Uncovering the Impact of Trust and Perceived Fairness on the
Acceptance of Wind Power Plants and Electricity Pylons
Anika Linzenich and Martina Ziefle
Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, 52074 Aachen, Germany
Keywords: Trust, Fairness, General Acceptance, Local Acceptance, Electricity Pylons, Wind Power Plants.
Abstract: Success of the German energy transition towards renewables relies not only on technical and economic
factors, but also on the public acceptance of the required energy infrastructure, e.g., wind power plants and
power lines. In this paper, acceptance-relevant process characteristics (perceived fairness of project planning,
trust in stakeholders, and trust in technology) were investigated by comparing users’ acceptance for wind
energy and power line planning, using an online survey in Germany (n = 70). Acceptance, trust, and perceived
fairness were significantly higher for wind power plants than for electricity pylons. General acceptance of
wind power plants and electricity pylons was affected by trust, with trust in technology playing a more
important role than trust in stakeholders. Local acceptance was directly influenced by general acceptance and
perceived fairness. Trust indirectly affected local acceptance through general acceptance. The results
contribute to an improved planning of energy infrastructure by adequately addressing public requirements.
1 INTRODUCTION
The German “Energiewende” (energy transition
towards renewable energy resources) requires a
considerable expansion and restructuring of the
current energy infrastructure to increase the share of
energy from renewable resources in the electricity
supply (Federal Ministry for Economic Affairs and
Energy, 2015; n.d.). In addition to the construction of
renewable energy generation facilities (e.g., wind
farms or biomass power plants), new power lines are
necessary to connect energy production facilities to
the electricity grid (Federal Ministry for Economic
Affairs and Energy, 2015; n.d.).
The success of planned projects is often
challenged by local opposition seriously delaying or
endangering the development, although citizens are in
general supportive of the energy transition and
associated renewable energy technologies (Jones and
Eiser, 2009; Lienert, Suetterlin and Siegrist, 2015).
Thus, a favorable reception of energy infrastructure
technologies on a general and local level is an
important precondition for successful energy
infrastructure planning and, at a higher level, for
achieving the energy transition (Sütterlin and Siegrist,
2017; Wüstenhagen, Wolsink and Bürer, 2007).
Reasons for local opposition to energy infrastructure
such as wind farms or power lines are numerous.
They include perceived visual impacts due to
infrastructure elements (electricity pylons, wind
turbines) that are visible from a great distance, but
also concerns about negative consequences for
human health and the environment (Baxter, Morzaria
and Hirsch, 2013; Cotton and Devine-Wright, 2013).
Siting conflicts can also arise from planning and
decision making processes that are perceived as
unfair (Gross, 2007; Zoellner, Schweizer-Ries and
Wemheuer, 2008). This perceived unfairness of
planning procedures and their outcomes was found to
be related to trust in stakeholders involved in the
planning (Devine-Wright, 2013; Huijts, Molin and
Steg, 2012).
In the current paper, the influence of process
characteristics (trust in technology and stakeholders
and perceived fairness) on the acceptance of energy
infrastructure technologies is empirically examined.
By directly comparing perceptions of wind power
plants and electricity pylons, it will be investigated
whether acceptance-relevant process parameters are
similar across technologies or whether they are
indeed technology-specific. The results yield
valuable insights for planners on how to achieve a
socially accepted planning of energy infrastructure
projects.
190
Linzenich, A. and Ziefle, M.
Uncovering the Impact of Trust and Perceived Fairness on the Acceptance of Wind Power Plants and Electricity Pylons.
DOI: 10.5220/0006696001900198
In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018), pages 190-198
ISBN: 978-989-758-292-9
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

2 SOCIAL ACCEPTANCE OF
ENERGY TECHNOLOGIES
The discrepancy between the often high general
support for an energy technology and a lower local
acceptance of specific implementations is known as
the “social gap” in energy infrastructure planning
(Bell, Gray and Haggett, 2005). Findings from past
research on wind power projects show that local
acceptance of a specific project is considerably
affected by general support for the technology (Jones
and Eiser, 2009; Walter, 2014). Among others, the
following factors have been identified as vital for the
acceptance of energy technologies: perceived
benefits, barriers, and risks associated with the
technology, trust in stakeholders responsible for the
planning and implementation of projects, and
perceived process fairness (Devine-Wright, 2013;
Huijts et al., 2012; Visschers and Siegrist, 2014).
