Does Size Matter? Investigating Laypeoples’ Preferences for Roll-out
Scenarios of Alternative Fuel Production Plants
Katrin Arning
1
, Barbara Sophie Zaunbrecher
1
, Maximilian Borning
2
, Niklas van Bracht
2
,
Martina Ziefle
1
and Albert Moser
2
1
Human-Computer Interaction Center, Chair of Communication Science, RWTH Aachen University,
Campus Boulevard 57, 52074 Aachen, Germany
2
Chair and Institute of Power Systems and Power Economics, RWTH Aachen University, Schinkelstraße 6, 52062 Aachen
Keywords: Alternative Fuel, Production Infrastructure, Power System Design, Public Acceptance, Conjoint Analysis.
Abstract: The substitution of fossil fuels by alternative fuels (AF) is a promising approach to achieve climate protection
goals. Since the production of AF places considerable demands on the existing power system, planning
processes also have to consider the energy demand and supply of AF production plants. Apart from these
technical requirements, the acceptability of new AF production plants and their power supply infrastructure
also needs to be considered. An empirical study (n = 313, carried out 2018 in Germany) based on the conjoint
measurement approach was conducted, which investigated the impact of acceptance-relevant criteria on
preferences for infrastructure scenarios for AF production plants. Emissions of an AF production plant had
the highest impact on preferences, followed by the electricity mix, where surplus and renewables were
preferred as energy sources. Compensatory measures, especially price reductions for AF, and the application
field of AF were of minor relevance for preference decisions. The size of AF production plants was also not
relevant for scenario preferences, at least on an abstract meta-level of planning scenarios. Based on the results,
the integration of acceptance as soft factor into power system planning processes is discussed and
recommendations for future planning processes and -deployment activities for acceptable AF production
infrastructure are derived.
1 INTRODUCTION
Compared to conventional fuels such as petrol and
diesel, alternative fuels (AF) can significantly reduce
the emission of greenhouse gases (Chu et al., 2012).
Since the production of AF is very energy-intensive
(Stephanopoulos, 2007), significant additional
amounts of electricity are required, which places
considerable demands on the existing power system,
especially in combination with the integration of
fluctuating sources of renewable energies (wind and
photovoltaics) (dena, 2017). Therefore, infrastructure
planning for the power grid also has to consider the
energy demand and supply of AF production plants.
From other energy technology infrastructure
contexts, it is known that public attitudes towards new
technical infrastructures are not always supportive yet
being decisive for a successful rollout (e.g., Batel et
al., 2013). In order to avoid possible pitfalls with
regard to the acceptability of new AF production
plants, public perception, acceptance and social
requirements need be identified a priori. The present
study therefore aimed at an analysis of acceptance of
AF production plant infrastructure scenarios in order
to integrate public requirements and preferences into
planning processes.
1.1 Power System Design for AF
Production Infrastructure
The utilization of electrical energy for AF production
has a significant impact on the power system, where
it affects both, the generation stack and the underlying
grid infrastructure (dena, 2017).
In general, power generation and consumption
have to be balanced at any time. When dealing with
an increased load due to additional consumers,
particularly new installations of renewable energy
sources are needed to provide the respective amount
of emission-free energy. However, renewable
Arning, K., Zaunbrecher, B., Borning, M., van Bracht, N., Ziefle, M. and Moser, A.
Does Size Matter? Investigating Laypeoples’ Preferences for Roll-out Scenarios of Alternative Fuel Production Plants.
DOI: 10.5220/0007697100910099
In Proceedings of the 8th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2019), pages 91-99
ISBN: 978-989-758-373-5
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
91

energies that are politically favored (such as wind
turbines and photovoltaics) are characterized by
intermittent feed-in, which increases the need for
flexibility in the system (Borning et al., 2018). Thus,
both temporal flexibility options (e.g., storage
systems, load flexibilization) as well as the spatial
flexibility option of the grid are required for an
efficient integration of additional consumers and
producers into the power system. Therefore, suitable
technical and operational restrictions of deployed
technologies are of highest importance. Depending on
the precise location and capacity of the fuel
production plant, the impacts on the power system
significantly vary, which imposes high challenges on
future power system design.
