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E U R O P E A NCOMMISSION
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Europeans and Biotechnology in 2010
Winds of change?
This is the seventh in a series of Eurobarometer surveys on life sciences and biotechnology conducted in 1991, 1993, 1996, 1999, 2002, 2005 and 2010. This latest survey, carried out in February 2010, was based on a representative sample of 30,800 respondents from the 27 Member States, plus Croatia, Iceland, Norway, Switzerland and Turkey. Issues such as regenerative medicine, production of Genetically Modified Organisms (GMOs, both transgenic and cisgenic), biobanks, biofuels and other innovations such as nanotechnology and synthetic biology, in addition to broader issues such as the governance of science and the engagement of citizens, were investigated. These surveys provide an indication of the distribution of opinions and attitudes in the public at large and evidence of changes in these perceptions over time. To ensure the continuing independence and high reputation of this series of surveys, the Commission charged a team of social scientists throughout Europe with designing the questionnaire and analysing the responses.
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EUROPEAN COMMISSION
Europeans and biotechnology in 2010
Winds of change?
A report to the European Commission’s Directorate-General for Research
by
George Gaskell*, Sally Stares, Agnes Allansdottir, Nick Allum, Paula Castro, Yilmaz Esmer, Claude Fischler, Jonathan Jackson, Nicole Kronberger,
Jürgen Hampel, Niels Mejlgaard, Alex Quintanilha, Andu Rammer, Gemma Revuelta, Paul Stoneman, Helge Torgersen and Wolfgang Wagner.
October 2010
*George Gaskell ([email protected]) and colleagues designed, analysed and interpreted the Eurobarometer 73.1 on the Life Sciences and Biotechnology as part of the research project Sensitive Technologies and European Public
Ethics (STEPE), funded by the Science in Society Programme of the EC’s Seventh Framework Programme for Research and Technological Development (FP7). The opinions expressed in this report are those of the authors and do
not represent the view of DG Research
Directorate-General for Research Science in Society and Food, Agriculture & Fisheries, & Biotechnology2010 EUR 24537 EN
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Cataloguing data can be found at the end of this publication.
Luxembourg: Publications Office of the European Union, 2010
ISBN 978-92-79-16878-9doi 10.2777/23393
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Contents
Overview of key findings .................................................................................................... 6
Introduction......................................................................................................................12
1. Optimism about technology.......................................................................................13
2. Emerging technologies...............................................................................................21 2.1 Nanotechnology .....................................................................................................21 2.2 Biofuels .................................................................................................................26 2.3 Synthetic biology .....................................................................................................29
3. Biotechnologies for food production..........................................................................36 3.1 GM food .................................................................................................................36 3.2 Animal cloning for food production ............................................................................41 3.3 Transgenic and cisgenic apples .................................................................................46
4. Regenerative medicine...............................................................................................51
5. Biobanks.....................................................................................................................60
6. Governance and trust.................................................................................................69
7. Familiarity and engagement with technologies.........................................................80
8. Pillars of truth: religion and science ..........................................................................89
9. Climate change.........................................................................................................101
10. Public ethics, technological optimism and support for biotechnology ...................105
References ......................................................................................................................110
Annex 1 ...........................................................................................................................112
Annex 2 ...........................................................................................................................128
3
Figures
Figure 1: Generalised technological optimism and pessimism......................................................15
Figure 2: Optimism and pessimism regarding eight technologies, EU27........................................16
Figure 3: Index of optimism about six technologies....................................................................18
Figure 4: Awareness of nanotechnology, EU27..........................................................................22
Figure 5: Encouragement for nanotechnology (excluding DKs)....................................................23
Figure 6: Perceptions of nanotechnology as beneficial, safe, inequitable and unnatural, EU27
(excluding DKs).....................................................................................................................25
Figure 7: Opinions regarding first generation and sustainable biofuels, EU27................................27
Figure 8: Support for first generation and sustainable biofuels (excluding DKs).............................28
Figure 9: Awareness of synthetic biology, EU27.........................................................................30
Figure 10: Priority given to finding out about risks and benefits (versus other issues) in relation to
synthetic biology....................................................................................................................32
Figure 11: Approval of and ambivalence towards synthetic biology..............................................34
Figure 12: Awareness of GM food, EU27...................................................................................37
Figure 13: Support for GM food, EU27......................................................................................37
Figure 14: Perceptions of GM food as beneficial, safe, inequitable and unnatural, EU27 (excluding
DKs).....................................................................................................................................38
Figure 15: Awareness of animal cloning for food production, EU27..............................................42
Figure 16: Perceptions of animal cloning for food products as beneficial, safe, inequitable and
unnatural, EU27 (excluding DKs) .............................................................................................43
Figure 17: Encouragement for GM food and animal cloning for food products (excluding DKs)........45
Figure 18: Perceptions of transgenic and cisgenic apples, EU27 ..................................................48
Figure 19: Support for transgenic and cisgenic apples (excluding DKs).........................................49
Figure 20: Levels of approval of biomedical research and synthetic biology, EU27 .........................53
Figure 21: Levels of approval for embryonic and non-embryonic stem cell research and gene therapy,
2005 and 2010, Europe-wide ..................................................................................................55
Figure 22: Levels of approval for human embryonic stem cell research, 2005 and 2010 (excluding
DKs).....................................................................................................................................56
Figure 23: Public views on ethical positions and regenerative medicine, EU27...............................58
Figure 24: Principles of governance for synthetic biology (DKs excluded) .....................................72
Figure 25: Principles of governance for animal cloning (DKs excluded).........................................73
Figure 26: Public confidence in the 'biotechnology system' (excluding DKs) ..................................78
Figure 27: Familiarity with five technologies: percentages of people who have heard of each
technology............................................................................................................................82
Figure 28: Engagement with five technologies, EU27.................................................................83
Figure 29: 'Worry' index for three technologies, by level of engagement, EU27.............................85
Figure 30: 'Safety' index for three technologies, by level of engagement, EU27.............................86
Figure 31: ‘Benefits' index for three technologies, by level of engagement, EU27 ..........................87
Figure 32: Index of generalised optimism and index of pessimism, by religious denomination, 32
European countries (DKs excluded)..........................................................................................90
4
Figure 33: Ethical objection to human embryonic stem cell research, by religious denomination, 32
European countries (DKs excluded)..........................................................................................90
Figure 34: Should science prevail over ethics? By religious denomination, 32 European countries (DKs
excluded)..............................................................................................................................91
Figure 35: Ethical objection to human embryonic stem cell research, by religious attendance, 32
European countries (DKs excluded)..........................................................................................92
Figure 36: Should science prevail over ethics? By religious attendance, 32 European countries (DKs
excluded)..............................................................................................................................93
Figure 37: Parental and family university education/work in science, by age group of respondent,
EU27 (DKs excluded) .............................................................................................................94
Figure 38: Technological optimism and pessimism, by science in the family, EU27 ........................96
Figure 39: Technological optimism and pessimism, by science education, EU27 ............................96
Figure 40: Support for nanotechnology, by family science background, EU27 (DKs excluded).........98
Figure 41: Support for GM food, by family science background, EU27 (DKs excluded)....................99
Figure 42: Support for GM food and nanotechnology, by science education, EU27 (DKs excluded) ..99
Figure 43: Favoured solutions for halting climate change..........................................................102
Figure 44: Preference for 'changing ways of life' solution to climate change and confidence that one's
government will adopt such policies, by country......................................................................104
Figure 45: Relationships between clusters of countries.............................................................109
Tables
Table 1: Trends in the index of optimism for biotechnology/genetic engineering ...........................20
Table 2: Issues about which respondents would like to know more in relation to synthetic biology,
EU27 (excluding DKs).............................................................................................................31
Table 3: Trends in support for GM food (excluding DKs).............................................................40
Table 4: Perceptions of safety, environmental impacts and naturalness of GM food and transgenic
apples, EU27 (excluding DKs)..................................................................................................50
Table 5: Segmentation of the European public on principles of governance for synthetic biology,
EU27 (DKs excluded) .............................................................................................................70
Table 6: Segmentation of the European public on principles of governance for animal cloning, EU27
(DKs excluded)......................................................................................................................71
Table 7: Principles of governance for synthetic biology, technological optimism, and support for GM
food, EU27 (DKs excluded) .....................................................................................................74
Table 8: Principles of governance for animal cloning, technological optimism, and support for
nanotechnology, EU27 (DKs excluded) .....................................................................................74
Table 9: Trust in key actors and trends from 1999.....................................................................76
Table 10: Trend in trust surplus/deficit for the biotechnology industry (DKs excluded)...................77
Table 11: Percentages of science graduates by age group, EU27.................................................95
Table 12: Principles of governance for animal cloning and synthetic biology by science education,
EU27 (DKs excluded) .............................................................................................................98
Table 13: Public ethics: five clusters.......................................................................................106
Table 14: Public ethics and support for biotechnology..............................................................107
5
Overview of key findings
The latest Eurobarometer survey on the Life Sciences and Biotechnology, based on representative
samples from 32 European countries and conducted in February 2010, points to a new era in the
relations between science and society. While entrenched views about GM food are still evident, the crisis
of confidence in technology and regulation that characterised the 1990s – a result of BSE, contaminated
blood and other perceived regulatory failures – is no longer the dominant perspective. In 2010 we see a
greater focus on technologies themselves: are they safe? Are they useful? And are there 'technolite'
alternatives with more acceptable ethical-moral implications? Europeans are also increasingly concerned
about energy and sustainability. There is no rejection of the impetus towards innovation: Europeans are
in favour of appropriate regulation to balance the market, and wish to be involved in decisions about
new technologies when social values are at stake.
Technological optimism
A majority of Europeans are optimistic about biotechnology (53 per cent optimistic; 20 per cent say
‘don’t know’). In comparison, they are more optimistic about brain and cognitive enhancement (59; 20),
computers and information technology (77; 6), wind energy (84; 6) and solar energy (87; 4), but are
less optimistic about space exploration (47; 12), nanotechnology (41; 40) and nuclear energy (39; 13).
Time series data on an index of optimism show that energy technologies – wind energy, solar energy
and nuclear power – are on an upward trend – what we call the ‘Copenhagen effect’. While both
biotechnology and nanotechnology had seen increasing optimism since 1999 and 2002 respectively, in
2010 both show a similar decline – with support holding constant but increases in the percentages of
people saying they ‘make things worse’. With the exception of Austria, the index for biotechnology is
positive in all countries in 2010, indicating more optimists than pessimists – Germany joining Austria in
being the least optimistic about biotechnology. But in only three countries (Finland, Greece and Cyprus)
do we see an increase in the index from 2005 to 2010.
Nanotechnology
Only 45 per cent of Europeans say they have heard of nanotechnology, which in the survey is described
in the context of consumer products. Six out of ten EU citizens who expressed an opinion support such
applications of nanotechnology, with support varying from over 70 per cent in Poland, Cyprus, Czech
Republic, Finland and Iceland to less than 50 per cent in Greece, Austria and Turkey. For the opponents
of nanotechnology, safety is the pressing concern followed by the perceived absence of benefits.
Biofuels
A comparison of crop based (first generation) biofuels with sustainable (second generation) biofuels
made from non-edible material shows that overall, Europeans are positive towards both types. 78 per
cent of Europeans support crop based biofuels and 89 per cent support sustainable biofuels. It would
appear that debates about the downsides of crop based biofuels – on food security, food prices and
destruction of forests for crop cultivation –have had only a marginal impact on the public’s perceptions.
6
Synthetic biology
Following a description of synthetic biology respondents in the survey were asked – ‘Suppose there was
a referendum about synthetic biology and you had to make up your mind whether to vote for or against.
Among the following, what would be the most important issue on which you would like to know more?’
Our respondents were asked to select three from the list of seven issues of interest. 73 per cent selected
‘possible risks’; 61 per cent ‘claimed benefits’ and 47 per cent ‘who will benefit and who will bear the
risks’. Information about social and ethical issues was the least frequent choice at 19 per cent. Asked
about their views on whether, and under what conditions, synthetic biology should be approved, of those
respondents who expressed a view 17 per cent said that they do not approve under any circumstances;
21 per cent do not approve except under very special circumstances; 36 per cent approve as long as
synthetic biology is regulated by strict laws and only 3 per cent approve without any special laws.
Overall, Europeans consider synthetic biology a sensitive technology that demands precaution and
special regulations, but an outright ban would not find overwhelming support.
GM food
GM food is still the Achilles’ heel of biotechnology. The wider picture is of declining support across many
of the EU Member States – on average opponents outnumber supporters by three to one, and in no
country is there a majority of supporters. What is driving the continued opposition to GM food? Public
concerns about safety are paramount, followed by the perceived absence of benefits and worry – GM
food is seen as unnatural and makes many Europeans ‘uneasy’. Across the period 1996-2010, we see,
albeit with fluctuations, a downward trend in the percentage of supporters. Denmark and the UK, at the
higher end of the distribution of support, are exceptions, as is Austria, at the lower end. Those among
the ‘old’ EU countries with a ban on GM crops in place consistently show low values of support, with Italy
joining the group. In contrast, Member States where GM crops are grown tend to show among the
highest values, suggesting a link between private attitudes and public policies.
Animal cloning for food products
Cloning animals for food products is even less popular than GM food with 18 per cent of Europeans in
support. In only two countries – Spain and the Czech Republic – does animal cloning attract the support
of three in ten. This contrasts with 14 countries in which support for GM food is above 30 per cent. Is
this an indication of broader public anxieties about biotechnology and food? The idea of the ‘natural
superiority of the natural’ captures many of the trends in European food production, such as enthusiasm
for organic food, local food, and worries about food-miles. And if ‘unnaturalness’ is one of the problems
associated with GM food, it appears to be an even greater concern in the case of animal cloning and
food products.
Cisgenics
Cisgenics is the genetic modification of crops adding only genes from the same species or from plants
that are crossable in conventional breeding programmes. It could be employed, for example, in the
cultivation of apples to provide resistance to the common apple diseases and thereby reduce pesticide
use. In all EU countries, cisgenic production of apples receives higher support (55 per cent) than
7
transgenic apples (33 per cent), with the former attracting majority support in 24 countries (including
Austria).
GM food and transgenic apples are both seen to be unnatural by three out of four respondents.
However, support for GM food (27 per cent) is a little lower than for transgenic apples (33 per cent).
Transgenic apples are more likely to be perceived as safe and not to harm the environment. It is likely
that the preamble in the survey describing transgenic apples as a technique that would ‘limit use of
pesticides, and so pesticide residues on the apples would be minimal’’ suggested an attractive benefit
both to food safety and the environment. Cisgenics might be seen as a hypothetical example of the so-
called ‘second generation’ of GM crops. Here, the benefits of GM apple breeding are achieved with a
technolite process, a consumer benefit is offered and as such it achieves better ratings in terms of
benefits, safety, environment, naturalness, and double the support of GM food.
Regenerative medicine
Developments in regenerative medicine attract considerable support across Europe. 68 per cent of
respondents approve of stem cell research and 63 per cent approve of embryonic stem cell research.
Levels of approval for gene therapy are similar, at 64 per cent. Xenotransplantation – an application long
subject to moratoria in various countries – now finds approval with 58 per cent of respondents. And the
solid support for medical applications of biotechnology spreads over to non-therapeutic applications.
Moving from repair to improvement, we find that 56 per cent of the European public approves of
research that aims to enhance human performance. However, support for regenerative medicine is not
unconditional. Approval is contingent upon perceptions of adequate oversight and control.
Biobanks
While approximately one in three Europeans have heard about biobanks before, nearly one in two
Europeans say they would definitely or probably participate in one, with Scandinavian countries showing
the most enthusiasm. And people do not seem to have particular worries about providing certain types of
information to biobanks: blood samples, tissue samples, genetic profile, medical records and lifestyle
data elicit similar levels of concern. However, amongst those similar levels there are some nuances. In
twelve countries, providing one’s medical records provokes the most worry, and in ten countries it is the
genetic profile that is most worrying. Asked about who should be responsible for protecting the public
interest with regard to biobanks, we find a split between those countries opting for self-regulation (by
medical doctors; researchers; public institutions such as universities or hospitals) and those opting for
external regulation (ethics committees; national governments; international organisations and national
data protection authorities). Broadly speaking, respondents in those countries which show higher levels
of support for biobanks tend to favour external regulation more than self-regulation. In those countries
where biobanks are unfamiliar, self regulation is a more popular way of guarding the public interest. On
the issue of consent, almost seven in ten Europeans opt for specific – permission sought for every new
piece of research; one in five for broad consent, and one in sixteen for unrestricted. But of those more
likely to participate in the biobank, some four in ten opt for either unrestricted or broad consent.
8
Governance of science
Europeans’ views on the governance of science were sought in the context of two examples of
biotechnology: synthetic biology and animal cloning for food products. Respondents were asked to
choose between, firstly, decisions making based on scientific evidence or on moral and ethical criteria,
and secondly, decisions made on expert evidence or reflecting the views of the public. 52 per cent of
European citizens believe that synthetic biology should be governed on the basis of scientific delegation
where experts, not the public decide, and where evidence relating to risks and benefits, not moral
concerns, are the key considerations. However, nearly a quarter of Europeans take the opposite view: it
is the public, not experts, and moral concerns, not risks and benefits, that should dictate the principles of
governance for such technologies (the principle of 'moral deliberation'). For animal cloning (compared to
synthetic biology) some 10 per cent fewer opt for scientific deliberation and 9 per cent more opt for
moral deliberation. It seems that moral and ethical issues are more salient for animal cloning for food
products than for synthetic biology: altogether 38 per cent of respondents choose a position prioritising
moral and ethical issues for synthetic biology, with 49 per cent doing the same for animal cloning for
food. To put this another way, the European public is evenly split between those viewing animal cloning
for food as a moral issue and those viewing it as a scientific issue.
Trust in key actors
The re-building of trust in regulators and industry from the lows in the 1990s is in evidence. On an index
capturing a trust surplus or trust deficit, we find ‘national governments making regulations’ up 23 per
cent since 2005. ‘Industry developing biotechnology products’ is up 9 per cent since 2005 and 62 per
cent since 1999, and ‘the EU making laws across Europe’ is up 14 per cent since 2005. On this index,
‘university scientists’ maintain a trust surplus of around 80 per cent. There is a robust and positive
perception of the biotechnology system. It seems fair to conclude that Europeans have moved on from
the crisis of confidence of the mid to late 1990s. It is also notable that both national governments and
the EU carry almost equivalent trust surpluses in the majority of countries. It seems as if the idea of
national regulation within a framework of European laws is accepted amongst the publics of the
European Member States.
Familiarity and engagement
The link between familiarity and engagement with technology is not straightforward. On the one hand,
views of nanotechnology are clearly related to the extent of public familiarity and engagement. Those
who are actively engaged in finding out about nanotechnology tend to be much more inclined to
perceive of it as safe and beneficial and something not to worry about, compared to those for whom
nanotechnology is unfamiliar. On the other hand, when it comes to the two controversial
biotechnologies, GM food and animal cloning in food production, levels of familiarity and engagement are
only weakly related to perceptions of them. These technologies similarly tend to invoke worry, and are
perceived as less beneficial and safe than nanotechnology.
9
Religion and education
Overall, the non-religious are more optimistic about the contribution of technologies to the improvement
of everyday life and are more likely to support human embryonic stem cell research. But when faced
with a conflict between science and religion they are almost evenly split on which pillar of the truth
should prevail – not that different to people in the major European religious denominations. Religious
commitment appears to be associated with greater concerns about ethical issues in stem cell research
and with a belief that ethics should prevail over scientific evidence. However, here again there are many
highly religious people who say that science should prevail in such a conflict of opinion.
As to the effect of education the findings show that socialisation in a scientific family and having a
university education in science are associated with greater optimism about science and technology, more
confidence in regulation based on scientific delegation, and more willingness to encourage the
development of both nanotechnology and GM food. However, the findings also show that scientific
socialisation either in the family or at university is not a magic bullet – it is not the panacea to the issue
of resistance to innovation. For example, a majority of those coming from a scientific family background
or with a degree in science are not willing to support the development of GM food.
Climate change
Across a number of questions it is apparent that there is widespread concern with climate change, and
more generally with sustainability. Respondents in all countries except two (Latvia and Malta) favour
changes in ways of living over technological solutions, even if this means reduced economic growth. Only
in 7 countries (Bulgaria, Poland, Estonia, Lithuania, Romania, Latvia and Malta) is support for the
‘changing ways of life’ solution below the ‘comfortable majority’ threshold of 55 per cent. In some
countries ( Finland, Denmark, or Switzerland) the support for the ‘changing ways of life’ solution is much
stronger than the support of the notion that technology will solve climate change (for instance, about six
times stronger in Finland, where only 14 per cent opt for the ‘technological solution’ and 84 per cent for
the ‘changing ways of life’ solution). The relatively small percentage of ‘don’t know’ responses shows
that people now feel ready to take a stance.
Whatever people’s view on climate change respondents, the majority is likely to assume that others
share their views and that their views will be reflected in national policies. Given that an individual’s
beliefs are reinforced by the support – actual or perceived - of others, that so many believe that others
share their views, is an indication of just how difficult is the task of changing beliefs about climate
change.
Public ethics, technological optimism and support for biotechnologies
Analysing the range of questions in the survey that address issues of public ethics – the moral and
ethical issues raised by biotechnology and the life sciences – we find five clusters of countries. Key
contrast emerge between clusters of countries. First, those that prioritise science over ethics and those
that prioritise ethics over science, and second those countries that are concerned about distributional
fairness and those who are not. In combination these contrasts are related to people’s optimism about
10
the contribution of technologies to improving our way of life and support for regenerative medicines and
other applications of biotechnology and the life sciences. Where ethics takes priority over science,
concerns about distributional fairness lead to a profile of lower support; but in the absence of
sensitivities about distributional fairness, the profile of support is relatively higher. When science taking
priority over ethics is combined with concerns about distributional fairness, then we find only moderate
support; but here again the absence of sensitivities about distributional fairness reveals a profile of high
support.
