Blending Acceptance as Additional Evaluation Parameter into

Carbon Capture and Utilization Life-Cycle Analyses

K. Arning

, B. Zaunbrecher

, A. Sternberg

, A. Bardow

and M. Ziefle

Human Computer Interaction Center (HCIC), RWTH Aachen University, Campus-Boulevard 57, 52074 Aachen, Germany

Chair of Technical Thermodynamics, RWTH Aachen University, Schinkelstr. 8, 52062 Aachen, Germany

Keywords: Life-Cycle Analysis, Carbon Capture and Utilization, Acceptance, Perception, Awareness, Conjoint Analysis,

Survey.

Abstract: Carbon Capture and Utilization (CCU) captures and uses CO

as a feedstock to produce carbon-based saleable

products. However, sustainable technology innovations are only attractive to investors and justify (public)

subsidies if they provide economical or ecological added value. Therefore, life-cycle analyses (LCA) are

applied to identify the environmentally most optimal option of a technology scenario. Since LCA do not

address the social dimension of sustainable innovations so far, a study is presented, where acceptance is

assessed as additional life-cycle evaluation parameter. A prestudy (qualitative interviews, n = 25 participants)

was run to identify acceptance-relevant parameters of CCU site deployment. In a conjoint study (n = 110),

which investigated the acceptance of CCU site deployment scenarios, the profitability, CO

-source, and type

of CO

-derived product were systematically varied as acceptance-relevant criteria. Findings show, that

profitability had the highest impact on CCU technology scenario preferences. Fuel was the most attractive

CCU product option and steel plants were the most preferred CO

-source. In sensitivity analyses specific

acceptable and nonacceptable CCU technology scenarios were identified. The assessment of acceptance for

CCU deployment scenario parameters allows to include acceptance as additional evaluation and weighting

parameter into life-cycle analyses of CCU technology scenarios.

1 INTRODUCTION

Fighting climate change caused by greenhouse gas

emissions is one of the greatest worldwide challenges

today. Carbon capture and utilization (CCU)

represents a range of technologies, developed to

reduce CO

emissions and fossil resource use by

capturing, “recycling”, and using CO

as a feedstock

to produce carbon-based saleable products. Most

CCU technology applications still have low

technology readiness levels (Wilson et al., 2015), but

first CO

-derived products are now reaching the end-

consumer market. Apart from the technical feasibility

of CCU, the technology must provide added

ecological and economic value and – most important

for its long-term success – reach public acceptance.

So far, the public perception of CCU is an under-

researched field (Jones et al., 2017), which also

applies to the consideration of acceptance in the

design and evaluation of CCU technology

deployment scenarios.

1.1 Carbon Capture and Utilization

Carbon Capture and Utilization (CCU) has gained

attention as climate change mitigation technology in

recent years, since it has the potential to limit or

reduce atmospheric releases of CO

and to replace

fossil resource use (Markewitz et al., 2012). CCU

refers to a broad range of eco-innovative and

sustainable technologies, which use CO

as a

feedstock for the production of diverse carbon-

derived products (Styring et al., 2015). Different

CCU production routes and resulting product types

have been developed: direct or physical utilization,

biological, and chemical utilization. In direct or

physical utilization, CO

is used as refrigerant, as

extinguishing agent or in cleaning processes. The

captured CO

can also be transformed via biological

processes into fuels or bio-oils. The chemical

utilization route of CO

allows the production of urea,

methanol, cyclic carbonates, salicylic acid, or polyol

(Markewitz et al., 2012). Here, CO

can be stored

partly permanent (e.g., in liquid fuels) or even for

Arning, K., Zaunbrecher, B., Sternberg, A., Bardow, A. and Zieﬂe, M.

Blending Acceptance as Additional Evaluation Parameter into Carbon Capture and Utilization Life-Cycle Analyses.

DOI: 10.5220/0006683000340043

In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018), pages 34-43

ISBN: 978-989-758-292-9

long-term time periods (e.g., in polymers or cement).

The production of plastic substances such as polyol,

polypropylene, and polyurethane based on carbonates

and polycarbonates allows access to the very high

demand and sales volumes in the plastics sector

(Coates and Moore, 2004). The chemical utilization

route of CCU is highly promising due to the high

availability of CO

, savings of fossil resources in the

production of plastic products, and a broad spectrum

of plastic product variants (Markewitz et al., 2012).