Due to high capital expenditures and long
lifetimes (40+ years) of the electrical assets, long-
term expansion planning presents a method to
anticipate future challenges and determine the most
reasonable investments (Skar et al., 2014). One
approach to consider public acceptance in power
system planning is to add hard restrictions (e.g.,
minimal distances of wind power plants from
residential areas) to the solution space (Cetinay et al.,
2017, van Bracht et al., 2018). Hence, in order to
provide robust investment recommendations, public
acceptance should be taken into account as a soft
factor in power system planning. Based on the
interdependencies between the fuel production
system and the electricity system, a first step is to
include social acceptance in the infrastructure
planning of fuel production plants. Thereupon,
resulting social requirements regarding specific
location types could be integrated within the long-
term expansion planning of the power system.
1.2 Relevance of Acceptance in AF
Production Infrastructure Design
AF have been researched from a social science
perspective mainly with regard to the acceptance of
fuel as final consumer product (e.g., Chin et al.,
2014). AF are mostly positively perceived, as long as
they are not significantly more expensive than
conventional fuels (Hackbarth and Madlener, 2016).
Further, ecologic aspects of reducing CO
2
-emissions
contribute to a positive perception, whereas the “fuel
vs. food”-debate in growing feedstock for biofuels
(Moula et al., 2017) or health or environmental risk
perceptions (Roche et al., 2010) led to negative
evaluations of AF.
The understanding of factors which influence AF
production facility acceptance, however, is still
limited. While economic and environmental impacts
of biorefineries were intensively studied (e.g.,
Santibañez-Aguilar et al., 2014), only a few studies
investigated AF production infrastructure acceptance.
Most studies focused on site selection and ways of
achieving community acceptance (e.g., Tigges and
Noble, 2012; Fortenbery et al., 2013). However, some
questions remain open: First, there is scarce
knowledge on the accepted size of AF production
plants. Second, since the energy system is currently
in transition and is not yet able to provide the required
electricity (dena, 2017), it would be valuable to know
if an energy mix of renewable and conventional
energy sources for AF production will be accepted by
the public. Third, little is known about other factors
which influence the acceptance of AF production
plants, such as positive (local jobs, income, fuel price
reduction incentives) or negative local effects (e.g.
pollution and noise) (Lee et al., 2017). Especially
smell emissions (Soland et al., 2013) and noise during
the operation of the plant or increased transport
negatively impact acceptance (Lee, 2017).
Compensatory measures on the local level might also
affect the acceptance of AF production plant siting.
Beyond financial incentives, we want to find out
which type of measure is preferred in this regard.
Furthermore, the area of application of AF might also
affect the acceptance of AF production plants (e.g.,
public transport or logistics).
Therefore, the present study examined the
following research questions:
1.) Which aspects of AF production plant
infrastructure design are most important for
public acceptance?
2.) Which specific AF production plant scenario
features are preferred by the public?
3.) How are future AF production plant scenarios
evaluated by the public?
2 METHODOLOGY
In order to answer the research questions, an
empirical, quantitative study was conducted using
conjoint analysis (CA).
2.1 Conjoint Analysis
CA allows for an ecologically valid investigation of
complex decision scenarios (Rao, 2014).
Respondents are asked to evaluate specific product
profiles or scenarios, which are composed of multiple
attributes and differ from each other in the attribute
levels. CA delivers information about which attribute
influences respondents’ choice the most and which
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level of an attribute is preferred. Preference shares
can be interpreted as indicators of acceptance
(Arning, 2017).
2.2 Selection of Attributes
The selection of attributes for the conjoint study was
based on interviews with laypeople and experts in
order to ensure that they reflect valid and relevant
aspects of power system planning for both groups:
Attribute 1: Size/distribution of AF production plants
with four levels “small plants”, “rather small plants”,
“rather large plants” and “large plants” (Figure 1).
Figure 1: Icons for the attribute levels "size/distribution".