11
Introduction
Eurobarometer 73.1 is the seventh in a series of surveys of public perceptions of the Life Sciences and
Biotechnology. The series started in 1991 with Eurobarometer 35.1 (INRA 1991) in the twelve Member
States of the European Community. It was followed by the second in 1993, Eurobarometer 39.1 (INRA
1993). In 1996, the third in the series, Eurobarometer 46.1(INRA 1997) covered the fifteen Member
States of the expanded European Union. The fourth in the series, Eurobarometer 52.1 (INRA 2000) was
conducted in 1999, the fifth (Eurobarometer 58.0) in 2002 (Gaskell et al. 2003) and the sixth
(Eurobarometer 63.1) in 2005 (TNS 2005). The new survey in 2010 covers the now 27 Member States of
the European Union plus Croatia, Iceland, Norway, Switzerland and Turkey.
The survey questionnaire for EB 73.1 includes key trend questions, designed to assess the stability or
change in aspects of public perceptions over the last ten years or more. It also includes new questions
that capture opinions and attitudes to emerging issues in the field of biotechnology: regenerative
medicine, synthetic biology and cisgenics. And as in 2005 there are questions on nanotechnology – in
part because nanotechnology has been heralded as the next strategic technology, but also on account of
its links with biotechnology, as seen in the emergence of the so-called converging technologies. As in
2005 there are questions about human embryonic and other types of stem cell research.
The Eurobarometer on Biotechnology and the Life Sciences, like other systematic survey research
studies, provides a representation of public voices – for the European public speaks not with one voice –
to policy makers, representatives of industry, journalists, civil society groups, scientists and social
scientists – and even to the public themselves. Surveys represent the world in particular ways;
depending on the perspective adopted, the representations will differ. Survey results do not have a
single, obvious and unequivocal meaning. Whether the glass is half full or half empty is a matter of
personal preference. In this report we provide our interpretation. But because other interpretations are
possible, we include the basic data in the Annexes to this report.
The report is divided into three sections. The first provides an analytic description of Europeans'
perceptions of biotechnology in 2010, with, where possible, comparable data from previous surveys to
illustrate trends. This is followed by two Annexes, containing the questionnaire and a codebook of basic
descriptive statistics for each question by country, with a technical note including details of survey
sampling and weighting. In the report we present results across the 32 countries. We also give Europe-
wide summaries for the current 27 EU Member States, with samples weighted to reflect their relative
population sizes. An expanding Europe is an inherent characteristic of these Eurobarometer reports.
However, note that were the summaries to include all 32 countries, they would change very little.
For ease of presentation the majority of results exclude those respondents who registered a ‘don’t know’
response. In this sense we report findings based on only those who expressed an opinion in the context
of a particular question. However, since the rates of ‘don’t know’ responses vary from question to
question, and from country to country, from about 5 per cent to 35 per cent, we encourage readers to
look at the codebook to assess the impact of differential rates of ‘don’t know’ responses.
12
1. Optimism about technology
The Lisbon declaration of 2000 set a strategic goal for the European Union (EU) to become the most
competitive and dynamic knowledge based economy in the world. The 7th Framework Programme (2007-
2013), with a budget of €53 billion to support research and technological development, was launched to
give a new impetus to increase Europe’s growth and competitiveness. In 2002, the EU’s Heads of State
and Government agreed to the Barcelona target to increase Research and Development to 3 per cent of
GDP.
The European Commission has reaffirmed the importance of innovation and research as one of the key
drivers of economic recovery. One of the seven flagship initiatives in the Europe 2020 strategy is the
Innovation Union and a commitment to ‘improve framework conditions and access to finance for
research and innovation so as to ensure that innovative ideas can be turned into products and services
that create growth and jobs’ (European Commission 2010a: 3).
But does the European public have the appetite for technology and innovation? Some theorists have
argued that we are in a post-materialist age in which the desire for economic growth is replaced by
concerns for the environment, personal development and civil liberties (Inglehart 1990). Others have
argued that uncritical enthusiasm for science and technology is typical of less developed economies, and
that the publics of the advanced industrial countries become increasingly critical, even sceptical, about
the contribution of science and technology to the quality of life (Durant et al. 2000).
However, such longer term changes in people’s values – for which it must be admitted the empirical
evidence is not overwhelming – can be reversed by period effects, such as a downturn in the economy.
Rising unemployment and other recessionary impacts focus people’s minds on how the economy can
deliver jobs, prosperity and improve the quality of life.
More prosaically there may be a habituation effect, whereby the novel of the past becomes the taken-
for-granted of the present, and even substantial breakthroughs in the past are no longer seen as such in
contemporary times. Think of personal computers, email and the lack of excitement that greets a new
computer operating system. People also recognize that the promises that accompanied past
developments were often hyperbole, and so they tend to discount similar claims attached to the current
crop of innovations.
In the Eurobarometer survey respondents were asked whether particular technologies ‘will improve our
way of life in the 20 years’, ‘will have no effect’, or ‘will make things worse’, and a ‘don’t know’ response
was accepted but not offered by the interviewer. This question has been asked since 1991 and it not
only provides an indicator of general sentiment towards technology and innovation but also places views
about biotechnology and the life sciences in the context of other technologies. Over the seven waves of
the Eurobarometer on biotechnology some of the target technologies have been retained in the survey,
others have been dropped and new technologies introduced to keep abreast of new developments.
13
In 2010 respondents were asked about eight technologies (the year in which the technology was
introduced is indicated in brackets here). The target technologies are computers and information
technology, and space exploration (from 1991), solar energy (from 1993), nuclear energy (from 1999),
nanotechnology (from 2002), wind energy (from 2005) and brain and cognitive enhancement (new in
2010).
From 1991 to 2005 a split ballot was used for biotechnology, with half of the sample asked about
‘biotechnology’ and the other half asked about ‘genetic engineering’. In 2010 the alternative descriptions
were combined into ‘biotechnology and genetic engineering’.
Generalised sentiment to technology
How optimistic are Europeans about new technologies? Our measures of generalised technological
optimism and pessimism are admittedly rather crude. We take the eight technologies (see above) and
count for each respondent: firstly, the number of technologies that they say will improve our way of life;
and, secondly, the number that will make things worse. We then compute for each country the average
(mean) number of technologies that are given the optimistic judgement (‘optimism’) and the average
(mean) number of technologies that are given the pessimistic judgement (‘pessimism’), and plot them
for the EU27 as a whole, and by country, in Figure 1.
Some caveats are in order. The eight technologies are not claimed to be representative of the full range
of technological innovations – they are a partial group. Civil nuclear power is hardly new and, as argued
above, innovation fatigue may have set in amongst sections of the public for computers and information
technology. But all of the technologies chosen may count as being ‘sensitive’, i.e. potentially raising
strong sentiments for various reasons beyond their technical characteristics and economic implications.
Our interpretation of the data is that lying behind an individual’s score on the scale is a representation
about the role of technologies in contributing to a better or worse future for society. And one might
expect that those countries in which, on average, more technologies are rated as likely to improve our
lives over the coming years, will tend to provide more support for political and economic policies that
support innovation.
14
Figure 1: Generalised technological optimism and pessimism
2.1
1.6
1.0
1.2
1.3
1.0
0.7
0.9
1.1
1.6
1.6
1.1
1.4
0.5
1.4
1.5
1.1
0.8
1.1
1.2
0.9
1.6
1.0
0.7
0.8
0.8
1.1
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1.0
0.8
0.8
0.7
1.1
3.8
4.2
4.3
4.3
4.3
4.3
4.4
4.5
4.6
4.6
4.7
4.7
4.7
4.8
4.9
4.9
4.9
4.9
4.9
5.0
5.0
5.0
5.1
5.2
5.3
5.4
5.4
5.5
5.6
5.6
5.7
5.7
4.9
0 1 2 3 4 5 6 7 8
Austria
Switzerland
Turkey
Romania
Lithuania
Portugal
Malta
Ireland
Poland
Luxembourg
Slovenia
Italy
Germany
Iceland
Belgium
Croatia
Netherlands
Bulgaria
France
Latvia
United Kingdom
Greece
Sweden
Hungary
Spain
Cyprus
Slovakia
Denmark
Czech Republic
Finland
Estonia
Norway
EU27
Average (mean) number of technologies
Optimism
Pessimism
15
Figure 1 shows that the greater majority of countries score between 4.5 and 5.5 out of 8 on this
measure of generalised technological optimism, indicating a degree of similarity in average levels of
optimism across European countries. The figure also shows the average (mean) number of pessimistic
responses; here only a small number of countries exceed 1.5. And while there is a negative relationship
between optimism and pessimism, it is not particularly large. The correlation coefficient which compares
optimism and pessimism between respondents (rather than between country-level averages) is -0.44,
where -1 would indicate a perfect one-to-one negative (linear) relationship between optimism and
pessimism, and 0 would indicate no such relationship.
So, is the glass half full or half empty? Does the European public hold a positive representation of
technology and does it depend on the particular technology? Figure 2 gives us some clues. For 7 out of
the 8 technologies optimists outnumber pessimists. Expectations about nuclear power are the exception
with an even split between optimists and pessimists.
Figure 2: Optimism and pessimism regarding eight technologies, EU27
39
41
47
53
59
77
84
87
10
9
29
7
11
7
6
5
39
10
13
20
11
11
4
4
13
40
12
20
20
6
6
4
0 20 40 60 80 100
Nuclear energy
Nanotechnology
Space exploration
Biotechnology and geneticengineering
Brain and cognitiveenhancement
Computers and informationtechnology
Wind energy
Solar energy
% respondents
Positive effect No effect Negative effect Don't know
Notably, a majority of Europeans are optimistic about biotechnology and genetic engineering. In
comparison, they are more optimistic about brain and cognitive enhancement, computers and
information technology, wind energy and solar energy, but are less optimistic about space exploration,
nanotechnology and nuclear energy.
The contrast between the four so-called strategic technologies of the post-World War II years is striking.
For biotechnology, 53 per cent are optimistic and 20 per cent are pessimistic. The comparable figures for
nuclear power are 39 per cent optimistic and 39 per cent pessimistic. For computers, 77 per cent are
16
optimistic and 11 per cent are pessimistic. For nanotechnology, which was acclaimed as a strategic
technology in the early 2000s, 41 per cent are optimistic and 10 per cent are pessimistic.1
Not surprisingly on account of its novelty, the percentage of ‘don’t know’ responses for nanotechnology
is above 40 per cent, much the same as in 2005. That biotechnology still elicits a ‘don’t know’ response
from one in five (again much the same as in 2005) suggests that either many people have still to make
up their minds about its prospects, or that it is difficult to weigh up pros and cons of the varieties of
biotechnology, for example across medical and agricultural applications.
Brain and cognitive enhancement, now the focus of attention of neuroethicists, is probably relatively
unfamiliar to many of the public (20 per cent give a ‘don’t know’ response), yet the idea of this
technology seems to engender widespread optimism, with optimists outnumbering pessimists by a ratio
of 5 to 1. Later in the survey, respondents are asked for their views on adequate levels of regulation of
research exploring ways of enhancing the performance of healthy people, for example to improve
concentration or to increase memory. The results are discussed in the context of views on regenerative
medicine in Chapter 4 of this report.
Nuclear power continues to be cited as an option in climate change and energy security debates. Here
we find equal percentages of optimists and pessimists (39 per cent). In contrast to the findings of the
Eurobarometer in 2005, in 2010 we find that judgements that it ‘will have no effect’ have declined from
18 to 10 per cent; the proportion of Europeans saying ‘it will improve our way of life’ has increased from
32 to 39 per cent; and roughly the same proportion of respondents say it ‘will make things worse’, with
an increase of just 2 percentage points to 39 per cent in 2010.
1 Synthetic biology - the latest strategic technology – was not included in this question set on account of its relative unfamiliarity. However, in Chapter 2 we report on the European public’s perceptions of this development.
17
Trends in technological optimism
To assess the changes in optimism and pessimism over time (1991 to 2010) we use a summary index.
For this we subtract the percentage of pessimists from the percentage of optimists and divide this by the
combined percentage of optimists, pessimists and those who say the technology will have no effect. In
excluding the ‘don’t know’ responses, this index is based on only those respondents who expressed an
opinion. A positive score reflects a majority of optimists over pessimists, a negative score a majority of
pessimists over optimists and a score around zero more or less equal percentages of the two.
This index has the following merits. Firstly, it is an economical way of presenting comparisons between
countries and over time; secondly, with substantial differences in rates of ‘don’t know’ responses across
countries, the raw scores can be misleading; and thirdly, it weights the balance of optimism and
pessimism in relation to all the respondents who express an opinion on the question.
Figure 3: Index of optimism about six technologies2
-20
0
20
40
60
80
100
1991 1993 1996 1999 2002 2005 2010
Year
Inde
x
Solar energy
Wind energy
Computers and IT
Nanotechnology
Biotechnology andgenetic engineering
Nuclear energy
The trends in the index of optimism (see Figure 3) show some interesting trajectories. Firstly, for all of
the energy technologies – wind and solar energy and nuclear power – an upward trend is seen. This
might be termed the ‘Copenhagen’ effect. The extensive media coverage of climate change and global
warming, making salient the issue of carbon emissions, may have helped increase public optimism about
the contributions of renewable energy sources and nuclear power. At the same time, new issues have
2 The countries included in each score for ‘Europe’ (weighted according to their relative population sizes) reflect the expanding membership of the EU: thus 1991 and 1993 scores are for the original 12 Member States, 1996–2002 for EU15, 2005 for EU25 and 2010 for EU27.
18
come to public attention, such as those represented by Al Gore in his An Inconvenient Truth (Gore
2006).
As an aside, how do those who are optimistic about solar and wind energy – the classic sustainable
energy solutions – view nuclear power, which is now claimed by some to be in the sustainable category
but completely rejected by others? In the event, the public are divided. While the optimists for solar
energy take the same position on wind energy, those who are optimistic about solar energy are split on
nuclear power between optimism (46 per cent) and pessimism (42 per cent).
In parallel, the second noticeable trend is that of recently declining optimism in biotechnology,
nanotechnology and computers and information technology. While computer and information technology
has been consistently around 80 per cent on the index, there is a small decline in the period 2005-2010.
While both biotechnology and nanotechnology had been on an upward trend since 1999 and 2002
respectively, in 2010 there is a similar decline in optimism. In both cases we see support holding
constant but changes in the percentages of ‘make things worse’ responses. These increase from 12 to 20
per cent for biotechnology and from 5 to 10 per cent for nanotechnology. Changes come not from a
reduction in ‘don’t know’ responses, but rather a decline in ‘make no difference’ responses.
Turning to European country-level data, Table 1 shows the index of optimism for biotechnology over the
period 1991 to 2010. The EU15 countries are ordered from the most to the least optimistic in 2010,
followed by the 10 new Member States of 2004, then Romania and Bulgaria and finally Iceland, Norway,
Turkey, Switzerland and Croatia (also ordered from most to least optimistic).
In all countries, with the exception of Austria, the index has positive values, indicating more optimists
than pessimists. But in only three countries (Finland, Greece and Cyprus) do we see an increase in the
index from 2005 to 2010. The table also shows little change in optimism over the last five years in Spain,
Ireland, the UK, France and Estonia, and that the non-EU countries Iceland and Norway stand amongst
the most optimistic countries. But in the rest of Europe there is a consistent decline in optimism about
biotechnology.
19
Table 1: Trends in the index of optimism for biotechnology/genetic engineering
1991 1993 1996 1999 2002 2005 2010
Spain 82 78 67 61 71 75 74
Sweden - - 42 - 61 73 63
Finland - - 24 13 31 36 59
Portugal 50 77 67 50 57 71 54
Ireland 68 54 40 16 26 53 51
UK 53 47 26 5 17 50 50
Italy 65 65 54 21 43 65 48
France 56 45 46 25 39 49 46
Denmark 26 28 17 -1 23 56 45
Greece 70 47 22 -33 12 19 35
Belgium 53 42 44 29 40 46 32
Luxembourg 47 37 30 25 29 55 32
Netherlands 38 20 29 39 39 47 31
Germany 42 17 17 23 24 33 12
Austria - - -11 2 25 22 -7
Cyprus - - - - - 74 78
Estonia - - - - - 79 76
Malta - - - - - 81 64
Hungary - - - - - 62 58
Czech Rep. - - - - - 71 53
Slovakia - - - - - 55 48
Latvia - - - - - 60 43
Poland - - - - - 59 41
Slovenia - - - - - 47 33
Lithuania - - - - - 66 28
Romania - - - - - - 36
Bulgaria - - - - - - 24
Iceland - - - - - - 79
Norway - - - - - - 70
Turkey - - - - - - 49
Switzerland - - - - - - 32
Croatia - - - - - - 25
20
2. Emerging technologies
2.1 Nanotechnology
Nanotechnology is a collective term for a variety of technologies for engineering matter on the atomic
and/or molecular level. Nanotechnology is considered a strategic technology par excellence; its many
uses and vast potentials cover medicines and medical processes as well as electronics, energy, materials,
filtration, consumer goods and food. As nanoscience emerged as a new discipline, scientists and policy
makers became conscious of the need to avoid a repetition of the GM food saga (David and Thompson
2008). In parallel, nanoethics emerged to debate the social, ethical and legal aspects of molecular
engineering. That it continues to be a socially sensitive technology is evidenced by a call of the European
Parliament to ban nanoparticles from food products.
For the Eurobarometer survey it was decided to select an area of nanotechnology that involved products
close to everyday life: cosmetics, sun creams and household cleaning fluids. Nanotechnology was
introduced to respondents in the following way:
‘Now thinking about nanotechnology: Nanotechnology involves working with atoms and
molecules to make new particles that are used in cosmetics to make better anti-aging creams,
suntan oils for better protection against skin cancer and cleaning fluids to make the home
more hygienic. Despite these benefits, some scientists are concerned about the unknown and
possibly negative effects of nanoparticles in the body and in the environment.’
Figure 4 shows that only around 25 per cent of Europeans have ‘engaged’ with nanotechnology, i.e.
talked about it or searched for information. More than half have not heard of it before the interview.
21
Figure 4: Awareness of nanotechnology, EU27
Not heard55%
Heard only20%
Talked about or searched for information
occasionally22%
Talked about or searched for information frequently
3%
First, we look at the distribution of supporters and opponents of nanotechnology in countries across
Europe. Figure 5 is based on only those respondents who expressed an opinion to question 10 below,
regarding encouragement for nanotechnology3. As can be seen from the figure, six out of ten EU citizens
support nanotechnology. Support varies, between all the countries in the survey, from 83 per cent in
Iceland to 48 per cent in Austria. Note that in the description of nanotechnology, both potential benefits
and risks were mentioned. It would appear that while opponents are concerned about safety issues, in
most countries this is a minority response. In all but three countries an absolute majority support
nanotechnology for consumer products.
3 That is, 63 per cent of respondents across the 32 countries.
22
Figure 5: Encouragement for nanotechnology (excluding DKs)
41
48
48
51
52
52
53
54
55
55
56
60
60
61
61
62
63
63
63
64
64
65
66
67
67
68
68
74
77
77
78
83
61
0 20 40 60 80 100
Turkey
Austria
Greece
Portugal
Slovenia
Malta
Luxembourg
Netherlands
Italy
Belgium
Croatia
Bulgaria
France
Germany
Denmark
Slovakia
Spain
Switzerland
Lithuania
Romania
United Kingdom
Estonia
Latvia
Sweden
Ireland
Norway
Hungary
Poland
Cyprus
Czech Republic
Finland
Iceland
EU27
% respondents who strongly agree or agree that nanotechnology should be encouraged
23
Respondents were asked a number of questions about nanotechnology (similar questions were also
asked about animal cloning for food products and GM food, which will be reported later):
1. Nanotechnology is good for the (NATIONALITY) economy
2. Nanotechnology is not good for you and your family
3. Nanotechnology helps people in developing countries
4. Nanotechnology is safe for future generations
5. Nanotechnology benefits some people but puts others at risk
6. Nanotechnology is fundamentally unnatural
7. Nanotechnology makes you feel uneasy
8. Nanotechnology is safe for your health and your family’s health
9. Nanotechnology does no harm to the environment
10. Nanotechnology should be encouraged
For each question, respondents were asked whether they totally agree, tend to agree, tend to disagree
or totally disagree. The first nine questions were designed to tap into four clusters of perceptions of
technologies. The final question, ‘should nanotechnology be encouraged?’ we take as a measure of
overall support.
• Questions 1 and 2 provide an index of the extent of perceived benefit;
• Questions 3 and 5 give as index of distributional equity – do people perceive this
technology to be fair or unfair in the distribution of both benefits and risks?
• Questions 4, 8 and 9 give an index of perceived safety/risk;
• And finally, questions 6 and 7 provide an index of worry related to unnaturalness. This
is similar to the ‘affective heuristic’ (Slovic et al. 2002).
For each respondent, a score was created for each of these four indices of benefit, safety, inequity and
worry (unnatural). Scores range from -1.5 to 1.5, where -1.5 indicates low perceived benefit, low safety,
and absence of both inequity and worry; and 1.5 indicates high perceived benefit, high safety, high
inequity and high worry. Zero marks the notional mid-point on the scale. Note, therefore, that the first
two indices are framed ‘positively’, with high scores indicating positive views about the technology,
whereas the second two indices are framed ‘negatively’, with high scores indicating concerns about the
technology.
