Article
Reference
Perspectives of informed citizen panel on low-carbon electricity portfolios in Switzerland and longer-term evaluation of informational
materials
VOLKEN, Sandra, XEXAKIS, Georgios, TRUTNEVYTE, Evelina
Abstract
Low-carbon transition is gaining momentum, but relatively little is known about the public preferences for low- and zero-carbon electricity portfolios given their environmental, health, and economic impacts. Decision science literature argues that conventional opinion surveys are limited for making strategic decisions because the elicited opinions may be distorted by misconceptions and awareness gaps that prevail in the public. We created an informed citizen panel (N=46) in Switzerland using technology factsheets, an interactive web-tool Riskmeter, and group discussions. We measured the evolution of the panel's knowledge and preferences from initial (uninformed) to informed and longer-term views four weeks after. In terms of energy transition, our elicited technology and portfolio preferences show strong support for the low-carbon electricity sector transition, especially relying on hydropower, solar power, electricity savings and efficiency, and other renewable sources. As these informed preferences are structurally different from the futures considered by many energy experts, we argue that these preferences should also [...]
VOLKEN, Sandra, XEXAKIS, Georgios, TRUTNEVYTE, Evelina. Perspectives of informed citizen panel on low-carbon electricity portfolios in Switzerland and longer-term evaluation of informational materials. Environmental Science & Technology, 2018, vol. 52, no. 20, p.
11478–11489
DOI : 10.1021/acs.est.8b01265
Available at:
in Switzerland and longer-term evaluation of informational materials
Sandra Volken, Georgios Xexakis, and Evelina Trutnevyte
Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01265 • Publication Date (Web): 12 Sep 2018 Downloaded from http://pubs.acs.org on September 17, 2018
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Perspectives of informed citizen panel on low-carbon electricity
1
portfolios in Switzerland and longer-term evaluation of informational
2
materials
3 4
Sandra P. Volken1, Georgios Xexakis1,2, Evelina Trutnevyte1,2* 5
6
1 Institute for Environmental Decisions (IED), Department of Environmental Systems Science, 7
ETH Zurich, CH-8092 Zurich, Switzerland 8
2 Renewable Energy Systems, Institute for Environmental Sciences (ISE), Section of Earth and 9
Environmental Sciences, Department of F.-A. Forel for Environmental and Aquatic Sciences, 10
University of Geneva, CH-1211 Geneva 4, Switzerland 11
* corresponding author (Uni Carl Vogt, Boulevard Carl Vogt 66, CH-1211 Geneva 4, 12
Switzerland; +41 22 379 06 62; [email protected]) 13
14
ABSTRACT 15
Low-carbon transition is gaining momentum, but relatively little is known about the 16
public preferences for low- and zero-carbon electricity portfolios given their environmental, 17
health, and economic impacts. Decision science literature argues that conventional opinion 18
surveys are limited for making strategic decisions because the elicited opinions may be distorted 19
by misconceptions and awareness gaps that prevail in the public. We created an informed citizen 20
panel (N=46) in Switzerland using technology factsheets, an interactive web-tool Riskmeter, and 21
group discussions. We measured the evolution of the panel’s knowledge and preferences from 22
initial (uninformed) to informed and longer-term views four weeks after. In terms of energy 23
transition, our elicited technology and portfolio preferences show strong support for the low- 24
carbon electricity sector transition, especially relying on hydropower, solar power, electricity 25
savings and efficiency, and other renewable sources. As these informed preferences are 26
structurally different from the futures considered by many energy experts, we argue that these 27
preferences should also inform the Swiss Energy Strategy 2050’s implementation. In terms of 28
methodologies in decision science, our factsheets, Riskmeter, and group discussions all proved 29
effective in forming the preferences and improving knowledge, but we also intriguingly found 30
that in a longer run the participants tended to revert back to their initial opinions. The latter 31
finding opens up multiple new research questions on the longer-term effectiveness of 32
informational tools and stability of informed preferences.
33 34
Keywords: public preferences; low-carbon transition; electricity generation; informed citizen 35
panel; technology impacts; usability evaluation 36
37
TOC art 38
39
40 41
1. Introduction
42
The transition to low-carbon electricity generation has gained momentum,1, 2 and it is key 43
to achieving the goals of the Paris Agreement to mitigate climate change to well below 2°C3-5. 44
Delineating robust pathways for the future in the fields of energy and climate change mitigation 45
requires bridging the analytical, factual assessment with the value-laden perspectives of the 46
wider public and key stakeholders6-10. In Europe, the wider public is increasingly supporting the 47
low-carbon transition as a whole11, 12 and renewable electricity generation in particular13-15. Most 48
existing studies to date have compared public preferences for low-carbon electricity generation 49
to conventional fossil fuel plants13, 15-17 and hence have found such increasing public support.