Attribute 2: Electricity mix with the levels
“conventional”, “renewables”, “mix (renewables and
conventional”, and “surplus renewables” (Figure 2).
Figure 2: Icons for the attribute "electricity mix".
Attribute 3: Field of application for AF with the
levels “public gas stations (private cars)”, “logistics”
(heavy-duty traffic and ships), and “air traffic”
(Figure 3).
Figure 3: Icons for the attribute "field of application".
Attribute 4: Emissions of an AF production plant with
the levels “none”, “smell”, “noise”, “smell and noise”
(Figure 4).
Figure 4: Icons for the attribute "emission".
Attribute 5: Compensation for AF production plant
deployment with the levels “local jobs”, “reduced
price for alternative fuel”, “local bus powered by
alternative fuel”, and “financial compensation”
(Figure 5).
Figure 5: Icons for the attribute "compensation".
2.3 The Questionnaire
For the design of the questionnaire, SSI Web
Software was used. The sample acquisition was done
by an independent market research company to obtain
a census-representative sample of people holding a
driving license.
The questionnaire items were developed based on
findings of qualitative pre-studies (interviews) and
checked for comprehensibility, length of interview
and wording. The questionnaire was structured as
follows: First, quota-relevant information (driving
license, gender, education, age, region) was assessed,
followed by an assessment of driving-related
characteristics. In the second part, the perception of
and attitude regarding AF was measured by using 6-
point Likert-scales (1 = “do not agree at all” to 6 =
“fully agree”). The third part started with an
introduction into AF production plants, followed by
the description of attributes and levels of the conjoint
study. Then, the conjoint part (choice-based conjoint)
with 9 choice tasks was presented, where participants
indicated their preferred AF production plant scenario
(Figure 6).
Figure 6: Screenshot of a choice task in the CBC-study.
2.4 The Sample
The sample (n = 313) was census-representative with
regard to age, gender, and education in Germany. The
mean age was M = 45.9 years (SD = 13.7, 18-80
years), with 50.8% male and 49.2% female
respondents. 47.6% held a primary educational
degree, 25.6% a secondary degree, and 26.2% a
tertiary degree.
Car ownership and driving experience. The
majority (88.3%) reported to use their own car, 5%
drive a company car, 0.7% use car-sharing, and 6%
drive a privately lent car. Most of these cars were
compensation
size / dimension
emissions
electricity mix
field of application
small plants rather large plants rather small plants
smell and noise none smell
renewables
air traffic
select
select select
mix (renewables
and conventionals)
surplus renewables
public gas stations
(private cars)
logistics (heavy duty
traffic and ships)
financial
compensation
reduced price for
alternative fuel
local bus powered
by AF
Does Size Matter? Investigating Laypeoples’ Preferences for Roll-out Scenarios of Alternative Fuel Production Plants
93

gasoline-powered (74.3%), followed by diesel-
powered cars (24.7%), only a small proportion were
cars with gas- (0.7%) or hybrid (0.3%) drive. Asked
for driving experience, the majority (46.3%) drove
their car daily, 35.1% several times a week, 10.5%
several times a month, 3.8% several times a year, and
4.2% reported to never use a car. Regarding annual
mileage, 17.7% drive less than 5,000km/year, 25.3%
drive 5,000-10,000km/year, 24.3% drive 10,000-
15,000km/year, 19% drive 15,000-20,000km/year
and 11% drive more than 20,000km/year. Based on
these data, the sample was considered sufficiently
familiar with the subject of driving and able to
provide valid data when evaluating alternative fuels.
Interest in AF was moderate (M = 3.7, SD = 1.2).
Self-reported AF knowledge levels were a rather low
(M = 2.9, SD = 1.2). The majority reported to have
very low (14.4%) or low (54.6%) knowledge about
alternative fuels, whereas only 29.1% reported to
have good and 1.9% to have very good knowledge
about AF and production processes.