Thus, from a technical perspective, CCU has great

ecological and economic advantages: by emitting less

, CCU can contribute to climate change

mitigation targets and the use, costs, and dependency

on expensive and limited fossil resources can be

reduced. However, “green” and sustainable

technology innovations such as CCU are only

attractive to investors and justify (public) subsidies if

they provide economical or ecological added value.

In the past, techno-economic assessments were most

commonly employed to identify the most cost

effective option. Currently, life-cycle assessments

(LCA) are increasingly used to identify the

environmentally most (or least) benign option of a

technology scenario.

1.2 Life-Cycle Assessment of Carbon

Capture and Utilization

“Green” technology innovations such as Carbon

Capture and Utilization require methods and tools to

assess and compare the environmental impact of their

products or services to the society. One specific

evaluation framework is the Life-Cycle Assessment

(LCA) (Rebitzer et al., 2004), which estimates and

assesses the environmental impacts attributable to the

life-cycle of a product in the form of environmental

impact measures such as climate change,

stratospheric ozone depletion, tropospheric ozone

(smog) creation, toxicological stress on human

health, and ecosystems, etc. In LCA, energy and

materials used and wastes released to the environment

are identified and evaluated regarding opportunities

to affect environmental improvements (Rebitzer et

al., 2004).

In the context of CCU, several life-cycle

assessments were conducted, which either focused on

the comparison of specific CCU process routes with

conventional process routes (e.g., von der Assen et

al., 2013) or the comparison of different CCU

technology process steps (e.g., von der Assen et al.,

2014). A study on life-cycle assessment of CO

utilization for polyurethane concluded that even

though chemical CO

-utilization does not lead to a

significant reduction in the global emissions budget,

significant amounts of fossil resources (mostly oil)

and CO

emissions resulting from the production can

be saved compared to the manufacture of

conventional products (von der Assen and Bardow,

2014). Not restricted to the field of CCU, but also for

other eco-innovations, “hot spots” and their impact on

the profitability and competitiveness of the

technology compared to conventional routes were

analysed (Castellani et al., 2017).

Even though LCA are referred to as holistic

approaches due to their broad evaluation perspective

from “cradle to grave” of a product, sustainable

innovations do not only exert environmental, but also

social impacts. So far, LCA do not address the social

dimensions of sustainable innovations, even though

the perception and acceptance of a technology,

product or service by the public can finally decide

about the success or failure of a technical innovation

(Batel et al., 2013). Public acceptance refers to a

positive reception or behavioral response (support) to

a technology. In contrast, missing acceptance can

result in protesting actions against the technology

infrastructure or avoidance of purchasing and using

the technology and its products (e.g., Wallquist et al.,

2012).

The present paper, therefore, aims for an

investigation of social indicators such as perception

and acceptance of the CCU technology in a LCA-

framework. Before the research approach is detailed

in section 2, the following section 1.3 focuses on the

state-of-the-art regarding the public perception and

acceptance of CCU.

1.3 Public Perception and Acceptance

of Carbon Capture and Utilization

Due to the young age of the CCU technology and the

comparably small number of mature carbon-based

products on the market (e.g., mattresses (Covestro,

2016); or synthetic methane (Audi, n.d.)), empirical

research about the perception and acceptance of CCU

is just emerging in the last years. These studies

mainly focus on the socio-political level of

acceptance (i.e., the acceptance of technologies by

major the general public, policy makers, and other

key stakeholders) as well as on market acceptance

(i.e., the acceptance of carbon-derived products by

potential consumers) (Wüstenhagen et al., 2007).

Most empirical studies on the perception and

acceptance of CCU were based on qualitative

methods, trying to identify underlying motives and

determinants of CCU acceptance (e.g., Jones et al.,

2016; Jones, 2015; van Heek et al., 2017), but first

Blending Acceptance as Additional Evaluation Parameter into Carbon Capture and Utilization Life-Cycle Analyses

quantitative studies directed on a quantification of

CCU acceptance levels (Perdan et al., 2017),

modeling CCU perception and acceptance (Arning et

al., 2017) or the decision process (van Heek et al.,

2017b) can be found today. These studies show, that

the general awareness and information level about the

CCU technology and carbon-derived products is

rather low in the general public (Perdan et al., 2017).