50
Yet, the contemporary debate in science and among many policymakers is no longer about 51
whether to mitigate climate change by switching to low-carbon alternatives, but how exactly to 52
do it4. Few studies have investigated the public preferences for low- to zero- carbon electricity 53
portfolios because they typically include substantial shares of fossil fuels and only several low- 54
carbon alternatives, such as solar, wind, or nuclear power13, 15-17. 55
No fundamental change, such as the low-carbon transition, can occur without unintended 56
environmental consequences18-21. A solid understanding of the public preferences for low-carbon 57
transition pathways should thus account for the multi-dimensional impacts of this transition as 58
well. Besides electricity generation costs or life-cycle greenhouse gas emissions16, 22, only a few 59
studies have additionally elicited the public’s preferences for trading off other impacts, such as 60
local air pollution, supply security, or land use explicitely23,24,25 or implicitly26. Yet, the 61
environmental, health, and safety impacts of the low- and zero-carbon technology portfolios are 62
much broader: nuclear power and large hydropower carry severe accident risks27, 28, hydropower 63
has negative effects on aquatic life21, enhanced geothermal systems could induce seismicity29, 30, 64
solar photovoltaic involve hazardous materials and toxic effluents during manufacturing21, 65
renewable technologies require more land31, and so on. Relatively little is known about the 66
public preferences for such a broader spectrum of environmental, health, and safety impacts of 67
the fully low-carbon portfolios. In fact, technologies with often-debated disadvantages, such as 68
nuclear power and its accident risk or wind power and its landscape impacts, often appear less 69
acceptable than their counterparts. Technical expert communities sometimes assume that, if the 70
public was aware that all low-carbon technologies have negative environmental, health and 71
economic impacts, more negatively viewed technologies would become more acceptable. There 72
is evidence that multi-dimensional information about the pros and cons of low-carbon 73
technologies can induce making trade-offs17,18, 20, 23, 32, 33
, but this has not been extensively tested.
74
Public preferences have mainly been assessed by public opinion surveys11, 13, 25, 32, 34
. 75
However, decision science literature argues that the elicited preferences may be biased due to 76
various knowledge gaps and misconceptions8, 14, 35, 36
. For example, past studies have shown that 77
natural gas may be perceived as renewable37, nuclear power as emitting greenhouse gases38, or 78
enhanced geothermal systems as potentially causing a volcanic eruption39. Although the 79
outcomes of such opinion surveys are useful, they are incomplete guides for strategic long-term 80
decision making. The elicited opinions may not be truly consistent with the values of the public, 81
as they are distorted by unfamiliarity and misconceptions35. The public may not yet even have 82
well-articulated values40 before going through the process of value articulation41 and preference 83
formation42. Thus, the preferences measured in conventional surveys might fail to reflect the 84
future support for emerging or even existing, less known technologies, for which the public has 85
not yet formed stable preferences. If any interventions are to be undertaken to actually shape 86
public support43, it is essential to set the well-informed public preferences as a benchmark 87
instead of relying only on unrepresentative preferences of experts and policymakers8, 44. 88
Previous researchers have adopted various approaches to form and elicit informed public 89
preferences for future energy portfolios. Mayer et al.14, 17 have used factsheets providing brief lay 90
summaries about individual electricity technologies and their environmental impacts. Such 91
factsheets have proved effective in informing the public about medical choices45. Trutnevyte et 92
al.20 have also used factsheets, but described full energy portfolios rather than single 93
technologies. Mayer et al.17, Bessette et al.22, Pidgeon et al.46, and Demski et al.16 have used 94
interactive tools in workshops or on the web, where the involved members of the public could 95
create their preferred energy portfolios by combining technologies given various technical, 96
resource, and environmental constraints. When tested for usability47, such tools facilitate learning 97
about the complexity of the energy transition46 and help elicit more stable preferences47. 98
Deliberative workshops14, 17, deliberative opinion polls48, consensus conference49, and focus 99
groups15, 50 are additional tools that enable learning through group discussions. Although some 100
earlier studies have combined several such tools14, 17, 20
, more empirical evaluative evidence 101
could be gathered on the usability and short- and longer-term effectiveness of these tools47, 51, 52. 102
With the aim to contribute to the search for robust energy futures as well as to decision 103
science literature on investigating the longer-term effectiveness of informational tools by non- 104
experts, we created an informed citizen panel (N=46) in the German-speaking part of 105
Switzerland to elicit the informed public preferences for low- and zero-carbon electricity 106
generation portfolios given information about the multi-dimensional environmental, health, and 107
economic impacts. We used technology factsheets, group discussions, and an interactive web- 108
tool and empirically evaluated their usability and effectiveness in a series of measurements 109
before, during, and after our process47, 53. The choice of creating an informed citizen panel in 110
Switzerland is particularly relevant. In May 2017, the Swiss population has approved the 111
implementation of the Energy Strategy 205054 in a nation-wide referendum and, for the first time 112
worldwide, legitimized a fundamental energy transition based on renewable energy and energy 113
efficiency. As the Energy Strategy 2050 sets broad transition goals, its implementation now 114
requires further choices on which low-carbon electricity generation technologies to deploy and to 115
what extent. Switzerland, thus, already now faces decisions that many other countries will 116
hopefully face soon as well.
117 118
2. Materials and methods
119 120
2.1 Procedure 121
Based on decision science literature, Figure 1 shows the procedure that was used to create 122
the informed citizen panel, follow the formation of its preferences for low-carbon electricity 123
May 2017 by advertising the study on online platforms and in various public places. The 125
advertisement asked the people to register by completing the online survey#0 with demographic 126
questions (demographics) and a question on the person’s preference for expanding specific types 127
of electricity generation in Switzerland to 2035 (technology preferences; 7-point Likert scale 128
from 1=completely disagree to 7=completely agree).
129
From 120 people who registered, we selected 55 participants who received an invitation 130
to take part in the study. From these 120 people, we first excluded those who worked in the 131
energy field in order to have only laypeople. Then, we purposefully sampled these 55 132
participants to invite a balanced group in terms of gender, age, living place (urban or rural area), 133
and, if possible, education. Using the technology preferences, we ensured that people with as 134
broad a range of high, medium, and low support for various technology categories (renewable 135
energy, nuclear, import, natural gas, and efficiency) would be invited in order to foster learning 136
through different perspectives in group discussions.