2.5 Data Analysis
The conjoint data were analyzed utilizing Sawtooth
Software (Sawtooth Software, 2017). Partial value
utilities were computed on the basis of Hierarchical
Bayes (HB) estimates and part-worth utilities
importance scores were calculated. They indicate
how important the attribute is for the preference
choice compared to all other attributes. By using zero-
centred differential part-worth utilities, which are
scaled to sum to zero within each attribute, it is
possible to compare differences between attribute
levels. Sensitivity simulations were carried out by
using the Sawtooth Market Simulator (Sawtooth
Software, 2009).
3 RESULTS
3.1 Alternative Fuel Production Plant
Acceptance
General acceptance of AF production plants was
positive (M = 4.0, SD = 1.1). Almost one quarter
(23.9%) reported a high acceptance of AF production
plants and 54.3% were positive about their
deployment, whereas 13.7% were negative or even
rejected (8%) the concept of AF production plants.
The local acceptance of AF production plants,
referring to a plant that is supposed to be built in the
immediate neighbourhood, was lower (M = 3.1, SD =
1.3). Most respondents (61.0%) rejected the
deployment of an AF production plant close to their
home, with 16.3% strongly rejecting the idea. A total
of 39% approved the deployment of an AF production
plant near their homes, with 2.9% being very much in
favour.
Regarding the perception of specific risks or
barriers, land use of AF production sites was the
greatest concern (M = 4.3, SD = 1.1). Other potential
barriers, such as smell emissions (M = 3.3, SD = 1.1),
a negative cost-benefit ratio of deploying AF
production plants (M = 3.3, SD = 1.2), perceived
health risks (M = 3.2, SD = 1.1), or noise emissions
(M = 3.2, SD = 1.0), were not perceived as critical
(ratings close to the midpoint of the scale (3.5)).
3.2 Impact of Attributes on AF
Production Plant Scenario
Preference Decisions
Relative importance scores were calculated to assess
the relative impact of the included acceptance-
relevant attributes on the preference decision for AF
production plant scenarios (Figure 7).
Figure 7: Relative importance scores for AF production
plant attributes in the CBC study.
The attribute “emissions” had the highest
importance score, i.e., the strongest relative impact on
the scenario choice (34.7%, SD = 15.9), followed by
the attributes “electricity mix” (24.2%, SD = 12.7)
and “compensation” (19.1%, SD = 9.4). The size of
an AF production plant was the second-least
important criterion in preference decisions (12.2%,
SD = 7.8), the attribute with the lowest impact on
scenario preferences was the “field of application”
(9.8%, SD = 6.4) of alternative fuels.
The findings indicate that potential emissions
from an AF production plant were the most dominant
attribute for AF production plant scenario acceptance.
The electricity mix used to supply energy to the AF
production facilities was also relevant, but with a
lower impact on the preference decision.
Interestingly, the size or distribution of AF
9.8
12.2
19.1
24.2
34.7
0 10 20 30 40
field of application
size/distribution
compensation
electricity mix
emissions
average importance in %
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production plants, one of the most relevant attributes
for power system planning, was of minor importance
for preference decisions relative to other acceptance-
relevant criteria.
3.3 Preferences for AF Production Plant
Scenario Features
Calculating the average zero-centred differential part-
worth utilities for all attribute levels revealed how
specific features within an attribute affected
respondents’ scenario choice. Levels with higher
scores were strongly preferred, whereas levels with
lower scores (in comparison to the other levels of the
same attribute) were rejected (Figure 8).
The utility values of the attribute “emissions”
showed the largest difference between part-worth
utilities due to the high importance score of the
attribute (see 3.2). “No emissions” was highly
preferred, as indicated by the highest utility value
(utility = 93.2, SD = 50.9). Compared to this, all other
emission-attribute levels were rejected. The weakest
rejection occurred for “noise” (utility = -4.1, SD =
19.3), followed by the rejection of the “smell”
emission (utility = -17.1, SD = 25.9). The
combination of “noise and smell” emissions from an
AF production plant was most strongly rejected by
respondents (utility = -72.7, SD = 41.7).