Even though CCU is generally positively perceived

due to its environmental benefits, especially technical

lay-people with a low awareness of CCU associate

higher risks with this technology. Frequently stated

risks or concerns in the context of CCU refer to health

risks (e.g., fear of headaches or suffocation due to

-leakage from CCU products), environmental

risks (e.g., fear of pollution during the production

process or during product disposal), product quality

risks (e.g., lower durability) or sustainability risks (no

long-term solution, when CO

is released after

product disposal) (Arning et al., 2017).

Summing up, empirical acceptance research on

CCU technology and products shows, that the

acceptance of a green technology innovation by the

public should not be taken for granted. To develop

technology scenarios, which are not only

economically and ecologically feasible and effective,

but also acceptable from the public or consumer side,

the present paper is directed on an integration of two

methodological approaches: life-cycle analyses and

empirical acceptance research. For the first time – to

best of our knowledge – acceptance parameter input

is assessed in a life-cycle scenario and is regarded as

additional life-cycle evaluation parameter. By

blending acceptance into life-cycle assessments,

technical innovations have a higher chance to evolve

into social innovations that meet people’s

requirements and expectations and yield less risk of

failure due to public opposition.

Therefore, the present study pursued the following

research aims:

1. Identifying acceptance-relevant criteria for a

CCU life-cycle scenario directed on the

deployment of a CCU site

2. Measuring acceptance of CCU site

deployment criteria and different CCU

deployment scenarios

3. Complementing technical and environmental

evaluation parameters of existing life-cycle

assessment approaches by acceptance

parameters

2 METHODOLOGY

In the following section, the methodological approach

of the study is detailed, i.e., the conjoint analysis

procedure, the prestudy, and the sample.

2.1 Conjoint Analysis

Conjoint analysis (CA) methods combine a

measurement model with a statistical estimation

algorithm (Rao, 2014). Compared to survey-based

acceptance studies, which are still the dominating

research method in information systems and

acceptance research, CA allow for a holistic and

ecologically more valid investigation of decision

scenarios (Alriksson and Öberg, 2008). Specific

product profiles or scenarios are evaluated by

respondents, which are composed of multiple

attributes and differ from each other in the attribute

levels. CA deliver information about which attribute

influences respondents’ choice the most and which

level of an attribute is preferred. Preference

judgments and resulting preference shares are

interpreted as indicator of acceptance (Arning et al.,

2014). In the present study, a choice-based-conjoint

(CBC) analysis approach was chosen, because it

closely mimics complex decision processes, where

more than one attribute affects the final decision

(Rao, 2014).

2.2 Selection of Attributes

A qualitative interview prestudy was conducted to

identify acceptance-relevant attributes and levels in

the context of CCU deployment to be used in the

conjoint analysis. Note, that the CCU life-cycle was

not operationalized from a technical perspective, but

from a social science perspective, taking only

acceptance-relevant factors of CCU deployment into

account. To identify these acceptance-relevant

factors, five focus-group discussions with n = 25

participants were conducted. Here, people with

differing technical expertise, environmental

awareness, age, and gender discussed perceived

benefits, barriers, and deployment requirements from

the publics’ perspective. All interviews were

recorded, transcribed, and analyzed by qualitative

content-analysis. The following attributes and levels

were extracted as being acceptance-relevant for CCU

technology deployment:

1. CO

-source with three levels a) chemical

plant, b) steel plant, and c) coal-fired plant.

The three point sources were chosen, because

even lay-participants were familiar with them

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

and first pilot- or demonstration sites are

already running in Germany.

2. Profitability of the CCU site with three levels

a) no public financing necessary, b) start-up

public financing for building the site, c) long-

term public financing necessary.

The aspect of profitability was emphasized by

experts, thereby differentiating between the

different production routes and types of CO

derived products (e.g., production of plastic

products versus production of fuel).

3. CO

-derived product with four levels a)

mattress, b) fertilizer, c) fuel, d) drugs.

Four different types of CO

-derived products

were chosen to represent the broad variety of

potential products, which can be produced

based on the CCU technology.

Because a combination of all corresponding levels

would have led to 36 (3 × 3 × 4) possible

combinations to evaluate, the number of stimuli was

reduced. Each respondent was presented with only 12

random tasks, i.e., some levels of attributes did not

appear together in one set. Therefore, a test of design

efficiency was applied to examine whether the design

was comparable to the hypothetical orthogonal design

(Sawtooth Software, 2013). Design efficiency was

confirmed with a median efficiency of 99% relative

to a hypothetical orthogonal design.