137
138
Figure 1. Procedure for creating the informed citizen panel and questions asked per stage 140
141
These 55 participants were invited to complete the online survey #1 with questions on the 142
self-rated knowledge about electricity supply (self-rated knowledge, 6 items with 7-point Likert 143
scale, available in Supplementary Information (SI)), interest in the topic in general and in the last 144
four weeks (self-rated interest, 6 items each with 7-point Likert scale), willingness-to-act on 145
energy (willingness-to-act, 7 items with 7-point Likert scale), a knowledge test on electricity 146
supply in Switzerland and in general (general energy knowledge; 20 true-or-false questions), 147
technology preferences, the respondent’s initial preference for Swiss electricity portfolio 148
(portfolio preferences (unrestricted), the respondents had to split 100% of the supply by their 149
preferred technologies without any technical, energy resource, or other restrictions), and on the 150
most important environmental, health, or economic impacts for evaluating each technology 151
(impact ratings).
152
The participants then received a workshop invitation letter with printed factsheets on 153
electricity technologies and their impacts (Section 2.2) and a request to spend up to an hour 154
reading these factsheets. Four workshops that lasted 2.5 hours and involved 8–15 participants 155
were organized; the 46 participants that showed up were randomly assigned to these workshops 156
and to the two discussion groups within each workshop. The workshops followed a script 157
adapted from similar studies14, 17, 50, 55
. After an introduction, each participant completed survey 158
#2, which tested whether the participants were familiar with and understood the information in 159
the factsheets (factsheet knowledge test). Subsequently, the participants discussed the individual 160
electricity technologies and their learning from the factsheets in two sub-groups facilitated by a 161
moderator. After a 25-minute discussion, the participants completed survey#3 that repeated the 162
questions on technology preferences and impact ratings that the participants answered about a 163
month ago in survey#1.
164
Next, a moderator introduced the interactive web-tool Riskmeter (Section 2.2), where the 165
participants could build and submit an electricity supply portfolio for Switzerland in 2035 under 166
technical and energy resource constraints (portfolio preferences (Riskmeter)). The participants 167
could ask questions about using the Riskmeter, but not about the electricity topics. Afterwards, 168
the participants completed survey#4 that included Riskmeter usability test questions, such as 169
true-or-false questions about the Riskmeter portfolio they have created and about the Riskmeter 170
itself. The participants were then asked to discuss their portfolios in a group for 25 minutes. A 171
screen that displayed all initially submitted portfolios was shown to start the discussion. After the 172
discussion, the participants could revise their submitted portfolios again (portfolio preferences 173
(Riskmeter)) and were asked to rate their satisfaction with their portfolio (satisfaction with the 174
Riskmeter portfolio).
175
Finally, the participants completed survey#5 that included four identical sets of evaluation 176
questions (evaluation of tools, 10 items with 7-point Likert scale, available in SI) for the 177
factsheets, Riskmeter, group discussions, and the workshop overall. Four weeks after the 178
workshop, they received a link to the last online survey#6 that repeated all questions from 179
survey#1 and the questions on the evaluation of tools from survey#5. To answer these questions, 180
they did not have access to the factsheets anymore because they were asked to give the factsheets 181
back just before the workshops. Forty-five respondents completed survey#6. The participants 182
received monetary compensation for their participation.
183 184
2.2 Materials 185
Our informational factsheets (available in English at https://portfolio- 186
builder.riskmeter.ch/static/basic_riskmeter/pdf/factsheets_en.pdf) described 13 alternatives that 187
could contribute to the Swiss electricity mix in 2035: (1) three hydropower types, including large 188
dams, large run-of-river, and small hydropower; (2) five new renewable technologies—solar 189
cells (photovoltaic), wind, deep geothermal, woody biomass, and biogas; (3) nuclear power (as 190
the Energy Strategy 2050 foresees nuclear phase-out in the long-run, only the existing Swiss 191
plants were considered, as some of them may still operate to 2035), (4) waste incineration and 192
large natural gas power plants (the latter was the only carbon-intensive Swiss source and it was 193
included in this study because it is part of the wider Swiss energy debate and because it also 194
helps investigating how our participants trade off climate change and other impacts), (5) net 195
electricity import from abroad (net on the annual basis), and (6) electricity savings and efficiency 196
improvements to reduce the electricity demand.
197
Each technology, its current status, resource potential, and environmental, health, and 198
economic impacts were described qualitatively and quantitatively on a double-sided A4 paper.
199
The impacts included climate change (CO2eq), local air pollution (PM10eq, SOx and NOx), water, 200
landscape and land use (m2 of land use), flora and fauna, accidental impacts, resource use and 201
waste (kWh of non-renewable energy used for 1 kWh of electricity), electricity costs (Rp. per 202
kWh), and electricity supply reliability. The impacts were assess using data from literature21, 56, 203
prioritizing the Swiss-specific data as much as possible57, 58 and including qualitative 204
explanations for non-experts. The factsheets were accompanied by a glossary and a 205
supplementary overview table that applied a five-color indicator system to reflect the severity of 206
impacts across technologies. In order to tailor our factsheets to the information needs of our 207
participants59, we conducted 12 semi-structured interviews before this study39. In these 208
interviews, we checked what non-experts know about electricity generation in Switzerland and 209
its environmental, health, and economic impacts as well as what awareness gaps and 210
misconceptions need to be addresses in the factsheets. The factsheets were reviewed for 211
understandability by a public communication specialist.