Referring to the levels of the attribute “electricity
mix”, the second-most important attribute, the most
preferred energy sources were “surplus renewables”
(utility = 29.8, SD = 29.2) and “renewables” (utility
= 29.1, SD = 35.8). Compared to that, the “mix
(renewables and conventional)” received lower
preferences (utility = 6.8, SD = 21.2). Using
“conventional” power supply for AF production
(utility = -65.7, SD = 51.3) was the only level which
was strongly rejected.
Focusing on the third most important criterion,
“compensation”, most features received slightly
positive evaluations, such as the creation of “local
jobs” (utility = 7.7, SD = 46.3), “reduced price for
alternative fuel” (utility = 7.6, SD = 27.3) or
“financial compensation” (utility = 2.7, SD = 45.5).
The only “compensation”-feature, which was rejected
was “local bus powered by alternative fuel” (utility =
18.1, SD = 37.3).
For the second-least important attribute
“size/distribution” of AF production plants no
systematic preference pattern showed, which might
be due to the low importance score. “Large” (utility =
3.9, SD = 30.7) and “rather small plants” (utility =
3.8, SD = 21.9) were favoured in comparison to
“rather large” (utility = -1.4, SD = 27.2) and “small”
AF production plants (utility = -6.3, SD = 33.9).
Regarding the least important attribute “field of
application”, the usage of AF for “private cars, AF
available at public gas stations” (utility = 13.2, SD =
21.9) was preferred in relation to “logistics - heavy
duty traffic and ships” (utility = 6.0, SD = 19.6) and
“aviation” (utility = 19.3, SD = 21.0), which was most
strongly rejected.
Figure 8: Part-worth utilities (zero-centred diffs) for AF
production plant scenario levels in the CBC study.
However, the high standard deviations in level
judgements for the attributes “compensation” and
“size/distribution” indicate that respondents differed
in their perception and evaluation of different
compensation measures.
3.4 Preference Simulations of Future
AF Production Plant Infrastructure
Scenarios
Sensitivity simulations were carried out in a next step.
The market simulator allows to estimate shares of
preference for different scenarios and can be used as
decision support tool in the roll-out planning stage of
AF production plant scenarios. For this purpose, the
13.2
6.0
-19.3
3.9
-1.4
3.8
-6.3
2.7
-18.1
7.6
7.7
29.1
6.8
-65.7
29.8
-4.1
-17.1
-72.7
93.9
-100 -50 0 50 100 150
public gas stations (private cars)
logistics (heavy-duty traffic and ships)
air traffic
large plants
rather large plants
rather small plants
small plants
financial compensation
local bus powered by alternative fuel
reduced price for alternative fuel
local jobs
renewables
mix (renewables & conventional)
conventional
surplus renewables
noise
smell
noise and smell
none
field of application
size/
distribution
compensation
electricity mix
emissions
part-worth utility
Does Size Matter? Investigating Laypeoples’ Preferences for Roll-out Scenarios of Alternative Fuel Production Plants
95

most probable scenarios from power systems design
perspective for the anticipated temporal development
of AF production plants over the years 2025, 2035
and 2050 were analysed with regard to their social
preference. In the scenario definition it was assumed
that the entire energy system will be strongly
decentralised, and renewables will play an important
role. By 2050, the dominant electricity source for AF
production will be “surplus renewables”. Moreover,
new innovative and cross sectorial technologies will
lead to AF usage in all fields of application.
The “2025 scenario” with the scenario features
“large plants”, “electricity mix”, “no emissions” and
“private mobility” led to a preference share of 38.7%
(SE = 1.4%). In the “2035 scenario” it was assumed
that the AF production system was more
decentralized with “rather small” plants, “no
emissions”, using an “electricity mix” for “logistics”
purposes. The preference share for the “2050
scenario” was 31.2% (SE = 1.0%) for the logistics
application context. The “2050 scenario” with the
features “small plants”, powered by “surplus”
electricity, “no emissions”, where fuel for the aviation
context was produced, received a preference share of
30.1% (SE = 1.2).