2.3 The Questionnaire

SSI Web Software was used for questionnaire design.

The questionnaire consisted of three parts: First,

participants received an introduction into basic terms,

functioning, and purpose of the CCU technology.

They were also introduced into the decision scenario.

To ensure that participants correctly understood all

attributes and levels, all of them were defined and

comprehensively described in the introduction.

Second, participants answered the 12 conjoint choice

tasks. They were asked to decide under which

conditions they would accept the roll-out of the CCU

technology. An example for a choice task is shown in

Figure 1. The third part of the questionnaire consisted

of general CCU acceptance items, assessing CCU as

“beneficial”, “useful”, “risky”, and “threatening”,

local CCU site deployment acceptance (all assessed

on 6-point Likert-scales (1 = “do not agree at all” to

6 = “fully agree”)) as well as demographic data (age,

gender, and education) and the general awareness and

knowledge level about the CCU technology in

specific (assessed on Likert-scales from 1 = “very

low” to 6 = “very high”).

Figure 1: Example of a choice task in the CBC study.

Respondents were asked to indicate the most preferred

CCU deployment scenario.

2.4 The Sample

Data of n = 110 participants was analyzed (only

complete data sets were included into the analysis),

with 53.6% male and 46.4% female respondents. The

mean age was M = 28.5 years (SD = 8.8), ranging

from 17-59 years. The sample was highly educated

with 60 respondents holding an university degree and

50 respondents holding a school leaving certificate.

The awareness and information level about the

CCU technology was very low in the sample. The

majority reported to have very low (69.1%) or low

(17.3%) knowledge about CCU, whereas only 4.5%

reported to have a good or very good (1.8%)

knowledge about the CCU technology.

2.5 Data Analysis

Data analysis of conjoint data was carried out by

using Sawtooth Software (SSI Web, HB, SMRT)

(Sawtooth Software, 2013). Part-worth utilities were

calculated based on Hierarchical Bayes (HB)

estimation and part-worth utilities importance scores

were derived. They provide a measure of how

important the attribute is relative to all other

attributes. Part-worth utilities are interval-scaled data,

which are scaled to an arbitrary additive constant

within each attribute, i.e., it is not possible to compare

utility values between different 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 or scenario simulations where

carried out by using the Sawtooth market simulator.

Likert ratings in the questionnaire were analysed

descriptively (M, SD). Ratings > 3.5 were interpreted

as approving, ratings < 3.5 as rejecting evaluation.

-source

-derived

product

Profitability

Chemical

plant

Coal-fire

plant

Steel

plant

mattress drugs fuel

No public

financing

Long-Term

public

financing

Start-up

public

financing

Blending Acceptance as Additional Evaluation Parameter into Carbon Capture and Utilization Life-Cycle Analyses

3 RESULTS

In this section, the findings regarding the acceptance

of CCU site deployment are presented as well as the

conjoint data analysis findings, i.e., relative

importance scores for the three attributes, part-worth

utility estimation findings for the respective attribute

levels, and the simulation of preferences for different

CCU site deployment scenarios.

3.1 CCU Deployment Acceptance

The general perception of the CCU technology was

positive (M = 4.3, SD = 0.8). A minority (10.8%) of

respondents evaluated the CCU technology as

nonacceptable, whereas 78% perceived CCU as

positive or highly positive (11.2%). Respondents

evaluated CCU as beneficial (M = 4.3, SD = 1.0),

useful (M = 4.4, SD = 0.9), not being risky (M = 2.9,

SD = 0.9) or threating (M = 2.5, SD = 0.9).

Asked for local acceptance of CCU site

deployment, 11% would react with protest, 56.4%

would tolerate the site, and 16.4% would approve the

deployment of a CCU site in their neighbourhood.

3.2 Relative Importance Scores

To evaluate the main impact factors on preferences

for CCU deployment scenarios, the share of

preference was calculated by applying Hierarchical

Bayes Analyses. The relative importance scores of

the attributes examined in the present study are

presented in Figure 2.

Figure 2: Importance scores for CCU deployment attributes

in the CBC study.

The attribute “profitability” had the highest

importance score (44.0%, SD = 18.0), followed by

“CO

-derived products” (34.6%, SD = 15.3) and

“CO

-source” (21.4%, SD = 14.8). The results

indicate that the profitability of CCU site was the

most dominant attribute to influence CCU

deployment scenario acceptance. The type of CO

derived product was also important, but to a lesser

extent. Interestingly, the CO

-source was the least

important attribute affecting CCU deployment

scenario acceptance.