In previous studies, two dimensions of process
fairness have been distinguished: procedural fairness
of planning decisions and distributional fairness
relating to how benefits, costs, and risks are shared
among the population (e.g., Gross, 2007; Huijts et al.,
2012). Planning procedures perceived as fair enable
citizens to participate in the planning process and they
take the interests of all citizens into account (Gross,
2007; Keir, Watts and Inwood, 2014). As found,
opposition to energy infrastructure projects was not
solely directed against the technology per se but also
against planning processes and the distribution of
benefits and costs that were perceived as unfair
(Gross, 2007; Keir et al., 2014; Walker and Baxter,
2017). Distributional fairness refers not only to a fair
distribution of benefits and risks or costs in the
population in general but was also considered on a
local level for residents living near proposed
installation sites of energy infrastructure (Gross,
2007; Walker and Baxter, 2017). Perceived fairness,
especially procedural fairness, was found to be
related to trust in stakeholders (e.g., Devine-Wright,
2013).
When investigating the influence of trust on
energy technology acceptance, recent studies mainly
considered trust in stakeholders responsible for the
technology such as energy companies and political
actors (Bronfman, Jiménez, Arévalo and Cifuentes,
2012; Huijts et al., 2012; Visschers and Siegrist,
2014). In other technology contexts (e.g.,
AAL/medical technologies, e-commerce), also trust
in technology was identified as acceptance-relevant
parameter (Grabner-Kräuter and Kaluscha, 2003;
Montague, Kleiner and Winchester, 2009).
So far, studies investigating the relationship between
trust in technology and acceptance of large-scale
energy technologies are scarce (e.g., Achterberg,
Houtman, van Bohemen and Manevska, 2010). As
trust in stakeholders and trust in technology might not
be the same, the influence of both trust types on
acceptance should be investigated.
Studies examining the impact of process
characteristics (trust and fairness) on energy
technology acceptance have most often been limited
to a single technology or compared technologies
referring to the same part of the energy supply such
as different energy sources (e.g., Bronfman et al.,
2012; Visschers and Siegrist, 2014; Zoellner et al.,
2008). But so far, it is still not understood if
acceptance-relevant process parameters are similar
across different elements of the energy supply system
(e.g., electricity generation and transmission).
Taking wind power plants and electricity pylons
as example for different elements in the energy
supply chain, the research aims of the present study
were: 1) A direct comparison of acceptance, trust in
technology and stakeholders, and perceived fairness
of project planning for wind power plants and
electricity pylons. 2) An investigation of acceptance-
relevant process characteristics for wind energy and
power line planning.
3 METHOD AND MATERIAL
In the following, an overview of the online survey and
the survey sample is given.
3.1 Questionnaire Design
The questionnaire items were chosen based on a
literature analysis of previous acceptance studies in
the energy infrastructure context. First, respondents
were surveyed for demographic data and attitudinal
variables: age, gender, technical self-efficacy
(evaluated using four items from Beier, 1999),
individual risk orientation, i.e., a person’s general
attitude towards risk and safety (assessed by four
items from Rohrmann, 2005), and self-assessed
knowledge about the wind energy and power line
technology (item “I feel well informed about the wind
turbine [electricity pylon] technology”).
In the second part, participants were asked to rate
electricity pylons and wind power plants in terms of
general and local acceptance, trust in the underlying
technology and involved stakeholders, and perceived
fairness. To enable a direct comparison of both
technologies, the same items were used to assess
Uncovering the Impact of Trust and Perceived Fairness on the Acceptance of Wind Power Plants and Electricity Pylons
191

evaluations of wind power plants and electricity
pylons.
