor analysis results: NEP is distinct from relational value
Our exploratory factor analysis shows that survey takers responded differently to relational value
prompts than NEP statements (Table 4.3 and Figure 4.1). The proportion of variation attributed
to Factor 1, the “Relational” Factor (0.24), is higher than the proportion attributed to Factor 2,
“NEP” factor (0.21).
Table 4.3. Factor Weights
Variable
comm_rel
Factor 1
Factor 2
Relational
0.54
NEP
wild_rel
0.61
iden_rel
0.78
kin_rel
0.75
other_rel
0.52
0.35
abuse_nep
0.31
0.68
bal_r_nep
0.5
spaceship_nep
bau_nep
0.67
0.36
0.78
crisis_r_nep
Factor 1
Factor 2
Relational
NEP
Eigenvalues/SS
loadings
2.43
2.11
Proportion
Variation
0.24
0.21
Cumulative
Variation
0.24
0.45
87
Figure 4.1. Graphical results of Factor Analysis.
Our factor analysis results show a grouping of the relational questions that is distinct from the
NEP statements. The crisis NEP statement is an outlier in the pooled data (Figure 4.1), which is
discussed in greater detail in the discussion.
4.3.3
Principal components analysis: NEP is distinct from relational values
A Principal Components Analysis was used to demonstrate how relational statements group
together as a separate factor from NEP statements, with the NEP vectors going in different
directions from the relational vectors (see Appendix L).
88
Table 4.4. PCA loadings based on correlation matrix.
PC is principle component, h2 is communality (variance shared with other variables, which is equivalent to
the sum of squares of common factor loading for a variable).
abuse_nep
bal_r_nep
crisis_r_nep
spaceship_nep
bau_nep
comm_rel
wild_rel
iden_rel
kin_rel
other_rel
SS loadings
Proportion Variation
Cumulative Variation
Proportion Explained
4.3.4
PC1
0.32
0.17
-0.09
0.32
0.4
0.69
0.73
0.81
0.77
0.62
PC1
3.04
0.3
0.3
0.55
PC2
0.73
0.7
0.49
0.68
0.75
0.13
0.17
0.14
0.13
0.34
h2
0.64
0.52
0.25
0.56
0.72
0.49
0.56
0.67
0.61
0.5
u2
0.36
0.48
0.75
0.44
0.28
0.51
0.44
0.33
0.39
0.5
com
1.4
1.1
1.1
1.4
1.5
1.1
1.1
1.1
1.1
1.5
PC2
2.49
0.25
0.55
0.45
High levels of agreement and consistency with types of environmental value
statements
Strong relational value statements resonate with diverse populations based on how the average
response to relational value and NEP statements was 4 (Agree). The responses to NEP
statements, on average, reflect relatively high ecological concern (see Table 4.5). NEP responses
were consistent (Tourist ! = 0.79 and M-Turk ! = 0.84), except for Costa Rican farmers (! =
0.35), largely due to the farmers’ wide variation in response to the “crisis” prompt (The so-called
"ecological crisis" facing humankind has been greatly exaggerated, see Table 4.1). We did not
include instrumental or intrinsic value statements when calculating ! because of the limited
number of statements in these categories.
89
Table 4.5. Cronbach’s alpha, mean response and standard deviation of responses across value statements.
Cronbach’s
alpha
NEP (5)
Full dataset
Farmers
Tourists
M-Turk
Relational (6)
Full dataset
Farmers
Tourists
M-Turk
Mean
Standard
deviation
0.73
0.35
0.79
0.84
4.0
4.3
3.7
4.0
0.75
0.49
0.81
0.74
0.80
0.73
0.79
0.79
4.0
4.4
3.9
3.9
0.68
0.43
0.75
0.61
Costa Rican Farmers responded differently to our value statements than the M-Turk and Tourist
samples. The Farmers on average responded with higher levels of agreement to relational value
prompts (mean = 4.4) as compared to Tourists (mean = 3.9) and M-Turk workers (mean =
3.9)(Table 4.5). Farmers on average scored higher on the NEP scale (mean = 4.33) than Tourists
(mean = 3.65) and M-Turk workers (mean = 3.96) (Table 4.5, Figure 4.2, Figure 4.3). The
relational and NEP statements as well as the distribution of Likert-scale responses across the
three populations is shown in the histograms in Figure 4.3. The x-axis is the number of
respondents and the y-axis is the items of the Likert scale (1 means strongly disagree to 5
meaning strongly agree).
90
Social Ecological Relational Value Statements
How I manage
the land, both
for plants and
animals and
for future
people, reflects
my sense of
responsibility
to and so
stewardship for
land
There are
landscapes
that say
something
about who we
are as a
community, a
people
I often think of
some wild
places whose
fate I care
about and
strive to
protect, even
though I may
never see
them myself
I have strong
feelings about
nature
(including all
plants,
animals, the
land, etc.)
these views
are part of
who I am and
how I live my
life
Plants and
animals, as
part of the
interdependent
web of life, are
like 'kin' or
family to me,
so how we
treat them
matters
My health, the
health of my
family and the
health of
others who I
care about is
dependent on
the natural
environment.*
Humans have
a responsibility
to account for
our own
impacts to the
environment
because they
can harm other
people
resp_rel
comm_rel
wild_rel
iden_rel
kin_rel
health_rel2
other_rel
4
Farmer
3
2
1
sub_pop
5
4
M−Turk
3
2
Farmer
M−Turk
Tourist
1
5
4
Tourist
Response
1 = Strongly Disagree; 5 = Strongly Agree
5
3
2
1
0
50 100 150 200
0
50 100 150 200
0
50 100 150 200
0
50 100 150 200
0
50 100 150 200
0
50 100 150 200
0
50 100 150 200
count
Humans are severely
abusing the
environment
The balance of nature
is strong enough to
cope with the impacts
of modern industrial
nations
abuse_nep
bal_r_nep
The so-called
"ecological crisis"
facing human kind
has been greatly
exaggerated
The earth is like a
spaceship with very
limited room and
resources
crisis_r_nep
If things continue on
their present course,
we will soon
experience a major
ecological catastrophe
spaceship_nep
bau_nep
5
4
Farmer
3
2
1
sub_pop
5
4
M−Turk
3
2
Farmer
M−Turk
Tourist
1
5
4
Tourist
Response
1 = Strongly Disagree; 5 = Strongly Agree
New Ecological Paradigm Statements
3
2
1
0
50
100
150
200
0
50
100
150
200
0
50
100
150
200
0
50
100
150
200
0
50
100
150
200
count
Figure 4.2. Mean and distribution of responses to relational value prompts and New Ecological Paradigm
Statements.
The sample includes Costa Rican farmers (n = 253), tourists in Costa Rica (n = 260) and US M-Turk workers
(n = 400).
*The health_rel prompt for the M-Turk population was worded “My health, the health of my family and the health of others who I care about is
not necessarily dependent on the natural environment.” Scores were reversed for this population when included in the analysis.
91
A shown in Figure 4.3, the M-Turk and tourist populations responded similarly to the
instrumental value statements (the standard errors overlap for 2 out of 3 instrumental value
prompts). Costa Rican farmers agreed more strongly with the metaphorical statements than the
M-Turk population. Except for the “crisis” statement, Costa Rican farmers scored the highest on
the NEP scale, followed by M-Turk then the Tourist population. The M-Turk and Tourist
populations responded similarly to the relational value prompts and lower than the farmers
Response
1 = Strongly Disagree 2 = Disagree; 3 = Neither Agree nor Disagree;
4 = Agree; 5 = Strongly Agree
(except for the similar responses to the responsibility prompt, “resp_rel”).
5
4
Farmer
3
M−Turk
Tourist
2
1
Extract Loss Clean Kin Resp Inden Other Decade Right Abuse Bal
met met met
Instrumental
Metaphor
Intrinsic
Crisis Space Bau Comm Wild Resp Iden
ship
rel
rel
New Ecological Paradigm
Kin Health Other
rel
Relational
Figure 4.3. Mean response with standard errors to value prompts across three populations.
Red circles indicate the mean response across the populations for each value statement.
Out of all of the environmental value statements that we tested, the highest average response for
the M-Turk and Tourist population was agreement with an instrumental value: It is important to
92
protect nature so we have clean air and water (“Clean”)(. Two NEP statements (“BAU” and
ponse to Value
Prompts
Abuse”)
ranked highest for the farmer population as shown in Figure 4.3 and Table 4.6.
Farmer
Value Prompt
Rank
1
M-Turk
Clean (4.69)
Tourist
Clean (4.6)
Farmer
BAU (4.81)
2
3
4
5
6
Other (4.34)
Abuse (4.25)
Other (4.09)
Community (4.07)
Right (4.00)
Other (4.4)
Responsibility (4.3)
Right (4.1)
Community (4.1)
Health (3.9)
Abuse (4.81)
Other (4.75)
Spaceship (4.74)
Community (4.70)
Responsibility (4.58)
Instrumental
M−Turk
Intrinsic
Metaphor
NEP
Relational
Tourist
risis
Table 4.6. Top six mean responses to environmental value statements across three populations.
The top four farmer scores are not statistically different from each other, effectively all being tied for first,
Type of
comm_rel is statistically different from the first two, bau_nep and abuse_nep.
bau spaceshipdecade
iden
loss health
wild
kin_m iden_m extract
Majority
of bal
M-Turk
sample have
positive
attitudes towards wind farms
4.3.5
Ecological Value Prompt
The majority of our M-Turk survey takers had positive attitudes towards wind power both at a
national and state level (see Figure 4.4).
In your opinion, construction of offshore
wind turbines off the coast of your state should be:
What is your attitude toward developing
wind power in the US?
60%
60%
40%
40%
20%
20%
0%
Attitude
re
su
ot
ib
ite
d
N
is
co
D
Pr
oh
ur
ag
e
d
ed
er
at
To
l
ed
ur
ag
En
co
eg
N
Ve
r
y
N
eg
at
at
ive
ive
l
tra
eu
N
iti
ve
Po
s
Ve
r
y
po
si
tiv
e
0%
Opinion
Figure 4.4. Attitude toward wind at the national (left) and state level (right).
93
As shown in Figure 4.5, a total of 77% of respondents thought that an offshore wind farm would
have no difference on if they went to the coast for recreation, 14% said less likely or much less
likely, while 10% said more likely or much more likely. Other responses to wind farm attitude
questions are reported in Appendix O.
Would the presence of a visible offshore wind farm make you
more or less likely to go to the coast for recreational purposes,
e.g., beach−going, boating, fishing, or walking along the coast?
80%
60%
40%
20%
lik
el
lik
el
y
y
e
uc
h
m
M
or
e
or
e
nc
re
N
o
di
ffe
ss
Le
M
M
uc
h
le
ss
lik
e
lik
el
ly
y
0%
Figure 4.5. Expected impact of an offshore wind on going to the coast for recreation.
4.3.6
Significant correlations between wind farm attitudes and environmental values
We calculated Pearson’s r correlation coefficients between indices comprised of the mean
responses to NEP, relational, instrumental and intrinsic value prompts and attitudes towards wind
farms using. See Appendix N for explanations of variables.
94
att_w_US
0.50
***
const_st
0.21
***
0.26
***
0.17
**
0.02
−
coast_rec
0.30
***
0.27
***
0.38
***
−
0.11
*
0.07
0.05
0.20
***
−
0.36
***
0.20
***
0.09
0.04
0.24
***
0.05
0.26
***
0.21
***
0.06
0.18
***
0.28
***
0.05
0.60
***
relational
0.26
***
0.13
*
0.06
0.22
***
0.19
***
0.02
0.45
***
0.71
***
metaphor
0.17
***
0.12
*
−
0.11
*
0.14
*
−
0.63
***
0.57
***
0.40
***
intrinsic
0.20
***
0.06
0.09
0.11
*
0.16
**
0.00
0.54
***
0.47
***
0.36
***
0.57
***
wf_rec
first_st
oper
NEP
instrumental
Figure 4.6 Correlation matrix of attitudes towards wind farms and environmental values.
Red denotes a negative correlation while blue is positive. P-value of < 0.0005 is "***", <0.005 is "**", <0.05
is "*".
As shown in Figure 4.6, the five types of environmental value indices positively correlate with
attitudes towards developing wind power in the US (p<0.0005) and attitudes towards supporting
a wind farm in your state if it was the first of many (p ranges from >0.05 to 0.0005). The
correlation is positive and significant between NEP, relational, metaphor and intrinsic value
indices and support for turbine construction along a respondent’s state’s coast (p ranges from
<0.0005 to <0.05). The five value indices positively correlate with each other (p <0.0005).
Additionally, the five value indices positively correlated with frequency of recreating on the
coast (p ranges from <0.005 to <0.005).
95
4.3.7
Environmental values influence wind farm attitudes at national and state level
We created three simple linear models for fixed effects to predict attitudes towards wind power
in the M-Turk population. Dependent variables of wind farm attitudes were predicted based on
indices of four types of environmental values (NEP, relational, metaphor, instrinsic and
instrumental) as well as demographic characteristics (gender, age, education level, and income).
Significant regression equations were found: 1) for wind power in the US (F(9, 390) = 9.771,
p<0.001), with an R2 of 0.165; 2) construction of a wind farm off a respondent’s states’ coast
(F(9, 372) = 3.040, p<0.001), with an R2 of 0.046; and 3) support of a wind farm in a
respondent’s state if it was the first of many (F(9, 390) = 5.357 p<0.001), with an R2 of 0.089.
See Table 4.7.
96
Table 4.7 Linear model results for fixed effects on attitudes towards wind power as anticipated by responses
to environmental value statements and demographic characteristics.
Dependent variable:
Wind power in the US | Wind farm off your state's coast | Support if first of many
(1)
(2)
(3)
NEP
0.286***
(0.060)
0.118**
(0.054)
0.194**
(0.085)
relational
0.051
(0.083)
0.186**
(0.074)
0.381***
(0.117)
metaphor
0.084*
(0.050)
-0.032
(0.046)
-0.053
(0.071)
intrinsic
-0.053
(0.049)
-0.007
(0.043)
-0.092
(0.069)
instrumental
0.057
(0.064)
-0.071
(0.056)
0.059
(0.090)
gender
0.194***
(0.066)
0.078
(0.057)
0.125
(0.093)
age
-0.005
(0.003)
-0.0001
(0.002)
-0.003
(0.004)
education
-0.025
(0.024)
-0.011
(0.022)
-0.054
(0.035)
income
0.030**
(0.013)
0.014
(0.011)
-0.010
(0.018)
Constant
2.727***
(0.318)
2.828***
(0.281)
1.950***
(0.449)
382
0.069
0.046
0.521 (df = 372)
3.040*** (df = 9; 372)
400
0.110
0.089
0.861 (df = 390)
5.357*** (df = 9; 390)
Observations
400
2
R
0.184
Adjusted R2
0.165
Residual Std. Error
0.610 (df = 390)
F Statistic
9.771*** (df = 9; 390)
Note:
*
p <0.1, **p<0.05***p<0.01
We found significant positive correlations between all of our environmental value indices and
attitudes towards wind power at a national level, offshore wind farms at a state level and support
for a wind farm if respondents knew it was the first of many (see Figure 4.6). The linear models
(see Table 4.7) suggest that different types of values play stronger and weaker roles in
influencing attitudes towards wind farms at different scales. NEP scores have a larger influence
97
on attitudes towards wind power at a national level than the other types of environmental value.
Both NEP scores and relational values influence attitudes towards wind at a state level.
Relational values appear to play a stronger role than NEP scores in influencing attitudes towards
supporting a wind farm if respondents are told it was the first of many (see Table 4.7).
4.4
Discussion
This research is a first step in seeking to operationalize a “relational values” construct in a survey
form in reference to other widely used constructs (intrinsic and instrumental) and a measure of
environmental concern (NEP). The following sections discuss the research questions in turn. The
first is associated with the relational concept in general, namely that diverse populations agree
with the statements, suggesting that what we refer to as a “relational framing” (in terms of the
phrasing rather than as an experimental design) is widely resonant. The following two sections
discuss how responses differed between the relational statements and the NEP, followed by how
there was consistency in responses to the relational statements, which could lead to treating this
set of statements as an index. Also, the correlations between wind farm attitudes and positive
relational and NEP responses, theoretical and policy implications of these findings and proposed
paths forward are discussed.
4.4.1
Diverse populations tend to agree with strong relational value statements
Agreement with relational values was higher than anticipated across populations. The mean
response for all three of the populations to the relational value statements was 4 (see Table 4.5,
Figure 4.2 and Figure 4.3), which is equivalent to “agree” on the Likert scale. The average for
each relational value prompt differentiated by population was higher than 3.6. We had expected
somewhat lower means given the explicit nature of the social-ecological linkage and our
98
deliberate attempt to phrase the prompts strongly to foster variation in our sample. The relational
prompts therefore push the bounds of how people think about the environment in relation to
themselves – such as thinking of wildlife as kin and considering the environment as part of their
identity. Although environmentalism may have become marginalized in the last decade (Marvier
and Wong, 2012, p. 292), these social-ecological relational statements clearly resonate with our
M-Turk, tourist and farmer samples (i.e., respondents tend to agree and strongly agree with the
value statements) (Figure 4.2).
The comparison between the relational value and metaphor statements is instructive, suggesting
that although social-ecological relations are lower in North American populations, associated
values remain strong in the populations we surveyed. M-Turk samples tend to be comprised of
~90% urban residents (Huff and Tingley, 2015). The farmers’ responses to the metaphor
statements were significantly higher than the M-Turk responses, and in the same range as their
relational responses. The M-Turk population responses to the metaphorical statements were
significantly lower than both the farmers and the M-Turk relational responses (Figure 4.3). We
speculate that the farmers are comfortable talking about nature in a deeply relational way, while
the M-Turk population is likely less comfortable with such ‘relationality’, but can still agree
with the moral conclusion expressed in the relational statements. We view this as further
indication that a relational framing may be an accessible way to engage diverse parties for the
purpose of conservation, including those who do not have an ecocentric worldview.
Relational value responses do not have the highest average among the types of value statements
in the three populations (Table 4.6). Out of the 17 statements presented to all three populations,
99
the overall highest ranked statements (in two of the three populations, tourist and M-Turk) was
the “clean” statement: “It is important to protect nature so we can have clean air and water.” We
classified “clean” as an instrumental statement (Table 4.1), but it is not narrowly self-oriented, in
that it implicitly includes concern for the well-being of others. The highest overall statement for
the farmers was “bau” (“If things continue on their present course, we will soon experience a
major ecological catastrophe,” i.e., business as usual). However since the farmers were so high in
their responses overall—their top 5 responses averaged over 4.7, meaning that the majority of
respondents answered 5— the differences between the top 5 are not significant (with the
exception of the fifth being different from the first and second rank based on t-test results—
Table 4.6), thus the top four could all be considered a top response.
It is not surprising that relational values were not noticeably higher in the farmer population as
compared to their NEP scores. We perceive the benefit of relational values is that it may allow
people to express environmental concern that they otherwise would not (on a scale like the NEP,
for example). For people with already high environmental values, it is not surprising they score
equally high in this alternative framing.
The top six overall mean scores of our three populations are depicted in Figure 4.3. For the
tourist population, four of the top six mean scores were relational statements. All three
populations included the “community” statement as the fifth highest. The M-Turk and farmer
population shared two of the top five (“community” and “other”). The community statement
refers to recognizing the uniqueness associated with place, where as “other” refers to
responsibility to reduce environmental harms felt by people elsewhere. All six relational
100
statements are represented in the top 6 value statements when all three populations are combined,
suggesting 1) there is resonance of relational statements in general, and 2) different aspects of
relational values resonate with different populations, that is, averaging across different
populations we see high levels of agreement with several relational statements.
4.4.2
Relational value responses are distinct from NEP
The factor analysis (FA) and PCA tests (Table 4.3, Figure 4.1, Table 4.4) reveal a distinction
between relational value responses and the NEP. Additionally, this analysis allows comparison
across statements and sets of question to determine the consistency with which individuals and
subpopulations responded to the survey, enabling underlying factors to emerge (Child, 1970).