212
In the workshops, we used an interactive web-tool Riskmeter (www.riskmeter.ch, Figure 213
2) that we developed to build a Swiss electricity portfolio in 2035 under technology and energy 214
resource constraints60. The Riskmeter required the manipulation of electricity produced by each 215
technology in TWh/year to meet the Swiss electricity demand of 70 TWh/year in 2035. With the 216
exception of nuclear, the technologies that are already built today and will last to 2035 were set 217
as the minimum, and the Riskmeter users could not exclude them from their portfolio. In the case 218
of nuclear, the minimum was set to zero because the Swiss Energy Strategy 205054 foresees 219
stepwise nuclear phaseout in Switzerland to 2035. The maximum potential of each technology 220
due to resource or technical constraints was also set and could not be exceeded by the Riskmeter 221
users60. If the users aimed to produce more electricity in Switzerland than is needed annually, the 222
net export value was calculated. As the participants varied the amount of TWh/year produced by 223
each technology, they could also observe the technology shares in the overall portfolio and the 224
contribution of individual power plants (the right panel of Figure 2). In contrast to other studies 225
that incorporated climate or economic impacts into the interactive portfolio builders14, 33, we 226
chose to have information on technology impacts only in factsheets. In this way, we were sure 227
that we do not distort the judgements of our participants about which impacts are more important 228
and require more attention (see Section 3.5). The Riskmeter has been pretested with energy 229
experts as well as non-expert users to optimize its usability.
230 231
232
Figure 2. Interactive web-tool Riskmeter (www.riskmeter.ch) showing the average preferred 233
portfolio of the informed citizen panel (N=46, survey#5). The means and standard deviations of 234
the individual supply options are as follows: large hydro dams (20.3±1.1 TWh/year), large run- 235
of-river hydropower (18.7±1.0 TWh/year), solar cells (11.3±5.7 TWh/year), nuclear (5.0±8.0 236
TWh/year), small hydropower (4.5±0.9 TWh/year), electricity savings (3.7±2.5 TWh/year), 237
waste incineration (2.7±0.5 TWh/year), wind (2.0±1.5 TWh/year), large natural gas (1.0±2.5 238
TWh/year), net import (0.9±3.4 TWh/year), deep geothermal (0.8±1.3 TWh/year), biogas 239
(0.7±0.4 TWh/year), woody biomass (0.3±0.3 TWh/year). The red lines mark the minimum of 240
each option for the users (i.e. the technologies are already built and will last to 2035).
241 242
2.3 Participants 243
Our 46 participants came from the German-speaking part of Switzerland; 73% lived in 244
the canton of Zurich and 48% lived in a medium or large city (over 30,000 or 100,000 245
inhabitants, respectively). They were all born in Switzerland and lived here for over 10 years.
246
The age of our participants ranged from 18 to 77 years (M=42.1, SD=16.6; median=44); thus our 247
participants were slightly younger than the general Swiss population (M=41.37 years)61. 50 % 248
were female, similar to the Swiss gender ratio of 50.9%61. 66.7% had graduated from high school 249
(the Swiss Matura), and 40% had completed at least a bachelor’s degree at a university; our 250
participants were therefore better educated than the Swiss average of 11.6% with high school 251
graduation and 16.9% with a bachelor’s degree at a university62. Most importantly, as discussed 252
in Section 2.1, our informed citizen panel was not set up to be representative, but rather to be as 253
diverse as possible in demographics and initial technology preferences.
254 255
3. Results and discussion
256
3.1 Initial, informed and longer-term preferences consistently show support for 257
renewable electricity and efficiency 258
Figure 3 shows the evolution of technology preferences elicited from our participants 259
before the study (survey#1), after reading factsheets and a group discussion (survey#3), and in 260
the longer term four weeks after the workshops (survey#6). The preferences per discussion group 261
are provided in the SI. The initial, informed, and longer-term preferences indicate a strong 262
preference of the participants for both solar cells and electricity savings and efficiency. This 263
findings is consistent with previous research in Switzerland6, 20. For solar cells, the technology 264
ratings decreased significantly (t=3.122, p=0.003) after participants read the factsheets and 265
discussed them, but four weeks after the workshop the preferences returned closer to the initial 266
values (t= –2.379, p=0.022 between survey#3 and survey#6). For electricity savings and 267
efficiency, the initial preferences in survey#1 increased even more in survey#3 after the 268
factsheets and group discussion (t= –2.916, p=0.006). Similar to solar cells, the factsheets had a 269
(positive) short-term effect only as the preferences dropped again closer to initial values four 270
weeks after the workshops (t= 2.367, p=0.022 between survey#3 and survey#6).
271
272
Figure 3. Measured technology preferences in terms of respondents’ view of expanding specific 273
types of electricity generation in Switzerland to 2035 (7-point Likert scale, 1=completely 274
disagree to 7=completely agree). Statistically significant differences (p<0.05) are: (a) between 275
survey#1 and survey#3, and (b) between survey#1 and survey#6. The informed preferences from 276
survey#3 are: electricity savings (6.2±1.6 TWh/year), solar cells (5.4±1.6 TWh/year), waste 277
incineration (5.4±1.5 TWh/year), wind (5.2±1.8 TWh/year), large run-of-river hydropower 278
(5.1±1.8 TWh/year), large hydro dams (4.9±1.8 TWh/year), small hydropower (4.9±1.8 279
TWh/year), biogas (4.5±1.6 TWh/year), deep geothermal (3.6±2.0 TWh/year), woody biomass 280
(3.6±1.7 TWh/year), net import (3.4±1.9 TWh/year), nuclear (2.8±2.2 TWh/year), large natural 281
gas (2.6±1.7 TWh/year).
282 283
As in the previous Swiss studies13, 63, Figure 3 shows that the participants also expressed 284
strong support for large hydropower dams because dams are a familiar and trusted technology in 285
Switzerland. These preferences remained largely unaffected by the information provided. The 286
initial preferences for large run-of-river hydropower showed that people were rather cautious 287
about expanding it, but after the factsheets and group discussion the preferences increased and 288
neared those for large hydropower dams. In fact, there was a statistically significant effect in the 289
longer run too, between survey#1 and survey#6 (t= –2.871, p=0.006), as the increased 290
preferences for run-of-river hydropower stayed four weeks after the workshop. Our data do not 291
indicate why there was such a longer-term effect, except for the changed views to landscape and 292
land use impacts (see Section 3.5). Large run-of-river hydropower, although prevalent in 293
Switzerland64, tends to receive less attention in the media than the highly visible large dams or 294
often-debated small hydropower. Our participants therefore may not have been familiar with 295
large run-of-river hydropower at the start, but during the process they learnt about its 296
characteristics and impacts and thus revised their preferences. The participants’ preferences for 297
small hydropower remained stable in all assessments and the technology was on average judged 298
just above the line of neither agree or disagree (Figure 2).