Figure 6 shows average (mean) scores for respondents in EU27 countries, both overall (yellow bars). We
then take the final question, number 10, and split the sample between supporters (those who agree that
nanotechnology should be encouraged) and opponents (those who disagree). In the figure, the
supporters are denoted with green bars and opponents with red bars.
The figure shows that, across the European public (the first bar in each cluster, in yellow), the balance of
opinion is that nanotechnology is somewhat more likely to be beneficial than not; to be unsafe rather
24
than safe; to be inequitable rather than equitable; and not particularly worrying (though equally, not
particularly unworrying). Taken as a whole, perceptions of nanotechnology emerge as rather neutral in
character. But dig beneath the surface and we find division in perceptions between supporters and
opponents. Supporters (denoted by the middle bar in each cluster, in green) are much more likely than
opponents (the last bar, in red) to agree that nanotechnology is beneficial, safe, equitable and not the
cause of worry. When comparing opponents and supporters, the most pronounced contrast is in the
issue of safety. Supporters and opponents are most in agreement on the issue of inequity, which
supporters returning a neutral verdict on this issue, and opponents somewhat concerned.
Multiple regression is a statistical technique that allows us to find out the extent to which the four indices
(benefit, safety, inequity and worry) make a separate (independent) contribution to the explanation of
variation in overall support. If the four indices are making independent contributions to explaining overall
support, then they flag up distinct concerns rather than merely some overall attitude, for example,
‘technological optimism’. The multiple regression4 shows that all four indices make a statistically
significant contribution to the explanation of overall support. Here, safety is by far the most influential,
followed by benefit, worry and lastly inequity.
Figure 6: Perceptions of nanotechnology as beneficial, safe, inequitable and unnatural, EU27 (excluding DKs)
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5Beneficial Safe Inequitable Unnatural
Ave
rage
(mea
n) s
core
Overall Supporters Opponents
4 Specifically, we used a binary logistic regression model, with the response variable dichotomised into ‘agree or totally agree’ that nanotechnology should be encouraged, versus ‘disagree or totally disagree’ that it should be encouraged. Respondents answering ‘don’t know’ to this question were excluded from this analysis. ‘Statistically significant’ results are so at the 1% significance level.
25
2.2 Biofuels
When biofuels made from edible crops were first introduced, they were heralded as one of the more
exciting applications of modern biotechnology, offering an apparently sustainable means to produce
energy resources and lower dependence on Middle-Eastern oil, as well as providing farmers in Europe
and the US with a new market. The EU announced targets for the introduction of biofuels, and motorists,
even airlines, sought out biofuels as a response to climate change. Relatively quickly, some unintended
consequences became apparent, with negative impacts appearing in the developed world – increased
speculation in commodity crops and food prices and in the developing world – increased destruction of
rain forests for crop cultivation.
In our questions on biofuels, respondents were asked sequentially about the first generation of crop
based biofuels and then about the second generation of more sustainable biofuels. The introductions
went as follows:
(First generation)
‘Let’s speak now about biofuels. Biofuels are made from crops like maize and sugar cane that
are turned into ethanol and biodiesel for airplanes, cars and lorries. Unlike oil, biofuels are
renewable, would reduce greenhouse gas emissions and make the European Union less
dependent on imported oil. Critics, however, say that these biofuels take up precious
agricultural land and may lead to higher food prices in the European Union and food shortages
in the developing world.’
(Second generation)
‘Now, scientists are working on more sustainable biofuels. These can be made from plant
stems and leaves - the things we don’t eat, or from trees and algae. With these second
generation biofuels, there is no longer the need to use food crops.’
Figure 7 summarises the balance of opinion about two generations of bio-fuels across the European
Union. Overall, feelings are positive towards all kinds of biofuels across Europe. 72 per cent of Europeans
support crop based biofuels. It would appear that the discussions about the downsides of crop-based
biofuels have not had much impact.
However, Europeans are even more optimistic about the second generation biofuels: 83 per cent
approve of sustainable biofuels made from non-edible material.
26
Figure 7: Opinions regarding first generation and sustainable biofuels, EU27
51
34
32
38
7
13
4
7
7
8
20 40 60 80 100
Sustainable biofuels
First generation biofuels
% respondents
Should definitely be encouraged Should probably be encouragedShould probably not be encouraged Should definitely not be encouragedDon't know
Figure 8 shows the levels of approval towards biofuels by country, ordered according to their overall
levels of support for sustainable biofuels. Respondents in all countries support sustainable bio-fuels more
than the crop based variety. In every country the majority support traditional biofuels, with highest level
of support in Slovakia, Denmark, Hungary and Baltic States (more than 80 per cent). Hence, there is an
overwhelming preference for such biofuels across Europe. Large gaps between the approvals of the two
generations of biofuels emerged in Scandinavia and Central Europe. Probably the term ‘sustainable’ is
considered particularly favourable in these countries while in countries such as Portugal or Turkey, where
differences are much less, the issue of sustainability has not gained such prominence.
27
Figure 8: Support for first generation and sustainable biofuels (excluding DKs)
60
55
70
76
81
57
68
73
83
85
70
81
78
84
79
82
86
77
84
67
88
87
77
75
68
91
87
91
78
91
86
88
78
67
74
78
83
85
86
87
87
88
88
88
88
89
89
91
91
92
92
93
93
93
93
93
94
94
94
94
95
95
97
97
98
89
0 20 40 60 80 100
Turkey
Switzerland
Luxembourg
Italy
Austria
Malta
Germany
Greece
Romania
Portugal
France
Czech Republic
Belgium
Spain
Bulgaria
United Kingdom
Estonia
Slovenia
Croatia
Norway
Hungary
Poland
Sweden
Netherlands
Iceland
Latvia
Ireland
Slovakia
Finland
Lithuania
Cyprus
Denmark
EU27
% respondents
Sustainable biofuels
First generation biofuels
28
2.3 Synthetic biology
Synthetic biology is an emerging field in which scientists seek to turn biology into an engineering
discipline. Rather than introducing one or a few genes into existing organisms, they want to construct
novel organisms and their genomes from scratch, using genetic ‘building blocks’ that ideally could be
freely combined. For example, the scientist Craig Venter and colleagues in May 2010 announced that
they had managed to introduce a functioning fully synthetic genome into a bacterium. Such results
currently meet with considerable media attention, but when it comes to public perceptions, it must be
assumed that synthetic biology has hardly entered public awareness. Nevertheless, and not unlike
nanotechnology, scientists are concerned that the new field could meet public resistance. Apart from
moral considerations over ’creating life’, a potentially sceptical public prompted scientists and regulators
to address ethical and social issues at a very early stage despite the lack of almost any current practical
applications.
In this section, we ask how people deal with emerging technologies – such as synthetic biology – that
still are unfamiliar to them. Confronted by such an innovation, what information is important to them?
How and in what ways does familiarity with the technology influence its evaluation? What is important to
people when it comes to decision-making and regulation?
Based on the assumption that synthetic biology still is widely unknown, respondents in the
Eurobarometer were, first of all, presented the following description:
Synthetic biology is a new field of research bringing together genetics, chemistry and
engineering. The aim of synthetic biology is to construct completely new organisms to make
new life forms that are not found in nature. Synthetic biology differs from genetic engineering
in that it involves a much more fundamental redesign of an organism so that it can carry out
completely new functions.
Respondents were then asked whether they had heard anything about synthetic biology before, and if
they had, whether they had talked with anyone about it or searched for further information. The results,
shown in Figure 9, indicate that synthetic biology is an unfamiliar technology to most Europeans. 83 per
cent indicate that they have not heard about it. Out of those having heard about it (17 per cent), 8 per
cent say that they have (passively) heard but not talked about it nor searched for any information. Only
9 per cent have talked about or searched for information occasionally or more. The innovation is most
familiar in Switzerland (30 per cent having heard) and least familiar in Turkey (10 per cent having
heard).
29
Figure 9: Awareness of synthetic biology, EU27
Not heard83%
Heard only8%
Talked about or searched for information
occasionally8%
Talked about or searched for information frequently
1%
Even if people are unfamiliar with a technology, they nevertheless are sometimes called upon to make
up their minds. While it makes little sense to ask people whether they support an unknown technology or
not, it is worthwhile asking what information they would be interested in to learn more about the new
development. What pieces of information do they regard as relevant, and what questions would they like
to be answered?
Respondents were presented with the following scenario:
Suppose there was a referendum about synthetic biology and you had to make up your mind
whether to vote for or against. Among the following, what would be the most important issue
on which you would like to know more?
Respondents were offered a list of seven issues and asked to choose the three options that were of most
interest to them. 84 per cent of those asked5 indeed chose three questions. The remaining 16 per cent
chose fewer issues; this group consisted predominantly of respondents who gave ‘don’t know’, ‘none’ or
‘other’ responses. There are considerable country differences in these responses. The highest number of
such ‘don’t know’ responses is found in Turkey (41 per cent); In the remaining countries the proportion
of such responses ranges from 6 per cent (Czech Republic) to 22 per cent (Latvia). To ensure
comparable base rates, for the following analyses only those respondents who chose three of the
following issues are included.
5 The questions on synthetic biology were part of a split ballot, i.e. only half of respondents were asked.
30
Table 2: Issues about which respondents would like to know more in relation to synthetic biology, EU27 (excluding DKs)
Issue % respondents selecting the issue
What are the possible risks 73
What are the claimed benefits 61
Who will benefit and who will bear the risks 47
What the scientific processes and techniques are 37
What is being done to regulate and control synthetic biology 34
Who is funding the research and why 28
What is being done to deal with the social and ethical issues involved 19
Other/none 1
Note: percentages sum to 300 because respondents chose three pieces of information
Clearly, potential risks and benefits related to synthetic biology are of upmost interest to respondents.
However, all the other issues are of interest to a not insignificant proportion of the European publics.
Remarkably, information about social and ethical issues clearly comes last in the list, while the scientific
processes involved meet considerable interest.
The most frequent out of 35 possible combinations are risks, benefits and the distribution of risks and
benefits (16 per cent); risks, benefits and scientific processes (11 per cent); risks, benefits and
regulation (9 per cent); and risks, benefits and funding (7 per cent). All other combinations are less
frequent (less than 5 per cent). The most frequent combinations all include interest in information on
both risks and benefits.
Risks and benefits are of high interest in all countries. Germany is the only country where interest in
benefits is higher than in risks; in all other countries risks are of highest interest. While in Belgium, the
Czech Republic, Estonia and France, interest in risks almost double that in benefits, in most other
countries the interests in risks and benefits are more balanced.
Figure 10 highlights the importance of risks and benefits relative to other issues in different European
countries. While in Greece, Lithuania, Portugal and Malta, risks and benefits combined represent the
most important concern, there are other countries where issues such as the distribution of risks and
benefits, scientific details, control and regulation, funding, or social and ethical issues play a more
prominent role. Of all countries, interest in the distribution of risks and benefits is highest (more than 60
per cent) in the Netherlands, the Czech Republic and in Slovakia; interest in scientific details is most
pronounced (more than 50 per cent) in the Czech Republic, in Bulgaria, Estonia and Slovenia; a demand
for information on control and regulation is particularly high (more than 40 per cent) in Sweden, France,
Iceland and Switzerland; the issue of funding attracts most interest (more than 30 per cent) in Romania,
Luxemburg and Ireland; and social and ethical issues are of highest interest (more than 30 per cent) to
respondents in the Netherlands, Denmark, Iceland and Sweden.
31
Figure 10: Priority given to finding out about risks and benefits (versus other issues) in relation to synthetic biology6
194
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0 20 40 60 80 100 120 140 160 180 200
Netherlands
Czech Republic
Sweden
Switzerland
Slovenia
Belgium
Ireland
United Kingdom
Luxembourg
Estonia
Latvia
Norway
Denmark
France
Iceland
Slovakia
Turkey
Finland
Poland
Croatia
Germany
Austria
Romania
Bulgaria
Italy
Hungary
Cyprus
Spain
Malta
Portugal
Lithuania
Greece
EU27
% respondents
Risks and benefits
Other issues
6 Note: Percentages within each country sum to 300 because respondents have chosen three pieces of information. Only those respondents who chose three pieces of information are included in this graph: those who responded ‘don’t know’ or who mentioned only one or two types of information are excluded from the graph. 32
Should synthetic biology be supported or not?
Finally, respondents were asked about their views on whether, and under what conditions, synthetic
biology should be approved. Not surprisingly, a substantial percentage across Europe (23 per cent) say
they don’t know (9 per cent in Greece, 43 per cent in Turkey). The remaining respondents, however, are
willing to voice a view despite the technology’s unfamiliarity. Some (17 per cent) say that they do not
approve under any circumstances and 21 per cent do not approve except under very special
circumstances. More than a third (36 per cent) approve as long as synthetic biology is regulated by strict
laws and only 3 per cent fully approve and do not think that special laws are necessary. Overall, it seems
safe to say that Europeans consider synthetic biology a sensitive technology that demands for precaution
and special laws and regulations, but an outright ban would not find overwhelming support.
Figure 11 shows that, across Europe, the numbers of those approving and non-approving are roughly
equal, an indication that synthetic biology potentially may become a controversial issue. Furthermore,
the picture of a divided Europe emerges: the proportions of those approving and non-approving vary
considerably. While in half of the countries under consideration, supporters outnumber critics, the
opposite is true for the other half of the countries. People in central European countries such as
Germany, Slovenia, Austria and the Czech Republic (as well as Iceland) are particularly cautious (50 per
cent or more do not approve at all or only under very special circumstances). Support, in contrast, is
more frequent in Southern (Portugal, Spain) and Eastern countries (Romania, Estonia, Hungary), as well
as in Ireland. In these latter countries, the majority of respondents express approval of the technology if
regulated by strict laws.
33
Figure 11: Approval of and ambivalence towards synthetic biology
24
38
29
35
23
19
12
19
26
20
13
43
25
35
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24
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Portugal
Ireland
Spain
Romania
Estonia
Hungary
Belgium
France
Luxembourg
United Kingdom
Norway
Turkey
Italy
Malta
Denmark
Lithuania
Slovakia
Switzerland
Poland
Netherlands
Bulgaria
Sweden
Greece
Latvia
Croatia
Cyprus
Finland
Czech Republic
Austria
Slovenia
Iceland
Germany
EU27
Net a
ppro
val
N
et d
isap
prov
al
net % respondents
Approve' minus 'do not approve'Don’t know
34
The grey bars indicate percentages of ‘don’t know’ responses. Red bars indicate the difference between
approval and non-approval with negative values indicating higher proportions of non-approval and
positive values indicating higher proportions of approval. ‘Do not approve’ comprises ‘do not approve
under any circumstances’ and ‘do not approve except under very special circumstances’, and ‘Approve’
comprises ‘approve as long as this is regulated by strict laws’ and ‘fully approve and do not think that
special laws are necessary’.
How is familiarity with synthetic biology related to the technology’s evaluation? Those who have heard
about synthetic biology are much more likely to approve, as long as it is regulated by strict laws. Those
who have not heard about the innovation are both more likely to so say that they don’t know or that
they do not approve under any circumstances. The fact that those familiar with synthetic biology are
more supportive should be interpreted with caution though; it is only a small group of respondents who
have heard about it. It is possible that familiarity leads to more support, but it is also possible that it is a
technophile avant-garde that – because of its affinity to and support of technologies – has heard about
synthetic biology7 in the first place. Whether familiarity will lead to more support for a broader public,
remains an open question.
In summary, a large majority of Europeans is unfamiliar with synthetic biology, giving us the opportunity
to investigate how European citizens deal with fundamentally unknown issues. Asked what information
they would like to be offered, risks and benefits are the preferred options across Europe. However, other
issues – such as the distribution of risks and benefits, funding, scientific details, regulation and social and
ethical issues – also represent important concerns to relevant proportions of the European public. When
it comes to the evaluation of synthetic biology, Europe seems to be evenly split: the proportion of those
approving of synthetic biology equal those not approving. About half of the countries included are
predominantly cautious, while the other half is predominantly supportive. It should be noted, though,
that support is almost always conditional on strict laws and regulation.
However, Europeans, on the whole, are not technophobic. They want to be informed about what to
expect from the innovation and to ensure prudent regulation. While those familiar with synthetic biology
are more likely to express (conditional) approval than those unfamiliar, it remains an open question
whether increasing familiarity with the topic will make European citizens more supportive of synthetic
biology in general or not.
7 Means for technology optimism seem to support this view: those having heard of synthetic biology are more optimistic about other technologies than those who have not heard (not heard of synthetic biology M = 4.78, SD = 2.14; passive awareness: M = 5.51, SD = 1.90; active awareness: M = 5.44, SD = 2.06).
35
3. Biotechnologies for food production
3.1 GM food
20 years after the first EU directive on deliberate release was released, the issue of GM crops and food is
still unresolved. Only two crops have formal approval for cultivation – Monsanto’s MON 810 Maize and,
most recently, BASF’s Amflora potato. At present only six countries have planted GM crops – Spain, the
Czech Republic, Portugal, Romania, Poland and Slovakia– about 95,000 hectares in total in 20098,
compared to 134 million hectares world wide. However, currently six countries have bans on GMOs using
the ‘safeguard clause’: Austria, France, Germany, Greece, Hungary and Luxembourg. Italy has said that
it will defy the EC and refuse to allow GM crop to be grown, but has not done so formally. Confronted by
this opposition, the European Commission is taking the subsidiarity route. Member States, it is proposed,
will have the legal right to decide whether to cultivate GM crops or not (European Commission 2010b).
GM food was introduced to respondents in the following way:
‘Let’s speak now about genetically modified (GM) food made from plants or micro-organisms
that have been changed by altering their genes. For example a plant might have its genes
modified to make it resistant to a particular plant disease, to improve its food quality or to help
it grow faster.’
Figure 12 shows that the majority of Europeans are familiar with GM food. Nearly half of them have not
only heard about it but also talked about it or searched for information. Only about 18 per cent have not
heard of it before the interview. Levels of engagement seem then to reflect continued media attention of
the issue.
8 GMO Compass, http://www.gmo-compass.org/eng/agri_biotechnology/gmo_planting/392.gm_maize_cultivation_europe_2009.html
36
Figure 12: Awareness of GM food, EU27
Not heard18%
Heard only27%
Talked about or searched for information
occasionally46%
Talked about or searched for information frequently
9%
Figure 13 presents the levels of support for GM food for both EU27 in 2010 and for comparative
purposes EU25 in 2005. In 2010, combining ‘totally agree’ and ‘tend to agree’ we find 27 per cent in
support. By the same token, 57 per cent are not willing to support GM food. The comparison between
2010 and 2005 shows no substantial changes in the public’s perception of GM food.
Figure 13: Support for GM food, EU27
5
6
18
21
28
29
33
28
16
16
0 20 40 60 80 100
2010, EU27
2005, EU25
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree Don't know
To explore what may be driving the public perceptions of GM food, we used the same set of question as
nanotechnology (see Chapter 2.1). With these questions, we have the four indices of whether
respondents perceive GM food as beneficial, safe, inequitable and worrying. In Figure 14 the first
(yellow) bar in each cluster shows overall perceptions of the four dimensions. Contrary to scientific and
industry opinion, the European public see GM food as not offering benefits, as unsafe, as inequitable and
as worrying.
37
Splitting the overall sample into those who support GM food (the middle, green bar in each cluster) and
those who oppose it (the last, red bar in each cluster), we see that the dimension that most
differentiates supporters and opponents is the issue of safety. This is followed by benefit and worry.
Even the supporters are lukewarm about benefits and safety, and on balance only marginally convinced
that GM food is equitable and worry-free. The views of opponents run in the opposite direction, and are
considerably more extreme. The perceived safety deficit suggests that the risk assessment for GMOs in
place according to EU rules is not considered valid. It could also be interpreted as an entrenched
attitudinal association between GM food and a lack of safety, notwithstanding institutional efforts to
demonstrate the opposite.
Figure 14: Perceptions of GM food as beneficial, safe, inequitable and unnatural, EU27 (excluding DKs)
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5Beneficial Safe Inequitable Unnatural
Aver
age
(mea
n) s
core
Overall Supporters Opponents
Carrying out logistic regression analysis (see Section 2.1) to explain levels of support for GM food, we
find that all four indices have an independent effect on levels of overall support. Here, safety is by far
the most influential, with the other three making smaller, albeit statistically significant, contributions.
Using questions on GM food from previous waves of the Eurobarometer survey, we can track levels of
support over time. In Table 3 we have three blocks of countries. Block 1 (from UK to Greece) comprises
EU15 plus Switzerland and Norway, who were included in some of the earlier Eurobarometers. Block 2
(from Czech Republic to Cyprus) covers those additional Member States when the EU expanded to 25.
Block 3 takes us to EU27, with Bulgaria and Romania, and also includes Iceland, Croatia and Turkey.
In Table 3 we show the percentage of respondents in each country who agree or totally agree that GM
food should be encouraged. We base the calculations on only those who express an opinion. Highlighted
in bold, green font are those countries in which GM crops are currently cultivated. It is noticeable that in
these countries, support for GM food tends to be amongst the highest. Romania is an exception to the
38
rule. Highlighted in italicised, red font are the countries which have bans on the cultivation of GMOs.
Apart from Hungary, at 32 per cent support, levels of support in these countries are amongst the lowest
in Europe.
Across the period 1996-2010, we see, albeit with fluctuations, a downward trend in the percentage of
supporters. Denmark and the UK, at the higher end of the distribution of support, are exceptions, as is
Austria, at the lower end. Those among the ‘old’ EU countries with a ban on GM crops in place
consistently show low values of encouragement, with Italy obviously joining the group. In contrast,
Member States where GM crops are grown tend to show among the highest values, which might suggest
a link between private attitudes and public policies.