The statements cluster in the factor analysis differently as individual populations (see Appendix
J) as compared to pooled results (Figure 4.1), but in all four cases the distinction between the two
sets is clear. Examining uniqueness of the relational statements as compared to the NEP, the
former has a higher proportional variation in the pooled data set (Figure 4.1), meaning the
relational statements are more tightly knit as a group than the NEP.
The PCA reveals two principle components, which consist of sets of variables that correlate with
each other. This can be seen in the trajectories of the vectors in the graphical results of the PCA
(Appendix L) and the weightings of PC1 and PC2 columns (Table 4.4). Both the PCA and factor
analysis are used for similar objectives but make different assumptions (see Methods section).
Both demonstrate that the relational statements fall into distinct components or factors, which
supports the hypothesis that the relational framings induce a different but coherent response
101
pattern. This response is also consistent, as evidenced by the high α across the relational
statements (Table 4.5).
4.4.3
Relational statements can be a single construct and have potential as new index
Our Cronbach’s alpha scores suggest, somewhat to our surprise, that the six relational values
statements cluster together strongly as an index. The six statements capture different aspects of
values about relationships with nature, and are not intended as multiple expressions of the same
idea, so it is interesting how strongly the statements do cluster. This result was echoed in the
tourist and M-Turk population, with α scores of 0.79 and 0.84 respectively, whereas the farmers
had a score of 0.35. The exception driving this unexpected result is the farmer response to the
crisis statement; the widely distributed spread of responses for this statement can be seen in
Figure 4.2.
Typically, the expectation is that those with a tendency toward an ecocentric worldview will
score low for this statement (until it is reversed for the purpose of analysis), and those with
anthropocentric worldview will score highly. The farmer results across all statements (see Figure
4.2 and Figure 4.3) demonstrate consistently high mean responses that are also statistically
higher than the other two populations as noted by the t-test results. This rural population of
predominantly small-holder Costa Rican farmers are reliant upon environmental conditions for
their livelihoods, and thus their strong environmental values (as understood through all of their
responses) are expected. This is reflected in their high scores, and in the case of the abuse
statement, statements where not a single farmer answered lower than a 4 (i.e. all respondents
answered agree or strongly agree). This brings in the question of why the farmers did not follow
102
the pattern of eco-centrism, which is associated with strong environmental values and evident
here.
We propose two possible explanations for the anomaly, but do not believe this is problematic for
our overall results. The first possibility is wording. The statement reads, “the environmental
crisis is greatly exaggerated,” with the expectation that those answering 4 or 5 (agree or strongly
agree) are not as concerned about the environment as 1 or 2 (strongly disagree or disagree). It is
conceivable in this region that those answering highly are deeply concerned about environmental
issues, but it is such a focal point that from their perspective it is overemphasized. That is, their
agreement with the statement speaks to the strong wording of “great exaggeration” rather than
suggesting environmental issues in their region are not present. An additional possibility is that
these farmers are better equipped to cope with change than their neighbours, thus reducing an
overall sense of urgency. All farmers who responded 4 or 5 to this question (about 30%)
responded in the expected NEP pattern matching an ecocentric worldview, so we do not believe
that our subset of farmers lack ecocentric views. In any case, this result did not impact the
analysis dramatically insofar as the NEP and relational factor analyses remained separate across
all populations and as demonstrated in Table 4.3 and Figure 4.1.
Farmer anomaly aside, the inclusion of NEP statements enabled us to demonstrate that for the
most part the statements correlated as expected, and our populations behaved consistently with
NEP experiments elsewhere. The high Cronbach’s alpha scores across the individual populations
and all three pooled means people responded consistently to the NEP and social-ecological
relational statements. In general, an alpha of 0.7 and higher is considered strong (Mohsen
103
Tavakol, 2011). Our high relational value alpha of 0.8 suggests there may be potential to
generate a scale or index when considered collectively as a group, and we consider the
development of such an index an avenue for future research.
4.4.4
Theory implications
As proposed in the introduction, we see potential to utilize relational values as a means to
solidify or enhance connections to the natural world, by invoking other held values that are not
necessarily environmental. That is, instead of thinking of nature as external or “outside of
oneself,” by connection to family, places we care about, and human well-being, ‘nature’
becomes part of an individual’s realm of care.
We refer to relational values as a framing rather than as a novel way of thinking about the
environment to recognize and emphasize that we are not suggesting this is entirely new
conceptual territory. Environmental values have been studied extensively, along with their
connections to attitudes and behaviours (Stern et al., 1995, Dietz et al., 2005, Spash et al., 2009).
Likewise, the attributes captured by our value statements were selected based on existing studies
and theory that suggest associations with family, community, and identity are powerful and
meaningful ideas that people will take action to protect and uphold (Martín-López et al., 2007;
Nichols, 2014). Our eventual aim is to examine whether this new value-frame can augment and
support existing theories of value that posit pathways between different categories of values (and
beliefs in the NEP sense of the word) and behaviour. This study is not sufficient to do so, but our
data does point to some encouraging possibilities for continuing along this path. Here we discuss
104
how we envision the relational framing to contribute to the values, beliefs and norms framework
(Dietz et al., 2005; Stern et al., 1999).
Values, beliefs and norms (VBN) theory of concern for the environment suggests that there are
relationships linking 1) the acceptance of basic values; 2) believing that something important is
threatened; and 3) the activation of a personal norm (obligation) to take action to restore those
values (Dietz et al., 2005; Stern et al., 1999). VBN posits that values influence our worldviews,
which in turn influence our beliefs of how environmental change has consequences for our
values, and these beliefs underlie norms from which we take action (Dietz et al., 2005). Figure
4.7 outlines the VBN theory in green, and highlights in purple how we imagine our selected
relational value dimensions contribute to this pathway. Our results are far too limited and
preliminary to support the hypothesis that social-ecological relational framing influences
behavioural intention (let alone behavior —even the VBN theory does not claim to
comprehensively explain pro-environmental behaviour), but we propose future studies to test
this.
105
Figure 4.7. Value-belief norm model (green) with our proposed ways in which relational framings (purple)
could influence steps of this pathway (black dashes).
We acknowledge the variety of barriers between behavioral intention and pro-environment behavior (dashed
blue line).
Figure 4.7 highlights where our relational value framings might support the theorized linkages to
the VBN. We propose that by leveraging some of the components of the model—namely
responsibility to others (both human and non-human) and personal norms—the pathway may be
strengthened or some of the other components may be bypassed. For example, a mother with
anthropocentric views and little understanding of consequences of a particular threat where she
lives (such as climate change influencing flooding), may still be induced to support a new coastal
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protected area in her community, if doing so is consistent with notions of good parenthood or
citizenship.
Reflecting upon our results in the context of this diagram, we note that the highest scores from
the relational statements were those that referred to groups in which they are a part or to which
they feel a sense of responsibility, including family and community. Psychological evidence
points to the importance of in-groups, social norms, and peer-pressure to influence behavior,
both in general and with pro-environmental behaviours specifically (Cialdini and Goldstein,
2004; Crompton and Kasser, 2010). While instrumental and intrinsic values tend to focus on
individual ways of thinking about the world, we propose relational framings have the capacity to
establish or enhance social influences that encourage action.
4.4.5
Policy and practical implications
Governments, NGOs, and decision-making bodies wrestle with how to effectively engage
communities in environmental decision-making processes (Reed, 2008). Regulatory bodies and
environmental impact assessment require consultation, yet assessments tend to focus on
biophysical impacts and have struggled to capture cultural ecosystem services, due to their less
tangible and less quantifiable nature. We propose there is a gap in the traditional tools that
explore and explain values on how we relate to the environment. Relational values may be used
to frame or facilitate discussions in decision-making processes linking environmental change to
tangible and intangible values. Here again we refer to framing in terms of a value construct,
rather than comparative framing used in experimental designs. Methods to assess socialecological relational value could be further refined to characterize how communities or
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individuals think about the environment. Invoking relational values may be key to reframing
conservation policy approaches (Berbés-Blázquez et al., 2016).
Framing conservation with relational values may offer more powerful leverage for conservation
than emphasis on instrumental or intrinsic values. Intrinsic values in and of themselves are
enough to motivate only a minority of people to achieve conservation goals (Armsworth et al.,
2007). A potentially broader array of people can be motivated by appeals to financial benefit and
self-interest in the name of conservation, but such appeals reinforce ‘extrinsic’ values—those
associated with the pursuit of prestige, power, image and status. Psychological research has
shown that reinforcement of extrinsic values can suppress intrinsic values, which are linked to
concern for others and the environment, kindness, understanding, appreciation, tolerance and
protection of people and nature (Blackmore et al., 2013). Furthermore, an instrumental-value
basis for conservation can only motivate conservation that is demonstrably useful (Chan et al.,
2007).
Relational value statements could be a part of how the International Union for Conservation of
Nature’s Key Biodiversity Areas partnership conducts biodiversity documentation, which would
include consistently collected information that assists policy advocacy on-site, as well as broader
analysis to prioritize areas for conservation. This partnership, as just one example of a potential
application of relational values, identifies important sites for various taxa, and is currently
consolidating a variety of partners to create a framework for assessment (threats, associated
ecosystem services, etc.) (Eken et al., 2004). These data could support prioritizing conservation
actions and policies that resonate with people locally. In a similar vein, diverse
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conceptualizations of values are incorporated in the conceptual framework of the International
Panel for Biodiversity and Ecosystem Services (IPBES). Relational value statements may help
operationalize these diverse conceptualizations in the planned regional assessments.
We anticipate the concern that employing community values or framing options could be used to
merely leverage instrumental values. Though we do not explicitly test that, our hypothesis relates
to encouraging environmental values in those who may not already feel strongly by anchoring
them to something they already care about and with which they already identify (e.g.,
community, family).
Our results linking environmental values to attitudes towards wind power and offshore wind
farms suggests that strong social-ecological relational value may influence support for a wind
farm at a state level and if it is a pioneering project, leading the way for many others to come.
Relational values do not have a statistically significant impact on attitudes towards wind power
at a national scale, suggesting that relational values may have more influence at a state level.
Our intention is not to find another avenue to “sell” the environment and its associated benefits
to a broader audience. As highlighted by Chan et al., “To be more than mere marketing,
environmental management must reflect on and possibly rethink conservation in the context of
local narratives and struggles over a good life” (p. 1464).
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4.4.6
Proposed paths forward
Our first pass at assessing social-ecological relational values resulted in a preliminary assessment
scale that can help launch future research. Our objective was not to create a new, universally
valid scale for social-ecological relational values. Although we capture diverse types of
relational values, we do not claim to have captured all aspects of “relationality.” We
acknowledge there may be different and/or additional statements that could enrich a socialecological relational index. We can imagine several research trajectories, as well as how other
future research may augment the ambitions of this preliminary study.
•
Expand and refine social-ecological relational statements. Our six relational
statements are likely not comprehensive. We can imagine further dimensions to be tested,
such as the extent to which natural elements contribute to a sense of belonging. Index
development in the psychological literature entails including more overlap between
statements to probe similar themes in multiple ways and test agreement with various
statements in different cultural settings (if universality–to the degree it is possible–is the
goal). The list should be refined list until there is greater certainty of its appropriateness
and accuracy for assessing the presence and strength of social-ecological relational
values.
•
Explore social-ecological relational values with other methods. Surveys can be useful,
but other methods, such as interviews and focus groups, can help delve into the
complexity and context-specific dimensions of social-ecological relational values.
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•
Use social-ecological relational value statements as an index in before/after or
control/impact studies. Such research would shed light on values in the context of
various environmental management and conservation interventions.
•
Embed social-ecological relational values research in scenarios with real-world
constraints. We envision empirical testing of relational values in the context of tradeoffs
and/or external constraints, including scenarios or choices to more accurately reflect the
types of decisions people make on a daily basis. One particular set of people whose
behaviours are of particular interest includes consumer responses to relational framings,
and testing consumption behavior when the disconnect between consumption practices
and environmental impact are made more explicit.
•
Further test relational value statements in comparative framing experimental
designs to estimate influence of relational values on renewable energy development
and energy conservation. Our exploratory analysis suggests that relational values may
influence attitudes towards wind farms at a state level. Future research could focus on
local levels and if relational value frames could influence support or rejection of sites for
renewable energy development. We also suggest research on the extent to which
relational value considerations could increase motivation for energy conservation if direct
connections are made between energy consumption and ecological consequences.
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4.5
Conclusion
The study provides preliminary empirical evidence of widespread support for social-ecological
relational values, an emergent topic in conservation (Berbés-Blázquez et al., 2016; Chan et al.,
2016). We foresee diverse paths forward to test this idea of relational values as a means of
overcoming the instrumental vs. intrinsic value of nature debate.
Self-interest tends to prevail when instrumental values dominate communications, campaigns
and debates (Blackmore et al., 2013). Instrumental values, however, are one type of the various
values that can come into play when we make decisions. Insights from cognitive psychology
highlight how we often make decisions and act based on affective responses to situations rather
than mental calculations of utility associated with different outcomes (Kahneman, 2011; Levine
et al., 2015). Similarly, while we acknowledge the logic behind instrumental justifications for
biodiversity conservation, studies show numerous other values, beliefs and attitudes motivate
conservation action, including, but not limited to, identity and social norms, biophilia, altruism
and notions of reciprocity. Leveraging these motivators in relational terms might engage more
people and enable individuals and communities to rethink conservation in the context of local
narratives and what it means to pursue a good life, which goes far beyond focusing on
instrumental values (Chan et al., 2016).
This study suggests a relational value framing as a new direction for innovation when it comes to
ecosystem service assessments, designing conservation initiatives and potentially building
support for renewable energy. This could not only inform, but also inspire the action necessary to
cultivate a future better for humans and other species.
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Chapter 5: Will communities “open-up” to offshore wind? Lessons learned
from New England islands
Sarah C. Klain, Terre Satterfield, Suzanne MacDonald, Nicholas Battista, Kai M.A. Chan
Preface
This chapter was the result of Sarah Klain’s participation in the UBC Public Scholars Initiative,
an innovative program to support collaborative scholarship that contributes overtly to the public
good. Sarah Klain collaborated with the non-profit organization Island Institute. Together, they
devised a transdisciplinary research agenda to understand successes and shortcomings across a
set of community engagement efforts pertaining to proposed offshore wind farms. A major
project goal was to better link academic research with civic practice and decision-making.
5.1
Introduction
The scientific consensus regarding the urgency of climate change mitigation has coalesced
(IPCC, 2014) while ideological and economic debates about appropriate actions and energy
policies have become increasingly polarized (Campbell and Kay, 2014; Dunlap and McCright,
2008; Kahan et al., 2012; McCright and Dunlap, 2011). Achieving the IPCC’s goal of 1.5°C or
less of warming entails a transformation of various modes of production and consumption,
including massive changes in our energy infrastructure (Johansson et al., 2016). Transitioning to
low carbon sources of electricity largely depends on the extent to which people act at various
scales to obstruct (e.g., file lawsuits), accommodate or champion low-carbon energy technology.
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Switching to greater reliance on renewable energy can diversify sources of energy, reduce carbon
emissions, reduce air pollution and meet growing demands for electricity (Jacobson and
Delucchi, 2011). Accordingly, renewable energy infrastructure is becoming increasingly
common in and near where people live. In 2015, the U.S. committed to increasing nonhydroelectric renewable energy generation to 20% of the U.S. total by 2030. This includes a
projected 22,000 MW of offshore wind, which could power 4.5 million homes (DOE EIA, 2015;
OPS, 2015).
Siting offshore wind farms and other renewable energy infrastructure has often been
controversial, resulting in project delays and cancelations (Kimmell and Stalenhoef, 2011;
Roberts et al., 2013). Bell et al. (2005) identified a ‘social gap’ when it comes to understanding
why national opinion polls reveal high levels of public support for the development of renewable
energy while specific applications for its development have low success rates. Proposed
explanations for this ‘social gap’ include the following: 1) self-interested NIMBY-ism (not in my
backyard), defined as “an attitude motivated by concern for the ‘common good’ and behaviour
motivated by ‘self-interest’” (Bell et al., 2005, p. 460); 2) democratic deficit in that a small,
unrepresentative number of opponents dominate the decision processes; 3) qualified support in
that national surveys may report high levels of public support, but this support may in reality be
based on certain conditions being met (e.g., related to noise, size, number of turbines,
environmental protection, community engagement, fairness of decision process, and fair
allocation of economic benefits); and 4) place protectors, who perceive higher place value in a
specific location without the renewable energy development (e.g., rejecting a development due to
its impact on local biodiversity or the historic qualities of a particular landscape), but may accept
114
the development in another location (Bell et al., 2013). If renewable energy targets are to be
achieved, this “social gap” must be bridged to mitigate, accommodate or otherwise work through
concerns and hostility of local communities to particular renewable energy projects (Bell et al.,
2005; Haggett, 2011).
Social science can elucidate why and how renewable energy controversies might be ameliorated
via robust public engagement strategies, including those that seek to clarify both concerns and
possible outcomes or alternatives. Public participation in decision-making has the potential to
enhance the quality of decision outcomes while improving the capacity of those involved to
meaningfully engage in policy processes (Dietz and Stern, 2008). Scholars of risk, technology
and the social dimensions of renewable energy recommend shifting governance away from
reliance on primarily technocratic evaluations of risks and benefits. Instead, scholars have called
for methods that ‘open-up’ the capacity for people with diverse perspectives to participate in
analytic deliberative processes to determine what constitutes appropriate development of a
technology (Devine-Wright et al., 2011; Stirling, 2008). Analytic-deliberative methods are
approaches to public engagement in decision-making that involve assessment and dialogue to
reconcile technical as well as expert knowledge with citizen values (Burgess et al., 2007). Such
methods can result in increased trust among those involved and acceptability of outcomes (Renn,
2008; 1999). “Opening up” decision-making processes entails recognition and accounting for the
numerous factors driving the development and deployment of technology, including “individual
creativity, collective ingenuity, economic priorities, cultural values, institutional interests,
stakeholder negotiation, and the exercise of power” (Stirling, 2008, p. 263). And yet, when done
poorly (i.e., closing down decision making), deliberative processes can ‘close’ down both
115
discussion of new technologies and so too the possibility of innovations, such as the
development of offshore wind farms in North America.
Although numerous articles have been published on public opinion of offshore wind (Firestone
et al., 2012; 2009), few academic studies have focused on identifying and characterizing both the
successes and challenges of community engagement practices involving this technology in North
America, and how this relates to theory about analytic deliberative processes. Addressing this
gap is an opportunity for social science research to inform the development of this industry and
siting developments in general.
We conducted research on the experiences of three New England islands to explore both the use
of deliberative designs and logics of acceptability or unacceptability of offshore wind farms. Our
goal was to parse how public engagement has occurred and the types of engagement practices
that built or eroded support for wind farms. We used normative theory on key components of
analytic-deliberative processes to explain characteristics of community engagement that worked
well versus those that resulted in relatively higher levels of frustration among various parties.
Our research identifies similarities, differences and gaps between this normative theory and our
three island community contexts to identify characteristics of community engagement that may
minimize frustration and increase satisfaction with decision processes and outcomes among local
stakeholders.
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5.1.1
Theorizing public engagement processes
A normative theory of public participation in decision-making has sought to conceptualize and
identify principles for reaching legitimate outcomes (Figure 5.1) (Abelson et al., 2003; Renn,
1992). Concepts of ideal speech situations and communicative competence are central to this
theory. An ideal speech situation involves the aspirational goal of reaching a rational consensus
wherein communication follows implied rules, no coercive or non-rational pressures exist and
assertions made are based on reason and evidence only (Habermas, 2004; Renn, 2008).