Since the application context of AF for private
mobility purposes was the most preferred, we also ran
the simulation for the years 2025, 2035 and 2050 for
the private mobility purpose (all other scenarios
settings remained the same). Here, the scenario
“2050” was the most preferred with a preference
share of 39.5% (SE = 1.1%), followed by the scenario
for the year “2025” (31.1%, SE = 1.3) and for the year
“2035” (29.4%, SE = 0.9).
4 DISCUSSION
We investigated the acceptance of AF production
plant infrastructure scenarios to integrate public
requirements into future planning processes to design
“acceptable” AF production infrastructure scenarios.
4.1 Perception and Acceptance of
Alternative Fuel Production Plants
AF production plants and their required infrastructure
were generally positively perceived. In line with other
studies, local acceptance, i.e., being personally
affected by an AF production plant, reduced
acceptance ratings (e.g., Lee et al., 2017). Looking
more specifically at risks associated with AF
production plant roll-out, respondents were not too
concerned by potential health risks or emissions from
an AF production plant. The highest risk perception
referred to large land requirements by AF production
facilities. Interestingly, the size or distribution of AF
production plants only played a minor role in
determining preferences, as shown in the CBC-study.
We assume that the meta-level of planning scenarios
and missing local affectedness of respondents by AF
production plant planning processes is the reason for
the low relevance of this factor in our study.
However, it would be wrong to assume that the size
of AF production facilities is not acceptance-relevant
at all. As soon as location decisions are made and
local communities are chosen for AF production plant
roll-out, the physical dimensions and land
requirements of production facilities become relevant
and might lead to protests (e.g., Fortenbery et al.,
2013). This leads to one important conclusion: The
planning of AF production infrastructure must always
start at the local level, otherwise no valid predictions
can be made for public acceptance.
The factor “emissions” of an AF production plant
exerted a considerably higher impact on scenario
preferences, supporting the thesis that local effects
are important for acceptance. Noise and especially
smell emissions were the strongest determinants of
acceptance. From a technical point of view, emissions
play a minor role in technical infrastructure planning
processes at this stage. However, planners should
therefore work on preventing emissions, especially of
unpleasant smell emissions (Soland et al., 2013).
Beyond that, the strong acceptance-determining
influence of "emissions" has another important
significance for AF infrastructure planning processes.
It shows that acceptance decisions can be shaped by
dimensions that are not taken into account by the
technical side because they are (not yet) part of the
technical planning process. The “acceptance-
relevance” of “technically irrelevant” factors was also
found in other acceptance contexts, such as the
perception of the CCU technology, where the
disposal of CO2-derived products in particular
influenced their acceptance (van Heek et al., 2017).
Technical infrastructure planners should therefore be
aware that apparently unimportant factors can
strongly affect acceptance and can act as "NoGos".
Investigating public acceptance in early stages of
technology infrastructure planning can provide added
value by identifying these factors and by taking them
into account at an early stage in the roll-out process.
The preferences for the “electricity mix” used to
supply energy for AF production plants showed that
renewable energy resources are strongly preferred
compared to conventional energy sources. Surplus
energy was the most preferred, which explains the
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highest preferences for the AF production scenario in
the year 2050, which was assumed to be based on
surplus energy. Until the power system will be able to
provide surplus energy to supply AF production
plants with energy in the year 2050, the production of
alternative fuels should not be operated using
electricity from conventional sources. Otherwise,
there is a risk that the population might perceive this
as "greenwashing", i.e. the use of "dirty" energy
sources for the production of "green" products (e.g.,
Plec and Pettenger, 2012).
Further factors mentioned as acceptance-relevant
in the prestudy-phase but exerting less influence in
the scenario judgments were “field of application”
and measures of “compensation”, which could be
offered to the local population near AF production
plants. With regard to the perception of compensation
measures for the deployment of an AF site (e.g.
financial compensation), serving either as
compensation or as incentives for AF site deployment
(e.g. free public transport), there are also mixed
findings in the literature. While in some infrastructure
projects compensatory measures had a positive effect
on acceptance during roll-out (Upham and Shackley,
2006), there were also cases where compensation did
not impact acceptance (Soland et al., 2013). We
suggest therefore that a positive effect of
compensations cannot be assumed per se but that such
measures must be developed in a participatory way
with the affected community in order to act as an
incentive and positively influence acceptance. In the
context of AF production plant rollout, more research
is needed in this regard.