39
Table 3: Trends in support for GM food (excluding DKs)
% respondents who agree or totally agree that GM food should
be encouraged
1996 1999 2002 2005 2010
United Kingdom 52 37 46 35 44
Ireland 57 45 57 43 37
Portugal 63 47 56 56 37
Spain 66 58 61 53 35
Denmark 33 33 35 31 32
Netherlands 59 53 52 27 30
Norway 37 30 30
Finland 65 57 56 38 30
Belgium 57 40 39 28 28
Sweden 35 33 41 24 28
Italy 51 42 35 42 24
Austria 22 26 33 24 23
Germany 47 42 40 22 22
Switzerland 34 20
Luxembourg 44 29 26 16 19
France 43 28 28 23 16
Greece 49 21 26 14 10
Czech Republic 57 41
Slovakia 38 38
Malta 51 32
Hungary 29 32
Poland 28 30
Estonia 25 28
Slovenia 23 21
Latvia 19 14
Lithuania 42 11
Cyprus 19 10
Iceland 39
Romania 16
Bulgaria 13
Croatia 13
Turkey 7
40
3.2 Animal cloning for food production
Using the technique that created ‘Dolly the sheep’, animal cloning for food products has been offered as
a commercial service. It is claimed that consumers will benefit simply because the offspring of clones will
produce better meat and milk products. Because cloning is costly, it is the progeny (F1s) that will enter
the food chain and not the clones (F0s). This is an important distinction as it has been argued that
labeling would be restricted to the F0s and would not be necessary for the F1s. Whether the public will
agree with the scientists that the F1s are the same as conventionally bred animals is a moot point;
parents perceived to be unnatural may lead to perceptions of unnatural offspring.
Scientific opinions on animal cloning for food products have been published by the Center for Veterinary
Medicine at the US Food and Drug Administration9 and by the European Food Safety Authority. Both
concur that cloning poses no increased risk for food consumption. However, they also agree that cloning
raises questions about animal health. The health risks include large offspring syndrome with animals
showing abnormalities of the lungs and other organs, increased incidence of cardiovascular and
respiratory problems, and increased rates of mortality and morbidity compared to sexually reproduced
animals. Those developing cloning claim that these problems will be minimized as the technology
matures.
In the formulation of their opinions the FDA and EFSA invited comments from the public. Here EFSA
notes that a large majority of submissions that did not support cloning ‘were not scientific views’. The
same occurred in the US, leading the FDA to stress that ‘the Agency is not charged with addressing non-
science based concerns such as the moral, religious, or ethical issues associated with animal cloning for
agricultural purposes’ (FDA 2008). Apparently, for the public on both sides of the Atlantic, cloning raises
issues beyond the strictly scientific. (The issue of scientific versus moral criteria for governance is taken
up in Chapter 6.)
Such concerns have also been voiced by the European Group of Ethics of Science and New Technology
(EGE 2008), which reports to the President of the European Commission. The EGE conclude that while
‘there are no categorical arguments against animal cloning for breeding with the purpose of food
production, the EGE is not convinced so far that there are enough good reasons to alleviate the ethical
concerns’. These include: moral unease at such a new dimension to animal breeding; the effects on
animal welfare and health; the need for traceability and labelling; the requirement for further research
efforts on key issues; and the need for a comprehensive public discussion. Perhaps influenced by these
concerns, the European Parliament voted overwhelmingly for a ban on cloned animals for food. But what
does the European public think of animal cloning for food products?
9 (FDA; see http://www.fda.gov/cvm/cloning.htm)
41
In the survey, cloning was described as follows:
‘Let’s speak now about cloning farm animals. Cloning may be used to improve some
characteristics of farmed animals in food production. Due to the high cost of cloning, this
technique would mainly be used to produce cloned animals which will reproduce with non-
cloned animals. Their offspring would then be used to produce meat and milk of higher quality.
However, critics have raised questions about ethics of animal cloning.’
Figure 15 shows that 71 per cent respondents have heard about animal cloning, with four in ten having
talked about or searched for information on the topic. Given the very extensive coverage of Dolly the
sheep in 1997, it is perhaps not surprising that this is a familiar issue for most people.
Figure 15: Awareness of animal cloning for food production, EU27
Not heard27%
Heard only30%
Talked about or searched for information
occasionally39%
Talked about or searched for information f requently
4%
To gauge the attributes of public perceptions of animal cloning, we used the same question set used for
nanotechnology and GM food (see Chapters 2.1 and 3.1), providing an indicator of support and
assessments of whether respondents perceive animal cloning as beneficial, safe, inequitable and
worrying. The yellow bars in Figure 16 show overall perceptions of the four indices. Similar to GM food,
the European public see animal cloning as not offering benefits, as unsafe, as inequitable and as
worrying. The similarities between perceptions of animal cloning and GM food are striking. Here, it is
worth noting that these topics were in different sections of the split ballot design used in the
42
Eurobarometer. Those who answered the questions on GM food did not answer the questions on animal
cloning, and vice versa.
Splitting respondents into those who support animal cloning for food products (green bars) and those
who oppose it (red bars), we see a considerable degree of differentiation on the issue of safety. This is
followed by benefit and worry. As with GM foods, supporters are not greatly convinced about benefits or
safety, and while they do not think it is inequitable, they are on average as likely to worry about it as not
to.
Regression modelling (see Section 2.1) shows that safety is the strongest predictor of support, with
benefit, equity and worry making separate, but much smaller, contributions to the explanation of support
for animal cloning.
Figure 16: Perceptions of animal cloning for food products as beneficial, safe, inequitable and unnatural, EU27 (excluding DKs)
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5Beneficial Safe Inequitable Unnatural
Aver
age
(mea
n) s
core
Overall Supporters Opponents
Figure 17 juxtaposes support for GM food and animal cloning for food products across Europe. As
mentioned above, questions about these two technologies were in different sections of the split ballot
used in the survey. Thus, we cannot determine the association between the two at the level of
individuals. At the country level, however, we can assess the correlation between levels of support for
the two technologies. It is moderately high, at 0.65.10
10 Pearson’s correlation coefficient, often called ‘Pearson’s r’. 0 would indicate that levels of support for GM food were unrelated to levels of support for animal cloning; 1 would indicate that they were essentially the same within countries; -1 would indicate that levels of support for GM food are exactly opposite to levels of support for animal cloning.
43
In only two countries are as many as three in ten supporters of animal cloning, among those who
express an opinion. This contrasts with 14 countries in which support for GM food is above 30 per cent.
Is this an indication of public anxieties about biotechnology and food? ‘The natural superiority of the
natural’ captures many of the current trends in European food production – organic, local, food-miles,
etc. And if ‘unnaturalness’ is one of the problems confronting GM food, it appears to be an even greater
concern for animal cloning and food products.
44
Figure 17: Encouragement for GM food and animal cloning for food products (excluding DKs)
7
10
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11
13
13
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0 10 20 30 40 50 60 70 80
Turkey
Cyprus
Greece
Lithuania
Croatia
Bulgaria
Latvia
Romania
France
Luxembourg
Switzerland
Slovenia
Germany
Austria
Italy
Estonia
Sweden
Belgium
Finland
Poland
Norway
Netherlands
Hungary
Malta
Denmark
Spain
Portugal
Ireland
Slovakia
Iceland
Czech Republic
United Kingdom
EU27
% respondents who 'agree' or 'totally agree' to encouragement
GM food Animal cloning
45
3.3 Transgenic and cisgenic apples
The preceding chapter on animal cloning suggested that ‘unnaturalness’ might be a reason for concern
or even rejection among the public. For plants, new biotechnological methods are being developed that
might be considered more ‘natural’ than conventional genetic modification, and at the same time reap
the benefit from modern molecular breeding approaches. Is this a viable strategy when it comes to
public concerns? Will such a ‘technolite’ solution be deemed more acceptable than conventional
transgenic techniques?
Commercial apple growers spray crops with pesticides and fungicides on a frequent basis – in some
locations 20 to 25 times a year – in order to prevent diseases such as canker, scab and mildew. This is
both costly and a potential health risk. Public concern about pesticide residues in fruit and vegetables
has been documented in a Eurobarometer survey sponsored by the European Food Safety Authority
(EFSA 2008). With proposals for the introduction of maximum residue levels (MRLs), there may be
pressure on the industry to look for alternative ways to protect crops from common diseases.
It has been found that crab apples, a closely related species that can cross naturally with food apples,
have genes that provide resistance to the common apple diseases, but classical breeding to introduce
such genes into modern varietals would be a painstakingly slow process. Cisgenics is the genetic
modification of crops adding only genes from the same species or from plants that are crossable with the
recipient plant in conventional breeding programmes. Thus, cisgenics might be thought of as
biotechnologically informed ‘green fingers’, reducing the time to introduce new strains of fruit from
decades to a matter of a few years.
Cisgenics, a technique also used to develop new strains of potato that are resistant to potato blight (a
contributory factor in the Irish famine in the mid 19th century), can technically be compared to
transgenics. In transgenics genes are taken from other species or bacteria that are taxonomically very
different from the gene recipient and transferred into plants to promote resistance to herbicides or to
insect pests – the latter by the incorporation of a gene that codes for Bacillus thuringiensis (Bt) toxin, for
example.
How might the public respond to cisgenics? Would the transfer of genes within a genus (‘life form’) be
more acceptable than transfers of genes across the genus? The species combined in a genus are
generally perceived to be phenotypically equivalent and genetic transfers may be imagined as much
more ‘morally acceptable’.
46
Cisgenics was introduced in the survey with the following description:
Some European researchers think there are new ways of controlling common diseases in
apples– things like scab and mildew. There are two new ways of doing this. Both mean that
the apples could be grown with limited use of pesticides, and so pesticide residues on the
apples would be minimal. The first way is to artificially introduce a resistance gene from
another species such as a bacterium or animal into an apple tree to make it resistant to mildew
and scab…. The second way is to artificially introduce a gene that exists naturally in wild/ crab
apples which provides resistance to mildew and scab.
Respondents were then asked to what extent they agreed or disagreed with a number of statements in
relation to these techniques:
1. It is a promising idea (transgenic)/ it will be useful (cisgenic)
2. Eating apples produced using this technique will be safe (transgenic)/it will be risky
(cisgenic)
3. It will harm the environment
4. It is fundamentally unnatural
5. It makes you feel uneasy
6. It should be encouraged.
These questions allow us to make two main comparisons. The first is between transgenics and cisgenics
in apple production: Is genetic modification within a species more acceptable to the public than
modifications which cross the species barrier? Secondly, we can also compare public perceptions of
transgenic apples with perceptions of GM food. In principle there should be no difference as the process
described in the survey of creating transgenic apples is identical to the process of creating other GM
food. However, it may be that GM in the context of food in general carries other negative connotations
that drive public perceptions. Some perceived risks become almost stigmatised, with the mere mention
of them leading to negative perceptions.
Figure 18 shows the contrast between perceptions of the indices of transgenic and cisgenic apples.
Across EU 27, 55 per cent support cisgenisis, some 22 per cent more than those who support
transgenics. As can be seen, cisgenic apples are more positively perceived on all the indices. They make
people feel less uneasy than transgenic apples; they seem more natural, less problematic for the
environment, safer and more useful/promising.
47
Figure 18: Perceptions of transgenic and cisgenic apples, EU27
12
22
23
13
16
10
39
23
28
16
7
14
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41
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9
9
8
8
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15
0 20 40 60 80 100
Promising: transgenic apples
Useful: cisgenic apples
Not safe: transgenic apples
Risky: cisgenic apples
Harm the environment: transgenic apples
Harm the environment: cisgenic apples
Unnatural: transgenic apples
Unnatural: cisgenic apples
Makes me feel uneasy: transgenic apples
Makes me feel uneasy: cisgenic apples
Encourage: transgenic apples
Encourage: cisgenic apples
Percentage respondents
Totally agree Tend to agree Tend to disagree Totally disagree Don't know
Figure 19 shows the country profile of support for transgenic and cisgenic apples. In all countries,
cisgenic apples receive higher support than transgenic apples. In 24 countries an absolute majority of
those who expressed an opinion are supportive of cisgenic apples.
48
Figure 19: Support for transgenic and cisgenic apples (excluding DKs)
17
30
23
37
23
22
28
26
39
37
28
37
38
33
27
18
37
31
38
36
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55
0 10 20 30 40 50 60 70 80 90
Luxembourg
Turkey
Switzerland
Italy
Croatia
Slovenia
France
Germany
Netherlands
Belgium
Austria
Spain
Portugal
Denmark
Sweden
Greece
Malta
Romania
Iceland
Ireland
Lithuania
Bulgaria
United Kingdom
Poland
Slovakia
Latvia
Norway
Estonia
Czech Republic
Finland
Hungary
Cyprus
EU27
% respondents who 'agree' or 'totally agree' to encouragement
Cisgenic apples
Transgenic apples
49
In contrast to transgenics, it seems that people may perceive cisgenic apples as not transcending the
‘life-form’ barrier separating living beings. Hence, cisgenics appears to be more natural, perhaps
comparable to hybridization in ‘natural’ horticulture.
What of the comparison between GM food and transgenic apples, which were both described in the
survey as the result of a similar process of genetic modification?
For EU 27, support for GM food is 27 per cent among those who expressed an opinion, while the
comparable figure for transgenic apples is 37 per cent. Can we determine from other questions what
differentiates GM food from transgenic apples that might account for the latter receiving 10 per cent
more support?
Table 4 shows the contrasting perceptions of the safety, environmental impacts and ‘naturalness’ of GM
food, transgenic apples and cisgenic apples.
Table 4: Perceptions of safety, environmental impacts and naturalness of GM food and transgenic apples, EU27 (excluding DKs)
GM food Transgenic apples Cisgenic apples
% responses Safe/not risky11 27 37 53
Not harmful for the environment
30 55 63
Unnatural 76 78 57
Support 27 33 55
While both GM food and transgenic apples are seen to be ‘unnatural’ by three out of four respondents,
transgenic apples are more likely to be perceived as safe and not to harm the environment. This
suggests that the preamble describing transgenic apples as a technique would ‘limit use of pesticides,
and so pesticide residues on the apples would be minimal’’ may have suggested a benefit both to the
environment and to food safety.
It has long been suggested that the Achilles’ heel of current GM crops and food has been the perceived
absence of benefits for the public and their imagined threat to nature’s integrity. Cisgenics might be seen
as a hypothetical example of the so-called ‘second generation’ of GM crops. Here, the benefits of GM
apple breeding are achieved with a technolite process, consumer benefits are apparent and as such
more acceptable, by a factor of two.
11
The criterion of safety was captured by different questions for each item: for GM food, agreeing that it is ‘Safe for your health and your family’s health’; for transgenic apples, agreeing that ‘Eating apples produced by this technique will be safe’; and for cisgenic apples, disagreeing that ‘It will be risky’. 50
4. Regenerative medicine
Throughout the series of Eurobarometer surveys on life science and society, biomedical research has
enjoyed more public support than agricultural applications of biotechnology (Bauer 2005). Biomedicine, it
seems, still encapsulates the very idea of progress in the public mind, having greatly contributed to the
alleviation of disease and suffering and having led to greater quality of life. Both public and private
investments in various medical applications of biotechnology have been significant in Europe. However,
there is one field within medical biotechnology that repeatedly attracted criticism. Regenerative
medicine, “the process of creating living, functional tissues to repair or replace tissue or organ function
lost due to age, disease, damage, or congenital defects” (according to a definition by the NIH) promises
significant improvements for an ageing population. However, it is beset with intriguing moral dilemmas
surrounding the origin of living cells and tissue. No wonder, the regulation of regenerative research has
been challenging. At times it has escalated into heated political controversies.
The field of human embryonic stem cell research epitomises some of the central tensions. Promoters
herald the potential of such research to contribute to the alleviation of human suffering and restore
dignity to patients and their families. This position has become a conflict of principle. On the one hand,
safeguarding the freedom of scientific research to push back the frontiers of knowledge; on the other
hand, using cells from human embryos is seen as an affront to the dignity of human life.
Human embryonic stem cell research not only raises religious opposition (especially from Catholics) but
is also seen as going against the public order that highly values the sanctity of human life. Other fields of
regenerative medicine have been also a cause of concern for regulators. Gene therapy has been in the
pipeline for almost two decades but has repeatedly been halted because of safety issues. Another
controversial application is xenotransplantation, regarded as an important source of cells and tissues for
transplantation into humans but fraught with issues over potential risks (for example porcine
endogenous retroviruses) arising from crossing species.
Questions in the Eurobarometer cover these issues. We also include questions that move from repair to
improvement, attempting to capture public views on human enhancement, that is using techniques of
regenerative medicine not only to repair debilitated bodily functions to the normal level but also to
improve certain aspects of human performance beyond this level. This raises questions over risk and
benefit, what is to be considered normal, and distributional equity.
In this section we first report on public views on the regulation of regenerative medicine and human
enhancement in 2010 and briefly discuss the most significant changes from 2005. We then attempt to
disentangle the ethical positions or dilemmas of the debate that are driving public views.
51
Respondents were presented with the following questions:
Let’s speak now about regenerative medicine which is a new field of medicine and clinical
applications that focuses on the repairing, replacing or growing of cells, tissues, or organs.
1. Stem cell research involves taking cells from human embryos that are less than 2 weeks
old. They will never be transplanted into a woman’s body but are used to grow new
cells which then can be used to treat diseases in any part of the body. Would you say
that...?
2. Now suppose scientists were able to use stem cells from other cells in the body, rather
than from embryos. Would you say that...?
3. Scientists can put human genes into animals that will produce organs and tissues for
transplant into humans, such as pigs for transplants or to replace pancreatic cells to
cure diabetes. Would you say that...?
4. Scientists also work on gene therapy which involves treating inherited diseases by
intervening directly in the human genes themselves. Would you say that...?
5. Regenerative medicine is not only about developing cures for people who are ill. It is
also looking into ways of enhancing the performance of healthy people, for example to
improve concentration or to increase memory. Would you say that...?
The response alternatives were:
• You fully approve and do not think that special laws are necessary
• You approve as long as this is regulated by strict laws
• You do not approve except under very special circumstances
• You do not approve under any circumstances
52
Figure 20: Levels of approval of biomedical research and synthetic biology, EU27
15
12
11
11
11
3
54
51
52
46
44
36
15
17
18
18
20
21
9
13
11
17
17
17
7
7
8
7
7
22
0 20 40 60 80 100
Non-embryonic stemcell research
Embryonic stem cellresearch
Gene therapy
Xenotransplantation
Human enhancement
Synthetic biology
% respondents
Fully approveApprove if strict laws to regulateDo not approve unless special circumstancesDo not approve under any circumstancesDon't know
Figure 20 presents the overall results for the EU27 countries as a whole. The greater majority of this
European public is willing to express an opinion on regenerative medicine. We find less than 10 per cent
‘don’t know’ responses. In contrast, one fourth of the European public has not formed an opinion on the
emerging field of synthetic biology (included here for comparative purposes, but discussed in greater
detail in Section 2.3).
In general, levels of approval are rather high. If we combine the two positive statements (‘fully approve
and I do not think that special laws are necessary’, ‘approve as long as this is regulated by strict laws’),
some 68 per cent approve of stem cell research and 63 per cent approve of embryonic stem cell
research. Levels of approval for gene therapy are similar, at 64 per cent . Xenotransplantation – an
application long subject to moratoria in various national contexts and an application which was seen as
even more critical than GM food in the 1996 Eurobarometer (Gaskell et al. 1998, p207) – is now
approved by 58 per cent of respondents. The solid support for medical applications of biotechnology
spreads over to non-therapeutic applications, moving from repair to improvement we observe that 56
per cent of the European public approves of research that aims to enhance human performance. This
result is consistent with expectations of brain and cognitive enhancement where 59 per cent said they
were optimistic about such developments. The new horizons opened up by biomedical research exploring
and enhancing the functions of the brain, perhaps the ultimate frontier in science, is apparently
favourably viewed by the European public in general.
The substantial levels of approval of these lines of research are, however, not unconditional. Approval is
clearly contingent upon perceptions of adequate oversight and control to guide developments. For
53
example, levels of approval when there are strict rules in place ranges from 44 per cent for human
enhancement to 54 per cent for non-embryonic stem cell research. The percentages of those who do not
approve but would allow developments in exceptional circumstance, ‘sceptical approval’, range from 15
per cent for non-embryonic stem cell research to 20 per cent for human enhancement. Both general
approval and general rejection of regenerative biotechnology are only minority positions.
These results show a very clear picture that raises a range of important issues for processes of
governance of this rapidly growing field of research. The European public does not generally approve or
reject applications of regenerative medicine and human enhancement, but wants developments to be
kept under control.
National regulation of human embryo stem cell research varies greatly across the European member
states. UK, Sweden and Belgium have adopted the most permissive legislation with Germany and Italy
adopting the most restrictive. The 2005 Eurobarometer report was made public at a time when the rules
for eligible research funding for 7th FP 2007 to 2013 were being defined. The European Parliament and
the Council opted for an approach that allowed the allocation of public funds to research using human
embryo stem cells under strict condition. A year later the new EC directive on Advanced Therapy
Medicinal Products (1394/2007) came into force.
Next we look at shifts and trends in levels of approval for three types of research between 2005 and
2010 (Figure 21). The views of the European public have become somewhat more decided, and across
the applications, levels of conditional approval have increased. This is due to a small increase in those
who do not approve under any circumstances and a somewhat larger decrease in the percentages of
‘fully approve’ responses.