3.3 Part-worth Utility Estimation

The average zero-centred differential part-worth

utilities for all attribute levels are shown in Figure 3.

The attribute “profitability” displayed the highest

range between part-worth utilities, which caused the

high importance scores (see 3.1).

Focusing on absolute utility values of the attribute

“profitability”, the level “no public financing” was

highly preferred, as indicated by the highest utility

value (53.2, SD = 40.7). “Start-up public financing”

was also accepted (utility = 12.8, SD = 18.4), whereas

“long-term public financing” (-66.0, SD = 35.1)

received the lowest utility value and was rejected by

respondents.

Looking at CO

-derived products, the most

preferred product type was “fuel” (utility = 19.2, SD

= 48.2), followed by “drugs” (utility = 9.5, SD =

48.8), which were also positively evaluated. Using

to produce fertilizer was evaluated neutrally

(utility = 0.0, SD = 36.0). The only product, which

received negative evaluations was the mattress

consisting of CO

foam (utility = -28.6, SD = 26.6).

Figure 3: Part-worth utilities (zero-centred diffs) for CCU

deployment attributes and levels in the CBC-study.

Regarding the CO

-source, the most preferred

point source was the “steel plant” (utility = 18.8, SD

= 0.7). The other two CO

-sources, the “chemical

plant” (utility = -2.9, SD = 23.6) and - to an even

higher extent – the “coal-fired plant” (utility = -15.9,

SD = 34.1) were rejected in a CCU deployment

scenario.

21.4

34.6

44.0

0 10 20 30 40 50

CO2-source

CO2-derived

product

Profitability

Importance scores

-2.90

18.78

-15.88

-28.62

9.50

-0.09

19.21

53.20

12.82

-66.02

-80 -60 -40 -20 0 20 40 60

chemical plant

steel plant

coal-fired plant

mattress

drugs

fertilizer

fuel

no p. financing

start-up p. financing

long-term p. financing

CO2-source

CO2-derived

products

Profitability

Part-worth utilities

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

3.4 Preference Simulations for CCU

Deployment Scenarios

In a next step, sensitivity simulations were carried out

by using the Sawtooth market simulator. In the

simulations, we investigated preferences for specific

CCU deployment scenarios. Based on the preference

patterns we identified in section 3.3, we simulated

public preferences for a “best case” scenario and for

four different CCU deployment scenarios.

The “best case CCU deployment scenario” with a

steel plant as CO

-source, fuel as CO

-derived

product, and no required public financing was

accepted by 77.4% (SE = 2.8%) of respondents. A

lower profitability in the beginning, which requires a

start-up public financing led to only marginally

reduced acceptance rates (77.2%, SE = 2.8%). In case

of public long-term financing, the acceptance rates

declined to 55.8% (SE = 3.2%).

In addition to the “best case”-scenario, four

realistic and technically feasible scenarios were

simulated.

 Scenario 1 – “Dream factory”: This scenario was

based on the “Dream factory” project of Bayer

(Bayer Material Science, n.d.), producing CO

derived mattresses with CO

from a coal-fired

power plant. It was characterized by a coal-fired

plant as CO

-source, a mattress as CO

-derived

product and was running profitable (i.e., no

public financing necessary).

 Scenario 2 – “Mattress factory / chemical

industry”: This scenario resembled scenario 1

regarding the product (a mattress) and the

profitability, only differing regarding the chosen

(but also feasible) CO

-source, which was a

chemical plant.

 Scenario 3 – “Fuel production”: The fuel

production scenario was composed of a coal-

fired plant as CO

-source manufacturing fuel as

-derived product. The fuel production site in

this scenario requires public subsidies due to

high energy costs in the production of hydrogen,

which is not cost-effective, so far.

 Scenario 4 – “Fertilizer production”: The fourth

scenario refers to the physical use of CO

produce fertilizers by chemical industry, which

provides the CO

-source in this scenario. No

decomposition of molecules is necessary for the

use of CO

in fertilizers. Thus, a profitable

operation of a CO

-derived fertilizer production

site is feasible.

Figure 4 displays the results of the preference

simulation of the four scenarios.