299
Initially, our participants also on average supported the expansion of all other 300
decentralized, mostly renewable electricity technologies (survey#1). For waste incineration, 301
biogas, and wind power the preferences remained comparatively high after the factsheets and 302
group discussions (survey#3) and in the longer run (survey#6) with no statistically significant 303
changes between surveys (except for wind). The preferences for wind power increased after the 304
information (t= –2.538, p=0.015), which is consistent with observations from other studies after 305
inducing trade-off thinking6,20. In the cases of deep geothermal plants and woody biomass, the 306
process of reading factsheets and discussing in groups led to a statistically significant decrease in 307
preferences. The preferences on average dropped below the middle point of “neither agree nor 308
disagree” (t=2.340, p=0.024 for deep geothermal; t=2.596, p=0.013 for woody biomass). This 309
observation is in line with the informed portfolio preferences reported in Section 3.3. The 310
participants’ impact ratings (Section 3.5) indicate that this change could have occurred due to 311
learning about accidental impacts (including seismicity), costs, climate change impacts, and 312
electricity supply reliability for geothermal, and due to local air pollution, land use, water use, 313
and climate change impacts for woody biomass. Although in the longer run (survey#6) the 314
preferences returned close to the initial preferences, more information seems to have led to 315
declined support for the emerging deep geothermal technology and less-visible woody biomass.
316
This is different from the case of often-debated wind power39, 65, where the preferences increased 317
with more information.
318 319
3.2 The attitudes are rather negative for nuclear, natural gas, and import 320
Across all three survey#1, survey#3, and survey#6, technology preferences for large 321
natural gas and nuclear plants as well as for net electricity import (counting on annual basis) 322
were on average below the midline of “neither agree nor disagree.” This finding indicates rather 323
negative attitudes of our panel toward these technologies. As we created the panel to have as 324
diverse initial technology preferences as possible (Section 2.3), there were also nuclear 325
proponents participating. Factsheets and group discussions did not affect the panel’s preferences 326
for nuclear and net import because there were no statistically significant differences between the 327
surveys in Figure 3. But there was a further statistically significant decrease in preferences for 328
natural gas plants (t=2.393, p=0.021 between survey#1 and survey#3; t=3.002, p=0.004 between 329
survey#1 and survey#6). All these technologies are typically viewed negatively in the public 330
opinion surveys in Switzerland13, 20, 63. But additional information diminished the preferences for 331
the only fossil fuel option even more. However, it must be noted that Switzerland already now 332
has negligible share of fossil fuels in electricity generation64, even if new gas plants were 333
discussed as part of the Swiss Energy Strategy 205054, 60, 64, 66
. 334
335
3.3. Informed preferences for full portfolios are more ambitious about renewable 336
energy and efficiency than expert studies 337
We consider the portfolio preferences (Riskmeter) in survey#5 as the best-quality 338
judgement of our informed citizen panel because the participants have become familiar with our 339
informational materials and have had a chance to discuss their perspectives in two group 340
discussions. Figure 2 shows the average preferred portfolio of the informed citizen panel.
341
Compared to technology preferences, the portfolio preferences (Riskmeter) also ensured that the 342
participants consider the electricity demand, technology and energy resource constraints in 343
creating full portfolios. As illustrated in the right part of Figure 2, hydropower contributed to the 344
portfolio the most (on average at 39.0 TWh/year for two types of large hydropower and 4.5 345
TWh/year for small hydropower). This is consistent with the current Swiss energy perspectives 346
for 2035 (43.0 TWh/year)64. The participants have on average included 11.3 TWh/year of solar 347
cells (photovoltaic), which is much more than in the Swiss energy perspectives (7.0 TWh/year)64 348
or other Swiss energy scenario studies60. Compared to their maximum potential, electricity 349
savings and efficiency as well as wind power were included on average to use half of their 350
potential from Riskmeter (3.7 TWh/year for savings and efficiency and 2.0 TWh/year for wind).
351
The other renewable decentralized technologies were also included to some extent, typically at 352
less than a half of their respective maximum potential (Figure 2). Net import and large natural 353
gas plants were kept to the minimum (3.7 TWh/year for net import and 2.0 TWh/year for natural 354
gas).
355
As discussed later in Section 3.6, the Factsheets knowledge test and Riskmeter usability 356
test showed satisfactory-to-good mastery of our informational materials. Hence, we argue that 357
the Portfolio preferences (Riskmeter) in survey#5 were sufficiently thought through by our 358
participants and are good-quality preferences with minimal effects of misconceptions or 359
knowledge gaps. This argument is also supported by the fact that, compared to Portfolio 360
preferences (Riskmeter) in survey#4 before the second group discussion, the Portfolio 361
preferences (Riskmeter) after this discussion were revised very little; the only statistical 362
difference was an increase in solar cells from 10.6±5.5TWh/year to 11.3±5.7TWh/year; t= – 363
2.585, p=0.013. This result indicates that our participants have had already formed their stable 364
preferences at that point.