54
Figure 21: Levels of approval for embryonic and non-embryonic stem cell research and gene therapy, 2005 and 201012, Europe-wide
23
12
28
15
18
11
36
51
37
54
36
52
17
17
14
15
20
18
9
13
7
9
8
11
15
7
14
7
18
8
0 20 40 60 80 100
Embryonic s tem cell research 2005
Embryonic s tem cell research 2010
Non-embryonic s tem cell research 2005
Non-embryonic s tem cell research 2010
Gene therapy 2005
Gene therapy 2010
% respondents
Fully approveApprove if strict laws to regulateDo not approve unless special circumstancesDo not approve under any circumstancesDon't know
Human embryonic stem cell research continues to be a contentious issue so next we look more deeply
into the changes between 2005 and 2010.
12 Based on the 25 Member States in 2005 and 27 Member States in 2010
55
Figure 22: Levels of approval for human embryonic stem cell research, 2005 and 201013 (excluding DKs)
53
40
60
59
75
65
65
51
53
56
65
55
53
53
79
77
66
78
76
72
71
50
74
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74
54
57
58
74
77
63
74
39
49
50
50
52
55
57
57
61
61
61
63
64
68
69
69
70
72
74
75
75
75
77
78
80
6769
0 20 40 60 80 100
Croatia
Switerland
Turkey
Norway
Iceland
Bulgaria
Romania
Austria
Slovenia
Slovakia
Germany
Czech Republic
Cyprus
Poland
Malta
Latvia
Lithuania
Luxembourg
Ireland
Greece
Finland
Italy
Hunary
Portugal
Sweden
Belgium
France
Netherlands
Estonia
Denmark
Spain
UK
EU27/EU25
% respondents
2010
2005
13 Based on the 25 Member States in 2005 and 27 Member States in 2010. 56
Figure 22 shows the changes in levels of approval of embryonic stem cell research between 2005 and
2010 in the countries that were members of the EU in 2005. A comfortable majority (55 per cent or
more) support embryonic stem cell research in 19 countries, from the UK at the top, down to Poland.
Support has increased by 8 per cent or more in Estonia, Finland, Greece, Ireland, Latvia and Slovenia. In
contrast, support has declined by 8 per cent or more in Hungary, Italy, Poland, Cyprus, The Czech
Republic, Germany, Slovakia and Austria. While data for 2005 and 2010 do not constitute a trend, the
decline in support across these eight countries may indicate problems to come. Finally, in the countries
not included in the 2005 Eurobarometer survey, all bar Croatia show a comfortable majority in support.
Interestingly enough, analyses for non-embryonic stem cell research and gene therapy point to similar
trends.
The debates over the regulations of biomedical research have been strongly characterised by diverse
ethical arguments and dilemmas. Respondents were asked to what extent they agreed or disagreed with
the following statements relating to ethical considerations involved in regenerative medicine:
1. It is ethically wrong to use human embryos in medical research even if it might offer
promising new medical treatments.
2. We have a duty to allow research that might lead to important new treatments, even
when it involves the creation or use of human embryos.
3. Immediately after fertilisation the human embryo can already be considered to be a
human being.
4. Mixing animal and human genes is unacceptable even if it helps medical research for
human health.
5. Research involving human embryos should be forbidden, even if this means that
possible treatments are not made available to ill people.
6. Should ethical and scientific viewpoints on regenerative medicine differ, the scientific
viewpoint should prevail.
7. You do not support developments in regenerative medicine if it only benefits rich people.
8. Research on regenerative medicine should be supported, even though it will benefit only
a few people.
9. Research into regenerative medicine should go ahead, even if there are risks to future
generations.
57
Figure 23: Public views on ethical positions and regenerative medicine, EU27
15
16
14
12
27
51
29
11
5
23
27
36
33
26
23
27
32
23
34
31
23
25
25
10
21
25
31
18
16
16
14
11
8
12
20
27
10
11
12
16
11
8
11
12
13
0 20 40 60 80 100
Embryo research wrong even if promising for medicine
Ethically wrong to use embryos in research
We have a duty to allow research for new treatments
Science over ethics
Mixing animal and human genes unacceptable
Do not support regenerative medicine for the rich only
Embryo as a human being
Regenerative medicine should be supported
Regenerative research to go ahead despite risks
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree Don't know
Figure 23 shows that an overwhelming majority of respondents claim not to support the lines of research
in question if they only benefit the rich. The views of Europeans clearly diverge on most other issues
relating to ethical positions, such as the sanctity of human life and the essence of the human, the
prospects of future risks and the imperative to further research in regenerative medicine.
58
Figure 24: Sanctity of human life versus utilitarian positions
Norway
Switzerland
Croatia
Iceland
Turkey
Romania
Bulgaria
Slovenia
Slovakia
Poland
Malta Lithuania
Latvia
Hungary
Estonia
Czech Republic
Cyprus
United Kingdom
SwedenPortugal
Austria
Netherlands
Luxembourg
Italy
Ireland
France
Finland
Spain
Greece
Germany
Denmark
Belgium
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35 40
% totally agreeing we have a duty to allow research involving human embryos
% to
tally
agr
eein
g th
at h
uman
em
bryo
nic
stem
cel
l res
earc
h it
ethi
cally
wro
ng.
Figure 24 maps the percentages within each country, of those believe we have a duty to allow research
that can bring benefits even if human embryos are used (indicative of a utilitarian principle) against
those who would like to see a ban posed on human embryonic stem cell research (indicative of a sanctity
principle). It clearly shows that the fault lines in European public views cannot be construed as a simple
divide between Roman Catholic and Protestant countries. In a group of mainly Scandinavian countries
(Iceland, Sweden, Norway, Denmark), UK and Spain, we find support for utilitarian ethics and little
support for a ban. While the other countries are low on the utilitarian factor, they differ according their
support for a ban on the use of human embryos for research. In Portugal, France, Belgium, Italy and
Estonia, there is as little support for a ban as in the Scandinavian countries and the UK. At the other
extreme countries that tend towards favouring a ban are Cyprus, Slovenia, Turkey, Austria, Germany and
Croatia.
59
5. Biobanks
Biobanks collect data on biological and environmental/lifestyle characteristics of individuals. They do so
on a very large scale, with the aim of teasing apart genetic and lifestyle factors in the risk of diseases
and the maintenance of health. Scientists hope to develop new methods for better understanding many
common diseases and arrive at new effective treatments. The pharmaceutical industry is interested and
likely to be a major investor in the development and maintenance of biobanks. According to the scientific
journal Nature (24 September 2009) there are more than 400 biobanks in Europe. The EU is funding
biobank research as well as the development of an integrated system for sharing the vast amounts of
data they contain. Collecting biological information from people with illnesses has a long history, but
collecting data from healthy people is relatively novel and key to biobanks. The issues of altruistic duty to
contribute to research, privacy of very sensitive personal data on health, life habits and genetic profiles,
commercialisation of the results from research on biobank data and governance issues have been widely
debated (Elger et al. 2008, Gottweis and Petersen 2008).
Biobanks were described to respondents in the following way:
‘And now thinking about biobanks for biomedical research: These are collections of biological
materials (such as blood and/or tissues) and personal data (medical records, lifestyle data)
from large numbers of people. Using biobanks, researchers will try to identify the genetic and
environmental factors in diseases, to improve prevention, diagnosis and treatment.
Participation in biobanks is voluntary. Critics, however, raise questions about privacy,
confidentiality and commercial interests regarding the biobanks and about who is going to
regulate them.’
Figure 25: Awareness of biobanks, EU27
Not heard67%
Heard only16%
Talked about or searched for information
occasionally15%
Talked about or searched for information f requently
2%
60
As yet, it appears that many European citizens are unaware of biobanks. Two thirds of respondents had
not heard about biobanks before they were interviewed, and only 17 per cent can be described as
having actively engaged with the topic, through discussions or seeking out information about them.
Nevertheless, how do people feel about participating in biobanks? Figure 26 shows a range of support –
from 92 per cent Icelanders (where a highly publicised initiative more than a decade ago resulted in the
setting up of a large commercial biobank) say they definitely or probably would be willing to provide
information to a biobank, to 24 per cent Latvians expressing the same view. Turkish respondents as a
group return similar expressed levels of enthusiasm, or lack of it, for biobanks, but with a great deal
more ambivalence too – 33 per cent Turkish respondents say ‘I don’t know’ to this question.
Figure 26: Would you be willing to provide information about yourself to a biobank?
11
6
8
7
10
4
4
10
6
13
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15
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13
10
18
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12
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54
14
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4
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3
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3
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6
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4
7
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6
9
7
12
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8
5
2
7
16
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3
5
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4
4
2
0
10
0 20 40 60 80 100
Turkey
Latvia
Lithuania
Bulgaria
Romania
Greece
Austria
Poland
Slovakia
Hungary
Germany
Croatia
Slovenia
Malta
Czech Republic
Switzerland
France
Ireland
Italy
Spain
United Kingdom
Belgium
Luxembourg
Portugal
Cyprus
Estonia
Netherlands
Finland
Denmark
Sweden
Norway
Iceland
EU27
Percentage
Yes, definitely Yes, probably No, probably not No, never DK
61
What kinds of deposits might be made to a biobank? Do certain types of information invoke more anxiety
amongst the public than other types? On the whole, the data suggest that people do not seem to be
markedly more worried about some than other types of information we asked about. At the EU-level (i.e.
pooling all the data), people seem less concerned about giving information about their lifestyle (e.g. diet,
exercise habits etc.) to biobanks. In half of the countries in the survey, fewer than 20 per cent
respondents mention being concerned about this. By contrast, in only two countries do fewer than 20
per cent respondents mention being concerned about giving their genetic profile to a biobank.
Regarding the relative distribution of concerns within countries, some more subtle differences emerge.
The primary concern about genetic profiles in most countries tends to be followed closely by the relative
levels of concern about giving medical records from one’s doctor. This is notably the case for a collection
of countries in the rather supportive Scandinavian area, and also in Luxembourg. These are countries
where biobanks are well established, and where concern about giving blood or tissue samples to
biobanks is low.
Generally, indeed, people tend to mention pairs of concerns together: for example, those who say they
would be concerned about giving blood samples to a biobank are more likely to say they are also
concerned about giving tissue samples than they are to be concerned about any other type of
information; those who are concerned about giving lifestyle information are more likely to also be
worried about giving medical records than anything else. So we have concerns based around
physiological samples on the one hand, and around personal descriptive information on the other.
Genetic profiles appear to span both types of information, and there does not seem to be a strong
connection between concern about particular types of information and levels of enthusiasm about
participating in biobanks.
62
Figure 27: Levels of concern about giving different types of information to a biobank
18
13
26
16
17
11
27
25
19
31
28
25
24
29
35
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7
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13
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0 50 100 150 200 250
Luxembourg
Iceland
Turkey
Finland
Sweden
Norway
Portugal
Italy
Denmark
Bulgaria
Malta
Estonia
Croatia
Poland
Romania
Lithuania
Cyprus
United Kingdom
Ireland
Switzerland
Hungary
France
Latvia
Spain
Belgium
Netherlands
Czech Republic
Slovenia
Greece
Slovakia
Austria
Germany
EU27
% respondents concerned about each type of information
Blood samplesTissue collected during medical operationsYour genetic profileMedical record from your doctorLifestyle (what you eat, how much exercise you take, etc.)
63
Whether those conducting research on data in biobanks have obligations to the donors has been
explored in the ethics literature. Some argue in favour of a version of informed consent while others
argue that on pragmatic grounds this is simply not feasible. Salvaterra and colleagues note that models
of consent differ widely and that the regulations covering biobank research are ‘characterised by a maze
of laws, policies and ethical recommendations’ (Salvaterra et al. 2008: 307). What do Europeans think?
In the survey respondents were asked
‘In a hospital doctors ask the patient to sign a form giving permission to carry out an operation
– this is called ‘informed consent’ and it is also required of medical researchers who do
research involving members of the public. When a scientist does research on data in a
biobank, what do you think about the need for this kind of permission? Researchers should…’
• Not need to ask for permission (unrestricted consent)
• Ask for permission only once (broad consent)
• Ask for permission for every new piece of research (specific consent)
• Don’t know
Figure 28 shows that 67 per cent of people in EU27 wish for a strict interpretation of informed consent –
permission being required for every piece of research. The figure also shows that there is a comfortable
majority (55 per cent plus) in all the countries covered by the Eurobarometer, with the exception of
Denmark. Asking for permission ‘only once’ is the preference of 18 per cent of EU27 and a mere 6 per
cent say permission is not needed. It is notable that countries such as Iceland, Sweden and the
Netherlands, all with long established biobanks, have relatively high percentages of people saying
permission is needed once only – up to around 1 in 3. Yet this is still a minority response.
These findings will represent a significant concern for the proponents of biobanks, whether national
governments, research institutions or private companies. Of course, in the survey situation respondents
do not have the opportunity to deliberate on their responses, the ethics of informed consent are
complex, and in such a context some people may opt for a precautionary response. Weighing up the
prospective collective benefits against the interests of the individual donor is not a simple matter. At
minimum, the findings suggest that at first sight, informed consent, as in hospital operations, is the
legitimate procedure, in the sense of familiarity from custom and practice. The promoters of biobanks
cannot take the public for granted, and will need to cultivate public confidence.
64
Figure 28: Form of consent for biobank research
51
55
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0 20 40 60 80 100
Denmark
Finland
Romania
Turkey
Netherlands
Italy
Ireland
Norway
Iceland
Poland
Portugal
Sweden
United Kingdom
Estonia
Lithuania
Spain
Slovakia
Luxembourg
Croatia
Belgium
Austria
Czech Republic
Switzerland
Latvia
Malta
Slovenia
Cyprus
Germany
France
Bulgaria
Hungary
Greece
EU27
% respondents
Ask for permission for every new piece of research (specific)Ask for permission only once (general)No need to ask for permission (unrestricted)Don't know
65
Figure 29: Probability of participation and preferred form of consent (excluding DKs)
5
3
6
16
11
17
26
28
84
79
68
56
0 20 40 60 80 100
No, never
No, probably not
Yes, probably
Yes, definitely
% respondents
No need to ask for permission (unrestricted)Ask for permission only once (broad)
Ask for permission for every new piece of research (specific)
But, for the proponents of biobanks there are some grounds for optimism. Figure 29 provides a cross-
tabulation of agreeing to participate in a biobank with the preferred form of consent. The figure shows
that those who say they will definitely participate in a biobank are much more likely to say researchers
don’t need to ask for permission (16 per cent) or permission granted once only (28 per cent). With 44
per cent of Europeans taking a relaxed view on the issue of consent the pool of potential volunteers is
around 150 million (taking account of the 10 per cent ‘don’t know’ responses).
Who should be responsible for protecting the public interest when it comes to biobanks? Respondents
were asked who, from a list, they would choose first and second to protect the public interest. The
majority of Europeans would entrust this responsibility to medical doctors and researchers first. But there
are some patterns in responses which vary interestingly between countries. First of all, certain pairs of
responses are more highly correlated than others – that is, people tend to choose types of actors in
clusters. Those who choose ‘doctors’ are more likely to also choose ‘researchers’ than to choose ‘national
governments’. Those who choose ‘national governments’ are more likely to also choose ‘international
organisations’ than to choose ‘researchers’. So we can see a difference in emphasis, between:
� self-regulation (medical doctors; researchers; public institutions such as universities,
hospitals); and
� external regulation (ethics committees; national governments; international
organisations such as the European Union or World Health Organisation; national data
protection authorities)
Figure 30 plots for each country the percentage of people who select one or more of the self-regulation
agents, against the percentage who select one or more of the external regulation agents. The pattern of
points in the scatterplot – countries lie very roughly on a line from top left to bottom right – illustrates
66
these clusterings of concerns. In some countries, such as Iceland and the Netherlands, respondents tend
to choose external regulation more often than self-regulation. In other countries, such as Greece and
Slovakia, respondents tend to choose self-regulation more often than external regulation. Broadly
speaking, respondents in those countries which show higher levels of support for biobanks tend to
favour external regulation more than self-regulation. In those countries where biobanks are unfamiliar,
specialists in the substance of biobanks tend to be more popular as guardians of the public interest. The
differing levels of support for external regulation may reflect broader issues in national politics – for
example, general trust in government.
Figure 30: External regulation versus self-regulation of biobanks
Norway
Switzerland
Croatia
Iceland
Bulgaria
Slovenia
Slovakia
Malta
LithuaniaLatvia
Hungary
Estonia
Czech Republic
CyprusUnited Kingdom
Sweden
Austria
Netherlands
LuxembourgIreland
France Finland Spain
Greece
Germany Denmark
Belgium
50
60
70
80
90
100
50 60 70 80 90 100
% respondents choosing one or more self-regulators
% re
spon
dent
s ch
oosi
ng o
ne o
r mor
e ex
tern
al re
gula
tors
Figure 31 shows the levels of support for sharing personal and biological materials amongst biobanks in
different European countries. There is almost no association between support for international
integration of biobanks and the agents that should protect the public interest; those who are in favour of
international integration are marginally more likely (than those against) to choose international bodies
like the EU as the primary guardians, but really only marginally. Levels of support for the sharing and
exchange of biobank data between EU Member States broadly echoes levels of support for biobanks per
se.
67
Figure 31: Support for sharing and exchange of personal data and biological materials
12
6
18
14
9
23
16
14
12
12
14
20
25
21
17
14
27
19
19
17
22
21
19
25
27
31
17
25
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20
29
52
19
12
33
25
32
39
25
32
35
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31
29
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43
30
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41
37
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36
34
32
47
42
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49
40
25
34
12
26
21
11
10
21
21
15
16
19
20
11
20
13
20
15
14
16
18
23
19
13
16
17
14
19
18
17
16
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17
7
17
16
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26
8
5
19
19
14
5
22
15
13
19
9
14
10
17
5
15
7
14
12
14
11
14
14
9
10
11
9
9
4
15
47
14
10
36
38
12
12
22
28
8
14
25
6
21
10
18
12
21
9
12
9
16
11
11
12
5
9
6
5
4
5
13
15
0 20 40 60 80 100
Turkey
Austria
Germany
Romania
Bulgaria
Switzerland
United Kingdom
Ireland
Poland
Greece
Latvia
Malta
Netherlands
Croatia
Hungary
Italy
France
Portugal
Estonia
Czech Republic
Sweden
Lithuania
Slovenia
Luxembourg
Spain
Denmark
Slovakia
Norway
Belgium
Finland
Iceland
Cyprus
EU27
% respondents
Yes, definitely Yes, probably No, probably not No, definitely not Don't know
68
6. Governance and trust
This chapter provides an overview of how European citizens think about the governance and regulation
of science and technology, as well as how trustworthy they think the key actors involved in the field of
biotechnology are.
Principles of governance
Given that time and knowledge are scarce, citizens are open to the idea that sometimes the
responsibility of developing public policy should be solely in the hands of the experts, the ones who are
deemed to ‘know best’. This cannot be generalised though; some issues are deemed too sensitive to be
left solely in the hands of experts. To what degree, then, do European publics feel they ought actively to
be involved in such decisions? And to what degree do they believe that they should defer to the
judgements of experts?
In the survey, respondents were asked two forced-choice questions. First, should decision-making be left
primarily to the experts or based mainly on the views of the public? And second, should decisions be
made largely on evidence related to the risks and benefits or based on moral and ethical considerations?
In the survey a split ballot was used. Half the respondents in each country answered the questions in the
context of synthetic biology, while the other half answered the questions in the context of animal cloning
for food products. Both of these topics had been the subject of prior questions in the survey.
The pairs of questions forced respondents to make a choice between the options offered; there was no
scope for saying ‘I would like to see scientific assessment informed by ethical and moral considerations’,
or ‘I would prefer to have experts taking note of the public’s views’. The intention of the question was to
push respondents. When it comes to the crunch, who do Europeans want to make decisions and what
sort of evidence should be privileged in the decision-making process?
The responses to the questions allow us to divide the public into four ‘types’ reflecting different principles
of governance (Gaskell et al., 2005). Opting for decisions based on expert advice rather than the views
of the public, and on the grounds of scientific evidence rather than moral and ethical considerations is
labelled the principle of scientific delegation. An institutional equivalent would be, for example, an expert
commission on risk assessment. By contrast, those who want decisions to be based on scientific evidence
and to reflect the views of average citizens are opting for the principle of scientific deliberation.
Institutionally, this could be reflected in a consensus conference, where lay people discuss aspects of an
issue with the help of specialists’ expertise. By the same token, those who would prefer decisions to be
based primarily on the moral and ethical issues involved (rather than scientific evidence), and on the
advice of experts rather than the general public, we refer to as adopting a principle of moral delegation.
The respective institution would be an ethics committee. And those who prioritise moral and ethical over
scientific considerations, whilst favouring the views of the general public over those of the experts, we
69
label as adhering to a principle of moral deliberation. Such a view could best be accommodated with the
help of instruments of public deliberation such as a peoples’ initiative.
Underlying these four principles of governance are beliefs about social progress and how science and
technology should be organised towards that goal. Can experts and sound science remain the basis for
deciding the direction of progress? Is science and technology developing along the right moral and
ethical lines? Can experts be trusted to take account of the public interest? (Gaskell et al. 1998).
Tables 5 and 6 and present the results for synthetic biology and animal cloning for food products,
respectively. For synthetic biology (Figure 5), a small majority (52 per cent) of European citizens believe
that the technology should be governed on the basis of scientific delegation where experts, not the
public decide, and where evidence relating to risks and benefits, not moral concerns, are the key
considerations. However, nearly a quarter of Europeans take the opposite view: it is the public, not
experts, and moral concerns, not risks and benefits, that should dictate the principles of governance for
such technologies (the principle of 'moral deliberation').