The best-rated scenario in our simulation was

scenario 4 (“Fertilizer production”). This scenario

was preferred by 47.7% of all respondents. Less

acceptable (19%) was scenario 1 (“Dream factory”),

which corresponds to a production site of foam

mattresses in Germany.

Figure 4: Preference simulation for CCU deployment

scenarios.

Scenario 2, which resembled Scenario 1, except

taking a chemical plant as CO

-point source was

preferred by 11.4%. The least preferred scenario was

scenario 3 (4.5%), which contained a fuel production

site subsidized with public funds and a coal-fired

plant as CO

-source. All four scenarios were rejected

by 17.5% of participants.

Summing up the findings of the conjoint analysis,

the profitability of the CCU site was the most

acceptance-relevant criterion. Regarding the

preferences for specific CCU technology and product

parameters, respondents mostly preferred fuel as

-derived product variant and steel plants as CO

point source. In the scenario analysis, the most

preferred scenario was the cost-effective production

of fertilizer, the least preferred scenario was the

subsidised production of fuel.

4 DISCUSSION

Social indicators such as public perception and

acceptance of technologies and products are not

considered as evaluation parameters in LCA so far,

even though they might exert considerable impact on

the successful adoption of a technology. The present

study was, therefore, a first attempt to address and

include the social dimension of sustainable eco-

innovations in life-cycle analyses. Based on a multi-

level empirical approach, acceptance-relevant criteria

of CCU site deployment were identified, assessed,

and weighted in a conjoint study.

19.0 11.4 4.6 47.7

Scenario 1:

Dream factory

Scenario 2:

Mattress

factory

Scenario 3:

Fuel

production

Scenario 4:

Fertilizer

production

Preference shares in %

Blending Acceptance as Additional Evaluation Parameter into Carbon Capture and Utilization Life-Cycle Analyses

4.1 Perception and Acceptance of CCU

Deployment Scenarios

In line with other empirical studies assessing CCU

acceptance (Arning et al., 2017; Jones et al., 2016;

Perdan et al., 2017), the CCU technology was

positively perceived by most respondents. Compared

to general acceptance, the local acceptance, i.e., the

acceptance of a CCU site in the surrounding

neighbourhood, was lower, but still backed by the

majority.

Apart from general evaluations of CCU

technology acceptance, the present study yielded

insights into the acceptance of design parameters of

CCU site deployment scenarios. The most important

criterion was the profitability of an industrial CCU

project. This is in line with findings, where economic

considerations were identified as most important

predictor of renewable energy acceptance in

Germany (Zoellner et al., 2008). Even with a strong

involvement of public funding, the approval for a

CCS site was 55%. If it was possible to operate a CCU

site profitably, the acceptance increased to 77%.

Another indicator for the strong influence of

profitability can be seen in the findings of the

sensitivity analysis (scenario 3): the most preferred

product (fuel) is not attractive enough to achieve high

acceptance levels – a prerequisite for reaching public

acceptance is a cost-effective production. For CCU

deployment scenarios we can conclude, that an

economic process route is always preferred by the

public. Future technical CCU research should,

therefore, be directed on the development of

profitable deployment scenarios and business models.

However, depending on the scenario, start-up

financing is also accepted, whereas long-term

financing of the CCU technology is generally

rejected.

The type of CO

-derived product also exerted

considerable impact on preferences. Apparently, the

public integrates the technology process outcomes,

i.e., the different types of CO

-derived products, into

the assessment of the technology and its

infrastructure. Looking at the different CCU product

types, the fuel option was the most preferred. When

using CO

for fuel production, many participants

perceived the protection of fossil resources as a key

advantage, given the high fuel demand worldwide

and the high value of maintaining individual

motorized mobility. On the other hand, the near-time

combustion of the CCU fuel and thus, the rapid

release of the previously bound CO

was perceived

critically. Since the fuel production based on the CCU

technology is a highly energy-intensive process,

future CCU fuel production scenarios should also

integrate the energy supply and implications for

power system and infrastructure design. Moreover,

future technical research should improve the

economic efficiency of this process route to achieve a

higher acceptance of the CCU technology.

Using the CCU technology to produce drugs

represents another positively perceived use case in the

context of CCU. However, in focus group discussions

respondents criticized the comparably small amount

of CO

being used (and saved) in drug production and

unknown health consequences of using CO

for

edible products. These perceptions can be explained

by inaccurate mental models of laypeople, where CO

is perceived as a toxic substance, causing negative

health effects (e.g., headaches, suffocation) (van

Heek et al., 2017a).