General acceptance was measured using two
items from Lienert et al. (2015) and Zoellner et al.
(2008). Local acceptance was assessed by asking
respondents to evaluate their reactions (supportive,
happy, concerned) to the construction of a
(hypothetical) wind power plant / electricity pylon in
their neighborhood using three items from Lienert et
al. (2015), O’Garra, Mourato and Pearson (2008), and
Soland, Steimer and Walter (2013). To assess trust in
wind energy and power line projects, participants had
to indicate their trust in the underlying technology as
well as trust in wind farm / grid operators and politics.
The two items on trust in actors (one for companies,
one for politics as a whole) were based on Bronfman
et al. (2012) and Huijts, Midden and Meijnders
(2007). Perceived fairness in wind energy and power
line planning was assessed in terms of procedural and
distributional issues. Covered procedural fairness
aspects were perceived fairness and publicness of the
siting process (two items based on Baxter et al., 2013,
and results from Gross, 2007), the consideration of
interests of all citizens, and opportunities for public
participation during the planning process (two items
based on Zoellner et al., 2008, and Soland et al.,
2013). Distributional fairness was assessed by two
items on the fair distribution of benefits and risks in
the population, especially considering the
benefit/risk-ratio for residents living near proposed
installation sites (based on MacGregor, Slovic and
Morgan, 1994, Wolsink, 2000, and results from
Gross, 2007).
Like measures for individual characteristics, all
items on wind power plant and electricity pylon
perceptions were assessed on six-point Likert scales
(1 = “do not agree at all”, 6 = “fully agree”). Thus,
values > 3.5 signify approval to and values < 3.5
indicate rejection of a statement. Results of reliability
testing are depicted in Table 1.
Table 1: Results of reliability testing.
Construct
Technology
Number
of items
𝛼
General
acceptance
Wind power plant
2
.82
Electricity pylon
2
.81
Local
acceptance
Wind power plant
3
.86
Electricity pylon
3
.84
Trust in
projects
Wind power plant
3
.81
Electricity pylon
3
.68
Perceived
fairness
Wind power plant
6
.82
Electricity pylon
6
.82
3.2 Sample
The online survey was conducted in November 2016
in Germany. Respondents were invited to participate
personally, via e-mail, discussion forums, and social
media. 114 people took part in the study. The
participants were volunteers who were not rewarded
for their participation. After excluding incomplete
data sets and internally inconsistent answering
patterns, 70 data sets were used for further analysis,
which corresponds to a response rate of 61%.
The mean age of the sample was 30.4 years
(SD = 12.3, range: 15-62 years) with 52.9% females
and 47.1% males. 21.4% of the participants reported
to hold a university degree and an equal share of
respondents had completed vocational training.
Another 44.3% of participants had obtained a
certificate for university entrance as highest
educational achievement, while 8.5% reported to
have a general certificate of secondary education or a
lower secondary school qualification. 4.3% stated to
have no educational attainment (yet).
The majority of respondents stated to live in the
city center (58.6%), whereas 30.0% indicated to
reside in the outskirts of a city or a suburb. 11.4% of
the sample lived in a rural area. The sample reported
to have a positive technical self-efficacy (M = 4.19,
SD = 1.33) but self-assessed specific knowledge
about the power line technology was rather low on
average (M = 2.64, SD = 1.39). Participants felt
significantly better but still not well informed about
the wind turbine technology (M = 3.06, SD = 1.51;
F(1,69) = 15.65, p < 0.001, 𝜂
2
= .19). The risk
orientation (general willingness to take risks) was
medium (M = 3.33, SD = 1.01).
4 RESULTS
First, perceptions of wind power plants, electricity
pylons, and process characteristics of wind farm and
power line projects are reported. In a second step, the
influence of trust and perceived fairness on the
acceptance of wind power plants and electricity
pylons is examined.