Tables 5 and 6 tell very similar stories for synthetic biology and animal cloning in relation to the
principles of moral delegation (around 15 per cent) and scientific deliberation (around 10 per cent). But
there is an interesting contrast between synthetic biology and animal cloning in levels of support for
scientific delegation and moral deliberation. For animal cloning (compared to synthetic biology) some 10
per cent fewer opt for scientific deliberation and 9 per cent more opt for moral deliberation. It seems
that moral and ethical issues are more salient for animal cloning for food products than for synthetic
biology: altogether 38 per cent of respondents choose a position prioritising moral and ethical issues for
synthetic biology, with 49 per cent doing the same for animal cloning for food. To put this another way,
the European public is evenly split between those viewing animal cloning for food as a moral issue and
those viewing it as a scientific issue.
Table 5: Segmentation of the European public on principles of governance for synthetic biology, EU27 (DKs excluded)
Based mainly on the advice of experts
Based mainly on the general public’s view
Based primarily on scientific evidence about the risks and benefits involved
Scientific delegation 52%
Scientific deliberation 10%
Based primarily on the moral and ethical issues involved
Moral delegation 15%
Moral deliberation 23%
70
Table 6: Segmentation of the European public on principles of governance for animal cloning, EU27 (DKs excluded)
Based mainly on the advice of experts
Based mainly on the general public’s view
Based primarily on scientific evidence about the risks and benefits involved
Scientific delegation 42%
Scientific deliberation 9%
Based primarily on the moral and ethical issues involved
Moral delegation 17%
Moral deliberation 32%
Figures 32 and 33 stratify the principles of governance results by country. As can be seen 11 of the
countries have a comfortable majority (55 per cent or more) in favour of scientific delegation for
synthetic biology while only 2 have a comfortable majority in favour of scientific delegation for animal
cloning and food.
A comparison of the percentages of respondents in each country opting for moral deliberation (public
ethics) over moral delegation (institutionalised ethics) might lead to the tentative conclusion that ethics
committees have yet to gain widespread public confidence. To achieve greater public confidence, ethics
committees may need to, and be seen to, take more account of the public voice.
On the governance of animal cloning, in all countries (with the exception of Norway) a similar or larger
percentage opt for moral deliberation than for moral delegation. For synthetic biology seven mainly
north-western countries have a higher percentage opting for moral delegation – Belgium, Finland,
Sweden, Norway, Netherlands, Iceland and Malta. It would appear that apart from North Western
Europe, moral delegation to ethics committees is yet to emerge as an accepted intermediary between
the wider public and the policy process.
On the other hand, a variety of technologically highly developed countries seem to be at odds with the
default solution to dealing with scientific uncertainty. Germany, Austria, Denmark and Switzerland show
less than 30% support for scientific delegation. Apparently, sound science is not enough especially when
it comes to potentially morally contentious issues such as animal cloning. In contrast, moral deliberation
enjoys high esteem, it seems – in Austria, more than half prefer this governance principle.
71
Figure 24: Principles of governance for synthetic biology (DKs excluded)
32
35
37
38
39
39
40
42
43
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48
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52
52
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66
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0 20 40 60 80 100
Germany
Malta
Denmark
Austria
Iceland
Switzerland
Bulgaria
Slovenia
Cyprus
Ireland
Greece
Croatia
Netherlands
Latvia
Turkey
Poland
Norway
Estonia
Sweden
Portugal
Finland
United Kingdom
Luxembourg
France
Belgium
Lithuania
Slovakia
Italy
Czech Republic
Spain
Romania
Hungary
EU27
% respondents
Scientific delegation Moral delegation Scientific deliberation Moral deliberation
72
Figure 25: Principles of governance for animal cloning (DKs excluded)
23
23
26
28
30
31
34
36
36
37
37
38
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33
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28
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28
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22
35
27
35
36
43
26
30
24
36
32
24
25
26
22
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20
33
0 20 40 60 80 100
Germany
Austria
Denmark
Switzerland
Sweden
Bulgaria
Iceland
Croatia
Cyprus
France
Luxembourg
Ireland
Slovenia
Netherlands
Norway
United Kingdom
Malta
Portugal
Latvia
Greece
Finland
Poland
Estonia
Slovakia
Czech Republic
Belgium
Turkey
Lithuania
Hungary
Italy
Romania
Spain
EU27
% respondents
scientific delegation moral delegation scientific deliberation moral deliberation
It is also interesting to see whether these different preferences for the principles of governance are
related to support for technology in general as well as to specific technologies, namely GM food and
Nanotechnology. Tables 7 and 8 present the results. Table 7 suggests that support for technology in
general is lower as publics move away from scientific delegation and closer towards moral deliberation
when thinking about synthetic biology; the same is true for GM foods (a decline from 31 per cent to 17
per cent). Table 8, however, demonstrates that no clear linear relationship exists between the principles
73
of governance for animal cloning and support for technologies in general, although those who take a
moral deliberation position are evidently more sceptical of technology in general compared to others.
Nonetheless, a clear linear relationship does exist in relation to support for Nanotechnology; scientific
delegators are significantly more supportive of this technology than moral deliberators (70 per cent
compared to 50 per cent).
Table 7: Principles of governance for synthetic biology, technological optimism, and support for GM food, EU27 (DKs excluded)
Mean score on technological optimism
(additive scale 0-8, where 8 equals high optimism)
% who encourage GM food
Scientific delegation 5.5 31
Scientific deliberation 5.0 30
Moral delegation 4.8 22
Moral deliberation 4.3 17
Table 8: Principles of governance for animal cloning, technological optimism, and support for nanotechnology, EU27 (DKs excluded)
Mean score on technological optimism
(additive scale 0-8, where 8 equals high optimism)
% who encourage nanotechnology
Scientific delegation 5.4 70
Scientific deliberation 4.8 61
Moral delegation 5.1 60
Moral deliberation 4.4 50
Taken together, it appears as if scientific delegation and moral deliberation mark two extremes, with
scientific deliberation and moral delegation somewhere in between, when measured against support for
a potentially sensitive technology. In other words, scientific delegation can be expected to deliver
accepted results in those cases only where a technology is not considered sensitive. More generally, the
call for moral deliberation may be expected in those cases where a technology is particularly sensitive
with respect to public sentiments.
74
Trust in key actors
Trust is a key attribute of a functional society. Without a degree of trust and confidence in many and
varied people in charge of transport, education, food production etc, life would be more or less
impossible. As a part of the division of labour, trust allows us to delegate responsibility for our safety and
security to others. In an ideal world, trust eliminates concerns about risk. However, trust may be
challenged when the ‘other’ is thought to be insufficiently informed, incompetent, or acting purely on the
basis of self interest.
Trust is part of the equation of scientific and technological innovation, where risk and uncertainty are
often unavoidable. When failures occur people may wonder are these actors competent? Are the sources
of information credible? Are they motivated by sectional interests and do they have the public good in
mind?
During the mid-1990s, in the heydays of the controversy over various food issues such as BSE, hormone
beef or GM soya, the public was said to have lost trust in key actors for example scientists involved in
risk assessment and regulators involved in risk management. The intricate relations between trust in
responsible actors and political decision-making has made a severe impact on technology policy both at
the national and the EU level. No wonder that decision-makers are eager to secure a sufficient level of
trust in institutions and responsible persons. To this end, various measures have been implemented
aiming at increasing transparency and accountability in the pursuit of good governance and effective
policy making.
In the survey, respondents were asked:
‘Now I’m going to ask you about some people and groups involved in the various
applications of modern biotechnology and genetic engineering. Do you suppose they are
doing a good job for society or not doing a good job for society?’
Saying 'doing a good job for society' is likely to express a view that the actor is both competent and
behaves in a socially responsible way. Thus, ‘doing a good job’ constitutes a proxy measure of trust and
confidence.
Table 9 is in two parts. Shown in the first two columns is the percentage of all Europeans saying 'good
job' and 'not doing a good job' for each of the nine actors presented. ‘Don’t know’ responses are not
included in the table.
Looking at the percentages for 2010 (data columns 1 and 2), 70 to 80 per cent of Europeans have
confidence in doctors, university scientists, and consumer organisations. Between 60 and 69 per cent
have confidence in environmental groups and in newspapers and magazines. All the other actors – the
EU, industry, government and shops – attract the confidence of between 54 per cent and 59 per cent of
Europeans.
75
In the final four columns the confidence surplus or deficit is shown for 1999, 2002, 2005 and 2010. This
is the difference between the percentages saying 'doing a good job' and 'not doing a good job'; a
positive score denotes a trust surplus, while a negative score a trust deficit. For this calculation, the
‘don’t know’ responses are excluded. The index thus provides, for those Europeans who expressed an
opinion, a relative ranking of levels of confidence for comparisons across actors and across time. The
trust surplus/deficit time series index, from 1999 to 2010, shows that, broadly speaking, doctors,
university scientists and consumer organisations retain a high trust surplus and newspapers and
magazines a moderate trust surplus. Shops show a dip in trust in 2002 and again in 2005. In 2010 they
return to a surplus of 46 – the level in 1999. Respondents’ national government, environmental groups,
the European Union and industry all show sizeable increases in trust surplus since 2005 and generally
increases over the last decade. The gain in trust in industry remains most remarkable with a 62 point
rise over the period.
Table 9: Trust in key actors and trends from 1999
% in 2010
(Base: DKs included) Trust surplus/deficit (Base: DKs excluded)
Doing a good job
Not doing a good job
1999 2002 2005 2010
Medical doctors keeping an eye on the
health implications of biotechnology
78 8 72 80 79 82
University scientists doing research in
biotechnology
74 8 - 73 78 80
Consumer organisations checking
products of biotechnology
70 11 72 73 76 74
Newspapers and magazines reporting
on biotechnology
62 20 53 57 49 50
The European Union making laws on
biotechnology for all European Union
countries
58 16 - 48 42 56
Industry developing new products with
biotechnology
56 19 -12 20 41 50
Environmental groups campaigning
against biotechnology
63 15 54 56 35 62
Our government in making regulations
on biotechnology
54 20 22 27 33 46
Shops making sure our food is safe 59 22 46 39 32 46
76
Table 10: Trend in trust surplus/deficit for the biotechnology industry (DKs excluded)
Percentage 1999 2002 2005 2010
Finland 24 47 68 72
Sweden -46 -10 11 70
Belgium 9 22 61 66
Netherlands 31 35 62 64
Denmark -20 15 44 57
Luxembourg -10 18 56 56
United Kingdom -16 29 58 55
France -35 15 37 52
Spain 2 32 67 50
Austria -9 47 45 50
Portugal 31 33 41 50
Ireland -30 17 46 44
Italy -32 -3 37 44
Germany 3 20 20 32
Greece -38 23 31 10
Slovakia 68 78
Czech Republic 77 76
Latvia 71 74
Cyprus 82 73
Hungary 51 66
Poland 54 65
Lithuania 62 58
Malta 75 54
Estonia 61 46
Slovenia 40 10
Iceland 74
Romania 70
Croatia 64
Lithuania 58
Switzerland 50
Norway 46
Bulgaria 40
Turkey 38
Table 10 shows how the trust surplus/deficit for industry has changed across the countries with time
series data where it is available. Substantial increases in the trust surplus are evident in Sweden,
Denmark, UK, France, Austria, Italy and Germany. While there are recent declines in Spain, Greece,
77
Slovenia, Malta and Estonia, the broader picture is of Europeans generally much more likely to think
industry is doing a good rather than a bad job.
Figure 26: Public confidence in the 'biotechnology system' (excluding DKs)
85
87
85
77
92
91
86
91
87
84
86
94
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55
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65
58
58
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50
63
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68
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90
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73
0 50 100 150 200 250 300 350 400
Slovenia
Norway
Ireland
Turkey
Estonia
Germany
Bulgaria
United Kingdom
Greece
Italy
Austria
Denmark
France
Switzerland
Portugal
Luxembourg
Croatia
Spain
Malta
Lithuania
Poland
Belgium
Romania
Latvia
Iceland
Czech Republic
Hungary
Slovakia
Netherlands
Finland
Sweden
Cyprus
EU27
% respondents
University scientists The EU Industry Government
78
Finally in this section on trust, Figure 26 concerns the extent of public confidence in what might be called
the ‘biotechnology system’. This comprises the actors that create and regulate biotechnology – research
scientists, industry and national and European regulators (Torgersen et al. 2002).
Notwithstanding the continuing controversy over GM food and crops and respondents concerns about
various technologies that have featured in this Eurobarometer survey, there is a robust and positive
perception of the biotechnology system. It seems fair to conclude that Europeans have moved on from
the crisis of confidence of the mid to late 1990s. It is also notable that both National Governments and
the EU carry almost equivalent trust surpluses in the majority of countries. Perhaps, the idea of national
regulation within a framework of European laws is accepted amongst the publics of the European
Member States.
79
7. Familiarity and engagement with technologies
Public engagement with science and technology has been a priority area within Directorate General for
Research in the European Commission for fifteen years. Engagement with issues technological, however,
may be a double-edged sword.
On the one hand, there is a long-standing belief that familiarity with a technology increases its positive
evaluation by the public. Familiarity not only refers to the active use of a technology and its products but
also to a basic knowledge of the principles and methods involved. In other words, promoters claim that
the public not only needs to be passively confronted with the technology at stake: rather, they have to
actively engage in searching for information and dealing with the issue.
On the other hand, there is a school of thought that see the public in the driving seat when it comes to
decision-making over the implementation of a (possibly risky) technology (Sclove 1995). Since the public
will be affected, the public should decide – so the normative argument proposes. In order to be able to
decide, the public needs to engage in the issue. Views on the technology may change – not necessarily
in favour of the technology – according to levels of engagement and as people acquire knowledge about
technical risks and benefits, and also about matters of public interest and distributional fairness.
Along the history of public engagement in Europe, starting with the Danish consensus conferences in the
1980s, varieties of this ‘Danish model’ have emerged in many Member States. Consensus conferences
have been introduced with different aims in mind, ranging from lay participation in real decision-making
to mere public relation exercises. These aims reflect diverse views on the role of the public in relation to
science and technology policy running from the extremes of ‘only the elite can decide such matters’ to
policy making by referenda or popular initiatives. Underlying these extreme views, and all those positions
in between, are a number of normative and pragmatic considerations.
In the Eurobarometer survey we are interested in finding out how engagement in science and
technology, by the public themselves, relates to their views. Do those who are more active in attending
and/or finding out about issues of science and technology hold different views from those for whom such
issues are of little interest?
Familiarity and engagement with a range of technologies
Figure 35 below is an illustration of public familiarity with a range of technologies within the life sciences.
It gives the percentages of respondents who report having heard of GM foods, animal cloning for food
production, nanotechnology, biobanks, and synthetic biology, prior to the interview. The top bar
illustrates the European weighted average (EU27), followed by a separate bar for each of the 32
countries included in the survey. The countries have been ordered according to aggregate familiarity
across the five technologies, i.e. by adding together the percentages who report having heard of each of
the five technologies in question.
80
In Europe as a whole, there is widespread familiarity with both GM food and animal cloning in food
production. After more than a decade of controversy related to GM food, awareness is generally high.
Three out of four people have heard about animal cloning in food production. Since 2005, familiarity has
remained constant at about 80 per cent for GM food and about 45 per cent for nanotechnology. About a
third of Europeans have heard of biobanks. Among the five different technologies presented below,
biobanking is the area where levels of familiarity vary most between countries. For example, In Iceland,
80 per cent of the public have heard of biobanks. In Turkey, Austria, and Portugal, familiarity is less than
20 per cent. Finally, the emerging area of synthetic biology is on average not very well known in Europe.
Only 17 per cent of the European population has heard of synthetic biology.
81
Figure 27: Familiarity with five technologies: percentages of people who have heard of each technology
49
68
59
70
68
79
69
81
80
85
83
74
74
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86
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46
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55
39
49
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46
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63
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19
10
17
21
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13
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17
0 50 100 150 200 250 300 350 400
Malta
Turkey
Portugal
Romania
Austria
Bulgaria
Slovakia
Poland
Ireland
Italy
Lithuania
Belgium
Hungary
Cyprus
France
Spain
Greece
Czech Republic
United Kingdom
Estonia
Latvia
Slovenia
Iceland
Germany
Croatia
Luxembourg
Denmark
Netherlands
Switzerland
Norway
Finland
Sweden
EU27
% respondents
GM foodAnimal cloning in food productionNanotechnologyBiobanksSynthetic biology
82
Figure 27 also shows that there are significant differences between countries. There appears to be a
cluster of Nordic countries on top, including Sweden, Finland, and Norway, where familiarity is very high
across the range of technologies, and also the remaining Nordic countries, Denmark and Iceland, are
characterized by relatively high levels of awareness of these technologies. The country comparisons
indicate significant differences in familiarity with the five technologies. Least familiarity is found in Malta,
Turkey, and Portugal.
In the questionnaire, respondents who confirmed having heard of these technologies were asked two
additional, follow-up, questions concerning the extent to which they had also engaged in active
discussion or information search on the subject. So, for example, people who reported having heard of
GM food, were subsequently asked whether they had ‘talked about GM food with anyone before today’
or ‘searched for information about GM food’ either ‘frequently’, ‘occasionally’, ‘once or twice’, or ‘never’.
In general, and particularly relating to synthetic biology and biobanks, very few people state that they
have frequently talked and / or searched for information. Among the five technologies, GM food is the
area in which most people have been actively engaged, in terms of talking with other people or
searching for information.
Figure 28: Engagement with five technologies, EU27
9
18
26
46
58
8
17
20
29
26
83
66
54
25
16
0 20 40 60 80 100
Synthetic biology
Biobanks
Nanotechnology
Animal cloning for food production
GM food
% respondents
Have heard and talked and/or searched for information
Have heard but not talked or searched for information
Have not heard
In Figure 28 above, response categories have been collapsed into three simple categories, indicating the
level of public engagement with the five technologies. The three categories include those who have not
heard of the technology, those who have passively heard but not actively talked about or search for
information, and finally those who have heard and also actively talked and/or searched for information
about the technology in question. The figure shows that 58 per cent of Europeans have had some
degree of active engagement with GM food prior to the interview, 26 per cent have heard about it
without engaging actively in discussion or information search, and the remaining 16 per cent are
unfamiliar with GM food. Almost half of the European population has actively engaged with animal
cloning, around 25 per cent of Europeans have actively engaged with nanotechnology, whereas only 18
83
per cent and 9 per cent have talked and/or search for information about biobanks and synthetic biology
respectively.
Engagement and affective reactions
In the survey, respondents were asked to what extent they agreed or disagreed with a number of
statements concerning animal cloning in food production, GM food, and nanotechnology. For each of
these areas in turn, respondents were asked to indicate whether they would ‘totally agree’, ‘tend to
agree’, ‘tend to disagree’ or ‘totally disagree’ that such technological applications are ‘fundamentally
unnatural’ and ‘makes you feel uneasy’. The responses to these two statements provide a measure of
what could be considered an affective dimension: do these technologies, i.e. animal cloning in food
production, GM food, and nanotechnology invoke anxiety or concern? Are Europeans worried about such
technological applications?
We might have expected that familiarity and active engagement with these technologies would have a
positive impact on the affective dimension, in the sense that those Europeans who had heard, actively
discussed and/or searched for information about animal cloning in food production, GM food, and
nanotechnology prior to the interview would be least worried about these technologies. In many
situations, people tend to be more concerned or worried about the issues that they are least familiar
with and least well-informed about. What the survey demonstrates, though, is that the relation between
familiarity on the one hand and unease on the other hand is not straightforward, but depends on the
particular technology in question.
Figure 29 below gives the average scores on an index of ‘worry’ related to the three technologies. The
index ranges from -1.5 to 1.5. Average scores above 0 indicate, that more people tend to agree that the
technologies are ‘fundamentally unnatural’ and ‘makes you feel uneasy’ and fewer people tend to
disagree with these statements. By comparing the average scores of those who have not heard, those
who have heard but not actively talked or searched for information, and those who have actively talked
or engaged in information search, we see some striking differences between technologies.
84
Figure 29: 'Worry' index for three technologies, by level of engagement, EU27
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Animal cloning in foodproduction
GM food Nanotechnology
Ave
rage
(mea
n) s
core
Have not heardHave heard but not talked or searched for informationHave heard and talked and/or searched for information
For nanotechnology, higher levels of familiarity and engagement clearly have a soothing effect on
Europeans. Among those who have not heard of nanotechnology, the average score on the index is
0.23, which means that in this group most people tend to agree that nanotechnology is fundamentally
unnatural and makes them feel uneasy. Among those who have heard of nanotechnology, but not
actively talked or searched for information, the average score is -0.01, which means that about an equal
amount of people either agree or disagree that nanotechnology is worrying. Finally, the average score
among those who have actively talked about nanotechnology or searched for information is -0.22,
indicating that in this group most people tend to disagree that nanotechnology is unnatural and ‘makes
you feel uneasy’. In the case of nanotechnology, then, the differences between these groups
demonstrate that higher levels of familiarity and engagement significantly reduce the extent of worrying
about nanotechnology.
For GM food and animal cloning in food production, the picture is rather different. First, on average
people are more affected by these technologies, and those who agree that animal cloning in food
production and GM food are ‘fundamentally unnatural’ and ‘makes you uneasy’ outnumber those who
disagree, irrespective of the level of familiarity and engagement. Furthermore, the relationship between
engagement and concern for these technologies is opposite to nanotechnology. For GM food and animal
cloning in food production, people who are most familiar and engaged are also those who worry the
most. These biotechnological applications appear to be so sensitive and controversial that higher levels
of involvement accelerate concern rather than ease the worry. This could well be part of the explanation
for the continued disapproval of GM food among the European public. Rising levels of familiarity over
time does not lead to less concerns, in fact the opposite seems to be the case.