Communicative competence is “the ability to use language…to create understanding and
agreement… This requires people enter into a discourse [i.e., discussion or deliberation
exercises] with an attitude oriented toward reaching understanding. People must be committed to
reflecting on their personal beliefs, values, preferences, and interests, they must be open to
alternative definitions of reality, and they must listen to other people’s arguments with an open
mind” (Webler, 1995, p. 44). Competence also means that the people involved in the deliberation
are able to assimilate information to reach an adequate understanding of the issue and
appropriate procedures are in place to choose the relevant knowledge to inform the process.
Principles of fairness are linked to competence to the extent that legitimate outcomes depend not
just on competence, but fairness as concerns equality of inclusion in the decision process,
procedural fairness throughout the deliberation, and mutual respect among all involved. Lastly,
fairness is transgressed when 1) the role of power is ignored or is not neutralized; and/or 2) when
political institutions make the deliberative process an end-creating activity, rather than the means
for generating an outcome. These obstacles can block the achievement of legitimate outcomes
(Figure 5.1).
117
Equality of access
Norma)ve theory of
public par)cipa)on in
decision-making
Revision of Habermas’ ideal
speech and communica%ve
competence
Fairness
Procedural fairness
Mutual respect
Relevant knowledge and
understanding of issue via
informa%on access and
interpreta%on
Competence
Appropriate procedures
used to select
knowledge used to
inform process
Ignores or
neutralizes role
of power
Public
par%cipa%on
can be purposeor end-crea%ng
ac%vi%es
Legi%mate
Outcomes
Figure 5.1. Normative theory of public participation in decision-making, adapted from Abelson et al. (2003).
The meta-principles of fairness and competence are necessary (Habermas, 2004; Renn, 1992) but arguably
not sufficient to reach legitimate outcomes (Ryfe, 2005). Neglecting the role of power and participation as an
end unto itself rather than a means to an outcome can be barriers to reaching legitimate outcomes (Abelson et
al., 2003).
Abelson et al. (2003) expand and operationalize this normative theory into pragmatic principles
for evaluating public participation in decision-making with more explicit recognition of the role
of power in deliberative processes (e.g., the availability and use of particular information can be
a source of power). This highly cited review, with over 720 citations on Google scholar as of
2016, documents how no simple formula exists for designing an optimal public engagement
process, but four key topics require attention: 1) representation; 2) procedural rules; 3)
information employed in the process and 4) the outcomes including decisions resulting from the
process. Representation refers to determining who fairly represents the “public” in a decisionprocess. This can be challenging because fair and legitimate processes that provide meaningful
opportunities for learning and recognition of diverse perspectives tend to be time-intensive and
118
relatively exclusive processes that can only involve a small number of people. Further
complicating fair representation is that citizens are more likely to get involved if they fear losing
something they value (Abelson et al., 2003). Situations can arise when a majority of people
support or feel neutral towards a proposal, but they are a “silent majority” because they choose
not to get involved with the decision process (Stephenson and Lawson, 2013). Abelson et al.
(2003) documents how procedural rules can help manage this potential self-selection of who gets
involved. They also identify the importance of being upfront and transparent about the timing
and extent of public engagement as well as responsiveness on the part of an authority who
compiles and responds to public input. Providing ample time for those involved to challenge the
information presented in the process is important, as is maintaining mutual respect throughout
the deliberation. Choices about information are crucial, specifically what information is selected
then how it is presented and interpreted. Finally, not just the process leading to the decision, but
also the outcome (the decision) needs to be associated with legitimacy and accountability
(Abelson et al., 2003).
Abelson et al. (2003) identified these key components of public participation in analytic
deliberative processes based on experiences in the health sector. Numerous other studies uphold
them in the design of deliberative processes related to sustainability issues (Antunes et al., 2009;
Blackstock et al., 2007; Burgess and Chilvers, 2006; Demski et al., 2015; Gregory et al., 2012;
Pidgeon et al., 2014; Webler et al., 2014), though some emphasize a smaller set of these
theoretical principles. For example, Demski et al. (2015) conducted an analytic-deliberative
workshop to better understand public values when it comes to system-wide energy transitions
with explicit attention paid to representation, procedural rules and information used in the
119
process. We identify and characterize components of three decision processes associated with
offshore wind project proposals, then relate our findings from our qualitative analysis to the
evaluation components from Abelson et al. (2003).
Our investigation of community engagement processes that worked well and those that could be
improved focuses on three New England islands at the forefront of offshore wind debates due to
their locations near proposed wind farm sites as well as economic and cultural connections to
adjacent ocean spaces (e.g., reliance on fishing, sense of place reinforced by aesthetic views).
Due to their proximity to the first offshore wind projects in North America, New England island
residents are likely to be among the first positively and/or negatively impacted by this
technology.
Three questions drove this work and were also relevant to our community partner, the non-profit
Island Institute. Given the public engagement already occurring in New England on developing
offshore wind: 1) What worked well regarding the process of community engagement and its
outcomes near proposed offshore wind farms near three New England islands? 2) What were the
major challenges with community engagement in these contexts? 3) What insights on community
engagement likely apply elsewhere as renewable energy infrastructure proposals become more
common? How this industry and other low carbon energy technologies unfold has implications
for the rate at which carbon emissions from electricity production are reduced and the timing and
extent to which we address climate change.
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5.2
Methods
Our three pragmatic research questions informed how we collected qualitative data from
interviews and relevant documents (e.g., meeting minutes, newspapers, magazines and online
news articles), iteratively reviewed and coded the data, compared and contrasted the experiences
on three islands, identified common themes, and then related these themes to a theoretical
framework, specifically Abelson et al.’s (2013) key components of public participation in
deliberation. We identified ways in which our findings resonate with and differ from these
components in the analytic-deliberative literature.
5.2.1
Context of study: collaboration with community-based organization
Our project was based on a collaboration between academic social scientists and staff of a nonprofit community development organization, Island Institute. This organization has advocated for
meaningful public engagement during decision-making processes, including those involving
island communities and offshore wind. Using various media, business and community-based
strategies, Island Institute has engaged local stakeholders, developers, scientists, engineers, state
and federal agency decision-makers and others to learn from each other and consider the tradeoffs involved in various development proposals. The Community Energy program staff at Island
Institute has worked with New England coastal and island communities on energy issues since
2008. Our aim with this project was to co-produce knowledge relevant to the communities with
which Island Institute works and academic audiences.
We selected three islands based on Island Institute’s long-term engagement with community
members, government authorities and wind farm developers involved in the consideration of
121
offshore wind near these particular islands. The proposed wind farms near Block Island,
Martha’s Vineyard and Monhegan Island (see Figure 5.2) are at different stages of project
development. The company Deepwater Wind began constructing the Block Island Wind Farm in
the summer of 2015. The Vineyard Power Cooperative officially partnered with Offshore MW, a
European wind farm company, in January of 2015. Together, they obtained a lease from the
Bureau of Ocean Energy Management (BOEM) to develop their project in federal waters 12
miles south of Martha’s Vineyard. The University of Maine was not successful in its 2014 bid
for funding from the U.S. Department of Energy (DOE) to develop a deep-water floating
offshore wind test site near Monhegan Island, but they did secure a smaller grant to continue
refining the design of their turbines and they may yet receive a larger DOE grant ($40 million) to
deploy and study a full scale prototype (Turkel, 2016).
122
Maine
Vermont
Monhegan
Island, Maine
New Hampshire
Martha’s Vineyard,
Massachuse<s
Massachuse<s
Block Island,
Rhode Island
Connec8cut
Wind resource
poten8al
Poor
Fair
Good
Excellent
Outstanding
Figure 5.2. Map of focal islands .
Wind data and categorization from NREL (2015).
5.2.2
Data collection and analysis
Island Institute staff conducted unstructured, key informant interviews to collect impressions,
opinions and experiences of people closely involved with community engagement in our study
sites. These included interviews with town council members, community leaders, government
agency employees, leaders of an electricity cooperative and wind farm developers.
We conducted participant observation in that one academic co-author (SK) was hosted by the
non-profit organization for 2.5 months to develop a collaborative relationship with Island
123
Institute staff and collect data via informal interviews with them as well as analysis of Island
Institute documents and online materials. We also made site visits to the study islands.
The document analysis involved compiling relevant newspaper articles, reports, meeting minutes
and information from websites pertinent to offshore wind and community engagement initiatives.
These initiatives were sorted into two categories, namely those that worked well, which research
participants associated with legitimacy and positive affect, and those that did not work well,
which were associated with expressions of frustration or other negative affect. The academic
researcher coded the interview notes and other documents based on qualities associated with
stakeholder satisfaction or lack there of, discussed initial themes with Island Institute partners
and refined the themes based on their discussions. Finally, these themes characterizing
engagement processes that worked well and those that did not work well were compared and
contrasted with analytic-deliberative literature on key components of public participation in
deliberation.
5.3
Results and discussion
Participants tended to be more satisfied with engagement processes that involved bi-directional
and accessible deliberative learning and the provision of custom-tailored community benefits.
Block Island and Martha’s Vineyard had largely successful community engagement processes
resulting in sufficient community buy-in, which contributed to the projects proceeding.
Monhegan Island was challenged with a compressed timeline and other initial challenges in
building community support. Our interviews, document analysis led us to identify two
overarching themes associated with perceptions of legitimate outcomes: accessible, deliberative
124
learning opportunities and community benefits. We then suggested ways to adapt and augment
key components of public participation in deliberation to siting renewable energy infrastructure
to better incorporate community benefits.
5.3.1
Focal island communities and wind farm engagement experiences
Our island communities differ from those connected by bridges or on the mainland largely based
of their relative isolation. We summarize basic characteristics of our three island communities in
Table 1.
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Table 5.1. Key differences between New England island study sites and mainland communities relevant to
engagement on energy issues.
The population and economy characteristics apply to many small towns while energy costs on islands tend to
be higher than on the mainland.
Characteristic Description
Consequences
Year-round
Population
Few technical experts
Local leadership positions are often part time or
volunteer positions
Small compared to adjacent
mainland communities
• Block Island: 1,051
• Martha’s Vineyard:
16,535
• Monhegan: 69
(U.S. Census, 2010)
Strong dependence on fishing
and tourism
Highly seasonal
Economy
Energy Costs
Can be higher than mainland,
e.g., residential electric rates
on Monhegan Island are
~$0.70 per kWh and ~$0.15 on
the mainland
Relatively vulnerable due to low economic
diversification
Year-round residents are likely more available to
participate in engagement efforts during low season
while seasonal residents and visitors are more likely
to engage during the summer
Strong interest in alternatives that could reduce
energy costs, particularly on islands without a grid
connection
Richer descriptions of the context for each island’s engagement with offshore wind, including
direct quotes from interviewees, are in Appendix Q. Below, we provide a brief overview of
engagement processes relevant to the islands we studied.
5.3.1.1
Block Island: the ocean state’s offshore wind farm pioneers
Construction began on the first offshore wind farm in North America in the summer of 2015—a
30-MW, five-turbine wind farm three miles off the coast of Block Island. A formal state-level
marine spatial planning process resulted in the Rhode Island Ocean Special Area Management
Plan, referred to as SAMP (Nutters and Pinto da Silva, 2012). The SAMP was created and
126
disseminated before the wind farm was proposed. This meant that information about state waters
was already readily available and accessible and had been discussed with key stakeholders
(Nutters and Pinto da Silva, 2012), including the town council of New Shoreham on Block
Island, which actively followed and contributed to the SAMP process.
The developer and the town council discussed the town’s need for additional technical capacity
to make the proposed project more accessible and understandable to residents. The town selected
and hired consultants to represent their interests and the developer, Deepwater Wind agreed to
reimburse the town for the expense of these consultants (Island Institute, 2012a). Also,
Deepwater Wind hired a liaison who had grown up on the island and was well respected by the
local community to facilitate community involvement and hold informational meetings.
Questions about perceived objectivity (or lack thereof) did not arise in relation to these hires
during our analysis. These consultants served the function of a bridging organization between the
developers and the island community members. The consultants translated pertinent technical
details and locally relevant information to the town council. They shared information with the
broader community, fielded questions at community meetings, listened to community concerns
and translated these concerns into comments during the formal regulatory processes. The
expertise of the consultants provided the town council with greater confidence that community
concerns would be better integrated into the wind farm planning processes.
Local stakeholders, government officials and Island Institute staff were convinced that locallyrelevant community benefits played an important role in the success of this project. For example,
the Block Island wind farm development was done in conjunction with connecting the island to
127
the mainland electricity grid for the first time. The town negotiated to have fiber optic strands
included in the underwater electricity cable bundle that now connects Block Island to the
mainland grid. Residents and business owners report benefiting from his high speed internet.
Deepwater Wind and New Shoreham have also developed a formal Community Benefit
Agreement (CBA) in which the wind farm company will pay for improvements to town
infrastructure where the cable comes ashore. Further, the project is expected to generate 300 jobs
during the construction phase, including opportunities for local mariners and fishermen (Smith et
al., 2015).
Block island no longer needs to transport and burn approximately one million gallons of diesel
fuel per year to power the island’s generators (Economist, 2015). The island will rely primarily
on electricity generated from the wind farm, they will sell excess electricity on particularly
windy days and draw from the mainland utility when the wind farm is not operating. The
existing diesel system will remain on the island in case of cable failure. There has been some
discussion that this system be used occasionally if requested by mainland utilities, in which case
they would export some power back onto the cable during heavy load conditions.
5.3.1.2
Martha’s Vineyard: moving forward with a cooperative approach
Vineyard Power grew out of Martha’s Vineyard’s Island Plan, a sustainability strategy that the
Martha’s Vineyard Commission completed based on input from thousands of island residents in
2009 to “create the future we want rather than settle for the future we get” (MVC, 2009, p. 1).
This plan included a recommendation to create a community-owned renewable energy
cooperative so islanders could have more autonomy over their energy production and better
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ensure community benefits associated with renewable energy development. In 2009, Vineyard
Power began recruiting members. People joined for social reasons (e.g., inclusion in the decision
making processes in an island-owned, action-oriented group to create a more sustainable energy
future for their community) and financial rewards (e.g., ownership and control of local renewable
energy projects and stabilized electricity prices once a large-scale renewable energy project is
developed) (Nevin, 2010). The cooperative’s community benefits are embedded in the
cooperative’s mission: “to produce electricity from local, renewable resources while advocating
for and keeping the benefits within our island community” (VPC, 2015).
The cooperative has played an active role in engaging community members in the wind farm
decision process. They hosted an interactive offshore wind map viewer on its website to not only
inform but also solicit preferences from coop members and other engaged island residents to find
a suitable location for the wind farm. This website provided readily available and appropriate
information while encouraging participation in sharing local values related to proposed locations.
The website provided information about visual, ecological and human use impacts based on
various proposed sites, including data collected from local sources such as island fishermen. The
cooperative also hosted a series of community meetings to share wind farm visualizations and
solicit feedback (Peckar, 2015a).
In January 2015, BOEM auctioned the rights to lease offshore wind in areas in federal waters
south of Martha’s Vineyard. The wind farm developer, Offshore MW, received a 10% discount
on their bid price because they had executed a Community Benefit Agreement (CBA) with
Vineyard Power. The CBA outlined opportunities to investigate local benefits to the island
129
including job creation, an operations and maintenance facility, and local equity ownership in the
project (VPCOMW, 2015). The size of the wind farm has not yet been confirmed.
5.3.1.3
Monhegan: confronting deep water and community challenges
The tumultuous path of offshore wind in Maine provides insights regarding mutual learning,
timing and accessibility of information. In 2009, Maine set ambitious goals to become a national
leader in ocean energy (MCP, 2009) and created opportunities for the development of marine
renewable energy demonstration projects (MPUC, 2010). Discussions of offshore wind had
implications for the island of Monhegan, a remote community 12 miles out to sea with some of
the highest energy costs in the nation (MPUC, 2015). In state waters, Maine took initial steps to
engage stakeholders in its strategy to expedite the development of the industry by designating
three research and demonstration test sites within state waters. State government staff and
collaborators hosted a series of public meetings and small and informal discussions along the
Maine coast. They incorporated scientific data and local knowledge into their assessment process
by making mutual learning accessible, e.g., traveling to Monhegan where they asked fishermen
to rank their fishing activity effort around the island in order to identify a site of least impact for
wind turbines.
Efforts to site offshore wind in nearby federal waters underscored the importance of timing and
availability of information. The Maine Public Utilities Commission (PUC) began a 16-month
process during which they solicited and reviewed bids for and public comments on a long-term
power purchase agreement. This extended period of time provided an opportunity to engage
stakeholders prior to the announcement of a developer and the location of a site. During this
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time, the Island Institute worked as a bridging organization to facilitate mutual learning through
the Offshore Wind Energy Information Exchange, an outreach and education initiative to inform
and engage coastal and marine stakeholders, developers, and decision-makers on the potential for
offshore wind energy development in the Gulf of Maine. The initiative included deliberative
learning experiences, such as exchange trips to fishing communities as well as a wind farm, the
human use mapping project Mapping Working Waters (Island Institute, 2009), information
sessions at the annual Fishermen’s Forum in Maine (Island Institute, 2009), and readily available
and understandable fact sheets (Island Institute, 2012a). These efforts provided coastal
stakeholders and industry representatives with a baseline understanding of community priorities
as well as the offshore wind industry, while creating an opportunity for stakeholders to meet each
other informally and build relationships.
Maine PUC later announced its selection of an unsolicited proposal from Statoil – a
multinational corporation specializing in offshore energy infrastructure – for testing floating
turbine technology in federal waters in the state’s Midcoast region. By this time, marine users
and other stakeholders in the area had already participated in education and information
exchange opportunities, preparing them to more proactively and constructively engage in
discussions with the developer and decision-makers (Island Institute, 2015).
The University of Maine entered a federal funding competition with a new scope of activities at
the Monhegan test site. Subsequently, the Maine Legislature directed the PUC to reopen the
bidding process so that the University of Maine could submit a proposal on an accelerated
timeline, and Statoil withdrew its proposal for a project in federal waters. While these
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developments had statewide implications, this impacted Monhegan by significantly limiting the
timeframe in which the community could learn about the change in scope from small-scale
portable to large-scale, semi-permanent turbines – a 12 MW pilot project. The PUC opportunity,
which prompted many islanders to learn of the change in project scale, was announced during the
summer—the island’s busiest time of year.
The accelerated timeline and need for information initially strained relations between the island
community and Maine Aqua Ventus (MAV), the University-led consortium developing the
larger project, but both parties quickly committed to improve communications. They clarified
points of contact and expectations for communications so that MAV could be certain that project
updates were being shared widely. Island leaders created the Monhegan Energy Task Force
(METF) to prioritize information that the community needed and facilitate discussion of
community benefits associated with the proposed offshore wind project. METF and MAV
engaged in weekly phone calls to enhance the flow of information and worked to develop an
expectations document to ensure timely project communications. During this time, both parties
looked to Block Island for examples of how information was shared and community benefits
arranged. MAV also began to host semi-regular open house sessions on the island during which
residents and visitors could have more extended discussions about aspects of the project. In late
2015, MAV received additional federal funding ($3.7 mill) to continue refining their floating
turbine designs (Turkel, 2015). Based on our interviews, some residents still have concerns about
the Monhegan offshore wind project but the developer and community have laid a more solid
foundation upon which future communication can take place.
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5.3.2
Bi-directional deliberative learning and community benefits as key to good
engagement
Our qualitative analysis suggests that much of the myriad considerations for good analytic
deliberative processes and outcomes boil down to two key, integrative themes: ensure
bidirectional deliberative learning and custom-tailored community benefits. These two
overarching themes emerged from our iterative coding process (in which interview notes,
attitudes and opinions of various parties involved, engagement materials, meeting minutes and
newspaper articles were reviewed, categorized and discussed). Table 5.2 characterizes the two
overarching themes of bi-directional learning and community benefits. We discerned four
dimensions within the bi-directional learning theme: readily available and appropriate
information, trusted messenger, collaboration with bridging organizations and timing of
engagement. Reading vertically from the left hand side of the table, it is evident from Table 5.2
that these common themes and associated dimensions played out in various ways across sites.