365 366
3.4. Other preference measurements support the same findings 367
Figure 4 shows the portfolio preferences (unrestricted) in survey#1 and survey#6, where 368
the participants had to distribute 100% of electricity demand to be supplied by different 369
technologies without technical, energy resource or any other constraints. In contrast to the 370
Riskmeter that defined the minimum and maximum limits to technologies, this unrestricted 371
portfolio task was likely the most difficult task in our study because. It required thinking about 372
one’s technology preferences as well as the beliefs about the current installed capacity and 373
maximum potential of these technologies. There are multiple changes in means of portfolio 374
preferences (unrestricted) for technologies between survey#1 and survey#6, but statistically 375
significant increases are observed only for large run-of-river hydropower (t= –2.274, p=0.028) 376
and waste incineration (t= –2.910, p=0.006). Statistically significant decreases are observed for 377
deep geothermal (t=3.154, p=0.003), natural gas (t=2.970, p=0.005), and biogas plants (t=2.728 378
p=0.009). Although the survey questions for Figures 3 and 4 were different, the observed 379
changes in portfolio preferences (unrestricted) and in technology preferences between survey#1 380
and survey#6 are in a large part consistent. Our participants have learned with a longer-term 381
effect about run-of-river hydropower, deep geothermal, and natural gas plants and adjusted their 382
portfolio preferences accordingly. Although Figure 3 shows decreased longer-term preferences 383
(survey#6) for woody biomass and relatively stable preferences for biogas, we cannot rule out 384
that the participants might have mixed up biogas and woody biomass, when asked for portfolio 385
preferences (unrestricted) in survey#6 four weeks after the workshop.
386 387
388
Figure 4. Measured portfolio preferences (unrestricted) in terms of distributing 100% of Swiss 389
electricity demand by individual technologies in 2035 without any technical, energy resource or 390
other constraints (values in % were multiplied by 70 TWh/year for comparison with Figure 2).
391
(a) marks the statistically significant differences (p<0.05).
392 393
The portfolio preferences (unrestricted) in survey#6 are, in fact, more consistent in terms 394
of technology order with the portfolio preferences (Riskmeter) than portfolio preferences 395
(unrestricted) in survey#6. This observation hints at longer-term learning effects. Large dams 396
and run-of-river hydropower as well as solar cells have the highest shares in portfolio 397
preferences (unrestricted) in survey#6, although the participants still do not seem to recall how 398
high the share of large hydropower in Switzerland is. Compared to the initial preferences, the 399
participants in survey#6 also increased the share of electricity savings and efficiency, possibly 400
because they had learnt about the higher maximum potential of savings and efficiency than they 401
had expected (see Figure 2). All other technologies in the portfolio were included to a minor 402
extent only, except for large gas power plants and net import that were both again minimized.
403
Figure 3 presents the results of all study participants together. There were some variations 404
among the individual discussion groups in initial technology preferences and their change after 405
the discussions (see SI for detailed results). This means that the group dynamics and arguments 406
raised in the different groups could have influenced the outcomes. But the overall patterns of the 407
change in technology preferences, such as natural gas, deep geothermal, or run-of-river 408
hydropower, can be observed in multiple groups. Even more, the tendency in survey#6 for the 409
participants to revert to their initial preferences is also clearly observable across groups.
410 411
3.5 Supply reliability and climate change are the most decisive impacts, but the 412
relevant impacts generally differ across technologies 413
Figure 5 shows the impact ratings initially (survey#1), after reading the factsheets and 414
discussing in a group (survey#3), and four weeks after the workshops (survey#6). Overall, the 415
most often reported categories of important impacts were electricity supply reliability and 416
climate change. The least important categories were impacts in terms of accidents, water, as well 417
as flora and fauna. However, over the course of the study the importance of accidental impacts 418
increased owing to the revised impact ratings for nuclear and deep geothermal plants. By the end 419
(survey#6), the importance of local air pollution increased and the importance of electricity costs 420
decreased. In general, we found that for each technology a different set of impacts is important, 421
primarily including impacts where a specific technology has a very high or low negative impact.
422
This finding is in contrast to common practice in methods, such as multi-criteria assessment, 423
where a universal set of importance weights for impacts are applied across all technologies18, 20. 424
The changes between uninformed and informed impact ratings in survey#1 and survey#3 425
reflect the reactions to our information. Although some changes could have occurred because the 426
participants initially interpreted the impacts differently from the definitions in our factsheets, 427
multiple changes in impact ratings are still consistent with the changes in technology and 428
portfolio preferences (Figures 2 and 3). For example, ratings on water, land use, and cost impacts 429
were revisited for large run-of-river and small hydropower. Climate change impacts were 430
adjusted for woody biomass or solar cells. Ratings on supply reliability, costs, resource use and 431
waste, and air pollution for solar cells were revised as well. Whereas many impact ratings in 432
survey#6 returned to initial uninformed ratings, several longer-term learning effects can be 433
observed: for example, local air pollution for woody biomass and biogas, accident impacts for 434
deep geothermal, and electricity costs for net import.
435
436
Figure 5. Impact ratings that show which environmental, health, or economic impacts were rated 437
as the most important for individual technologies. Each participant could choose one to three 438
impact categories per technology; the responses were then normalized to three choices per 439
participant and rounded to zero decimals.