85
Engagement, risks and benefits
Similarly to the analyses of affective reactions to nanotechnology, GM food and animal cloning in food
production, also perceptions of risks and benefits are related to levels of familiarity and engagement.
In the 2010 barometer results, risk or safety has been a recurring and dominant issue in the way that
the European public relates to controversial technologies. In the questionnaire, three statements
particularly tap into the perceived safety of nanotechnology, GM food, and animal cloning in food
production. For each of these areas, respondents were asked to which extent they agree that the
technologies are ‘safe for future generations’, ‘safe for your health and your family’s health’, and ‘does
no harm to the environment’. Combined, these statements function as an index of ‘safety’, and
equivalent to the index of affect described above, the index for perceived safety ranges from -1.5 to 1.5,
with average scores above 0 indicating, that a majority of people agree that the technologies are safe,
and scores below 0 indicating that most people disagree that the technologies in question are safe.
Figure 30 shows that Europeans clearly on average tend to disagree that GM food and animal cloning in
food production are safe technologies, no matter how familiar they are with them. There are modest
differences between people who are unfamiliar and people who are more actively engaged. With regard
to animal cloning in food production, the engaged Europeans find this technology slightly safer than
people who have not heard of it at all. For GM food the relation is opposite, but also in this case the
differences between the active, information-searching segment of the population and those who are
unfamiliar with GM food, is modest.
Figure 30: 'Safety' index for three technologies, by level of engagement, EU27
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Animal cloning in foodproduction
GM food Nanotechnology
Ave
rage
(mea
n) s
core
Have not heardHave heard but not talked or searched for informationHave heard and talked and/or searched for information
86
Again, nanotechnology stands somewhat out. In relation to nanotechnology, levels of familiarity and
engagement clearly have an impact on perceived safety. Those who had not heard of nanotechnology
before the interview are less convinced that nanotechnology is safe, and the majority of people within
this group disagrees that nanotechnology is safe for future generations, the environment, and their own
and their family’s health. People, who have heard, but not actively talked or searched for information
about nanotechnology are fairly more likely to agree that nanotechnology is safe, and in the final group
of actively engaged respondents, a small majority tend to agree that nanotechnology is safe.
With regard to perceived benefits of nanotechnology, GM food, and animal cloning in food production,
these are similarly measured on an index, based on two statements, namely that the technology ‘is good
for the national economy’ and ‘is not good for you and your family’. The latter statement is reversed in
the overall index, which ranges from -1.5 to 1.5, so that scores above 0 indicate agreement that the
technology is beneficial and scores below 0 indicate disagreement.
Figure 31: ‘Benefits' index for three technologies, by level of engagement, EU27
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Animal cloning for foodproduction
GM food Nanotechnology
Ave
rage
(mea
n) s
core
Have not heardHave heard but not talked or searched for informationHave heard and talked and/or searched for information
Consistent with the previous results, the public assessment of nanotechnology differs from GM foods and
animal cloning in food production, and again, the level of engagement plays a significantly more
important role for Europeans’ perceptions of benefits in the case of nanotechnology. People who have
not heard of nanotechnology prior to the interview have an average score of 0.02 on the index, which
means that about an equal amount of these people either agree or disagree that nanotechnology is
beneficial. As familiarity increases, so do perceived benefits. People who have heard of nanotechnology
prior to the interview on average score 0.21 on the index, while those who have actively talked or
searched for information have an average score of 0.39, indicating that a majority in these groups agree
that nanotechnology is beneficial.
87
For both GM food and particularly animal cloning in food production, a vast majority disagrees that these
technologies are beneficial, irrespective of their level of familiarity and engagement with these
technologies. There are almost no differences between those who are unfamiliar and those who have
some degree of familiarity with animal cloning for food production, when it comes to assessing benefits.
For GM food, those who have not heard of it before the interview tend to be a bit less sceptical about
benefits than those who knew about GM foods before the interview.
On the whole, public familiarity and engagement with technologies appear to have a significant impact
on assessment in the case of nanotechnology. Those who know about and actively engage in
nanotechnology tend to be much more inclined to perceive of nanotechnology as safe and beneficial and
something not to worry about. On the other hand, when it comes to the two controversial
biotechnologies, GM food and animal cloning in food production, levels of familiarity and engagement
play a minor role for perceptions. These technologies generally invoke worry, and are perceived as less
beneficial and safe.
88
8. Pillars of truth: religion and science
Both science and religion are used as the basis of statements about the ‘truth’. But what happens when
these ‘truths’ collide? Religious authorities have made claims for the virtues of creationism and intelligent
design, and for these subjects to be included in the school curriculum in science. Others claim that a
collection of pluripotent stem cells are a human being, and that whatever the possible benefits, human
embryonic stem cell research should not be countenanced. From the scientific perspective, the positivists
have long argued that scientific truths trump any other form of knowing. Some scientific authorities have
argued that religion is at best wishful thinking, at worst a pernicious force in society. Such competition is
not, of course, inevitable. There are both scientists and religious leaders who see no intrinsic conflict
between these two pillars of the truth. But how do such positions play out with the public? How do
people in the major religious denominations of Europe view science, and what is the impact of the
strength of religious adherence on such views? In terms of views about science and technology, does a
scientific family background make a difference? And what is the impact of education in science from
school to university?
In the Eurobarometer respondents were asked questions about their religious denomination, their
religious beliefs, and behaviours. We explore the association between these facets of religion and a
selection of indicators of attitudes and beliefs about science and technology: generalised optimism and
pessimism about technologies; principles of governance for synthetic biology and animal cloning for food
products; and overall support for nanotechnology and GM food. Note that in this chapter the summaries
of Europe-wide responses are given for the 32 countries in the sample, rather than just for the 27
current Member States. This approach allows us to gain the maximum amount of information about
Muslim respondents, who are in very small numbers in all countries but Turkey.
Generalised technological optimism and pessimism
Technological optimism is based on a simple count of the number of technologies (see Chapter 1)
respondents say will ‘improve our way of life’. Similarly, technological pessimism is a count of the
number of technologies that respondents say will ‘make things worse’. As can be seen from Figure 32,
the non-religious are the most optimistic, while Muslim respondents are least optimistic. But Figure 32
also shows that Muslims are, along with the non-religious, the least pessimistic. The most pessimistic are
the adherents to the Orthodox Church. That said, apart from the difference in optimism between the
non-religious and Muslims, the other contrasts are relatively small, providing little basis for claims of
cleavages in the culture for science based on religious denomination.
89
Figure 32: Index of generalised optimism and index of pessimism, by religious denomination, 32 European countries (DKs excluded)
0
1
2
3
4
5
6
Non-religious Protestant Catholic Orthodox Muslim
Denomination
Ave
rage
(mea
n) s
core
Optimism Pessimism
We now look at responses to two questions that address the possible dilemma between science and
ethical positions. From the battery on regenerative medicine we take two questions and Figures 32 and
33 show how people in the different denominations responded:
It is ethically wrong to use human embryos in medical research even if it might offer
promising new medical treatments
Should ethical and scientific viewpoints on regenerative medicine differ, the scientific
viewpoint should prevail
Figure 33: Ethical objection to human embryonic stem cell research, by religious denomination, 32 European countries (DKs excluded)
12
21
19
23
36
24
28
32
35
29
39
32
34
30
24
25
19
15
12
11
0 20 40 60 80 100
Non-religious
Protestant
Catholic
Orthodox
Muslim
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree
90
Figure 33 shows that the non-religious are, by a considerable margin, more likely to disagree that human
embryonic stem cell research is ethically wrong. A majority of 64 per cent are, by implication, prepared
to support stem cell research if it offers medical treatments. Those most likely to agree that stem cell
research is ethically wrong are the Muslims, Orthodox Christians and Catholics. But, what is also striking
is that 35 per cent of Muslims, 42 per cent of Orthodox Christians and 49 per cent of Catholics support
stem cell research on what appears to be utilitarian grounds – potential health benefits outweighing
ethical concerns.
Figure 34: Should science prevail over ethics? By religious denomination, 32 European
countries (DKs excluded)
12
11
18
13
28
32
40
37
42
29
35
34
30
28
26
22
15
14
16
16
0 20 40 60 80 100
Protestant
Orthodox
Non-religious
Catholic
Muslim
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree
Figure 34 shows that across all the religions and amongst the non-religious, opinion is divided as to
whether, in a conflict between science and ethics, the scientific view should prevail. For the Muslims,
Catholics and the non-religious there is a slight majority leaning towards science, the Orthodox Christians
are equally divided, and among the Protestants the majority leans towards ethics. All in all, the striking
finding is of differences of opinion within the religious denominations and within the non-religious, rather
than differences between the religious and non-religious.
Now what of religious commitment? Here we take frequency of religious attendance in all the major
denominations as a proxy for commitment and look again at the above two questions about the ethics of
stem cell research and conflict between ethics and science.
91
Figure 35: Ethical objection to human embryonic stem cell research, by religious attendance, 32 European countries (DKs excluded)
17
15
18
23
29
24
29
34
31
33
35
39
33
31
26
24
17
15
15
12
0 20 40 60 80 100
Never
Once a year or less
Only on special holydays
Every one to threemonths
Every week or moreoften
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree
As can be seen in Figure 35, 62 per cent of those attending a religious service once a week or more
often are opposed to stem cell research even if it promises new medical treatments. And as the
frequency of religious attendance declines so do fewer people oppose stem cell research. That said it is
only among those who attend services once a year or less do we see a majority supporting stem cell
research. But, once again it is notable that even among the most committed a substantial minority – 38
per cent for ‘every week or more often’ and 46 per cent for ‘once in every one to three months’ resolve
the dilemma in favour of stem cell research.
What about our second dilemma – how should a conflict between science and ethics be resolved? Figure
44 shows, as we have seen before, that the greater the religious commitment the less are people
inclined to resolve such a conflict in favour of science. For those attending a service ‘every week of more
often’ the proportion is 5.5 to 4.5 in favour of ethics. At the other pole, of those who never attend a
service the proportion is the mirror image – 5.5 to 4.5 in favour of science.
All in all, the non-religious are more optimistic about the contribution of technologies in the improvement
of everyday life and are more likely to support human embryonic stem cell research. But when faced
with a conflict between science and religion they are almost evenly split on which pillar of the truth
should prevail – not that different to the major European religious denominations. Religious commitment
appears to be associated with greater concerns about ethical issues in stem cell research and with a
belief that ethics should prevail of scientific evidence. However, here again there are many highly
religious people who say that science should prevail in such a conflict of opinion.
92
Figure 36: Should science prevail over ethics? By religious attendance, 32 European countries (DKs excluded)
20
14
15
13
15
35
41
43
39
31
29
32
29
29
29
16
13
13
19
24
0 20 40 60 80 100
Never
Once a year or less
Only on special holydays
Every one to threemonths
Every week or moreoften
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree
Although it is clear that religious commitment is related to an ethical rather than a scientific orientation
(Figure 36) this could be due, in part, to other factors. For example older people or possibly women,
categories of people that tend to be less supportive of science and technology may be more likely to
frequently attend religious services. Multiple regression allows us to investigate such hypotheses. Here
responses to the question concerning the priority given to ethics or science are ‘predicted’ using three
indicators – age, gender and religious commitment. We find that age is not a significant predictor but
both gender and the frequency of religious attendance are separately highly significant. Being female
and attending a religious service once a week or more are strongly related to the tendency to prioritize
ethics over science. While this does not explain what actually leads people to this position, it does show
that attributing the effect solely to religious commitment is overly simplistic. Other characteristics of the
individual outside the scope of our survey are implicated.
Scientific background and education The last 20 years have seen a number of debates around the topic of science literacy. These range from
normative assertions about the need for citizen to know about matters scientific in order to participate in
the democratic process; concerns about the decline in the teaching of science and the rise in meta-
physical beliefs and the popularity of pseudo science, and the absence of scientific literacy feeding
resistance to scientific and technological innovation.
Attempts to measure science literacy have been controversial amongst the social scientific community
interested in science and technology. Miller and Durant were early initiators of the measurement camp
using a quiz format to assess people’s knowledge of scientific facts (Miller 1998). In a recent meta-
analysis Allum and colleagues showed a small but consistent positive correlation between various
measures of science literacy and support for science and technology (Allum et al. 2008). The critics of
93
this approach argue that factual knowledge is but a small component of the understanding of techno-
science. This approach to science literacy supports the infamous ‘deficit model’ cultivating a caricature of
the public as ignorant, distrustful and risk averse, and points the finger of blame, for example for the
problems over GM food, exclusively on the public and away from systemic institutional and political
failings in the governance of science – a democratic deficit (Jasanoff 2000).
Yet, there are still interesting questions to be asked about the drivers of support and resistance to
science and technology. Why are some people more optimistic about technological innovation than
others? Why are some more relaxed about risk? As we have seen religious beliefs may play a part, but
the part is far more complex than a simple religion versus science equation. What of family background
and education?
In the Eurobarometer respondents were asked two questions about their family background and their
education in matters scientific. First, respondents were asked:
Does/Did any of your family have a job or a university qualification in natural science,
technology or engineering (for instance, physics, chemistry, biology, medicine)?
Figure 37 shows the percentages of respondents across six age groups who say that their mother and/or
father, or another family member, had such a job or qualification.
Figure 37: Parental and family university education/work in science, by age group of respondent, EU27 (DKs excluded)
2
2
2
4
8
8
17
20
17
15
14
13
81
79
81
81
78
79
0 20 40 60 80 100
65+
55-64
45-54
35-44
25-34
15-24
Age
gro
up
% respondents
Mother and/or father Other family member No family member
94
As can be seen in Figure 37 about 1 in 5 people, regardless of age, come from a family background in
which their father, mother or another member of the family have a job or university training in science.
Across the sample as a whole 4 per cent have a parent educated or working in science and 17 per cent
have other family members with similar experience. It is notable that the prevalence of parents with
scientific experience increases among the younger age categories, increasing from 1 in 50 of those aged
65+ to 1 in 12 for the 15-24 year olds.
Now, what about the respondents themselves? In the survey the relevant question was:
Have you ever studied natural science, technology or engineering: at school, in college, in the
university or anywhere else?
With this question we divide the sample into those who have studied science at a university and those
who have not. As can be seen in table 9 around 8 per cent of Europeans have studied science at
university level. While 10 per cent of the 25-34 year olds have a university science education, not
unexpectedly it is lower, at 8 per cent, for the 15-24 year olds presumably because some of the latter
age group are not old enough to go to university.
Table 11 shows the prevalence of science education across the age groups.
Table 11: Percentages of science graduates by age group, EU27
Age group % respondents
15-24 8
25-34 12
35-44 8
45-54 7
55-64 9
65+ 5
Now, it is probably not unreasonable to expect that those socialised in a ‘scientific family’, or having
studied science at university will be not only more familiar with issues in science but also more
supportive of science led innovation. But the question remains by how much more, and do socialisation
and education have different impacts?
Figure 38 considers technological optimism and pessimism. On both counts those with a parent or other
family member, and those who have studied science at university are more optimistic and marginally less
pessimistic about the impact of the seven technologies than those with no family member educated or
working in science and those who have not studied science at university. Interestingly, there are no
differences in optimism between those respondents with a parent versus another family member
educated or working in science.
95
Figure 39 shows that a science degree is associated with both greater optimism and lower pessimism
compared to those without a science degree.
The contrast between the socialisation effect (Figure 38) and the educational effect (Figure 39) is rather
striking. Studying science at university is associated with significantly higher optimism and lower
pessimism compared to family socialisation in science.
Figure 38: Technological optimism and pessimism, by science in the family, EU27
0
1
2
3
4
5
6
No family member Other family member Mother and/or father
Science in the family
Ave
rage
(mea
n) s
core
Optimism Pessimism
Figure 39: Technological optimism and pessimism, by science education, EU27
0
1
2
3
4
5
6
No science degree Science degree
Studied science
Ave
rage
(mea
n) s
core
Optimism Pessimism
96
So far, we find that both family background in science and university education in science are associated
with respondents reporting greater optimism about science and technology. How do these groups
compare with others in their views about the governance of science. In the survey, respondents were
asked two questions. First, should decision-making be left primarily to the experts or based mainly on
the views of the public? And second, should decisions be made largely on evidence related to the risks
and benefits or based on moral and ethical considerations?
The responses to the two questions allow us to divide the public into four ‘types’ reflecting different
principles of governance. Opting for decisions based on expert advice rather than the views of the public,
and on the grounds of scientific evidence rather than moral and ethical considerations is labelled the
principle of scientific delegation. By contrast, those who want decisions to be based on scientific
evidence and to reflect the views of average citizens are opting for the principle of scientific deliberation.
By the same token, those who would prefer decisions to be based primarily on the moral and ethical
issues involved (rather than scientific evidence), and on the advice of experts rather than the general
public, we refer to as adopting a principle of moral delegation. And those who prioritise moral and ethical
over scientific considerations, whilst favouring the views of the general public over those of the experts,
we label as adhering to a principle of moral deliberation.
The survey involved a split ballot in which half the sample was asked about the governance of animal
cloning, while the other half was asked about synthetic biology. Hence we have two independent views
on the governance of these technologies. Table 12 shows the relevant percentages. Looking at the two
tables we see that opting for either moral delegation or scientific deliberation is not affected by studying
science at university. For both animal cloning and synthetic biology around 1 in 5 favour moral
delegation and about 1 in 11 favour scientific deliberation; whether a person has a degree in science or
not makes relatively little difference.
However, the contrast between moral deliberation and scientific delegation is rather striking. Those with
a science degree are 10 per cent more likely to opt for scientific delegation and about 10 per cent less
likely to chose moral deliberation compared to those without a degree in science. Hence, it may be
concluded that the study of science at university is associated with greater confidence in governance by
scientifically trained experts.
But having said that, it is worth noting that for animal cloning, 43 per cent of those with a science
degree opt for either moral deliberation or moral delegation. By implication, they recognise the moral
dimensions of animal cloning for food products and believe that the governance of this technology
should prioritise these. To a lesser extent we find the same for synthetic biology. Here 30 per cent of the
science graduates want to see the moral issues reflected in the governance of this technology. By the
same token, it is worth emphasising that amongst those without a degree in science 41 per cent opt for
scientific delegation in the case of animal cloning and 51 per cent in the case of synthetic biology.
97
Table 12: Principles of governance for animal cloning and synthetic biology by science education, EU27 (DKs excluded)
No science degree Science degree
% respondents Animal cloning Synthetic biology Animal cloning Synthetic biology
Moral deliberation 34 24 22 13 Moral delegation 17 15 21 17 Scientific deliberation 9 10 7 8
Scientific delegation 41 51 50 62 In a final analysis in this section we look at support for a familiar technology GM food and a less familiar
one nanotechnology. Many believe that if only the public knew more about science and technology, they
would be more willing to support innovation and be less prone to be influenced by the siren voices of
opposition. Thus we continue our analyses by asking whether socialisation in a scientific family and/or a
university education in science associated with more support for these two technologies?
First we look at family background in Figure 40. For nanotechnology support rises from 60 per cent for
those without a family background in science to 63 per cent with another family member educated or
working in science, and to 73 per cent for those respondents whose father and/or mother are educated
or work in science. The respective percentages for GM food (see Figure 41) are 26, 30 and 37. Clearly
exposure to science in one’s family background is associated with more support for both nanotechnology
and GM food. But, as noted in the earlier analyses, the issue is not black and white. While those with a
mother and/or father working or educated in science are the most supportive of GM food, a majority –
63 per cent – do not agree that the development of GM food should be encouraged.
Figure 40: Support for nanotechnology, by family science background, EU27 (DKs excluded)
12
16
29
48
47
44
26
23
21
14
14
6
0 20 40 60 80 100
No family member
Other family member
Mother and/or father
Fam
ily s
cien
ce b
ackg
roun
d
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree
98
Figure 41: Support for GM food, by family science background, EU27 (DKs excluded)
5
7
9
21
23
28
33
33
33
40
38
30
0 20 40 60 80 100
No family member
Other family member
Mother and/or fatherFa
mily
sci
ence
bac
kgro
und
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree
Looking at the impact of a science degree we find a rather similar picture – see Figure 42. Science
graduates are more supportive of nanotechnology than those without a science degree – 75 per cent
compared to 59 per cent respectively. The same pattern is observed for GM food. Support for GM food is
found among 34 per cent of science graduates compared to 26 per cent of those without a science
degree.
But, once again a majority of science graduates – 65 per cent – do not support the development of
GM food.
Figure 42: Support for GM food and nanotechnology, by science education, EU27 (DKs
excluded)
5
9
12
25
21
25
47
50
33
32
26
17
40
33
14
8
0 20 40 60 80 100
No sciencedegree
Science degree
No sciencedegree
Science degree
GM
food
Nan
otec
hnol
ogy
% respondents
Totally agree Tend to agree Tend to disagree Totally disagree
99
Broadly speaking these analyses show that socialisation in a scientific family and having a university
education in science are associated with greater optimism about science and technology, more
confidence in regulation based on scientific delegation, and more willingness to encourage the
development of both nanotechnology and GM food. However, the analyses also show that scientific
socialisation either in the family or at university is not a magic bullet – it is not the panacea to the issue
of resistance to innovation. A majority of those coming from a scientific family background or with a
degree in science are not willing to support the development of GM food.
Religion and Science Education These analyses point to some fairly consistent associations between views about science and technology
and both religious beliefs and commitment, and university education in science.
On average, compared to those respondents who say they are non-religious or atheist, those who say
they are a member of one of Europe’s major religious denominations are less optimistic about science
and technology’s contribution to a better future, less supportive of hESC research, and more likely to
support governance based on ethics rather than science. By contrast, science graduates and those with
one or other parent educated or employed in science related activities compared to others are more
optimistic about science and technology, have more confidence in regulation based on scientific
delegation, and more willingness to encourage the development of both nanotechnology and GM food.