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Table 5.2. Summary of good practices and challenges related to community engagement.
For more detail on engagement in three proposed offshore wind farm sites, see site descriptions and
Appendix Q.
Sites
Block Island, RI
Readily
available &
appropriate
information
- Town hired consultants to listen,
translate and represent community
interests
- Developer reimbursed town for
consultants
- Developer prioritized outreach to
community (Island Institute, 2012a)
- Developer hired local liaison to lead
outreach
Bi-directional Deliverative Learning
Trusted
messenger
Collaboration
- Consultants helped to bridge town and
with bridging
developer
organization
- Project preceded by RI Ocean Special
Area Management Plan (SAMP) process,
which was funded and supported by
federal, state and private entities (Nutters
and Pinto da Silva, 2012)
Timing of
engagement:
Iterative and
multi-year
Martha’s Vineyard, MA
Monhegan Island, ME
- Vineyard Power Cooperative hosted
interactive offshore wind map viewer to
inform participants about
environmental, human use and visual
impacts
- Island Institute developed peer-reviewed
fact sheets to address the questions raised
during community meetings (Island
Institute, 2012b)
- Cooperative founders and members
are island residents
- Partnership between local cooperative
and developer provides a bridge to the
community
- Leaders in Monhegan Energy Task Force
assumed role of messengers
- Island Institute served as bridging
organization between developer and
communities
- Process to create Martha’s Vineyard
Island Plan and energy coop entailed
substantial learning and sharing of
information and values
- Information Exchange site visits enabled
diverse stakeholders to meet repeatedly
and exchange information and
experiences
- Engagement with fishing industry
continued after SAMP completed
- Coop used online wind map viewer to
- Community meetings from 2009-2012 to solicit resident preferences for farm
create and adopt comprehensive energy location
plan for Block Island (IEC, 2012)
- Mapping Working Waters project
engaged fishermen to share local
knowledge and provided opportunity for
them to learn about wind farms (Island
Institute, 2009)
- Formal community engagement from
- SAMP process made information about
2006 to 2010 to create comprehensive,
state waters readily available before OSW
proactive Island Plan on various
farm was considered (Nutters and Pinto da
sustainability issues
Silva, 2012')
- University of Maine collected information
on turbines’ proximity to fishing areas,
created and shared visualizations, and
conducted tourism impacts study
- Timing of engagement around state
- Having participated in SAMP process,
waters test site activities created
- Recruited energy coop members over
offshore wind was not a new topic to local
challenges from which the community
multiple years starting in 2009
leaders when project was proposed
organized Monhegan Energy Task Force
emerged
Provision of Community Benefits
- Presentations about OSW in both winter
and summer to reach year-round and
seasonal residents
Deliberation to
determine
community
benefits with
flexible models
- Provides mainland grid connection
- Reduction in electricity rates
- Embedded in Vineyard Power
Cooperative’s mission and
organizational structure
- Island fishermen were hired to assist with
environmental monitoring and site
assessment
- Ends need to import 1 mill gallons of
diesel annually (Economist, 2015)
Fiber optic strands in cable bundle
provided to increase internet speed
-- Coop members steer siting decision
(VPCOMW, 2015)
- Preliminary discussions have included
possibility of mainland grid connection,
reduced electricity rates, improved
broadband internet
for custom
- On-island infrastructure improvements
- Community Benefit Agreement
tailored benefits - Local jobs provided: mariners and
enabled developer to get discount on
fishermen hired to provide security during
lease of ocean space
construction
- More engagement is needed to more
precisely identfy locally appropriate
community benefits
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5.3.2.1
Defining bi-directional deliberative learning
Based on our interviews and document analysis, bi-directional deliberative learning opportunities
improved stakeholder engagement in offshore wind project consideration and site development.
We use the term bi-directional in reference to mutual learning among developers, government
authorities and local community members. Deliberative learning is the exchange of both
knowledge and values in a group setting, which is important for developing trust, mutual respect
and reaching more satisfying outcomes among those engaged in decision-making processes
(Gregory et al., 2012). Numerous interviewees emphasized the importance of the developers
learning about local knowledge, values and priorities. Staff at boundary organizations involved
commented on the need to build a shared vocabulary (e.g., megawatt, microgrid) when
considering future energy scenarios on each island.
Island Institute staff explained their motivation for their Working Waters participatory mapping
exercise as collating different types of knowledge with the goal of sharing facts and values to
help address an often unequal power dynamic between project proponents “from away” and local
communities. Based on our analysis and relevant literature, wind farm proponents tend to benefit
from community engagement strategies in which they learn from the relevant experiences and
knowledge of people who could be directly impacted if the proposed development moves
forward (see J. Field, 2014).
From our qualitative analysis, we characterized four linked components that we categorize under
bi-directional deliberative learning: readily available and accessible information, employment of
a trusted messenger/communicator, collaboration with bridging organizations and timing as
135
related to iterative learning opportunities over multi-year time frames. These topics arose in
numerous interviews and documents as being crucial to the quality of community engagement.
5.3.2.1.1
Readily available and accessible information
Island Institute staff and local government officials in our study sites—echoing the academic
literature (Bell et al., 2005)—emphasized how people in adjacent communities needed easy
access to information in order to have informed opinions about the proposed wind farms. On the
three islands we studied, this information included background on wind farm technology,
specifics of a proposed project and how this development could impact individuals and their
communities. Island Institute staff and government authorities recognized how skill is needed to
translate technical scientific and engineering facts into language that helped lay people learn
without being alienated. They stressed the importance of using language accessible to public
audiences (e.g., translate megawatts generated into how many average households’ electricity
needs will be met in a year, explain what a cable to the mainland means for island residents,
explain a power offtake agreement). Island Institute responded to community concerns about a
lack of accessible information by creating wind farm fact sheets available in paper form and
online (Island Institute, 2012a). Wind farm information in our study sites was published in
locally popular newsletters, posted on bulletin boards, paper copies were provided in public
places and information was posted online.
Island Institute staff compiled local knowledge in their Mapping Working Waters project (Island
Institute, 2009) because they recognized local knowledge and values need to be translated for
wind farm project proponents, marine spatial planners and others working at regional and larger
136
scales to better understand the salience, credibility and legitimacy of local perspectives. This
type of local knowledge translation, such as fishermen’s expertise on suitable routes to lay the
cable (J. Field, 2014) and the location of prime fishing areas to be avoided, is also documented in
academic literature as helping to reach legitimate decision outcomes (Failing et al., 2007;
Gregory et al., 2012). The accessibility of information provided during these decision processes
was critical given that new information can influence opinions, especially when there are high
levels of uncertainty related to a proposed project (Dietz and Stern, 2008) and in situations with
widespread misconceptions (Bell et al., 2005; Ottinger and Williams, 2002).
5.3.2.1.2
Trusted messenger
Our interview data showed that Block Island wind farm developers recognized the importance of
hiring a trusted liaison from the local community to help facilitate the community engagement
process. Communication between community members and project proponents was an issue on
Monhegan Island, for various reasons including the compressed time frame to submit a federal
grant proposal and, potentially, because the developer had no local, Monhegan-based staff.
Consequently, more effort has been invested in relationship building, particularly between the
developer and a community energy group, the Monhegan Energy Task Force as the developer
prepares to apply for additional funding.
Our interpretation of the central role of trusted communicators aligns with numerous studies that
have documented how the messenger matters of information may matter more than the
information delivered (Cialdini and Goldstein, 2004; Kahan, 2010; 2012; Wynne, 1992). Studies
have shown that if a technology and its costs and benefits are not appropriately translated or
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people distrust the source of the information, stakeholders may feel alienated or disengage from
the decision process (Wynne, 1992; 1989), and potentially become entrenched in their opinion
regardless of new information that arises (Kahan, 2010). Information alone has a limited
influence on opinions (Kahan et al., 2012). People tend to “endorse whichever position
reinforces their connection to others with whom they share important commitments” (Kahan,
2010, p. 297). Arguably more important than technical information, the social context in which
information is shared and the person presenting it—the messenger—can exert substantial
influence on attitudes, opinions and behavior (Cialdini and Goldstein, 2004; Kahan, 2010). This
encompasses the personalities, communication styles and values of people sharing information
and facilitating community meetings and dialogues.
5.3.2.1.3
Bridging organizations
Island Institute as a bridging organization spearheaded participatory mapping of fishing effort to
inform marine spatial planning (Island Institute, 2009). Part of the rationale for this project was
to shift local stakeholders from playing the role of recipients of information to producers of
information that developers and government officials could understand, respect and use. Tobias
(2009) documents how boundary organizations can help provide such potentially empowering
experiences for local stakeholders.
The experiences of people involved in our offshore wind farm study sites reinforce the critical
role that boundary organizations can play in supporting community engagement. Echoing Cash
et al. (2006), boundary organizations assisted in the co-production and sharing of knowledge for
138
decision-making in our study sites. Boundary or bridging organizations can be defined with the
following characteristics (Cash et al., 2003):
•
Accountability to both sides of a boundary, e.g., local communities and project
proponents
•
Use of “boundary objects,” e.g., maps reports, and forecasts, which actors on different
sides of a boundary co-produce
•
Participation across the boundary involving
-
Convening
-
Translation
-
Coordination of complementary expertise
-
Mediation
Island Institute, SeaPlan, Gulf of Maine Research Institute and NOAA’s Sea Grant program are
examples of bridging organizations that played important roles in relation to the island
communities that we studied. Interviewees characterized them as more objective third parties
(i.e., more objective than the developers). These organizations helped run community
engagement and public outreach processes related to marine spatial planning and offshore wind
farm siting, but did not push for specific outcomes. On Block Island and Martha’s Vineyard, our
interviews and document analysis showed that project proponents and local government retained
organizations and people with excellent communication and facilitation skills who the
community already trusted. It is likely that part of the success of using these relatively neutral
people who served as communication bridges is that stakeholders are more likely to be open to
learning new information if the values of the messenger and/or bridging organization resonate
with them (Kahan, 2010).
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5.3.2.1.4
Timing: substantial iterative public engagement before site selection
Iteration emerged as a requisite characteristic of the community engagement processes
characterized by minimal participant frustration. These iterative learning opportunities unfolded
over multiple years. They involved joint fact-finding, such as Rhode Island’s Special Area
Management Plan process, and values clarification, such as the prioritization of sustainability
issues and potential solutions in the Martha’s Vineyard Island Plan.
Timing was problematic on Monhegan Island. From our interviews, we surmise that in all three
sites developers were often reluctant to share uncertain details, such as the specific location of a
site, before they were confirmed. During an early stage of the project, developers on Monhegan
Island tended to share only incomplete information when they engaged in community meetings,
which frustrated local stakeholders, some of whom perceived the developer as being dishonest
by withholding information. The uncertainty of the impacts also frustrated stakeholders.
The frustration that select interviewees expressed suggests that some public mistrust, skepticism
and opposition to the Monhegan renewable energy proposals may have been (or could be)
reduced with more frequent, meaningful and timely opportunities for locals to voice their
concerns in decision-making (Bell et al., 2005; Gregory et al., 2012). Literature on planning
processes and environmental management stresses the importance of engaging communities
early and often (Dietz and Stern, 2008; Gregory et al., 2012) yet, as our island examples show,
this can be challenging due to uncertainties inherent in early stages of project development. It
became apparent from our research that wind farm developers often spend years collecting the
requisite information to comply with regulatory requirements and determine optimal sites.
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Upstream research engagement can help navigate uncertainties associated with a new technology
and the impacts it may have. Scholars are beginning to study upstream deliberation regarding
offshore renewable energy (Wiersma, 2016; Wiersma and Devine-Wright, 2014). When
conducting upstream research, scientists, government authorities, bridging organizations and/or
developers can discuss a new technology with citizen groups before any choices are made
regarding if and where the technology may be used. Upstream research can help scientists and
developers to “open innovation processes at an early stage to listen, respond and value public
knowledge and concerns related to risks and ethical dilemmas” (Wilsdon and Willis, 2004, p.
28). This type of research can help answer people’s questions, including “Why this technology?
Why not another? Who needs it? Who is controlling it? Who benefits from it? Can they be
trusted? What will it mean for me and my family? What are the outcomes that this technology
seeks to generate? Could we get there in another, more sustainable and cost-effective way?”
(Wilsdon and Willis, 2004, p. 28).
We recommend that when state, tribal and federal agencies initiate ocean planning, they also
facilitate upstream research as pertains to potential new uses of ocean space that may not yet be
pressing issues. Ocean planning involves coordinating regional planning for current and future
ocean industry, conservation and recreation. Before areas are designated for specific ocean uses,
such as offshore renewable energy development, ocean planning initiatives have provided
opportunities for data collection, dialogue on various uses and values and sharing of information.
More of this kind of early engagement could help stakeholders learn about technologies and how
they could be managed without triggering place-protective opposition. Such opposition can stem
141
from perceived threats to specific places that may be important to people’s sense of identity and
to which they may have other strong attachments (Devine-Wright, 2009).
In addition to being included in ocean planning processes, BOEM also has the potential to
facilitate upstream research as the agency interacts with state, tribal and local governments
through task force meetings on specific offshore resource issues. This helps in providing
transparency regarding issues at different levels of government and provides opportunities for
stakeholders to learn and ask questions about areas of federal waters or specific projects. BOEM
has the authority to collect and share data on and then define boundaries of offshore ocean areas
that are available via leases to wind farm developers (Firestone et al., 2015). Through BOEM’s
task force meetings, information is directed to the specific set of stakeholders that an offshore
renewable energy project may affect. This type of early engagement with stakeholders is critical
in any ocean development project.
Our interviewees emphasized how early engagement dispelled community member’s fears of
finding out too late to become meaningfully involved in decision processes on Martha’a
Vineyard and Block Island. On Martha’s Vineyard, the steps of the process and the timeline for
making various decisions related to island sustainability in general and later offshore wind
enabled stakeholders to understand how and when to engage in the process. Boundary
organizations, developers, and government agency staff recognized time and resource challenges
around iterative and potentially multi-year stakeholder involvement in decision processes. Our
analysis showed that building trust among proponents, the selected ‘messengers’ and
communities takes time as does allowing for new information and questions to arise. Based on
142
the literature and our qualitative analysis, timely deliberation on identifying and procuring
community benefits can also build trust.
5.3.2.2
Provision of community benefits
Island Institute staff, community leaders and local government officials thought that explicit
inclusion of community benefits was key to successful engagement processes on Block Island
and Martha’s Vineyard. Engagement efforts in Monhegan did not include substantial discussion
on this topic prior to 2016.
By community benefits, we mean additional and distinct funds or investments that the developer
provides to communities, often near project sites (B. J. A. Walker et al., 2014). Benefits
associated with the generation of renewable electricity, such as carbon reduction, are diffuse and
tend to accrue at a global scale while several environmental, economic and landscape impacts are
concentrated and local. Providing community benefits above and beyond tax revenues can play
an important role in managing renewable energy scale-related distributional conflicts (Wolsink,
2007; Zografos and Martínez-Alier, 2009).
Whereas the term ‘community benefits’ has been used broadly, the experiences of those engaged
with our study sites suggest a need for a more nuanced theorization of this term. That is, whereas
the term itself could be viewed from a utilitarian perspective as simply providing net benefit to
the majority, our study sites demonstrate that such a narrowly utilitarian approach does not
sufficiently capture strongly held community concerns of fairness. Whereas one might think that
a community benefits from a project if the majority receives a net benefit, and the community-
143
scale aggregate is a net benefit, our data suggest that these are not sufficient criteria. Individuals
expressed concern that specifically impacted groups may require compensation, i.e., some island
leaders and boundary organization staff expressed how compensation should be considered for
fishermen who would lose fishing grounds. These individuals were not among those who would
be most directly impacted by an offshore wind farm. Accordingly, we seek to make explicit that
broadly acceptable community benefits are benefits to individuals and groups as seems fair and
appropriate from a community perspective.
This qualifier adds a broader relational perspective that integrates not only consequences but also
principles and notions of fairness at scales coarser than an individual. From this perspective,
individuals may oppose a project even if they might personally gain from it (e.g., a local barge
operator may get numerous contracts from an offshore wind project), if they seem unfair at a
community level, accounting for the existing and historic relationships and the prevailing values
of a place (e.g., the wind farm siting process may not be sensitive to the preferences of local
lobstermen).
Community benefits can help balance the provision of private and public benefits associated with
an offshore wind farm. Some perceive offshore wind development as privatizing the ocean,
which, historically, has been a public space for fishing, recreating and other activities (DevineWright and Howes, 2010; Firestone et al., 2009; Pomeroy et al., 2014). The federal management
agency overseeing the development of offshore wind, BOEM, has public good-oriented goals,
but they use market-based tools to achieve these (e.g., auctions involving private developers).
Part of BOEM’s mission is to “promote energy independence, environmental protection and
144
economic development” via delineating and auctioning areas of the ocean for different purposes,
including offshore wind farms (BOEM, 2015). We suspect that BOEM’s general public goodoriented goals are less salient to residents of communities adjacent to wind farm sites compared
to local concerns, such as displacement of fishermen from fishing grounds, but we did not
measure this (Island Institute, 2012b). In order to shift perception of benefit from the large scale
and general to the local and specific, developers may provide community benefits for various
reasons, such as to help earn the public’s trust and create a sense of fairness associated with the
project (Aitken, 2010; Cowell et al., 2011; Rudolph et al., 2015). However, as noted in European
case studies, the formation and provision of community benefits can erode or build trust and
perceptions of fairness (Aitken, 2010). Community benefits literature and our research
demonstrate how establishing trust and perceptions of fairness rest on both the process of coming
up with appropriate benefits as well as the models and mechanisms used to deliver the benefits.
5.3.2.2.1
Deliberation to determine community benefits
Relevant literature and our island-focused research point to the importance of collaboration
among developers, communities and government agencies to identify and provide community
benefits rather than only respond to government mandates about benefits (Aitken, 2010; Rudolph
et al., 2015). Community benefits are required by law in some contexts and voluntary in others.
For example, land-based wind developers in Maine must pay host communities according to the
number of installed turbines (Maine State Legislature, 2010) but offshore wind developers are
not required by law to provide community benefits in the UK (Aitken, 2010).
145
Our research and relevant literature supports how early discussions among government
authorities, developers and communities are needed to arrive at acceptable definitions and
understandings of communities, benefits, impacts and how they relate to each other (see Figure
5.3). We have thus far used the term community in reference to residents of particular islands,
but communities can be based on location (e.g., a town), interests (e.g. recreational boaters),
groups who are adversely impacted (e.g., commercial fishermen), organizations (e.g., an energy
cooperative) and/or other shared characteristics. Benefits can be understood as sharing economic
gains associated with tapping into a public natural resource (i.e., wind), recognition of hosts
(e.g., developer seeks to be a good neighbor, communities receive benefits for hosting substation
infrastructure), increasing local support (e.g., community groups or energy cooperatives who
receive benefits commit to supporting wind farm), accounting for impact (e.g., recognition of
local negative impacts), compensation for agreed upon and specific losses (e.g., funds to improve
habitat for birds at high risk of collision with turbines). Impacts can be perceived as positive
(e.g., provision of jobs and carbon neutral electricity) and/or negative (e.g., bird mortalities,
decreased visual amenities). Rudolph et al. (2015) developed a framework to achieve the
legitimate provision of community benefits via a set of interactions among communities, benefits
and impacts (Rudolph et al., 2015). Community engagement processes on two of the islands we
studied had substantial community support (Martha’s Vineyard and Block Island) and covered
the topics in this framework when they developed community benefits. Interviews documented
that wind farm developers for the Monhegan project have come to recognize the role of
community benefits in the other islands’ development processes and are working towards
discussion about what such benefits could be for Monhegan.
146
Government
Authori;es
Communi;es
Developers
Stakeholders
Who
should benefit?