4.1 Perceptions of Wind Power Plants
and Electricity Pylons
Mean values for perceptions of wind power plants
and electricity pylons are depicted in Figure 1.
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192

Figure 1: Ratings of general and local acceptance, trust in
projects, and perceived fairness for wind power plants and
electricity pylons (n = 70).
General acceptance of wind power plants was
positive (M = 4.91, SD = 1.04), while electricity
pylons were rated significantly lower and rather
neutral (M = 3.69, SD = 1.38; F(1,69) = 48.42,
p < 0.001, 𝜂
2
= .41). For both types of infrastructure,
local acceptance was lower than general support
(wind turbines: M = 3.85, SD = 1.21; electricity
pylons: M = 2.94, SD = 1.29), but again wind power
plants obtained a significantly higher rating
(F(1,69) = 27.75, p < 0.001, 𝜂
2
= .29).
The general trust in power line projects (mean
value summarizing trust in the underlying technology
and stakeholders involved in project planning) was
slightly negative (M = 3.20, SD = 1.25). In contrast,
trust in wind power projects was significantly more
positive (M = 3.82, SD = 1.00; F(1,69) = 22.93,
p < 0.001, 𝜂
2
= .25).
Looking deeper into the different types of trust
(Figure 2), trust in the technology itself was highest
and trust in politics lowest for both technologies,
while trust in wind farm and grid operators ranged in
between. Comparing mean values for wind power
plants and electricity pylons, participants had
significantly more trust in the wind turbine (M = 4.69,
SD = 1.06) than in the power line technology
(M = 3.64, SD = 1.35; F(1,69) = 34.36, p < 0.001,
𝜂
2
= .33). A significant difference between electricity
pylons and wind power plants was also found for trust
in wind farm / grid operators: Respondents reported a
slightly positive trust in wind farm operators
(M = 3.74, SD = 1.25) but slightly rejected trust in
grid operators (M = 3.11, SD = 1.50; F(1,69) = 13.79,
p < 0.001, 𝜂
2
= .17). Trust in politics was rated on a
Figure 2: Descriptive statistics for different types of trust in
wind farm and power line projects (n = 70).
similarly negative level for both technologies (wind
turbines: M = 3.04, SD = 1.52; electricity pylons:
M = 2.83, SD = 1.56).
Perceived fairness (Figure 1) was slightly
negative for wind farm projects (M = 3.20, SD = 0.86)
and significantly lower for power lines (M = 2.84,
SD = 0.93; F(1,69) = 12.07, p < 0.01, 𝜂
2
= .15).
Zooming into the different aspects of fairness (Figure
3), also the individual items were rated as rather
negative to neutral. The two items respondents least
agreed to were aspects of procedural fairness:
consideration of interests of all citizens (wind
turbines: M = 2.93, SD =1.21; electricity pylons:
M = 2.49, SD =1.29) and participation opportunities
in the planning process (wind turbines: M = 2.96,
SD = 1.17; electricity pylons: M = 2.53, SD = 1.26).
Compared with power line projects, wind power
planning was perceived as more adequately but still
not sufficiently considering interests of all citizens
(F(1,69) = 7.81, p < 0.01, 𝜂
2
= .10) and providing
opportunities for public participation (F(1,69) = 8.13,
p < 0.01, 𝜂
2
= .11). Wind energy and power line
projects did not significantly differ in perceptions of
siting processes – both were regarded as similarly fair
and open to the public – but in evaluations of
distributional fairness. Respondents did rather not
agree that risks and benefits were fairly distributed
among citizens during the construction and operation
of wind power plants (M = 3.10, SD = 1.07). For
electricity pylons, respondents perceived an even
higher level of unfairness (M = 2.73, SD = 1.21;
F(1,69) = 5.83, p < 0.05, 𝜂
2
= .08). Distributional
fairness on a local level (benefit-risk-ratio for
residents compared to the public) was perceived
slightly better but still not positive. Values were
neutral for wind turbines (M= 3.59, SD = 1.23) and
significantly more negative for pylons (M = 3.03,
SD = 1.25; F(1,69) = 13.47, p < 0.001, 𝜂
2
= .16)
4.91
3.85
3.82
3.20
3.69
2.94
3.20
2.84
1
2
3
4
5
6
General
acceptance
Local
acceptance
Trust in
projects
Perceived
fairness
Wind power plants Electricity pylons
Average ratings (1 = min, 6 = max)
mid-point of scale
2.83
3.11
3.64
3.04
3.74
4.69
1 2 3 4 5 6
Trust in
politics
Trust in
operators
Trust in
technology
Wind power plants Electricity pylons
Level of agreement
mid-point
"do not
agree at all"
"fully agree"
Uncovering the Impact of Trust and Perceived Fairness on the Acceptance of Wind Power Plants and Electricity Pylons
193

Figure 3: Descriptive statistics for aspects of perceived fairness related to wind farm and power line projects (n = 70).