The production of fertilizer based on CO

was

neutrally perceived. Similar to the perceptions of

-derived drug coating, negative perceptions of the

-derived fertilizer were related to health concerns

due to the “toxic” nature of CO

We assume that the

perceived closeness of the product to the body is the

underlying explanatory variable: the closer the

(potentially harmful) innovative product is to the own

body, the more threatening it is perceived, leading to

more negative product evaluations.

Compared to the other product alternatives, the

mattress received the most negative preference

ratings. Even though the savings of fossil resources in

the production of the mattress were acknowledged as

environmental benefit, the unknown health

consequences of long exposure times to a CO

derived mattress acted as strong barrier. Interestingly,

the CCU mattress received more positive acceptance

ratings, when it was the only CCU product example

being evaluated (e.g., Arning et al., 2017; van Heek

et al., 2017b). We assume that the direct comparison

of CCU product alternatives and their preference

assessment in holistic scenarios caused these different

outcomes. Hence, for a valid estimation of CCU

technology and product acceptance a multi-method

approach and mutual validation or triangulation of

findings is strongly recommended.

The source of CO

was the least acceptance-

relevant design parameter in the CCU deployment

scenario. However, for a successful technical rollout

of CCU, CO

should be taken from chemical and/or

steel industry to avoid any decline in acceptance.

Since CO

cannot yet be separated from steel industry

emissions, the CO

capacities of the chemical

industry should be fully exploited. If, nevertheless,

is extracted from coal-fired power stations, a

profitable operation and an acceptable product is

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

necessary to reach public acceptance of CCU

deployment.

4.2 Methodological Considerations and

Future Research

The present study successfully demonstrated the

assessment of public acceptance and preferences for

specific technology scenario criteria. Even though the

awareness and knowledge about (future) deployment

scenarios of the CCU technology was rather low,

respondents were able to express clear preferences

regarding the source of CO

, the manufactured CCU

product variants, and the profitability of running the

CCU site. The empirically based acceptance

evaluations can be used as additional evaluation

parameter in life-cycle assessments, where not only

the environmental impact of specific technology

routes is evaluated, but also their impact on public

acceptance. Since this study was a first attempt to

assess acceptance of specific CCU site deployment

scenarios, we did not fully portray the complete

technical life-cycle of CCU in our study. On the other

hand, qualitative studies about CCU product

acceptance suggest, that potential consumers

integrate dimensions into their acceptance

evaluations, which are not considered in life-cycle

analyses so far, such as the disposal of CCU products

(van Heek et al., 2017b). From a technical point of

view, the disposal of a product is not considered in

life-cycle analyses, since this process step does not

differ for conventionally manufactured or CCU-

based products. However, for the consumer the

disposal step (especially the way of disposal) is

highly acceptance-relevant and strongly influences

the overall perception of the CCU technology. Future

studies should, therefore, extend the acceptance

evaluation of the CCU life-cycle to gain a more

complete picture of acceptance and acceptance-

relevant “hot spots” in CCU scenarios. Moreover, we

work on the extension of this methodological

approach to other innovative and sustainable

technical scenarios (e.g., alternative fuels). Since

sustainable technical innovations do not only exert

environmental, but also economic impacts, market-

mediated effects should also be systematically

considered in future life-cycle approaches, such as

suggested in the Consequential LCA (CLCA)

(Kätelhön et al., 2016).

However, the low awareness level of CCU bears

the risk of assessing instable and nonvalid pseudo-

opinions about CCU. To reduce this risk, we put

special emphasis on the development of the

instruction in cooperation with technical experts and

iteratively improved their comprehensibility in pre-

tests.

5 CONCLUSIONS

Combining social science methodologies with

technical and economic assessment approaches

allows to include the complex concept of public

acceptance in sustainable technical scenario

development and respective life-cycle steps.

Moreover, the blending of acceptance into life-cycle

assessments allows the definition of an optimal

consumer product life-cycle scenario. This way,

sustainable technical innovations have a higher

chance of being acceptable and commercially

successful, when a conjunct development of

technology, sustainability, and acceptance is pursued.

ACKNOWLEDGEMENTS

This work has been funded by the European Institute

of Technology & Innovation (EIT) within the

EnCO2re flagship program Climate-KIC. André

Bardow thanks for support 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.

Special thanks go to Lukas Halbach for research

assistance.

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