440 441 442
3.6 Informational materials were effective in knowledge creation and liking, but 443
with little difference between factsheets and web-tool 444
Table 1 shows the empirical evaluative evidence of our procedure and materials. The 445
participants’ satisfaction ratings indicate that the factsheets, web-tool Riskmeter, group 446
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Accidental impacts (total for all participants: 105 in survey#1,
136 in survey#3, 151 in survey#6)
Survey#1 Survey#3 Survey#6
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Electricity costs
(total for all participants: 204 in survey#1, 203 in survey#3, 167 in survey#6)
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Climate change (total for all participants: 236 in survey#1,
200 in survey#3, 246 in survey#6)
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Resource use and waste (total for all participants: 177in survey#1,
190 in survey#3, 178 in survey#6)
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Electricity supply reliability (total for all participants: 271 in survey#1,
239 in survey#3, 226 in survey#6)
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Local air pollution (total for all participants: 168 in survey#1,
167 in survey#3, 218 in survey#6)
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Water
(total for all participants: 152 in survey#1, 143 in survey#3, 159 in survey#6)
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Accidental impacts (total for all participants: 105 in survey#1,
136 in survey#3, 151 in survey#6)
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Flora and fauna
(total for all participants: 146 in survey#1, 138 in survey#3, 129 in survey#6)
0 10 20 30 40 50 60
Large hydropower dams Large run-of-river hydropower Small hydropower Nuclear Solar cells (photovoltaic) Wind power Deep geothermal Large natural gas plants Woody biomass Biogas Waste incineration Net import
Lanscape and land use (total for all participants: 205 in survey#1,
171 in survey#3, 177 in survey#6)
Sum of times across all participants that the environmental, health or economic impact was judged to be among the three most important impacts for evaluating each technology
average responses are above the middle point of 35). This perception did not significantly change 448
after four weeks. There is a high internal consistency for all satisfaction scales before and after 449
the workshop (Cronbach’s α=0.71 before and 0.71 after the workshops for factsheets; α=0.77 450
and 0.86 for the Riskmeter; α=0.57 and 0.83 for group discussions; α=0.71 and 0.85 for the 451
overall workshops). Interestingly, there were no differences in the participants’ perception of 452
factsheets and Riskmeter, even if it is often assumed that interactive tools can make the 453
information more engaging and accessible to non-experts. The group discussions had the lowest 454
rating of all the tools used and multiple evaluative items were ranked near the middle point of 4.
455
But the participants still liked the discussions in general (M=5.2±1.2) and found them useful to 456
form an opinion (M =4.5±1.4).
457 458
Table 1. Evaluation of the materials and procedure.
459
Before the workshops (survey#1, N=46)
At the workshops (surveys#2-#5, N=46)
Four weeks after the workshops (survey#6, N=45) Evaluation of tools
(10 items, 7-point Likert scale, max.
score 70, available in SI)
• Factsheets M=54.7 ± 6.3 M=55.4 ± 7.3
• Web-tool Riskmeter M=53.7 ± 7.4 M=54.1 ± 8.0
• Group discussion M=46.5 ± 6.7** M=45.8 ± 9.7**
• Whole workshop M=56.1 ± 6.1*** M=56.0 ± 7.6
Self-rated knowledge
(6 items, 7-point Likert scale, max.
score: 42, available in SI)
M=25.6 ± 6.5* M=28.0 ± 5.0*
General energy knowledge test (20 true-or-false questions, max.
score: 20, available in SI)
M=9.8 ± 2.8* M=12.7 ± 2.5*
Self-rated general interest (6 items, 7-point Likert scale, max.
score: 42)
M=28.6 ± 7.5 M=28.7 ± 6.5
Self-rated interest in last four weeks (6 items, 7-point Likert scale, max.
score: 42)
M=18.1 ± 6.5* M=16.3 ± 5.6
Willingness-to-act
(7 items, 7-point Likert scale, max.
score 49)
M =34.9 ± 7.9 M=34.9 ± 9.1
* p < 0.01 between two measurements of the same variable at different times
** p < 0.01 for differences from the evaluation of other tools
*** p < 0.05 for difference from the factsheets and p < 0.01 for from the Riskmeter
460
As shown in Table 1, the self-rated knowledge about the electricity topics in survey#6 461
was significantly higher than before our process in survey#1 (t= –3.84, p=.000). This means that 462
our workshops helped the participants to feel more knowledgeable. The participants’ self-rated 463
knowledge ratings had high internal consistency in both surveys (Cronbach’s α=0.85 and 0.84).
464
In fact, the participants also objectively performed better in completing a general energy 465
knowledge test, where they scored much better in survey#6 than in survey#1 (t= –6.585, p=.000).
466
On average, 63% of answers were correct in survey#6 compared to 49% initially. In terms of 467
individual tools, the participants performed satisfactorily in answering factsheet knowledge tasks 468
that tested active mastery of factsheets’ information under time pressure (M=18.3±7.4, 59%
469
correct on average). The participants also performed satisfactorily in actively mastering the 470
Riskmeter (M=3.7±1.5, 73% correct on average) and understanding the information on 471
electricity portfolios (M=5.2±1.4, 74% correct on average). Eighty-five percent of participants 472
reported that they followed a specific strategy when creating their preferred Riskmeter portfolio 473
rather than randomly coming up with the portfolio50. The participants were also satisfied with 474
their preferred portfolio (M=23.3±3.4, the maximum score of 28). These findings show that our 475
study had positive short- and longer-term effects on energy knowledge and active mastery of 476
information, even if many of the participants’ preferences returned to the initial values in 477
survey#6.
478 479
3.7 No effect on interest or willingness to act was observed 480
In terms of participants’ self-reported general interest in energy topics (Table 1), our 481
study does not seem to have had the effect between survey#1 and survey#6. The interest ratings 482
had high internal consistency both before and after the workshop (Cronbach’s α=0.88 and 0.83, 483
respectively), and were significantly above the midpoint of “partly agree and partly disagree”
484
(t=4.238, p=.000 in survey#1 and t=4.898, p=.000 in survey#6). In terms of interest in energy 485
topics in the last four weeks (e.g., reading newspaper articles, attending events about the topic), 486
the participants’ interest dropped statistically significantly in survey#6 compared to survey#1 487
(t=2.765, p=0.008). The interest rating had high internal consistency in both surveys (Cronbach’s 488
α=0.89 and 0.82, respectively) and was significantly below the sum of the midpoints in the 489
scales, i.e. “once to twice a week” (t= –6.339, p=.000 and t= –9.194, p=.000). As the overall 490
energy interest did not change, it could be that after our intensive process with the factsheets and 491
workshops, the participants felt they spent less time with the topic in the four weeks after the 492
workshops.