4.2 Influence of Trust and Perceived
Fairness on Acceptance
Standard multiple regression analyses were conducted
to investigate the influence of trust and perceived
project fairness on acceptance of wind power plants
and electricity pylons. The enter method was used
because trust and fairness have already been
identified as acceptance-relevant parameters in past
research. As a first step, mean values of the trust and
perceived fairness scales were entered as independent
variables and general acceptance as dependent
variable.
The resulting regression models explained a
similar amount of variance in general acceptance:
33.2% of variance for wind power plant acceptance
(F(2,67) = 18.19, p < 0.001) and 33.7% of variance
for electricity pylon acceptance (F(2,67) = 18.56,
p < 0.001). In both cases, perceived fairness had no
impact and only trust in projects contributed
significantly to general acceptance (wind: ß = .60,
p < 0.001; electricity: ß = .57, p < 0.001) – with a
positive evaluation of trust increasing acceptance.
For the investigation of local acceptance, general
acceptance was added as independent variable
besides trust and fairness due to findings from past
research on wind farms according to which general
support considerably impacts local acceptance (e.g.,
Jones and Eiser, 2009; Walter, 2014). The model for
wind power plants explained 38.9% of variance for
local acceptance (F(3,66) = 15.64, p < 0.001) with
general acceptance being the strongest predictor
(ß = .53, p < 0.001), followed by perceived fairness
(ß = .22, p < 0.05).
Both factors influenced acceptance positively: the
higher general support and fairness ratings were, the
more favorable was local acceptance. Trust in wind
power projects did not directly influence local
acceptance of wind turbines.
Strikingly, in the power line context general
acceptance and process characteristics had a greater
predictive power: 66.4% of variance in local
acceptance were explained by general acceptance as
strongest predictor (ß = .76, p < 0.001) and by fairness
(ß = .30, p < 0.01; F(3,66) = 46.45, p < 0.001). Again,
the two factors increased acceptance, whereas trust in
projects had no impact.
To sum up so far, trust was found to impact
general acceptance. General acceptance and process
fairness were identified as promoters of local
acceptance for both technologies. In a next step, the
contribution of these factors was analyzed in detail by
looking deeper into trust and fairness aspects. The
aim was to find out which trust and fairness
parameters were most acceptance-relevant for wind
energy and power line projects by performing
stepwise regression analyses.
2.49
2.53
3.00
2.73
3.26
3.03
2.93
2.96
3.09
3.10
3.57
3.59
1 2 3 4 5 6
Consideration of interests of all citizens
Public participation opportunities in
the planning process
No concern for lack of publicness of
siting process
Fair distribution of benefits and risks
among the population
No concern for unfairness of siting
process
Fair distribution of benefits and risks
for residents
Wind power plants
Electricity pylons
Level of agreement
mid-point
"do not
agree at all"
"fully agree"
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194

First, the impact of trust items on general
acceptance was examined. The resulting regression
models are depicted in Tables 2 and 3.
Table 2: Regression model for the influence of trust types
on general acceptance of wind power plants.