Beneficiary communi2es
can be defined by
- Loca2ons: town, island
- Interests/prac2ces: fishermen, sailors
- Groups adversely impacted: fishermen
- Organiza2ons: energy coopera5ves,
conserva5on groups
- Other aJributes: demographic
characteris5cs
Why & how to
provide benefits?
What
are the impacts?
- Share economic
gains associated with
using public resource
- Recognize hosts
- Account for impact
- Compensate for
specific losses
- Other
- Environmental
- Social
- Economic
How are impacts
perceived?
- Posi2vely
- Nega2vely
To collabora2vely develop
Appropriate
Community
Benefits
Figure 5.3. A robust approach to developing community benefits.
This requires reaching a common understanding of impacts, communities, fair and appropriate benefits, and
their interactions among developers, communities and government authorities. Italics denote examples.
Adapted from Rudolph et al. (2015).
147
5.3.2.2.2
Flexible models for custom tailored benefits
Community benefits took different forms in our three study sites. They can be integrated into
various stages of a project, such as the planning, permitting, mitigation, operational and
decommissioning stages. We add to Rudolph et al.’s (2015) overview of common offshore wind
community benefit models and mechanisms:
•
•
•
•
•
•
•
•
•
•
•
Community funds (most common)
Other and pre-existing funds
Community ownership
Equal distribution of revenues
Direct investment and project funding (e.g., paying for infrastructure improvements)
Jobs, apprenticeships and studentships
Educational programs
Electricity discounts
Community benefit agreements
Indirect benefits from the supply chain
Indirect benefits via tourist facilities
It may be instructive for communities, government authorities and developers to look to Europe
when considering appropriate community benefits. In Denmark and regions of Germany,
community benefits are often based on cooperative models in which members own the business
and all profits after taxes are given back to members (Breukers and Wolsink, 2007). In the UK,
energy developers annually pay into a fund proportional to the megawatts (MW) of installed
capacity for community organizations to spend on local initiatives (Cowell et al., 2011). For
more detailed descriptions of different types of community benefits, see Rudolph et al. (2015).
Community benefits have the potential to enhance or degrade relationships between developers,
government authorities and local communities; they can be perceived as broadly beneficial or a
bribe that displaces civic duty (Sandel, 2012; B. J. A. Walker et al., 2014). Co-creating
community benefits so they are perceived as fair and appropriate from a community perspective
148
may reduce the perception among stakeholders of benefits as bribes. Establishing locallyappropriate community benefits involves clearly identifying their scale, role and purpose in order
to reduce this potential negative perception (Cowell et al., 2011). This process can also improve
clarity and diminish uncertainty about what will be provided so developers can discuss them
earlier in the planning stages. Rudolph et al. (2015) recommend that developers and authorities
negotiate with communities about various benefit models during early stages of wind farm
planning, ideally before submitting planning applications.
5.3.2.3
Relevance to components of public participation in deliberation
We conducted our qualitative analysis before reviewing principles for public participation in
deliberation. Many of the concepts that emerged from our analysis associated with successful
and/or frustrating parts of engagement processes reinforce principles from Abelson et al. (2003).
The principles from Abelson et al. (2003) that arose more than once in our qualitative analysis
are outlined in blue in Figure 5.4. We augment these principles with consideration of community
benefits in deliberative processes that may result in an imposition of one party’s interests on a
community (e.g., wind farm developers interests imposing on adjacent community member’s
interests). It is likely that Abelson et al, (2003) did not attend to community benefits because the
topic of their review was health policy and the presumed community benefit was improved
health. Explicit attention to community benefits, as depicted in orange boxes in Figure 5.4,
could apply broadly to community engagement with various types of infrastructure and
technology, not just to a developer building a wind farm.
149
Legi)mate and fair
selec)on process
Representa3on
Access to par)cipa)on
in decision process
Demographic
Representa)ve sample
Geographic
Legi)mate
Clarity in
selec)on process
Poli)cal
Fair
Par)cipant
selec)on vs. selfselec)on
Key
characteris)cs
Responsive
Public input
sought
Ample )me for
discussion
Key components
of public
par3cipa3on in
delibera3on
Procedural rules
Informa3on
used in
process
Outcomes and
decisions arising
from process:
Legi3macy and
accountability for
outcomes
Community
Benefits
Public input
incorporated
into final
decision
Decisions &
public input
into them are
communicated
to the public
Organiza)onal
level of public
par)cipa)on
and input
Iden)fy
impacts
Iden)fy and
provide customtailored benefits
deemed fair and
appropriate from
a community
perspec)ve.
Who should
benefit
Major features, e.g.,
agenda seEng
Minor features, e.g.,
order of who speaks
Who is listening
to public?
Who responds
to public?
Par)cipants have opportunity to challenge
process, informa)on presented and experts
involved
Characteris)cs
Appropriate selec)on
and presenta)on
Fair selec)on
Accessible, readable,
diges)ble
Sufficient )me to
consider, discuss
and challenge
informa)on
provided
Ci)zens are more informed about issues
Shared
understanding
across
communi)es,
authori)es &
developers
Reasonable
Decision-making authority responds to
public input
BeSer or different decision
Who chooses
informa)on
Who chooses experts
that provide informa)on
Iden)fy public input that
was incorporated
Answer why public input
was incorporated or not
Broad understanding and
acceptance of decision
Why and how to
provide benefits
Figure 5.4. Design and evaluation principles for public participation processes with community benefit
outcomes.
Blue outlines denote topics from Abelson et al. (2003) that arose in multiple interviews and our document
analysis despite how we did not provide specific prompts for these topics. Orange denotes attributes of
community benefits that were perceived as crucial to the success of the wind farm decision processes that we
studied. We recognize the importance of topics in black outlines from Abelson et al. (2003), even though they
were not common topics in our interviews or document analysis.
150
5.4
Conclusion
Proposals for renewable energy infrastructure are poised to rapidly proliferate, particularly if
countries follow through with carbon reduction commitments. The ways in which humanity
approaches, manages and responds to inevitable controversy over these technologies impacts the
pace and efficacy of addressing climate change and transitioning to low carbon energy sources
(Roberts et al., 2013). Based on results from the islands we studied and literature synthesis, we
see the critical importance of developers and decision makers engaging local communities to
address concerns about project impacts and benefits to achieve legitimate decision outcomes.
Communities may legitimately reject particular renewable energy technologies.
Furthermore, we augment established principles for public participation in deliberation that focus
on process with an explicit inclusion of a particular outcome. Specifically, if the project is
considered worthy of moving forward, we recommend outcomes of community benefits deemed
fair and appropriate by communities that incorporates viewpoints from government authorities
and developers.
Deliberative analytical decision processes involving extensive stakeholder engagement can be
resource and time intensive, but this initial investment can result in lower long-term costs with
potentially fewer delays, it may reduce the risk of litigation costs (Irvin and Stansbury, 2004;
Randolph and Bauer, 1999) and we suggest it may result in better long-term relationships among
those involved. Based on what we learned from the experiences of Block Island, Martha’s
Vineyard and Monhegan Island, building a foundation of both knowledge and trust is crucial for
151
the success of an offshore wind farm and likely other renewable energy technologies. Making
deliberative learning accessible and providing clear community benefits can help ensure that 1)
the decision-making processes around these projects are inclusive, effective and perceived as
fair; 2) local, scientific and political knowledge is considered; and 3) projects that are considered
appropriate after an analytic-deliberative process are properly sited.
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Chapter 6: Conclusion
The purpose of my dissertation was to shed light on controversies and potential solutions at the
confluence of climate change and the biodiversity crisis in a manner that addresses human
psychology, including fundamental desires for connection to others, both human and non-human.
I am convinced that such insights can help combat the currently bleak data trends and unfolding
disasters resulting from climate change and dramatic reductions in biological diversity and
abundance.
Scientists and economists have attempted to cut through decades of political debates on the
validity of climate change by repeatedly calling for “vigorous efforts to develop low-carbon
technologies” (2013, p. 326). Countries implemented this recommendation to a degree, but a vast
gulf separates the planet’s current trajectory from the atmospheric conditions we must achieve to
stabilize the climate. For example, non-hydropower renewable energy sources are growing faster
than any other source for new generation capacity globally. Yet, they comprised only 5% of the
total world electricity generation as of 2012 while coal generated 40% (DOE EIA, 2016).
Navigating this gulf entails re-considering societal priorities and the technologies we use. In
democratic societies, partial solutions to climate change and the biodiversity crisis require public
support, which led me to investigate perceptions of impacts and benefits associated with a
technology—offshore wind farms—that could contribute towards reducing greenhouse gas
emissions.
153
6.1
Realization of renewable energy research goals and research implications
This dissertation applies and integrates social studies of risk, ecosystem services, environmental
and relational values and theory on analytic-deliberative processes to barriers to scaling up
renewable energy. This integration addresses facets of public opposition to renewable energy
development based on concerns about social, financial and environmental consequences, value
orientations and flawed engagement practices.
Chapter 2 provides preliminary evidence that an important set of insights from risk perception
may apply to how people understand environmental impacts. Specifically, I showed that
components of the psychometric risk paradigm extend beyond its traditional domain of
environmental health concerns (e.g., carcinogens) to indirect impacts to people via concerns
about changes to ecosystem services. These findings would suggest that developers and
government authorities might anticipate stakeholder concerns more effectively by attending to
the characteristics of risks. For example, risks perceived to be uncontrollable, and those that
invoke dread—e.g., those associated with fatalities of animals—are more likely to induce higher
levels of concern. This preliminary evidence that perceived risk research also applies to ES gives
environmental researchers a new set of methods and conceptual tools for understanding the types
of environmental impacts that will likely loom large in the public's mind, and which impacts will
largely escape notice.
Whereas Chapter 2 focused on risk perceptions, Chapter 3 demonstrated that many people are
willing to pay to mitigate the harms of offshore wind farms—and are willing to pay even more
for the wind farms to have net positive ecological effects via enhanced habitat for underwater
154
species. The implications of this high latent demand for biodiversity-friendly offshore wind
farms is that it suggests willingness on the part of utility payers to fund renewable energy that is
not only clean climate-wise, but also ecologically beneficial. This public support could help
transform the energy landscape. The strongest preferences and consequently highest willingness
to pay amounts were for wind farms with contributions to species richness and abundance that
are net positive. People favor the regenerative options and are willing to pay for them. An
implication of Chapter 3 is that renewable energy proponents may increase support for their
proposed projects by going beyond mitigating risks to biodiversity to making the infrastructure
ecologically regenerative.
An important caveat for the experiment in Chapter 2 is that the sample that I surveyed
(Northeastern US) is a relatively environmentally oriented and wealthy part of the world.
Nonetheless, the choice experiment models point to a surprisingly large willingness to pay for
ecologically regenerative offshore wind farms.
Recognizing that not all values are monetary, Chapter 4 provided some empirical evidence
worthy of further investigation on the emerging concept of relational values. Specifically, I
showed that these relational values—values linking people and ecosystems via tangible and
intangible relationships as well as the principles, virtues and notions of a good life that may
accompany these—may be both strong and widely held. Furthermore, my results indicate that
people respond to relational value statements differently than how they respond to New
Ecological Paradigm statements, the latter being a common way to assess ecological worldviews.
I found preliminary evidence that this novel relational construct is predictive of attitudes and
155
preferences toward wind farms. Conservation scientists and practitioners may have been missing
this important relational dimension of attitudes and values about the environment. Relational
values need more testing, but they may be sufficiently cohesive and discrete to be an important
construct for understanding environmental values and motivations for pro-environmental
behavior.
In Chapter 5, my qualitative analysis demonstrated that, amongst the litany of criteria in the
literature, good public engagement in three island communities boiled down to two key themes:
enabling bidirectional deliberative learning and providing community benefit. That is, the
smoother decision processes included public engagement in dialogue in which participants,
including experts and local stakeholders, learned from each other while reconciling technical
expertise with citizen values. Outcomes included the provision of community benefits that have
important relational dimensions in that these benefits should be collaboratively negotiated. The
resulting benefits ought to be perceived broadly as fair and appropriate from a community
perspective. Attending to these two key themes may improve the quality of the interactions
among communities, government authorities and developers when deciding if and where to site
renewable energy infrastructure.
6.2
Limitations
As an initial foray into several rapidly expanding areas of research, my dissertation of course has
several notable limitations. First among these for Chapter 2 is the relative small size of my
sample. My exploratory in-depth mixed methods were tested on a small sample (n = 27). As
always, a larger sample size would make the statistical analysis and the corresponding findings
156
more robust. Also, in contrast to health risk studies comparing expert to lay people, I had no
independent measure of the magnitude of risks to ES. Instead, my scoring system was based on
interpreting my interview data and academic publications on this topic. Accordingly, it is
possible that some of what appeared to be a strong risk signal may actually stem from the fact
that some risks (like those to marine mammals and birds) are actually more likely to be large in
magnitude.
A potential limitation of Chapter 3 is that my survey respondents (Mechanical Turk workers) are
not a fully representative sample of residents of the geography that I targeted (coastal New
England states). As I mention in Chapter 3, the demographic characteristics of Mechanical Turk
workers differed from census data. I found, however, no evidence that demographic
characteristics influenced choices in the experiment. Intuitively and without empirical evidence,
I suspect that these online workers may be more accepting of novel technologies because they
have chosen to work within a relatively new online system. Although I did not find obvious
biases in how people with different demographic characteristics responded, I would not
completely rule out that their relative youth and higher levels of education may have made them
more likely to support environmentally friendly renewable energy. The expense of using an
online panel that is more closely representative of a targeted population may be worthwhile for
future studies on this topic.
Another limitation in Chapter 3 was my inclusion of marine habitat impacts in the design of my
choice experiment while excluding impacts to birds, which was a highly prominent concern in
157
Chapter 2. Future research could include wind farm design features with different levels of
positive and negative impact on avian life.
My estimates of willingness to pay (WTP) for artificial reef are higher than those found by
Börger et al. (2015) who used an additional annual tax to measure WTP and sampled from a
population in England potentially impacted by the development of an offshore wind farm.
Although I used monthly in the preamble to the survey and in the description of each choice,
respondents may not have reflected on how much this tax would cost annually, which may
partially explain my high WTP estimates. Future research could evaluate sensitivity to monthly
as compared to annual taxes in choice experiments related to offshore wind.
Lastly, I used vivid graphics to convey wind farm characteristics in the experiment. I did not,
however, assess if the high willingness to pay amounts could be partially attributed to the bright
images used to represent different qualities of artificial reef habitat. It is possible that
respondents may have made quick, intuitive decisions to choose the most colorful option without
reflecting much on the hypothetical cost incurred from this choice.
While it provides promising findings, Chapter 4 suffers from the normal limitations of a
preliminary exploration. Specifically, the six relational value statements each address a
potentially separate aspect of values about relationships (e.g., kinship with nature as distinct from
responsibility for impacts to others). While these six statements showed consistency as a set (an
interesting and somewhat unexpected finding in itself), some researchers will be interested in the
sub-component values separately. In the exploratory analysis described in Chapter 4, we did not
158
test whether each of these relational value statements has internal validity. Accordingly, we do
not know if slightly re-wording each type of relational value would change how people respond
to it.
As is the case with most site-based research, the results of Chapter 5 may be limited in their
generalizability across different types of renewable energy infrastructure in different regions of
the world. We grounded our assessment in more generalizable academic literature when we
evaluated if and how theoretical ideas about analytic-deliberative processes played out in three
sites. Given the limited number of sites, our results about bi-directional deliberative learning and
community benefits may not strongly resonate in other places. More research is needed to test
the generalizability of these qualitative results while recognizing the extensive literatures
demonstrating that both procedural and distributive justice matter.
6.3
Future research directions
Much research remains to better understand the complexity of public support and opposition to
sustainable energy transitions. I see a need for more human-centered energy-related research
methods (e.g., surveys, interviews, focus groups) to reveal additional underlying factors
motivating or hindering the adoption of offshore wind infrastructure and other renewable energy
technologies. Such research could also assess why, how and in what contexts attitudes and
behaviors towards energy technologies change. Pre and post surveys could be used to investigate
energy-related attitudes and behavior changes over time, which could help research and
consequently practitioners anticipate future attitude and behavior changes.
159
My research is one of a small handful of academic studies that have focused on community
engagement with the nascent offshore wind industry in the US. More extensive and longer-term
research that involved additional interviews and becoming more embedded in decision processes
relevant to marine planning could lead to additional insights.
Building on my wind farm choice experiment results in Chapter 3, additional research could be
done in collaboration with a renewable energy business to assess real willingness to pay, not just
hypothetical willingness to pay for renewable energy infrastructure that has regenerative design
features.
6.4
Towards ecologically and socially sustainable energy
The climate negotiations at COP16 established higher emissions reduction targets and more
accountability via emissions reporting requirements than preceding international climate change
agreements (UN, 2015). These ambitious targets may tempt policy-makers to streamline public
engagement processes to deploy the technologies faster. Such streamlining could be counterproductive, potentially increasing the rates of lawsuits and developers losing their social license
to operate. My research and other studies reinforce how we need well planned analytic
deliberative processes (Devine-Wright et al., 2011). More broadly, echoing Stehr (2016), my
research points towards confronting climate change as an opportunity for more democracy, not
less.
I identified large, latent support for ecologically regenerative renewable energy technology and a
strong suggestion that relational values could help propel a sea-change in actions as well as
social practice around reconfiguring our energy systems. Securing sustainable energy may prove
160
an illusive goal, but this research has helped bring some social and ecological facets of this goal
into sharper focus while providing a foundation upon which future research can be launched.
161
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Appendices
Appendix A Golden Bay interview consent form
University of British Columbia
Institute for Resources,
Environment & Sustainability
2202 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel: 604.822.7725
Fax: 604.822.9250
www.ires.ubc.ca
Consent Form: Exploring Perspectives on Energy and the Environment in Golden Bay
To:
Principal Investigator
Dr. Kai Chan
Co-Investigator
Sarah Klain
This research will contribute towards Sarah Klain’s PhD dissertation.
Purpose
You are invited to take part in this research because of your professional expertise and/or
community leadership. The purpose of this project is to collect information from a wide range of
experts and potential stakeholders to better understand attitudes towards energy security, a
hypothetical renewable energy project and its potential environmental impacts.
Sponsor
This project was made possible by a research grant from the New Zealand Ministry of Business
Innovation and Employment. The University of British Columbia is conducting this study in
collaboration with Cawthron Institute.
Study Procedures
Participating in this study entails an interview that will last approximately 45 minutes to one hour.
You will be asked questions about if and how you identify with Golden Bay and the wider region. The
interview will also include questions about energy security and a hypothetical offshore wind farm,
which will include an interactive visualization of a wind farm in Golden Bay. With your consent, the
interview will be audio recorded. After the interview, the digital audio recording will be transcribed
and the original files will be deleted to protect confidentiality.
Potential Risks
The topics of this interview, including energy security and a hypothetical change in infrastructure, may be
contentious. To minimize and avoid psychological stress, the confidentiality of the information that you share
is guaranteed and you are free to stop participating in the interview at any point.
178
To minimise the risk of accidental release of confidential information, we will code all interview data and
delete the original audio files. Only aggregated data and information that does not reveal the identity of any
participant will be published and presented publically.
Potential Benefits
Your participation in this study will help researchers and management agencies understand local and regional
concerns regarding energy security and potential developments in this area. Through this research we hope to
communicate the diversity of values and opinions associated with a hypothetical change to Golden Bay. As
someone whose profession involves direct work with ecosystems, energy and/or your community, we feel it is
important to include your perspective in this research. If you are interested in receiving a digital copy of the
output of this research, please email Sarah Klain at XXX.
Confidentiality
Your identity and participation in this research will be kept strictly confidential. All notes and digital audio
recordings will be coded and stored on an external hard drive that will be kept in a locked file cabinet.
Participants will not be identified by name in project reports.
Remuneration/Compensation
To thank you for your participation, you will be entered in a draw to win a $75 gift certificate redeemable at
any FreshChoice supermarket.