493
The participants’ willingness-to-act ratings suggested an overall positive attitude toward 494
energy action before and four weeks after the workshops (Table 1). The ratings had high internal 495
consistency in survey#1 and survey#6 (Cronbach’s α=0.79 and 0.83, respectively) and were 496
significantly above the midpoint of “partly willing-to-act and partly not willing-to-act” (t=6.025, 497
p=.000 and t=5.089, p=.000).
498 499
3.8 Implications for low-carbon energy transition 500
In terms of implications for the energy transition, our informed citizen panel in the 501
German-speaking part of Switzerland demonstrates strong support for low-carbon electricity 502
sector, especially relying on hydro, solar power, electricity savings, efficiency improvements, 503
and to a lesser extent on a diverse mix of other renewable sources. The alternatives of new 504
natural gas power plants or significant shares of nuclear power are not supported by our panel. In 505
fact, we observe a strong backing by our panel for building new plants locally in Switzerland to 506
minimize the annual net electricity import into the country. With climate change impacts and 507
electricity supply reliability as the key criteria in forming one’s opinion, the participants seem to 508
generally consider and balance a wider set of pros and cons of generation technologies on the 509
environment, health, and economy. We thus argue that the informed preferences of our citizen 510
panel could feed into the implementation of the Swiss Energy Strategy 2050, especially for 511
specifying the targets of renewable energy and energy efficiency. Interestingly, our panel seems 512
to express different preferences from what the Swiss energy experts model in future energy 513
projections and scenarios60, 64, 66. Solar power and net import are the two biggest disparities and 514
future work could further explore the origins of these disparities. Similar observations have been 515
made in other countries16. 516
517
3.9 Implications for decision science literature, especially on longer-term 518
effectiveness of information 519
In terms of effectiveness of our informational materials, the participants were satisfied 520
with the materials, including the technology factsheets, interactive web-tool Riskmeter, and the 521
overall workshops. We could also measure a change in preferences and thus, indirectly, learning 522
effects, especially in the shorter term. This means that the processes of preference formation42 523
and value articulation40 were happening. With the exception of some technologies, such as large 524
run-of-river hydropower, deep geothermal, or woody biomass, we still observed relatively stable 525
public preferences consistent with earlier opinion surveys in Switzerland13, 20, 63
. This is not 526
surprising because the public energy discussions have been happening for several years now and 527
intensified before the Energy Strategy 2050 referendum in May 2017. Our panel therefore likely 528
already had relatively formed and stable preferences. The situation could differ significantly in 529
other countries though17, 46. 530
Whereas some learning effects remained in longer run, we generally observed that four 531
weeks afterwards the participants tended to revert back to their initial opinions. This is a new 532
finding in the literature because previous research has not empirically investigated longer-term 533
evolution of informed preferences in the energy context14, 17, 24, 67
. Elaboration likelihood theory68 534
hypothesizes two processes of information-induced change in attitudes. First, when individuals 535
are motivated and able to engage with new information, they thoughtfully consider it and this 536
leads to a lasting change in attitudes. Second, individuals that are not motivated or able to engage 537
with the new information new information. It is possible that some of our participants were not 538
motivated enough for the first process to occur because they only had low to medium interest in 539
energy topics without personal relevance. However, after additional investigation we did not 540
reveal any differences in the longer-term stability of preferences between the participants with 541
high or low interest in the energy topics. It is also possible that our participants were not able to 542
engage with the information because they did not have relevant prior knowledge or there were 543
too many distractions and time pressure at the workshops. In contrast to other studies23, 33, 69, we 544
had practically no decision support for our participants in making tradeoffs between multi- 545
dimensional pros and cons of electricity technologies. Possibly, our information was therefore 546
too extensive and too complex for the participants to thoroughly engage with it for a longer-term 547
effect. After all, there is still the dimension of memory70, where the continuous exposure to 548
information may help creating stable preferences in the long run. Future research in decision 549
science could unpack this temporal dimension of learning and preference formation too.
550 551
3.10 Limitations and future research needs 552
Our elicited preferences of the informed citizen panel are only one piece in the overall 553
understanding of the public view to low- and zero-carbon energy transition8, 71, 72
. These 554
preferences are naturally conditional to our informational materials because there is no neutral 555
framing of information16. For example, we merely provided information on electricity generation 556
costs without mentioning wider electricity grid or storage costs and hence possibly measured 557
high support for solar power. Moreover, we investigated the environmental, health, and 558
economic impacts of electricity generation technologies in general without disaggregating how 559
the different Swiss regions would be affected. As the impacts can vary notably across the sub- 560
national regions73-75 and such information can change public preferences20, future work is needed 561
to investigate the spatial dimension of public preferences. Our study focused on the electricity 562
generation that needs to play a pivotal role in low-carbon transition76, but future work should 563
also investigate the informed public preferences for the energy system as a whole16, 46. Our 564
technology factsheets included complex multi-dimensional information and we did not provide 565
decision support to help making tradeoffs and hence potentially engaging with the information 566
better. This could be done in the future23, 69. Finally, we only investigated short-term and longer- 567
term evolution of preferences in the course of several weeks. Energy transition, however, will 568
need to span decades. Future research could therefore analyze the evolution of public preferences 569
over long periods of time. In fact, the methodologies and tools from this study could be used to 570