Regarding the “field of application” for AF we
can conclude from the acceptance evaluations, that
the public is more likely to accept the roll-out of AF
production plants if they can benefit directly from it,
i.e. if the fuel produced can also be used for their own
mobility. Compared to that, the other application
purposes did not exert positive effects on scenario
preferences (logistics or aviation). With regard to
further research on AF and the roll-out of AF
production sites, it is therefore advisable to primarily
develop fuels and infrastructure systems for private
mobility and to supply the logistics and aviation
industries with alternative fuels in a second step. This
prioritization can help to increase the acceptance of
alternative fuels in the population.
In order to predict public preferences and
acceptance for concrete technical rollout planning
scenarios, sensitivity analyses were simulated for
three future AF production plant infrastructure
scenarios for the years 2025, 2035 and 2050. The
preference simulations showed that the area of
application for alternative fuels, i.e. for private
mobility, is a strong acceptance driver. The highest
preference for the scenario in the year 2050 can be
attributed to the intended use of surplus energies for
the production of alternative fuels. This shows that
the German “Energiewende” towards renewable
energies in AF production system infrastructure
design is not only a technical challenge for power
system design, but also a concrete demand or social
requirement by the public.
Even if the simulations do not consider all
technically relevant factors and do not cover the level
of local roll-out planning, they demonstrate how
future roll-out scenarios can be complemented by an
a priori acceptance assessment. So far, public
acceptance of sustainable energy rollout scenarios has
only been captured a posteriori, i.e. after the planning
stage was completed and the deployment stage was
initiated. The procedure presented here can contribute
to promoting sustainable technology development
that is also accepted by the population.
4.2 Implications for the Power System
Long-term planning tools for the power system
generally use optimization models and mathematical
programming to capture complex developments in
the future. The objective function of those
optimization problems is based on numerical values
for the parameter (usually costs) being minimized or
maximized (Luenberger et al., 2016). In the current
study, a first attempt was made to include acceptance
evaluations as further parameters into planning
models. In a next step the acceptance evaluations
need to be transformed to integrate them into power
system planning models. Effects of the assessed
acceptance-relevant attributes for AF production
plants have to be either monetized or alternatively
taken into account, e.g., within a multi-criteria
objective function. By doing so, acceptance-relevant
attributes can be considered as soft factors, which can
serve as decision-support in making trade-off
decisions for power system scenarios of AF
production infrastructure.
4.3 Methodological Considerations and
Future Research
Future studies should differentiate between a general
acceptance level and local acceptance, since directly
affected residents living close to AF production plants
assess the preferred size of production facilities or the
type/intensity of emissions differently. As indicated
by high standard deviations in respondents’
Does Size Matter? Investigating Laypeoples’ Preferences for Roll-out Scenarios of Alternative Fuel Production Plants
97

judgements, future research should integrate
individual factors to develop more target-group
oriented recommendations and communication
strategies (e.g., Arning et al., 2018). Further, the study
should be replicated with a larger representative
sample in Germany, but also with international
samples to allow cross-cultural comparisons of AF
production plant infrastructure acceptance.
5 CONCLUSIONS
The present study successfully identified and
assessed acceptance-relevant factors with regard to
AF production plant infrastructure design. Although
the integration of acceptance evaluations as soft
factor into power design planning tools needs further
methodological refinement, insights on drivers of AF
production plant infrastructure acceptance were
gained, which allowed to simulate preferences for
future AF production plant roll-out scenarios.
ACKNOWLEDGEMENTS
This work was supported by the Cluster of Excellence
“Tailor-Made Fuels from Biomass”, which is funded
under Contract EXC 236 by the Excellence Initiative
by the German federal and state governments to
promote science and research at German universities.
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