B
SE B

T
Constant
1.32
.36
3.64
Trust in wind power plant
technology
.77
.08
.78**
10.10
Adjusted R
2
= 0.59; ** p < 0.01; n = 70
Table 3: Regression model for the influence of trust types
on general acceptance of electricity pylons.
B
SE B

T
Constant
1.18
.36
3.32
Trust in electricity pylon
technology
.69
.09
.67**
7.50
Adjusted R
2
= 0.44; ** p < 0.01; n = 70
For both wind power plants and electricity pylons,
trust in the underlying technology was the sole trust
variable that contributed significantly to general
acceptance (wind: ß = .78, p < 0.001; electricity:
ß = .67, p < 0.001). In the wind power context, trust
in technology explained 59.4% of variance in general
acceptance (F(1,68) = 102.02, p < 0.001), whereas for
electricity pylons, the model had a slightly lower
predictive power (44.4%; F(1,68) = 56.21, p < 0.001).
Subsequently, the influence of fairness
parameters on local acceptance was investigated to
identify the critical hotspots of planning. Results for
wind power plants are depicted in Table 4.
Table 4: Regression model for the influence of fairness
characteristics on local acceptance of wind power plants.
B
SE B

T
Constant
2.44
.41
5.92
Fairness of benefit-risk
distribution for residents
.39
.11
.40**
3.63
Adjusted R
2
= 0.15; ** p < 0.01; n = 70
The model for local acceptance explained 15.0% of
variance in wind turbine ratings (F(1,68) = 13.16,
p < 0.01) and only included the fair distribution of
benefits and risks for residents (ß = .40, p < 0.01): the
more positive evaluations of local distributive
fairness, the higher was local acceptance. Fairness
characteristics had a noticeably higher impact on the
local acceptance of electricity pylons (see Table 5).
48.8% of variance in local acceptance were explained
by fairness items (F(3,66) = 22.95, p < 0.001). Again,
the fair distribution of benefits and risks for residents
exerted the highest positive influence (ß = .69,
p < 0.001). Also, the adequate consideration of
interests of all citizens increased local acceptance
(ß = .32, p < 0.01). In contrast, acceptance was lower
for respondents who were less concerned about the
siting process not being open to the public (ß = -.25,
p < 0.05).
Table 5: Regression model for the influence of fairness
characteristics on general acceptance of electricity pylons.
B
SE B

T
Constant
.68
.34
1.97
Fairness of benefit-risk
distribution for residents
.71
.10
.69**
6.97
Consideration of interests
of all citizens
.32
.09
.32**
3.59
No concern for siting
process being non-public
-.23
.09
-.25*
-2.46
Adjusted R
2
= 0.49; * p < 0.05, ** p < 0.01; n = 70
5 DISCUSSION AND
CONCLUSION
To unveil whether requirements for a fair and trusted
project planning are the same for different parts of the
energy supply system or whether they are technology-
specific, the present study took wind power plants and
electricity pylons as examples and directly compared
the impact of trust (meaning trust in stakeholders and
trust in technology) and perceived fairness on
acceptance.
In the present study, general and local acceptance
were significantly higher for wind power plants than
for electricity pylons, which indicates a comparably
higher potential for opposition to power line projects
and mirrors findings from Zaunbrecher et al. (2014).
Similar patterns across technologies were spotted
for general trust and fairness ratings and their
influence on acceptance. For wind energy and power
line projects, trust in technology was evaluated more
positively than trust in wind farm / grid operators. In
contrast to past studies on large-scale technology
acceptance (e.g., Huijts et al., 2007), the present
results showed a lower trust in politics compared to
companies regarding the planning and operation of
wind power plants and power lines. This might reflect
the current political and societal situation in
November 2016 (e.g., the Edelman Trust Barometer
found trust in politics and other established
institutions to have decreased compared to the
previous year in Germany and many other countries
around the world; Edelman, 2017). Still, this finding
underlines the importance for political actors to make
transparent and consistent decisions that take account
Uncovering the Impact of Trust and Perceived Fairness on the Acceptance of Wind Power Plants and Electricity Pylons
195

of environmental and citizen needs to successfully
introduce energy infrastructure projects.