Contact for information about the study
If you have questions or want to know more information about this study, please call or email Sarah Klain at
XXX.
Contact for concerns about the rights of research subjects
If you have any concerns about your treatment or rights as a research subject, you may contact the Research
Subject Information Line in the UBC Office of Research Services at 604-822-8598 or if long distance e-mail to
RSIL@ors.ubc.ca.
Consent
Your participation in this study is entirely voluntary and you may refuse to participate or withdraw from the
study at any time.
Your signature below indicates that you have received a copy of this consent form for your own records. Your
signature also indicates that you consent to participate in this study.
____________________________________________________
Subject Signature
Date
179
Appendix B Golden Bay Interview request letter
Exploring Perspectives on Energy and the Environment in Golden Bay
Dear
Based on your expertise and experience, you are invited to take part in a research project to better
understand perspectives on energy security and environmental impacts of energy-related
developments in Golden Bay. Provided you are willing to take part in this study, you will be asked
question about if and how you identify with Golden Bay and the wider region. The interview will also
include questions about energy security and an interactive visualization of a wind farm in Golden
Bay.
Participating in this study entails an interview that will last approximately 45 minutes. Only
aggregated data and information that does not reveal the identity of any participant will be
published and presented publically.
This research project is made possible by a grant from the New Zealand Ministry of Business
Innovation and Employment. Researchers at the University of British Columbia in Canada are
conducting this study in conjunction with Cawthron Institute, based in Nelson as part of a wider
study on Tasman and Golden Bay. The purpose of this project is to collect information from a wide
range of experts and potential stakeholders to better understand attitudes towards energy security,
a hypothetical renewable energy project and its potential environmental impacts. As someone who is
involved with decision that effect ecosystems, energy and/or your community, we feel it is important
to include your perspective in this research.
To thank you for your participation, you will be entered in a draw to win a $75 gift certificate
redeemable at any FreshChoice supermarket.
If you are able to make time for an interview between 8 and 25 April, please contact Sarah Klain (tel
XXX). Also, if you wish to obtain a digital copy of the output of this research, please email Sarah Klain.
Sincerely,
180
Sarah Klain
PhD Student
Institute for Resources, Environment and
Sustainability
University of British Columbia
Principal Investigator
Dr. Kai Chan
Institute for Resources, Environment and
Sustainability
University of British Columbia
This research will contribute towards Sarah Klain's PhD dissertation
181
Appendix C Golden Bay interview protocol
Exploring Perspectives on Electricity and the Environment in Golden Bay
Interviewee #_______
Date _______
Interview Protocol
Introduction
Introduce yourself and the project. Thank the participant in advance. Provide:
Project description
Study region is Golden and Tasman Bay
Overview of interview
A reminder that this is an exploration and there are no right or wrong answers
Consent form and confidentiality agreement
Start the digital recording device.
Participant information
What is your name?
What year were you born?
What town do you live in?
Can you tell me about your current occupation?
Initial Ranking
The topic of this interview may seem far from your area of expertise but bear with me. I’m
interviewing you based on your experience in [her/his line of work].
182
When thinking about new sources of electricity, which of the following concerns are most important?
Please choose your top four concerns, ranking these from most important (1=most important, 2= 2nd
most important, 3=3rd most important, 4=4th most important).
[Display cards randomly, i.e., don’t use same order every time. Then ask them to sort their top four
concerns from high to low, which you can then record as a number above the “__”]
__Using more local resources to generate electricity rather than imported resources
__Minimizing capital cost of the technology (the one-time cost of the new infrastructure)
__Ensuring that utility bills don’t increase more than 10% to cover new costs
__Prioritize low carbon source of electricity
__Reduce or mitigate strongly any local environmental impacts
__Ensure any visual or aesthetic impacts of energy infrastructures are locally acceptable
__Ensure noise associated with electricity generation is locally acceptable
__ Ability for the energy system to withstand or recover quickly from natural hazards, e.g., an
earthquake or storm events
__ Other, specify _________________
[If people are really struggling with choosing, reduce the list by removing the 1st (local) and 4th (low
carbon) items]
Can you say more about why [x,y,z] are your top 3 concerns?
New Zealand’s Electricity
New Zealand gets most of its electricity from hydroelectric dams but heavily relies on fossil fuels for
transportation. [show and explain graphics]
Figure 1. New Zealand’s Electricity Sources. Hydroelectric dams
provide over half of New Zealand’s electricity.
Oil
0%
Coal
7%
Wind
5%
Biogas
1%
Gas
17%
Hydro
56%
Geo- thermal
14%
183
Figure 2. New Zealand’s Total Primary Energy Supply (TPES). TPES is
the sum of domesGc producGon of energy plus imported sources of
energy, subtracGng energy exports and energy used for internaGonal
transport. Primary energy can be renewable or non-re
Gas
19%
Hydro
11%
Geothermal
20%
Oil
34%
Coal
7%
Other
Renewables
9%
Figure 3. New Zealand’s Consumer Energy Demand by Sector.
The transportaGon sector heavily relies on oil.
Electricity
250
Renewables
Natural Gas
Petajoules
200
Oil
150
Coal
100
50
0
Agriculture, Industrial Commercial Transport ResidenGal
Forestry and
Fishing
Sector
Electrifying the
transportation sector could reduce carbon emissions. This would entail developing additional sources
of low carbon electricity.
184
Energy Security
Now I’d like to talk about energy in this region, in particular at the top of the South Island.
Let’s begin with the term energy security.
What does energy security mean to you?
What other words or terms come to mind when you hear the phrase ‘energy security’.
I’d like to read one widely accepted definition of the term energy security so that I can be sure that
we’re talking about the same thing. Energy security is based on the extent to which energy sources
are available, accessible, affordable and acceptable. This includes safety of energy fuels and services,
energy efficiency, diversification of supply and minimization of price volatility.
Is that an acceptable definition or would you like to offer another one?
Yes ____ No ____
[if no, Other definition ____________________________________]
On a scale of 1 (not secure) to 5 (highly secure), how energy secure do you think this region is?
[If appropriate] What would a more energy secure top of the south look like?
Do you think energy security is playing a significant role in how this region is developing?
What, if any role, should energy security play in how this region develops?
Renewable Electricity Sources
The next questions are about renewable sources of electricity, defined as electricity from natural
resources that are continuously replenished. Examples include solar power, wind power,
hydropower, geothermal and biomass.
To what extent do you agree with the following statements [show scale]:
1 -----------------2------------------3-----------------4--------------------5
strongly disagree
disagree
neutral
agree
strongly agree
We need more development of renewable electricity nationally.
We need more development of renewable electricity regionally.
Can you elaborate on your answers? What best explains why you agree/disagree/feel neutral?
Are there particular renewable electricity projects that you support? Why?
Are there particular renewable electricity projects that you oppose? Why?
Here’s a map of existing and proposed land-based wind farms.
185
Land-Based Wind Farms
!
Proposed
!
Operating or under construction
Awhitu
Hauauru Ma Raki
Taumatatotara
Taharoa
Hawkes Bay Titiokura
Central Wind
Waitahora Turitea
Mt Munro Puketoi
Castle Hill
New Zealand Wind Energy Association Long Gully
Lake Grassmere
Hurunui
Mt Cass
Mt Stalker
Kaiwera Downs
±
Slopedown
Flat Hill
0 25 50
100
150
200
km
Proposed and existing wind farms in New Zealand.
Do you have an opinion about any of the land-based wind farms that have been proposed and/or
built in New Zealand?
Do you: strongly disagree, disagree, neutral, agree, strongly agree with the following statement?
New Zealand should prioritize the development of renewable electricity sources other than
hydroelectric dams.
1 -----------------2------------------3-----------------4--------------------5
strongly disagree disagree
neutral
agree
strongly agree
Earthquake Risk
Anyone living on the south island is no stranger to earthquake risks. Geologists estimate a 30%
chance that the Alpine Fault will rupture in the next 50 years, producing a large earthquake in the
range of the major Christchurch quakes in 2010 and 2011. Conceivably, this could significantly disrupt
the electricity supply to the region. [Show map]
186
Power Stations & Fault Lines
South Island, New Zealand
Power Station Capacity
Fossil Fuel (MW)
Faults
Transmission lines (kV)
0 - 51
11
52 - 140
33
141 - 264
50
265 - 500
66
501 - 850
110
Hydroelectric (MW)
220
0 - 51
350
52 - 140
141 - 264
265 - 500
501 - 850
±
0 15 30
60
90
120
km
South Island fault zones, powerlines and power stations.
Because the electricity supply to the top of the south Island is only a few transmission lines that run
near fault lines, an earthquake of this magnitude would likely have major consequences for the
energy grid.
If local renewable energy development significantly reduced this vulnerability, would you
[increase/decrease/not change] your support of localized energy development? Discuss.
In general, did this map influence how you think about energy security? Please explain.
Is the map surprising? Please explain.
Offshore Wind Energy
Next I’d like to talk about electricity generated by offshore winds. Have you heard of offshore wind
farms?
[If even vague yes] Can you describe for me any impressions or ideas of what this is and how you
think it works? What words or terms come to mind when you hear offshore wind farm?
What are your general concerns about developing offshore wind farms?
What do you think are the benefits of developing offshore wind?
Place Attachment & Identity
Now I want to get you to think about the region where you live.
Are you attached to the Golden Bay/Tasman Bay region? If so, to what extent are you attached?
187
1 -----------------2------------------3-----------------4--------------------5
unattached
mildly attached moderately
attached
strongly attached
Why?
Is being ‘from’ Golden/Tasman Bay important to your sense of ‘who you are’ or ‘where you belong.’
1 -----------------2------------------3-----------------4--------------------5
Strongly No
No
neutral
yes
strongly yes
Can you tell me why?
How long have you lived in this area? This region of Tasman and Golden Bay?
Are there places on the landscape or seashore here that you are particularly attached to? Can you
name these and/or tell me why they matter to you?
Visualization Video
Now we’re now going to turn our attention to a video about this area and a hypothetical offshore
wind farm. [Show movie]
[Audio in movie]
New Zealand has exceptional wind resources. Golden Bay is one of the two best sites in the country for an offshore wind
farm due to its relatively shallow water depths and consistent wind.
The location of this hypothetical wind farm is based on a study conducted by the National Institute of Water and
Atmospheric Research. This study identified where wind conditions are suitable for this technology.
The hypothetical farm was placed south of Farewell Spit because of the strong and consistent wind where the water depth
is less than 30m. Wind farms at depths greater than 30m are considerably more expensive.
Sailing tends to be permitted near offshore wind farms.
This type of turbine is used in the Horns Rev wind farm off the coast of Denmark. The turbine height from the tip of the
blade to sea level is 110 meters. Each turbine blade is 40m in length.
This view is from the beach near the Farewell Spit Café and Visitor Centre.
Golden Bay is important bird habitat. Research in Denmark showed that geese and ducks altered their flight behavior
after a wind farm was built to avoid colliding with the turbines. Similar studies have not yet been done on seabirds in New
Zealand.
On a clear day, boaters launching near Pohara may see the turbines on the distant horizon.
People in some northern coastal areas of Able Tasman National Park may be able to see the farm in the distance during
clear weather conditions.
Sound associated with the construction of offshore wind farms can disturb marine mammals.
The underwater foundations of the turbines may benefit marine mammals and other sea life because of the habitat they
create, which is called the artificial reef effect. An artificial reef is a human-made underwater structure. Algae and
invertebrates, such as barnacles and oysters, attach to the hard surfaces of an artificial reef. This marine life can provide
habitat and food for fish and other species.
188
Bottom trawling would not be allowed within an offshore wind farm or near the underwater electricity cables linking the
wind farm to land.
Recreational fishing may benefit from the artificial reef effect associated with the bases of the turbines.
To give you an idea of how much energy can be generated from a wind farm, a 2 MW offshore turbine in a place with
consistent, strong wind can power approximately 800 households. This hypothetical wind farm of 25 turbines could power
~20,000 households, which is more than the number of households in the Tasman District.
An offshore wind farm in Golden Bay would be a source of electricity with minimal carbon emissions.
[when the narration stops you can ask the following]
Is there any part of the video you would like to see again?
How does this visualization make you feel? [probe this] Why?
What does the visualization make you think about?
Do you think this technology would cause issues?
Ecosystem Services
There are commercial and recreational fisheries, recreational boating and aquaculture in this Bay.
This area is also habitat to marine mammals, birds and other species. Earlier you mentioned [XXX] as
[natural features or special places] that contribute to your attachment to this region.
If you think about the ways in which nature and this place is important to you, what do you think
could be lost if this project was developed?
What, if any, impact would it have on your livelihood?
What do you think could be gained if it went through?
Opinions on offshore wind
On a scale of 1(strongly disagree) to 5(strongly agree), what do you think of the following statement?
Offshore wind farms are a promising technology for Golden Bay.
1 -----------------2------------------3-----------------4--------------------5
strongly disagree disagree
neutral
agree
strongly agree
Why?
[Give interviewee 20 tokens] Using these 20 tokens, can you weight your concerns related to a
potential offshore wind development in Golden Bay? Please assign a higher number of tokens to the
issues that you are most worried about.
189
Why did you weight [X] the most?
Why did you weight [Y] the least?
[X] and [Y] are your top two concerns. Assuming an offshore wind farm was to proceed, in order to
ensure these impacts are mostly eliminated, would you be willing to increase your annual income tax
burden by: [circle]
1. $0
2. $50
3. $100
4. $150
5. $200
Can you weight the benefits that you associate with a potential offshore wind farm using 20 tokens?
190
Why did you weight [X] the most?
Why did you weight [Y] the least?
[X] and [Y] are your top two benefits. Assuming an offshore wind farm was to proceed, in order to
ensure these benefits are achieved, would you be willing to increase your annual income tax burden
by: [circle]
1. $0
2. $50
3. $100
4. $150
5. $200
Final Ranking
I’d like to return to one of my initial questions. When thinking about new sources of electricity in
general, after this interview, would you change how you rank the following? Again, please choose
your top four concerns, ranking these from most important to lesser importance.
__Using more local resources to generate electricity rather than imported resources
__Minimizing capital cost of the technology (the one-time cost of the new infrastructure)
__Ensuring that utility bills don’t increase more than 10% to cover new costs
__Prioritize low carbon source of electricity
__Reduce or mitigate strongly any local environmental impacts
__Ensure any visual or aesthetic impacts of energy infrastructures are locally acceptable
__Ensure noise associated with electricity generation is locally acceptable
191
__ Ability for the energy system to withstand or recover quickly from natural hazards, e.g., an
earthquake or storm events
__ Other, specify _________________
[if different from initial ranking] Can you tell me why you have a different ranking?
Let me know if you have any additional comments or questions about this interview.
Thank-you for your time.
[stop recording device]
192
Appendix D Full table of risk components
Scoring of risk characteristics from psychometric risk paradigm associated with perceived risks to ecosystem services from an offshore wind
farm. Our scores in blue are based on reviewing the biological and social science literature on offshore wind farms as well as interviews
conducted in our study site. Our risk characteristics (components of Factor 1 and 2) were inspired by the psychometric risk paradigm. Italics
denote components removed from correlation test because the scores to not vary across the potential ES consequences
(-) --> diminishes risk perception; (+) --> increases risk perception; WF is wind farm
Factor 1 Dread
Do those benefiting bear their
Can precautions be easily
Is a particular consequence
share of risks? Are risks and
taken to reduce the negative
fatal?
benefits equitably distributed
impact?
across society?
Does this pose a risk Do the risks
Is the potential scale of
to people in the
increase over
the consequence global?
future?
time?
Diminishes risk
perception
Controllable (-)
Consequences not fatal
Easily reduced (-)
(-)
Not globally
catastrophic (-)
Low risk to future Risk decreasing Voluntary
generations (-)
over time (-)
exposure (-)
Example
Car: driver can drive cautiously to
Bicycle, car
reduce severity of potential
accident
Medical x-ray
Fires, floods
Medical x-rays
Increases risk
perception
Uncontrollable (+)
Dread (+)
Consequences Fatal (+) Not easily reduced (+)
Example
Airplane: passengers relinquish
control to pilot, passengers do
not control severity of accident
Terrorism, shark attack, nuclear
Nuclear meltdown
meltdown
Displacement of
recreational fishing
-1
Not dread (-)
-1
Stakeholders generally have
Area displaced tends to be
opportunities to influence
relatively small in comparison to
location and size of wind farm;
the much larger extent of fishing
they tend to have some control in
grounds, this tends not to be not
relation to displacement and
a dreaded concern
consequently impact on fishing
Displacement of
commercial fishing
Displacement of
recreational boating
Negative impact on
tourism
Potential Ecosystem Service Consequence
Do people have
a choice in
exposing
themselves to
this risk?
Risk factor
question
Can the person who suffers
Does potential consequence
negative consequences control
evoke a feeling of dread?
the severity of the consequences?
Negative visual
impact
Impact to seabirds
Impact on marine
mammals
Equitable (-)
Medical x-ray: wear a lead
car: drivers benefit from cars
apron, bicycle: wear a helmet while facing risks of driving
Ocean acidification
-1
-1
Not fatal
As long as area of wind farm
is not prime or irreplaceable
fishing grounds, impact can
be reduced by moving fishing
effort elsewhere
Not Equitable (+)
Globally catastrophic High risk to future Risk increasing
(+)
generations (+)
over time (+)
Sea level rise due to climate
change: poor people, who emit
less carbon, will suffer most
severe consequences
Nuclear meltdown
1
WF would have inequitable but
small impact on rec fishermen
-1
-1
-1
-1
1
Same as above in relation to
commercial fishing
Area displaced is small relative
to size of bay, this is not a
dreaded concern
Not fatal
Impact easily reduced by
moving commercial fishing
effort elsewhere
WF would have inequitable but
small impact on commercial
fishermen
Local not global impact
-1
-1
-1
-1
1
Same as above in relation to
impact on fishing
No expressions of dread found in
literature in relation to
displacement of recreational
boating
Not fatal
Impact easily reduced by
recreational boating
elsewhere
WF would have inequitable but
small impact on recreational
boating; rec boaters likely
wouldn't benefit much
-1
1
1
-1
-1
No expressions of "dread" per se
found in literature in relation to
negative impact on tourism.
People are concerned, but we
did not find documentation of
widespread anxiety or fear (aka
dread).
Not fatal
1
-1
-1
-1
1
1
1
1
1
-1
Nuclear fall out
1
1
See above
See above
fishers
generally don't
choose this
-1
-1
-1
1
Local not global impact
See above
See above
boaters
generally don't
choose this
-1
-1
-1
1
See above
See above
tour operators
likely do not
choose this
-1
-1
1
See above
See above
XXXX
-1
1
-1
Placing the turbines further
Dread or fear does not
offshore to reduce visual
The negative affective reaction to
characterize most people's
impact is not feasible with
People living, working and
visual impact is subjective so not attitudes to a WF. Many dislike
Not fatal
existing technology given
recreating closer to coast would Local not global impact
controllable
and don't want it but it's not a
water depths at distances at experience greater visual impact
source of dread
which farm would not be
visible from land
1
1
1
1
1
-1
Extensive studies on bird
migrations have been
People strongly value region's
conducted to inform siting of
People tend not to control bird high density of nesting sea birds, Some bird mortalities are
WFs pose a higher risk to birds
WFs. Once constructed, few
behavior. Perception of high
they are highly concerned with
associated with wind
than other marine species. No
Local not global impact
options currently exist to
likelihood of collisions
development that could harm
turbine collisions
benefit to birds.
reduce risk of bird collisions
birds populations
with commercial scale
modern turbines
1
-1
Exposure to
pollutants often
increase health
risks over time
Involuntary
exposure +
-1
Not easily reduced: tourism
operations would likely need
to change their operations Concerns raised about impacts of
Local not global impact
that currently focus on
WF on tourism
wildness of land and
seascape
1
Climate change
Skiing,
skydiving
Same risk to present
and future
Risks stay the
generations, lifespan
fishers
same or decrease
Local not global impact of wind turbines is 20generally don't
as people adjust
30 years so minimal
choose this
to change
risk to people in
distant future
-1
Results are inconclusive regarding
if wind farms negatively impact
tourism. It is a common concern,
but tour operators control what
they advertise and show so they
could capitalize on the green tech
aspect of farm. Many tourists
may want tours of the farm
(Lilley, 2010).
Risk of cancer
after quitting
smoking
-1
Concerns raised about impact to
People dread potential harm to
Perception of fatal
marine mammals in interviews
whales as evidenced by strong collisions (although none
and past studies. Ecological
Can not control marine mammal affective response in interviews have been documented in
Interviewees do not know of studies suggest marine mammals
behavior with regards to wind
and to whale strandings and
WF studies); perception
technologies to safely keep may benefit from increased food Local not global impact
turbines, collision is a common
deployment of volunteer time that electromagnetic fields
whales away from turbines availability wind turbines. A small
concern
and resources to reduce
from underwater cables
minority of interviewees
fatalities of common whale
could effect whale
wondered if wind farm could
strandings in bay
strandings
decrease whale strandings)
-1
-1
See above
Birds may avoid
area around
turbines
not voluntary
(Lindeboom, 2011)
exposure
and learn to fly
below or above
-1
-1
1
See above
WF construction
phase has most
acute impacts,
operations have
minimal impact
(Snyder and
Kaiser, 2009)
not voluntary
exposure
193
Factor 2
Risk factor
question
Diminishes risk
perception
Are the consequences
observable?
Do the people exposed to the
consequences know about it?
Are the consequences of
exposure delayed?
Is the hazardous consequence
new to science?
Risk known to science (-)
Observable (-)
Known to those exposed (-)
Effect immediate (-)
Old Risk (-)
Example
Cars, bicycle
Flooding
Smoking
Flooding
Car, bicycle
Increases risk
perception
Risk Unknown to science (+) Not observable (+)
Unknown to those exposed
(+)
Effect delayed (+)
New Risk (+)
Radon (at least initially)
Exposure to many pollutants
Fracking
Example
Displacement of
recreational fishing
Displacement of
commercial fishing
Potential Ecosystem Service Consequence
Is the risk known to science?
Displacement of
recreational boating
Negative impact on
tourism
Long term impact of fracking
Fracking: impact on
underground ecosystems
and water is hard to
observe
-1
-1
-1
-1
-1
Impact of displaced recreational
fishing has been studied, easy
for people to imagine it has
known consequences
displacement can be
observed
Given visibly of WFs, this impact
would be known to those
displaced
No time delay
past developments have
displaced fishing effort, e.g.,
aquaculture, shipping
-1
-1
-1
-1
Impact of displaced commercial
fishing has been studied
displacement can be
observed
Given visibly of WFs, this impact
would be known to those
displaced
-1
-1
-1
-1
-1
Impact of displaced recreational
fishing has been studied
displacement can be
observed
Given visibly of WFs, this impact
would be known to those
displaced
No time delay
past developments have
displaced boating, e.g.,
aquaculture, shipping
-1
-1
-1
-1
-1
No time delay
past developments have
impacted tourism
-1
-1
No time delay
visual impact not new to science
-1
-1
Tourism impacts have been
Given visibly of WFs, this impact
studied. Studies suggests no or
impact on tourism can be
would be known to those
minimal impact to tourism.
observed
displaced
Results are not conclusive across
all study locations.
Negative visual
impact
-1
-1
-1
surveys can be used to
assess attitudes towards
visual impact
-1
Given visibly of WFs, this impact
would be known to those
displaced
-1
impacts to seabirds are
observable
This impact could be measured
and known
No time delay
impact to seabirds not new to
science
1
-1
-1
-1
-1
scientists have identified
mechanisms underpinning
impacts to marine mammals in
other locations from offshore
wind farms
impacts to marine
mammals are observable
This impact could be measured
and known
No time delay
impact to marine mammals not
new to science
Visual impact from offshore
wind farms has been studied
Impact to seabirds
Impact on marine
mammals
-1
past developments have
displaced fishing effort, e.g.,
aquaculture, shipping
1
scientists have identified
mechanisms underpinning
impacts to seabird in other
locations from offshore wind
farms
194
See: https://youtu.be/w_JYLRHi_Bc
195
Appendix E Choice experiment consent form
Principal Investigator
Dr. Kai Chan
University of British Columbia
2202 Main Mall
Vancouver BC, Canada
Co-Investigator
Sarah Klain, PhD Candidate
University of British Columbia
2202 Main Mall
Vancouver BC, Canada
We are conducting a survey about people’s preferences based on different text and imagebased descriptions. The survey will take approximately 20 minutes.
This research will contribute towards Sarah Klain’s PhD dissertation.
Sponsor
This research project was made possible by a grant from the Social Science and Humanities
Research Council of Canada (SSHRC).
Purpose
You are invited to take part in this research because you are a resident of New England and we
are interested in New Englanders preferences and opinions.
Study Procedures
If you consent, you will be directed to a survey and you will make choices based on your
personal preferences. We will also ask a few demographic and attitude-related questions.
Potential Risks
To minimize and avoid psychological stress, the confidentiality of the information that you
share is guaranteed and you are free to stop participating in this survey at any point. We ask for
your m-turk worker id, but no information that reveals your identity.
Potential Benefits
Information from your participation in this study may inform policy and development options.
You may find the survey educational. If you are interested in receiving a digital copy of the
output of this research, please email Sarah Klain at XXX.
Confidentiality
We are not collecting information that could identify who you are. The M-Turk system protects
the anonymity of its workers.
196
Remuneration/Compensation
You will be paid $1 to complete this survey via the M-Turk system.
Contact for information about the study
If you have questions or want to know more information about this study, please email Sarah
Klain XXX or Kai Chan at XX.
Contact for concerns about the rights of research subjects
If you have any concerns or complaints about your rights as a research participant and/or your
experiences while participating in this study, contact the Research Participant Complaint Line in
the UBC Office of Research Services at 604-822-8598 or if long distance e-mail RSIL@ors.ubc.ca
or call toll free 1-877-822-8598 (Toll Free: 1-877-822-8598).
Consent
Your participation in this study is entirely voluntary and you may refuse to participate or
withdraw from the study at any time without jeopardy to your employment.
Clicking “I consent to participating in this study” indicates your consent in choosing to take this
survey.
197
Appendix F Choice experiment Mechanical Turk request description
Requester: Sarah Klain
Qualifications Required: HIT approval rate (%) is higher than 50; Location is ME, MA, CT, NH, RI
Reward: $1.00 per HIT
HITs available: 1
University of British Columbia
We are conducting a survey about people’s preferences based on different text and imagebased descriptions. The survey will take approximately 20 minutes.
Make sure you know your M-Turk Id.
Responses will be checked before approval. Once approved, you will be paid $1.
Please follow these steps to complete the survey:
1. Accept the HIT
2. Open the survey in a different Tab or Window (right-click on link and select option):
https://ubc.qualtrics[xxx]
3. Complete the survey. A Completion Code will be shown when you finish this survey. This
code is necessary to process payment
4. Insert the Completion Code below:
Thank you for your interest!
198
Appendix G Choice experiment survey
Options for Electrifying the Future
Introduction
A wind farm is a cluster of wind turbines used to generate electricity. Based on US Department
of Energy studies, coastal New England has strong and abundant offshore wind resources as
shown in the map below.
Wind resource
potential
Poor
Fair
Rhode
Island$
Good
Excellent
Outstanding
1. Have you seen a wind turbine in operation?
o Yes
o No
2. What is your attitude toward developing wind power in the US?
o Very positive
o Positive
o Neutral
o Negative
199
o Very Negative
3. In your opinion, construction of offshore wind turbines off the coast of your state should
be:
o Encouraged
o Tolerated
o Discouraged
o Prohibited
o Not sure
4. Would the presence of a visible offshore wind farm make you more or less likely to go to
the coast for recreational purposes (e.g., beach-going, boating, fishing, or walking along
the coast)?
o
o
o
o
o
Much less likely
Less likely
No Difference
More Likely
Much more Likely
Choices of Electricity Sources
Research on how people make decisions shows that how people feel, their prior knowledge and
their past experiences affect how they make decisions. We need to know if you take the time to
read directions, otherwise the information you provide in this survey will not be useful. To
demonstrate that you have read the instructions, for the next question on how you feel about
wind turbines, please select “None of the above” as your answer.
Please check all the words that describe your feelings towards wind turbines:
Supportive
Interested
Apathetic
Opposed
Afraid
Enthusiastic
Disinterested Skeptical
Concerned
Curious
Appreciative None of the Above
For the purpose of this survey, please assume that your state has committed to increase energy
generation by 10%. Imagine that you have the opportunity to vote on either
1) An offshore wind farm with 100 wind turbines; or
2) A new coal or natural gas plant
200
Imagine that a wind farm is being considered for a site off the coast of your state. As part of
the negotiation with various stakeholders, you and other residents are given shares worth
$100 in the wind farm company (or cooperative) if the wind farm is developed.
A Google Earth visualization of an offshore wind farm. The eye altitude is 3 feet above the ocean. Typical offshore
wind farm towers rise to around 360 feet above sea level.
If an offshore wind farm is built, assume a renewable energy fee would be added each month
to your electricity bill. This fee would be used to offset construction and maintenance costs for
the lifespan of the wind farm, which is about 25 years.
We will ask you to vote for your preferred option while assuming that:
• The electricity generation option that receives the most votes will be constructed
• Each energy option generates an equal number of job opportunities
• Potential wind farm sites have equal wind resources
• Wind farm locations are outside of bird migration pathways and distant from bird
nesting areas
• Engineers and biologists can create underwater structures as part of the tower, which
supports the turbine blades. This tower could provide different levels of underwater
habitat quality.
This wind energy company could be a:
201
•
•
•
•
Cooperative: members own the business, all profits after taxes are given back to
members
Private company or corporation: owned by share holders who appoint a board of
directors who supervise the business
Municipal owned and operated initiative: the wind farm is publicly owned by the
municipal government
State owned and operated initiative: the wind farm is publicly owned by the state
government
202
Please consider the following set of options.
Effect&on&
marine&life!
Op#on&A&&
Wind&Farm!
Op#on&B&
Wind&farm!
Op#on&C&
Coal&or&Gas&Plant&
No&Wind&Farm!
• Large!loss!
• 60%!decline!in!diversity!and!
abundance!
• Turbine!structures!provide!
poor&habitat&for!underwater!
plants!and!animals,!e.g.,!an=>
fouling!paint!used!on!tower!
• Large!gain!
• 60%!increase!in!diversity!
and!abundance!
• Turbine!structures!provide!
excellent&habitat!for!
underwater!plants!and!
animals!
• No!wind!farm!
• Expansion!of!coal!or!
natural!gas!
• No!direct!impact!on!
marine!ecosystems!
• Associated!CO2!emissions!
contribute!to!ocean!
acidifica=on!!
!
!
Wind&farm&
Ownership!
&!
&!
Private!
Municipality!owned!
Ownership!not!specified
Visibility&
from&shore!
Highly!visible![play!movie]!
1!mile!from!shore!
Barely!visible![play!movie]!
≥10!miles!from!shore!
Built!on!land
Addi#on&to&
monthly&
electricity&
u#lity&bill!
&!
$1!
$20!
$0!
option would you vote for?
I would vote for:
!
!
Which
203
o Option A
o Option B
o Option C
[Repeat for a total of 8 Choice Sets. Each choice set varies the levels and attributes according to
my orthogonal array]
Imagine that a wind project off your state’s coast was the first of numerous North American
offshore wind projects. Would this influence your attitude towards the wind project? For
example, suppose that building 300 wind farms off the coast from Connecticut to Maine could
supply 30% of the electricity for New England coastal states. Together, these wind farms would
have a substantially larger impact on the ocean than one wind farm. However, 300 wind farms
could greatly reduce air pollution, foreign oil dependence, and reliance on fossil fuel linked to
climate change and sea level rise. If you knew that the farm near your state’s coast was the first
of many offshore wind farms, would you be more or less likely to support the wind farm?
1
2
3
4
5
|---------------------------------|---------------------------------|---------------------------------|---------------------------------|
Less likely
to support
No effect on
my decision
More likely
to support
Details about yourself to help us interpret our survey results
Are you female or male?
o Female
o Male
How old are you?
What is your zip code?
What is your race or ethnic origin? Check all that apply.
o American Indian or Alaska Native
o Asian
o Black or African American
o Hispanic, Latino or Spanish
o Native Hawaiian or Other Pacific Islander
o White European
o Middle Eastern
204
o North African
o Other ______
What is the highest level of education that you have completed? Please check one.
o Grade school
o Some high school
o High school graduate
o Some college credit
o Associate degree
o Bachelor’s degree
o Graduate degree or Professional degree
Which category best describes your household income before taxes in 2014?
o Less than $10,000
o $10,000-$14,999
o $15,000-$24,999
o $25,000-$34,999
o $35,000-$49,999
o $50,000-$74,999
o $75,000-$99,999
o $100,000-$124,999
o $125,000-$149,000
o $150,000-$174,999
o $175,000-$199,999
o $250,000 and above
What is your employment status?
o Employed for wages
o Self-employed
o Out of work
o A homemaker
o Student
o Retired
Have you heard of Mechanical Turk? To confirm that you are carefully reading instructions,
please select: Yes, I am an MT worker.
o
o
o
o
o
Never heard of it
No, what is that?
Vaguely, but I’m not sure
Yes, I am an MT worker
Yes, I have done many HITs
205
Please indicate your political affiliation:
o Democratic party
o Republican party
o Independent
o Other (please specify)
o None
Do you recreate on the coast? This could be a range of coastal or ocean-based activities such as
going to the beach, surfing, fishing, and/or boating.
o Frequently, 20+ times/year
o Sometimes, 10-20 times/year
o Every now and then, 5-10 times/year
o Rarely, 1-5 times/year
o Never
Attitudes
On a scale of 1 (Strongly Disagree) to 5 (Strongly Agree), to what extent do you agree with the
following statements?
Humans are severely abusing the environment.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Unsure
Agree
Strongly agree
The balance of nature is strong enough to cope with the impacts of modern industrial nations.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Unsure
Agree
Strongly agree
The so-called “ecological crisis” facing human kind has been greatly exaggerated.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Unsure
Agree
Strongly agree
The earth is like a spaceship with very limited room and resources.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Unsure
Agree
Strongly agree
If things continue on their present course, we will soon experience a major ecological
catastrophe.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Unsure
Agree
Strongly agree
206
On a scale of 1 (Strongly Disagree) to 5 (Strongly Agree), to what extent do you agree with the
following statements?
Plants and animals, as part of the interdependent web of life, are like ‘kin’ or family to me, so
how we treat them matters.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Neutral
Agree
Strongly agree
Humans have a responsibility to account for our own impacts to the environment because they
can harm other people.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Neutral
Agree
Strongly agree
I have strong feelings about nature (including all plants, animals, the land, etc.); these views are
part of who I am and how I live my life.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Neutral
Agree
Strongly agree
I often think of some wild places whose fate I care about and strive to protect, even though I
may never see them myself.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Neutral
Agree
Strongly agree
There are landscapes that say something about who we are as a community, a people.
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Neutral
Agree
Strongly agree
How I manage the land, both for plants and animals and for future people, reflects my sense of
responsibility to and so stewardship of the land
1 ------------------2------------------3------------------4--------------------5
Strongly disagree Disagree
Neutral
Agree
Strongly agree
I think about the forest and all the plants and animals in it like:
A family of which I am very much a part
o Yes, this is very much like how I think about the forest
o Yes, this is like how I think about the forest
o This is somewhat like how I think about the forest
o This is somewhat unlike how I think about the forest
o No, this is very unlike how I think about the forest
207
Beings to which we owe responsible citizenship and care
o Yes, this is very much like how I think about the forest
o Yes, this is like how I think about the forest
o This is somewhat like how I think about the forest
o This is somewhat unlike how I think about the forest
o No, this is very unlike how I think about the forest
Something that I identify with so strongly that it makes me, me
o Yes, this is very much like how I think about the forest
o Yes, this is like how I think about the forest
o This is somewhat like how I think about the forest
o This is somewhat unlike how I think about the forest
o No, this is very unlike how I think about the forest
A world that we must care for so that any damage doesn’t also negatively effect humans who
depend on it elsewhere
o Yes, this is very much like how I think about the forest
o Yes, this is like how I think about the forest
o This is somewhat like how I think about the forest
o This is somewhat unlike how I think about the forest
o No, this is very unlike how I think about the forest
Thank-you for completing this survey! Your opinions are important.
Here is your code to insert in Mechanical Turk to receive your payment: [XXXXX]
If you want to learn more about this research project and why we asked you certain questions,
click on Optional Debrief below.
Completion
Thank-you for completing this survey! Your opinions are important.
Here is your code to insert in Mechanical Turk to receive your payment: [XXXXX]
Let us know if you have any insights for improving this survey.
Optional Debrief
This research was designed to assess people’s preferences when it comes to making trade-offs
related to renewable energy development. We will use the results to estimate public levels of
support for a renewable energy technology that could be designed to increase the abundance
208
and diversity of marine ecosystems. We are also testing to see if the public prefers one type of
wind company ownership model over others.
In Europe and China, wind farm developers are building offshore wind farms on an industrial
scale. Offshore wind farms have not yet been built in North America. The higher construction
and maintenance costs of offshore as compared to land-based wind farms can be largely offset
by increased electricity generation since offshore wind tends to be stronger and steadier than
onshore wind. Currently, offshore wind farms cost more per unit of electricity generated than
most coal, natural gas or hydroelectric power stations, but operating a wind farm does not
generate carbon emissions nor does it impact river ecosystems.
If you’re interested in learning more about the science of offshore wind farms, here are some
sources of information:
The National Renewable Energy Laboratory, which is within the US department of Energy:
http://www.nrel.gov/wind/offshore_wind.html
The Natural Resources Defense Council, Renewable Energy for America site on offshore
renewables:
http://www.nrdc.org/energy/renewables/offshore.asp
209
Appendix H Variables in choice experiment
Variables used in discrete choice experiment regression models including description and means for survey
respondents (n = 400).
Variable
ASC
Description
Alternative-specific constant
Type of Data
choice A = 1; choice B = 1;
choice C = 0
big.loss
Choice attribute is 60% decline in
diversity and abundance
1 = yes; 0 = no
0.18
small.lossw
Choice attribute is 30% decline in
diversity and abundance
1 = yes; 0 = no
0.17
small.gain
Choice attribute is 30% increase in
diversity and abundance
1 = yes; 0 = no
0.19
big.gain
Choice attribute is 60% increase in
diversity and abundance
1 = yes; 0 = no
0.13
state
municipal
privatew
cooperative
mi1w
mi4
mi8
mi10
bill
state owned wind farm
municipal owned wind farm
privately owned wind farm
cooperative owned wind farm
wind farm 1 mile from shore
wind farm 4 miles from shore
wind farm 8 miles from shore
wind farm > 10 miles from shore
cost of OWF as addition to monthly
utility bill
1 = yes; 0 = no
1 = yes; 0 = no
1 = yes; 0 = no
1 = yes; 0 = no
1 = yes; 0 = no
1 = yes; 0 = no
1 = yes; 0 = no
1 = yes; 0 = no
$1; $5; $10; $20
0.18
0.17
0.20
0.12
0.16
0.17
0.20
0.14
5.38
white
female
age
univ_degr
Respondent is white
Respondent is female
Age of respondent
Respondent has a university degree
income
Household income before taxes
1 = yes; 0 = no
1 = yes; 0 = no
18-69
1 = university degree or
more, 0 = less than
university degree
1 to 12; 1 = less than $10k;
12 = more than $250,000
wages
self.emp
coast_rec
Employed for wages
Self employed
Sometimes or frequently recreates at coast
(10-20+ times/year)
wBase case used in effects coding.