For wind power and power line projects, fairness
parameters were (mostly) evaluated negatively. As
the two fairness characteristics rated worst were the
consideration of interests of all citizens and public
participation opportunities in the planning process,
planners should pay particular attention to interests
from different citizen groups and offer the public
better-suited ways to participate in the planning.
Corroborating findings from past research, (e.g.,
Devine-Wright, 2013; Visschers and Siegrist, 2014;
Zoellner et al., 2008), trust and fairness were revealed
as relevant to acceptance across technologies. But in
this study, trust and fairness affected different
acceptance levels. Trust in technology impacted
general acceptance of wind turbines and electricity
pylons, while general acceptance and perceived
fairness were factors influencing local acceptance.
As a first study, the current research revealed that
trust in the underlying technology had a significant
impact on general acceptance of wind power plants
and electricity pylons, indicating a general,
overarching pattern across technologies and
confirming results from Achterberg et al. (2010) for
hydrogen technologies. This is interesting because
previous studies have mainly focused on the role of
trust in stakeholders for the acceptance of energy
technologies, neglecting trust in the technology itself.
Technology-specific findings referred to the
influence of individual fairness parameters on local
acceptance of energy infrastructure projects. Fairness
aspects were revealed to play a more important role
for electricity pylon acceptance compared to wind
power plants. For both electricity pylons and wind
power plants, a fair distribution of benefits and risks
for residents was relevant to local acceptance. But
since this was also the best-rated fairness item, it
might not be the most acceptance-critical point
compared to procedural fairness issues. In the power
line context, local acceptance was also found to be
impacted by the consideration of interests of all
citizens and the “publicness” (or rather “non-
publicness”) of the siting process. Findings for
“publicness” of the siting process seem at first
contraintuitive: the less concerned respondents were
about the planning process not being open to the
public, the lower was local acceptance of power line
projects. A possible explanation could be that people
who perceive siting processes to be highly public
might have been more frequently confronted with
planned power line projects through media reports
and public discussions. This could lead to feelings of
ubiquity (i.e., “power lines are constructed
everywhere”), resulting in a decreased acceptance.
But this explanation remains speculative and needs to
be investigated in future studies.
Some methodological issues of the current study
should be considered in future research. In our study,
hypothetical scenarios for wind energy and power
line projects were compared. Hence, a direct
comparison of case studies on actual projects is
important for further insights into the relevance of
process parameters for project acceptance. A further
limitation is the small and skewed sample which was
referred to in this study. For the adopted approach, the
sample size is sufficient in a methodological and
statistical sense. However, one should consider that
participants volunteered to take part in the study and,
in addition, were highly educated, thus the findings
might not represent the “normal” population. Future
studies should aim for a census representing sample
to measure the view of an entire population on the
topic and should seek for a replication of the findings
with a larger and more balanced sample.
Another topic which needs a deeper focus in
future research, regards the role of trust in technology
for energy infrastructure acceptance by identifying
the factors which constitute trust in large-scale energy
technologies (e.g., perceived reliability, perceived
safety, or an interplay of benefits and costs; Montague
et al., 2009) and by investigating the relationship
between trust in technology and trust in stakeholders.
The results of the present study can be used to
inform project planning for energy infrastructure
technologies. Planners need to be aware of: 1) the
relevance of trust in stakeholders and (equally
important but so far largely neglected) trust in the
technology and 2) the need for a fair planning process
with a just distribution of benefits and risks in the
population.
ACKNOWLEDGEMENTS
The authors would like to thank Feyza Gölpinar for
research support. This work had been inspired by the
Excellence Initiative of German Federal and States
Government (Project UFO, urban future outline) and
has been funded by the European Institute of
Technology & Innovation (EIT) within the EnCO2re
flagship program Climate-KIC.
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