1 = yes; 0 = no
1 = yes; 0 = no
1 = yes; 0 = no
Mean
0.67
0.83
0.59
32.38
0.66
5.36
0.56
0.11
0.36
210
Appendix I Factor Analysis by population
Factor analysis results from tourist sample
Factor analysis results from farmer sample
211
bau_nep
0.5
abuse_nep
spaceship_nep
other_rel
clean_inst
health_rel
other_met
0.0
kin_met
iden_met
tech
extract_ins
loss_instr
decade_mor
right
−0.5
Factor2
comm_rel wild_rel
resp_met
iden_rel
kin_rel
bal_nep
crisis_nep
−0.2
0.0
0.2
0.4
0.6
0.8
Factor1
Factor analysis results from M-Turk sample
212
Appendix J Scree plot
Scree plot including responses to five NEP statements and six relational value statements across all three
populations. Parallel analysis, optimal coordinates and acceleration factors are different methods to
determine the number of factors to retain (Ledesma, 2011).
213
Appendix K Graphical PCA results
Graphical PCA results using data on responses to relational value and NEP statement
Graphical PCA results using data on responses to relational value and NEP statement
214
Appendix L M-Turk Cronbach’s alphas
Cronbach's alpha
0.8
0.6
0.4
0.2
)
EP
N
r(
ho
m
et
ap
(5
4)
)
(2
si
c
rin
in
t
tio
la
re
in
st
ru
m
en
ta
na
l(
l(
3)
6)
0.0
Type of Environmental Value Prompt
Cronbach alphas for M-Turk population. Note the different number of prompts in each category as shown
in parentheses after each environmental value type. We suggest testing additional intrinsic and
instrumental value prompts.
215
Appendix M Variables on wind farm attitudes and indices of environmental value
Variable
att_w_US
oper
const_st
wf_rec
coast_rec
first_st
Description
What is your attitude toward developing wind power in the U.S.?
Likert Scale Descriptor
Score
Very negative
1
Negative
2
Neutral
3
Positive
4
Very positive
5
No
1
Have you seen a wind turbine in operation?
Yes
2
1
In your opinion, construction of offshore wind turbines off the coast Prohibited
Discouraged
2
of your state should be:
Tolerated
3
Encouraged
4
1
Would the presence of a visible offshore wind farm make you more or Much less likely
Less likely
2
less likely to go to the coast for recreational purposes (e.g., beachNo difference
3
going, boating, fishing, or walking along the coast)?
More likely
4
Much more likely
5
Never
1
Do you recreate on the coast? This could be a range of coastal or
Rarely, 1-5 times/year
2
ocean-based activities such as going to the beach, surfing, fishing,
Every now and then, 5-10
3
and/or boating.
Sometimes, 10-20 times/year 4
Frequently, 20+ times/year
5
Much less likely to support
1
Imagine that a wind project off your state’s coast was the first of numerous
Less likely to support
2
North American offshore wind projects. Would this influence your attitude
3
towards the wind project? For example, suppose that building 200 offshore wind No effect on my attitude
More likely to support
4
farms could supply 30% of the electricity for New England coastal states.
Together, these wind farms would have a substantially larger impact on how
Much more likely to support
5
people currently use the ocean and the ocean environment than one wind farm.
However, 200 wind farms could reduce air pollution and reliance on fossil fuels
linked to climate change and sea level rise. If you knew that the farm near your
state’s coast was the first of many offshore wind farms, would you be more or
less likely to support the wind farm?
mean_nep
Mean response to New Environmental Paradigm prompts
Strongly Disagree
Disagree
Neither Agree nor Disagree
Agree
Strongly Agree
mean_rel
Mean response to relational value prompts
No, this is very unlike how I think
about the ocean
This is somewhat unlike how I
think about the ocean
This is somewhat like how I think
about the ocean
Yes, this is like how I think about
the ocean
Yes, this is very much like how I
think about the ocean
mean_inst
Mean response to instrumental value prompts
mean_met
Mean response to metaphor value prompts
mean_mor
Mean response to moral/intrinsic value prompts
Strongly Disagree
Disagree
Neither Agree nor Disagree
Agree
Strongly Agree
Strongly Disagree
Disagree
Neither Agree nor Disagree
Agree
Strongly Agree
Strongly Disagree
Disagree
Neither Agree nor Disagree
Agree
Strongly Agree
From M-Turk Sample
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
216
Appendix N Wind farm attitudes
If you knew that the farm near the coast of your
state was the first of many offshore wind farms,
would you be more or less likely
to support the wind farm?
40%
30%
20%
10%
t
t
or
pp
pp
su
su
to
to
y
y
uc
h
m
M
or
or
e
e
lik
el
lik
el
n
to
ef
fe
c
N
o
ss
Le
or
e
ud
at
tit
to
lik
el
y
to
y
lik
el
ss
le
M
M
uc
h
m
y
su
su
p
pp
po
or
rt
t
0%
Have you seen a wind turbine in operation?
0.8
Percent
0.6
0.4
0.2
0.0
No
Yes
Response
217
Appendix O Distribution of responses to value prompts
To what extent do you agree with these statements?
Extract
Loss
Clean
Kin
Resp
Inden
Other
Decade Rigth
Abuse
Bal
Crisis
Space
ship
Bau
Comm
Wild
Resp
Iden
Kin
Health
Other
4
Farmer
3
2
1
5
Instrumental
4
Metaphorical
M−Turk
3
2
1
Intrinsic/moral
NEP
Relational
5
4
Tourist
Response
1 = Strongly Disagree;
2 = Disagree; 3 = Neither Agree nor Disagree;
4 = Agree; 5 = Strongly Agree
5
3
2
1
200 0 200 0 200 0 200 0 200 0 200 0 200 0 200 0 200 0 200 0 200 0 200
200 0
0 00
200 0
0 00
200 0
200 0
0 00
200 0
200 0
0 00
0 00
200 0
200 0
0 00
0 00
200 0
0 00
0 00
00100
200
1
200
1
200
1
200
1
200
1
200
1
200
1
200
1
200
1
200
0100
200 0100
200 0100
200 0100
200 0100
200 0100
200 0100
200 0100
200 0100
200 0100
200 0100
200
count
218
Appendix P Detailed site descriptions
Case 1. Block Island: The Ocean State’s Offshore Wind Farm Pioneers
Construction began on Deepwater Wind’s 30 MW, five-turbine wind farm three miles off the
coast of Block Island in the summer of 2015 after a relatively smooth project development
process compared to the nearby Cape Wind proposal. This can be attributed to many factors,
including the groundwork established by the Rhode Island Coastal Resources Management
Council’s Rhode Island Ocean Special Area Management Plan (SAMP) shortly before the
project was proposed (Nutters and Pinto da Silva, 2012). Also, the relatively small scale of the
Block Island project likely contributed to its ability to move forward first. The Block Island
Wind Farm consists of five turbines compared to Cape Wind’s 130, the anticipated economic
impact on electric rates is smaller than Cape Wind’s, and it is a multi-million dollar project while
Cape Wind is a multi-billion dollar project (Smith et al., 2015). The Block Island Wind Farm
also benefited from the state’s long-term contracting legislation, as well as minimal federal
regulatory review due to the project’s location within state waters. While not without its
opponents (McGlinchey, 2013), this project has been met with support from island leaders, a
local Indian tribe, environmentalists and fishermen, in part due to well-defined benefits
(Economist, 2015).
Timing played a key role in the success of this project. Creating and disseminating the SAMP
before the wind farm was proposed meant that information about state waters was already readily
available and accessible and had been discussed with key stakeholders (Nutters and Pinto da
Silva, 2012), including the town council of New Shoreham on Block Island, which actively
followed and contributed to the SAMP process. When Deepwater Wind proposed a wind farm in
Rhode Island’s state waters, the New Shoreham Town Council was tasked with reviewing the
proposal and representing the community’s interests and concerns. The town council recognized
that it did not have energy experts on staff to review the associated technical documents within
the structure of the regulatory process. To prevent a defensive David versus Goliath mentality
219
(i.e., the small island community standing up to a large, well-financed development corporation),
Deepwater Wind and the town council discussed the town’s need for additional technical
capacity to make the proposed project more accessible and understandable to residents. The town
selected and hired consultants to represent their interests and Deepwater agreed to reimburse the
town for the expense of these consultants (Island Institute, 2012a).
These consultants served the function of a bridging organization between the developers and the
island community members. The consultants translated pertinent technical details and locally
relevant information to the town council. They shared information with the broader community,
fielded questions at community meetings, listened to community concerns and translated these
concerns into comments during the formal regulatory processes. The expertise of the consultants
provided the town council with greater confidence that community concerns would be better
integrated into the wind farm planning processes. A New Shoreham Town Council Member
recognized the importance of readily available information, hiring a trusted communicator and
securing community benefits:
The community [of Block Island] benefited greatly from the sharing of information via
the Ocean SAMP process, and by Deepwater Wind's commitment to putting in place a
trusted liaison as conduit for information... By employing [the liaison] and locating his
office on Block Island, Deepwater Wind was able to provide "up to the minute"
information and build relationships of trust. This was critical to success. By negotiating
with the developer a number of key community benefit items, the Town of New Shoreham
became a partner (albeit small) in the project, not just a passive venue to be utilized [or]
exploited…We became educated, conversant, increasingly confident, and responsible
citizens as we faced each phase of the process… We learned that even a small island
community can lead by example… There is no end to what needs to be learned and
stewarded.
Local stakeholders, government officials and Island Institute staff were convinced that locallyrelevant community benefits played an important role in the success of this project. Once the
220
farm is built, Block Island will, for the first time, be connected to the mainland grid. Deepwater
Wind anticipates that this wind farm and the submarine transmission cables connecting the
turbines and the island to the mainland electricity grid will lower the island’s electricity costs by
40% (Economist, 2015), which was a driver in garnering local support for the project.2 The
project developer, Deepwater Wind, anticipates that this wind farm and the submarine
transmission cables connecting the turbines and the island to the mainland electricity grid will
reduce the island’s electricity costs (Smith et al., 2015). As a result, once the wind farm is
completed, Block Island will no longer need to transport and burn approximately one million
gallons of diesel fuel to power the island’s generators (Economist, 2015). The town negotiated to
have fiber optic strands included in the electricity cable bundle that were provided for the town.
Faster Internet service will benefit residents and businesses that have struggled with the slower
microwave-based broadband, particularly during the busy summer months. Deepwater Wind and
New Shoreham have also developed a formal Community Benefit Agreement (CBA) in which
the wind farm company will pay for improvements to town infrastructure where the cable comes
ashore. Further, the project is expected to generate 300 jobs during the construction phase,
including opportunities for local mariners and fishermen (Smith et al., 2015).
Case 2. Martha’s Vineyard: Moving forward with a Cooperative Approach
Vineyard Power was an outgrowth of Martha’s Vineyard’s Island Plan, a sustainability strategy
that the Martha’s Vineyard Commission completed based on input from thousands of island
residents in 2009 to “create the future we want rather than settle for the future we get” (MVC,
2009, p. 1). Eight years after the controversial Cape Wind offshore wind project had been
proposed, the plan included a recommendation to create a community-owned renewable energy
cooperative so islanders could have more autonomy over their energy production and better
ensure community benefits associated with renewable energy development. To date, Vineyard
Power has developed five commercial-scale solar photovoltaic projects on Martha’s Vineyard
2
This anticipated cost reduction estimate did not account for the 2014 dip in oil prices. The offshore wind
farm, however, is anticipated to reduce the volatility of electricity prices on the island. In the long term,
natural gas and oil prices are expected to rise (EIA, 2015).
221
and continues to look to multiple renewable energy technologies going forward, including
offshore wind.
In 2009, Vineyard Power began recruiting members. The price of a membership in the coop
escalates over time, beginning at $50 and currently at $200 in 2015. People joined for social
benefits (e.g., inclusion in the decision making processes in an island-owned, action-oriented
group to create a more sustainable energy future for their community) and financial rewards
(e.g., ownership and control of local renewable energy projects and stabilized electricity prices
once a large-scale renewable energy project is developed) (Nevin, 2010). The cooperative’s
community benefits are embedded in the cooperative’s mission: “to produce electricity from
local, renewable resources while advocating for and keeping the benefits within our island
community” and the organization’s vision “to be Martha's Vineyard's community-owned energy
cooperative” (VPC, 2015).
Vineyard Power members have made community benefits a central theme in the development of
this offshore wind farm. Lack of perceived community benefits, arguably, played a more minor
role in Cape Wind, an earlier Massachusetts-based offshore wind farm proposal that has stalled
due to lawsuits, regulatory issues and problems with its Power Purchase Agreement (PPA).
Learning from the Cape Wind experience, Vineyard Power initially developed a wind farm
ownership model influenced by the project design and financing structure of the communityowned Fox Islands Wind Project on Vinalhaven Island, Maine where the size of the project was
linked to the amount of power consumed by the island (personal communication Peckar, 2015b).
The complexity, scale and scope of the currently proposed offshore wind farm, which could be
as large as 2,000 MW (Smith et al., 2015), vastly exceeds the three-turbine Fox Islands Wind
Project yet the focus on local control and benefit remains.
In January 2015, BOEM auctioned the rights to lease offshore wind in areas in federal waters
south of Martha’s Vineyard. Offshore MW received a 10% discount on their bid price because
they had executed a Community Benefit Agreement (CBA) with Vineyard Power. The CBA
outlined opportunities to investigate local benefits to the island including job creation, an
222
operations and maintenance facility, and local equity ownership in the project (VPCOMW,
2015).
The President of Vineyard Power Cooperative reinforced the importance of community
engagement, providing accessible information and community benefits when he said:
“Vineyard Power has always advocated for an open, community-based approach in the
development of renewable energy projects. We have been an extremely active participant
throughout the BOEM offshore wind leasing process and provide updates and information
to local municipalities, businesses and residents of our island to ensure our community and
stakeholders remain engaged. We also believe that any offshore wind farm development in
our surrounding waters should provide local benefits. We took control of our energy future
and decided to be an active participant in the process. Through years of outreach with our
members, local legislators and the local municipalities, BOEM recognized the nation’s first
Community Benefit Agreement between our organization and Offshore MW. Through this
CBA, we will ensure that our island community’s local economy will remain strong
through local ownership, and job creation.”
In earlier stages of the project’s development, the cooperative hosted an interactive offshore
wind map viewer on its website to not only inform but also solicit preferences from coop
members and other engaged island residents to find a suitable location for the wind farm. This
website provided readily available and appropriate information while encouraging participation
in sharing local values related to proposed locations. The website provided information about
visual, ecological and human use impacts based on various proposed sites, including data
collected from local sources such as island fishermen. The cooperative also hosted a series of
community meetings to share wind farm visualizations and solicit feedback (Peckar, 2015a).
Case 3: Monhegan Island: Confronting deep water challenges
The tumultuous path of offshore wind in Maine provides insights regarding mutual learning,
timing and accessibility of information. In 2009, Maine set ambitious goals to become a national
leader in ocean energy (MCP, 2009) and created opportunities for the development of offshore
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wind and tidal energy demonstration projects in both state and federal waters (MPUC, 2010). In
each of these jurisdictions, discussions of offshore wind had implications for the island of
Monhegan, a remote community 12 miles out to sea with a year-round population of about 60
and some of the highest energy costs in the nation at ~$0.70 kWh as compared to ~$0.15 kWh
for mainland residential electricity in Maine (MPUC, 2015).
In state waters, Maine took initial steps to engage stakeholders in its strategy to expedite the
development of the industry by designating three research and demonstration test sites within
state waters. Representatives of Governor Baldacci’s Ocean Energy Task Force worked with the
Maine Coastal Program (MCP) within the Maine State Planning office to host a series of public
meetings and “kitchen table” (i.e., small and informal) discussions along the Maine coast where
sites were under consideration. They incorporated scientific data and local knowledge into their
assessment process by making mutual learning accessible. For example, when MCP and other
state agency staff traveled to Monhegan to gather feedback on the potential to create a site two
miles from the island, they met with fishermen in a local fish house. They asked fishermen to
rank their fishing activity effort around the island in order to identify a site of least impact for the
turbines.
Efforts to site offshore wind in nearby federal waters underscored the importance of timing and
availability of information. On September 1, 2010, the Maine Public Utilities Commission
(PUC) began a 16-month process during which they solicited and reviewed bids for and public
comments on a long-term power purchase agreement. This extended period of time provided an
opportunity to engage stakeholders prior to the announcement of a developer and the location of
a site. During this time, the Island Institute worked as a bridging organization to facilitate mutual
learning through the Offshore Wind Energy Information Exchange, an outreach and education
initiative to inform and engage coastal and marine stakeholders, developers, and decision-makers
on the potential for offshore wind energy development in the Gulf of Maine. The initiative
included deliberative learning experiences, such as exchange trips to fishing communities as well
as a wind farm, the human use mapping project Mapping Working Waters (Island Institute,
2009), information sessions at the annual Fishermen’s Forum in Maine (Island Institute, 2012b)
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and readily available and understandable fact sheets (Island Institute, 2012a). These efforts
provided coastal stakeholders and industry representatives with a baseline understanding of
community priorities as well as the offshore wind industry, while creating an opportunity for
stakeholders to meet each other informally and build relationships.
In January 2013, Maine PUC announced its selection of an unsolicited proposal from Statoil – a
multinational corporation specializing in offshore energy infrastructure – for testing floating
turbine technology in federal waters in the state’s Midcoast region. By this time, marine users
and other stakeholders in the area had already participated in education and information
exchange opportunities, preparing them to more proactively and constructively engage in
discussions with the developer and decision-makers (Island Institute, 2015).
Later in 2013, the University of Maine entered a federal funding competition with a new scope
of activities at the Monhegan test site. Subsequently, the Maine Legislature directed the PUC to
reopen the bidding process so that the University of Maine could submit a proposal on an
accelerated timeline, and Statoil withdrew its proposal for a project in federal waters. While
these developments had statewide implications, this impacted Monhegan by significantly
limiting the timeframe in which the community could learn about the change in scope from
small-scale portable to large-scale, semi-permanent turbines. The PUC opportunity, which
prompted many islanders to learn of the change in project scale, was announced during the
summer, which is the island’s busiest time of year.
The accelerated timeline and need for information initially strained relations between the island
community and Maine Aqua Ventus (MAV), the University-led consortium developing the
larger project, but both parties quickly committed to improve communications. The first step was
to clarify points of contact and expectations for communications so that MAV could be certain
that project updates were being shared widely. Island leaders created the Monhegan Energy
Task Force (METF) as a way to prioritize information that the community needed and facilitate
discussion of community benefits associated with the proposed offshore wind project. METF and
MAV engaged in weekly phone calls to enhance the flow of information and worked to develop
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an expectations document to ensure timely project communications. During this time, both
parties looked to Block Island for examples of how information was shared and community
benefits arranged. MAV also began to host semi-regular open house sessions on the island
during which residents and visitors could have more extended discussions about aspects of the
project. In late 2015, MAV received additional federal funding ($3.7 mill) to continue refining
their floating turbine designs (Turkel, 2015).
The co-chair of the Monhegan Energy Task Force (METF) reflected on dispelling
misconceptions and improving communication between islanders and wind farm developers:
“As we try to keep our very small community running, it is easy to get lost in the “doing”
and not the “talking.” While dealing with Maine Aqua Ventus, the greatest challenge we
faced was how to quickly get correct information to the community. The key for Monhegan
Energy Task Force was to develop a plan for sharing information and for making research
resources accessible. We co-authored a communications MOU with Maine Aqua Ventus,
developed a website, sent mailings, and created an email list of stakeholders – making it
possible to “tell” while we were doing. Open communication between the community and
Monhegan Energy Task Force paired with open communication between Monhegan Energy
Task Force and Maine Aqua Ventus helped all parties keep up to date and kept
misinformation to a minimum.”
Based on our interviews, some residents still have concerns about the Monhegan offshore wind
project but the developer and community have laid a more solid foundation upon which future
communication can take place.
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