But while these are consistent trends, they are also consistently underwhelming in size. In all the groups
under consideration – the religious and the non-religious, those from scientific families or not, and those
with a degree in science or not, there are many that depart from the ‘consistent pattern’. So, some with
religious beliefs and devotional commitment seem to show solid support for science, and some science
graduates are very concerned about ethics and far from supportive of GM food. To this extent, any
generalisations from these findings on the role of religion and education in cultivating views about
science should not be overstated.
100
9. Climate change
In this section we turn to a theme affecting numerous issues addressed in this report - climate change,
global warming and sustainability. As we saw in chapter 1, all the energy technologies included in the
index of technological optimism – wind, solar and nuclear power – are increasingly believed to be likely
to improve our way of life over the next 20 years – an indication, perhaps, of public anxieties about the
impacts of climate change. Yet, while many scientists and political figures are also anxious and debates
highlight the need for action, the conference on climate policy in Copenhagen in autumn 2009 failed to
agree a compromise to take matters beyond the Kyoto protocol of more than a decade ago. Indeed the
parallel world-wide citizens’ conference on climate change arrived at more radical views than most
politicians would dare to countenance.
What do European citizens believe needs to be done about global warming and climate change?
In the survey, respondents were offered two possible ways of dealing with climate change and asked to
indicate which was closest to their opinion:
• Technology will stop climate change and global warming so we can maintain our way of
life and economic growth
• To halt climate change and global warming, we have to rethink ways of living even if it
means lower economic growth.
Figure 43 shows the percentages for the options chosen across EU 27 and for each country. There are
two trends of particular note.
First, there are relatively few ‘don’t know’ responses to this question - these range from 2 per cent in
Finland to 19 per cent in Lithuania and Ireland. It is remarkable that there are 20 countries with less
than 10 per cent of respondents answering don’t know. This suggests that in the light of the ten or more
years of debate about climate change, much of it mired in a seemingly inextricable entanglement of
conflicting interests, the European public feels ready to take a stance.
And second, the European public take a radical stance. Respondents in all countries except two – Latvia
and Malta – select the option of changes in ways of living over technological solutions, even if this means
reduced economic growth. Across EU27 more than two to one favour this option. In only seven countries
(Bulgaria, Poland, Estonia, Lithuania, Romania, Latvia and Malta) is support for the ‘changing ways of
life’ solution below the ‘comfortable majority’ threshold of 55 per cent. It is also of note that in eight of
the wealthier European countries support for changing life styles is above 70 per cent.
Of course, there is often a gap between ‘what people say and what people do’, particularly in social
surveys where the cost of answering a question in a socially desirable way is minimal. But taking into
account other findings in this Eurobarometer on optimism about energy technologies and support for
101
sustainable biofuels, we suggest that converging lines of inquiry point to a recognition that something
needs to be done about climate change and that both society and technology has a contribution to
make.
Figure 43: Favoured solutions for halting climate change
36
45
49
51
52
53
53
56
57
58
58
59
60
62
64
64
64
64
64
64
65
65
66
69
71
71
71
71
73
78
80
83
64
52
46
34
30
35
30
36
25
25
36
31
37
30
32
19
28
32
31
32
29
31
24
29
22
27
24
22
24
19
19
12
15
26
12
9
17
19
13
18
11
19
18
6
11
4
11
5
18
9
4
5
4
7
4
11
5
9
2
4
7
5
8
4
8
2
10
0 20 40 60 80 100
Malta
Latvia
Romania
Lithuania
Estonia
Poland
Bulgaria
Ireland
Portugal
Czech Republic
United Kingdom
Slovakia
Italy
Hungary
Turkey
Croatia
Cyprus
Belgium
Denmark
Norway
Iceland
France
Netherlands
Spain
Greece
Austria
Luxembourg
Sweden
Switzerland
Slovenia
Germany
Finland
EU27
% respondents
To halt climate change and global warming, have to rethink ways of living even if it means lower economic growth
Technology will stop climate change and global warming so we can maintain our way of life and economic growth
Don't know
102
Perceived consensus and policy expectations regarding the ‘changing ways of life’ solution
Overall, the support for the view that there is a need for changing lifestyles – even if this implies reduced
economic growth – is impressive. Do respondents perceive their views to be consensually shared and do
they think that their views will be adopted by politics in their country? Interestingly, whatever view on
climate change respondents hold, the majority is likely to assume that others share their views and that
their views will be reflected in national policies. Out of those who think that technology will stop climate
change, 58 per cent think that many other people share their views and 51 per cent assume that their
views will be adopted by their country’s policies (29 per cent and 36 per cent respectively do not think
so, with the remaining respondents saying that they don’t know). Out of those who think that a change
of life is needed to stop climate change, 63 per cent think that their views are shared by others but only
48 per cent think that their country will adopt their preferred policy (26 per cent and 37 per cent do not
think so). Given that an individual’s beliefs are reinforced by the support – actual or perceived - of
others, that so many believe that others share their views, is an indication of just how difficult is the task
of changing beliefs about climate change.
The expectations of the public concerning their government’s decisions are important in terms of future
social debate. When the publics have clear preferences for certain solutions and do not expect
governments to implement them, more social controversy and debate are to be expected. Figure 44
shows that in some countries (Finland, Switzerland, Greece, Sweden, Austria, Iceland) there is both a
strong preference for the ‘changing ways of life’ solution and high confidence that the country will adopt
policies consonant with this solution. In other countries, respondents – although strongly supporting the
‘changing ways of life’ solution – are less confident they will see corresponding policies (Germany,
Slovenia, Spain, France). Countries like Latvia, Romania, Estonia or Malta show a lower preference for
the ‘changing ways of life’ solution, but also low expectations regarding a consonant public policy.
103
Figure 44: Preference for 'changing ways of life' solution to climate change and confidence that one's government will adopt such policies, by country
NorwaySwitzerland
Croatia
Iceland
Turkey
Romania
Bulgaria
Slovenia
Slovakia
Poland
Malta
LithuaniaLatvia
Hungary
Estonia
Czech Republic
Cyprus
United Kingdom
Sweden
Portugal
Austria
Netherlands
Luxembourg
ItalyIreland
France
Finland
Spain
Greece
Germany
DenmarkBelgium
30
40
50
60
70
80
90
100
30 40 50 60 70 80 90 100
% respondents choosing 'changing ways of life' solution to climate change
% re
spon
dent
s w
ho t
hink
('de
finite
ly' o
r 'pr
obab
ly')
that
thei
r ow
n co
untry
will
ad
opt p
olic
ies
in li
ne w
ith th
eir v
iew
Note: the two lines added mark the ‘comfortable majority’ threshold (55 per cent) for ease of
interpretation.
104
10. Public ethics, technological optimism and support for biotechnology
This analysis and interpretation of the Eurobarometer 73.1 is a component of an EU funded project
Sensitive Technologies and European Public Ethics (STEPE)14. In the analysis of the survey we have
looked at the wider picture using summary scores across the EU27 countries, and presenting graphics
that show comparative data for the individual 32 countries. In this final chapter, we return to the
project’s wider goal of investigating European public ethics, and we do so using a statistical technique –
cluster analysis – that allows us to identify groups of countries that share broadly similar views on moral
and ethical issues in relation to science and technology.
The analysis is based on those questions in the survey that addressed moral and ethical sensitivity:
• The percentage of respondents who think that in a disagreement between science and ethics
in the context of regenerative medicine, the ethical view should prevail (ethics over science or
science over ethics).
• For GM food, nanotechnology and animal cloning, the average level of concern about
distributional fairness – whether ‘it will benefit some people but put others at risk’ and whether
‘it will help people in developing nations’. Rather than ‘distributional equity’ we call this
distributional fairness.
• The percentage of respondents who would want to know about the moral and ethical issues
involved in synthetic biology if they were deciding how to vote in a referendum (interest in
ethics).
• The percentage of respondents who think that the governance of science, in relation to
synthetic biology, and separately, animal cloning, should be based on moral and ethical
considerations rather than scientific evidence (moral governance versus scientific governance).
It is important to appreciate that cluster analysis is a procedure for summarizing a variety of data
sources. It provides a number of possible ‘solutions’, identifying different numbers of clusters, from
which the researcher chooses the most interpretable. In this sense, the outcome of a cluster analysis is
tentative and provisional. For our analysis, we selected a five-cluster solution for the 32 countries. Each
cluster comprises a set of countries, described in Table 13 below.
14 Funded by the Science in Society Programme of the EU’s Seventh Framework Programme for Research and Technological Development. For more information on STEPE, see http://www.stepe.eu
105
Table 13: Public ethics: five clusters
Cluster Countries Profile Sensitivities and
place of science
1 Belgium, Czech
Republic, Estonia,
France, Slovakia,
Sweden and UK
• Low concern over distributional fairness
• Balanced on governance of science
• Moderate interest in ethics
• Science over ethics
Interest in ethics
Science 1st
2 Croatia, Finland,
Latvia,
Luxembourg,
Norway, Poland,
Portugal, Turkey
• Moderate concern about distributional
fairness
• Balanced on scientific governance
• Low interest in ethics
• Science over ethics
Distributional fairness
Science 1st
3 Hungary, Italy,
Lithuania,
Romania and
Spain
• Moderate concern about distributional
fairness
• Scientific governance
• Low interest in ethics
• Science over ethics
Science 1st
Low to moderate
interest in ethical
issues
4 Austria, Bulgaria,
Cyprus, Germany,
Greece, Slovenia
and Switzerland
• High concern about distributional
fairness, particularly about GM food
• High support for moral governance
• Moderate interest in ethics
• Ethics over science
Distributional fairness
Science 2nd
5 Denmark, Iceland,
Ireland,
Netherlands and
Malta
• Low fairness concerns, particularly for
GM food
• Moral governance
• High interest in ethics
• Ethics over science
Moral governance
Science 2nd
We must take care in interpreting these clusters. First, Europe does not present a level playing field
when it comes to matters of science and society. Some countries have a longish history of bringing moral
and ethical issues into science; others have not. Equally, what constitutes ‘ethical concerns’ may vary
across countries due to their wider history and more specific experiences with science and technology.
For example, Austria’s referendum in 1996 set in train a long history of sensitivities around genetic
modification, and in the UK, the Human Embryology and Fertilisation Authority facilitated the
development of regenerative medicines well in advance of many other European countries.
Table 14 shows some quite nuanced differences between the clusters. Countries in cluster 4 are
characterized by a wide ranging moral and ethical imperative, while countries in cluster 5 are interested
in ethical issues, but apparently not concerned about distributional fairness.
106
In contrast to clusters 4 and 5, the countries in clusters 1, 2 and 3 all prioritise science over ethics.
Clusters 2 and 3 differ from countries in cluster 1 by a greater concern about issues of distribution
fairness. And in cluster 1 we see a greater interest in the ethical implications of synthetic biology in
comparison to clusters 2 and 3.
How do these patterns of ethical concerns relate to levels of support for science and technology? To
investigate this, we take three indicators:
• Technological optimism - the number of technologies that people say would improve our
way of life (optimism)
• Support for GM food, nanotechnology and animal cloning for food products - total
percentage of supporters (bio-nano)
• Support for the various regenerative medicines – see Chapter 4; total percentage of
supporters (regenerative medicine)
Table 14: Public ethics and support for biotechnology
Cluster Sensitivities Optimism Support for
bio-nano
Support for
regenerative medicine
1 Interest in ethics
science 1st
High High High
2 Distributional fairness
Science 1st
Medium Medium Low
3 Science 1st
Medium Medium Medium
4 Distributional fairness
Science 2nd
Low Low Low
5 Moral governance
Science 2nd
Medium High High
Table 14 shows some interesting associations between public sensitivities and levels of support for the
technologies. Cluster 4, predominantly German speaking countries, for whom all the moral and ethical
issues appear to be highly sensitivities show, relatively speaking, the lowest technological optimism and
lowest support for regenerative medicines and for bio-nano.
Cluster 5, which includes Denmark, the Netherlands and Ireland, also put science second and have
strong views on the importance of moral and ethical issues in governance. At the same time they are,
relatively speaking, among the most supportive of bio-nano and regenerative medicine, and show
moderate technological optimism. Reflecting on the recent history of Denmark and the Netherlands the
combination of public sensitivities about and support for science and technology might reflect the
107
successful embedding of societal issues in science – societies at ease with scientific progress, informed
by ethical principles.
By contrast, cluster 1, which includes France, Sweden and the UK, put science first but also show an
interest in, rather than possibly concerns about, ethics. In these countries distributional fairness is
apparently not an issue. In this cluster of countries, relative to others, technological optimism is high and
there are high levels of support for regenerative medicines and bio-nano.
Cluster 2 is a heterogeneous group of countries linked by putting science first, having some concerns
about distributional fairness but otherwise at the centre of gravity in Europe. They are, relatively
speaking, moderately optimistic about technology, not very keen on regenerative medicine and moderate
supporters bio-nano.
Cluster 3, which includes Italy, Spain and Hungary also put science first. But in these countries ethical
and moral issues are not on the public’s radar screen. In comparison with the other clusters, these
countries show moderate levels of technological optimism and equally moderate levels of support for bio-
nano.
Figure 45 shows how the clusters are statistically related to each other. Looking from the bottom to the
top of the graphic, it can be seen that clusters 1 and 2 are more similar to each other than to any other
cluster. Cluster 3 is more similar to clusters 1 and 2 than it is to any other cluster. Clusters 4 and 5 are
more similar to each other than to any other cluster. If we were to select a two cluster solution to these
data, we would split the countries between those in clusters 1, 2 and 3 on the one hand, and clusters 4
and 5 on the other. Another way of expressing this is to say that the greatest division between countries
is between those in the upper three rows of Table 14, and those in the lower two rows. And it turns out
that the key characteristic distinguishing those two groups of countries from each other is the relative
priority given to scientific versus ethical concerns. But having said that we need to move down the
graphic and note that clusters where distributional fairness is a concern are rather different in their
support for science and technology, than those clusters (1 and 5) where this is a lesser concern.
108
Figure 45: Relationships between clusters of countries
Science 1st Ethics 1st
1 2 3 4 5
Concerns about Distributional
Fairness
Support: High Mixed Moderate Low High
Looking at clusters 4 and 5 it is clear that we cannot conclude that giving priority to ethics over science
leads to a profile of low technological optimism and low support for biotechnologies. Rather,
technological optimism and support for biotechnologies must be seen as a combination of the priority
given to either ethics over science, or science over ethics; and crucially, whether distributional fairness is
a particular sensitivity. Where ethics takes priority, concerns about distributional fairness lead to a profile
of low support. And when science taking priority over ethics is combined with concerns about
distributional fairness, then support moderate. For the present we conclude that the relations between
perceptions of science and technology, and public ethics are intriguing. In our continuing research we
will dig deeper into the meaning and origins of distributional fairness, and into the wider implications of
the relative priority that people give to science versus ethics.
109
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Gore, A (2006) An Inconvenient Truth: The Planetary Emergency of Global Warming and What We Can Do About It. New York: Rodale Gottweis, H, and Petersen, A (ed.s) (2008) Biobanks. Governance in comparative perspective. Abingdon: Routledge Inglehart, R (1990) Culture shift in advanced societies. Princeton, NJ: Princeton University Press. INRA (1991) Eurobarometer 35.1: Opinions of Europeans on biotechnology in 1991. Report undertaken on behalf of the Directorate-General Science, Research and Development of the Commission of the European Communities http://ec.europa.eu/public_opinion/archives/ebs/ebs_061_en.pdf INRA (1993) Eurobarometer 39.1: Biotechnology and genetic engineering. What Europeans think about in 1993. Report written for the European Commission Directorate-General Science, Research and Development, Unit XII/E/1: Biotechnologies. http://ec.europa.eu/public_opinion/archives/ebs/ebs_080_en.pdf INRA (1997) Eurobarometer 46.1: The Europeans and modern biotechnology. Report written for the European Commission Directorate-General Science, Research and Development (first published in French) http://ec.europa.eu/public_opinion/archives/ebs/ebs_108_en.pdf INRA (2000) Eurobarometer 52.1: The Europeans and biotechnology. Report by INRA (Europe) – ECOSA on behalf of Directorate-General for Research Directorate B - Quality of Life and Management of Living Resources Programme http://ec.europa.eu/research/pdf/eurobarometer-en.pdf Jasanoff, S (2000) Designs on Nature: Science and Democracy in Europe and the United States. Princeton, NJ: Princeton University Press. Salvaterra, E et al. (2008) Banking together: a unified model of informed consent for biobanking. EMBO Reports 9, 30713 Sclove, R E (1995) Democracy and Technology. New York: The Guildford Press. Slovic, P, Finucane, M, Peters, E, and MacGregor, GG (2002) The Affect Heuristic, P.397 in: Gilovich, T, Griffin, D and Kahneman, D (ed.s) Intuitive Judgment: Heuristics and Biases. Cambridge University Press TNS Opinion and social (2005) Special Eurobarometer 224, Wave 63.1: Europeans, science and technology. Requested by Directorate General Research and coordinated by Directorate General Press and Communication http://ec.europa.eu/public_opinion/archives/ebs/ebs_224_report_en.pdf Torgersen et al. (2002) The framing of a new technology, 1973-1996. Ch.2 in Bauer, MW and Gaskell, G (ed.s) Biotechnology – the making of a global controversy. Cambridge: Cambridge University Press.
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Annex 1
EB Special 73.1 Biotechnology and the Life Sciences
Questionnaire: English version
QB1 I am going to read out a list of areas where new technologies are currently developing. For each of these, do you think it will have a positive, a negative or no effect on our way of life in the next 20 years?
(ONE ANSWER PER LINE)
(READ OUT) Positive
effect Negative
effect No effect DK
1 Solar energy 1 2 3 4 2 Computers and Information Technology 1 2 3 4 3 Biotechnology and genetic engineering 1 2 3 4 4 Space exploration 1 2 3 4 5 Nuclear energy (M) 1 2 3 4 6 Nanotechnology 1 2 3 4 7 Wind energy (N) 1 2 3 4 8 Brain and cognitive enhancement (M) 1 2 3 4 ASK QB2a TO QB4a ONLY TO SPLIT A - OTHERS GO TO QB2b
Let’s speak now about genetically modified (GM) food made from plants or micro-organisms that have been changed by altering their genes. For example a plant might have its genes modified to make it resistant to a particular plant disease, to improve its food quality or to help it grow faster.
QB2a Have you ever heard of genetically modified (or GM) foods before? (M) Yes 1 No 2 EB64.3 QB6a TREND MODIFIED ASK QB3a IF "YES", CODE 1 IN QB2a - OTHERS GO TO QB4a QB3a Have you ever…? (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Yes, frequently
Yes, occasionally
Yes, only
once or twice
No, never DK
1 Talked about GM food with anyone
before today 1 2 3 4 5
2 Searched for information about
GM food 1 2 3 4 5
112
ASK ALL IN SPLIT A QB4a For each of the following issues regarding GM food please tell me if you agree or disagree
with it. (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Totally
agree Tend to agree
Tend to disagree
Totally disagree
DK
1 GM food is good for the
(NATIONALITY) economy 1 2 3 4 5
2 GM foods is not good for you and
your family 1 2 3 4 5
3 GM food helps people in
developing countries 1 2 3 4 5
4 GM food is safe for future
generations 1 2 3 4 5
5 GM food benefits some people but
puts others at risk 1 2 3 4 5
6 GM food is fundamentally
unnatural 1 2 3 4 5
7 GM food makes you feel uneasy 1 2 3 4 5
8 GM food is safe for your health
and your family’s health 1 2 3 4 5
9 GM food does no harm to the
environment 1 2 3 4 5
10 The development of GM food
should be encouraged 1 2 3 4 5
ASK QB2b TO QB7b ONLY TO SPLIT B - OTHERS GO TO QB5a
And now thinking about nanotechnology: Nanotechnology involves working with atoms and molecules to make new particles that are used in cosmetics to make better anti-aging creams, suntan oils for better protection against skin cancer and cleaning fluids to make the home more hygienic. Despite these benefits, some scientists are concerned about the unknown and possibly negative effects of nano particles in the body and in the environment.
QB2b Have you ever heard of nanotechnology before? (M) Yes 1 No 2
113
ASK QB3b IF "YES", CODE 1 IN QB2b - OTHERS GO TO QB4b QB3b Have you ever…? (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Yes, frequently
Yes, occasionally
Yes, only
once or twice
No, never DK
1 Talked about nanotechnology with
anyone before today 1 2 3 4 5
2 Searched for information about
nanotechnology 1 2 3 4 5
ASK ALL IN SPLIT B QB4b For each of the following statements regarding nanotechnology please tell me if you agree
or disagree with it. (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Totally
agree Tend to agree
Tend to disagree
Totally disagree
DK
1 Nanotechnology is good for the
(NATIONALITY) economy 1 2 3 4 5
2 Nanotechnology is not good for
you and your family 1 2 3 4 5
3 Nanotechnology helps people in
developing countries 1 2 3 4 5
4 Nanotechnology is safe for future
generations 1 2 3 4 5
5 Nanotechnology benefits some
people but puts others at risk 1 2 3 4 5
6 Nanotechnology is fundamentally
unnatural 1 2 3 4 5
7 Nanotechnology makes you feel
uneasy 1 2 3 4 5
8 Nanotechnology is safe for your
health and your family’s health 1 2 3 4 5
9 Nanotechnology does no harm to
the environment 1 2 3 4 5
10 Nanotechnology should be
encouraged 1 2 3 4 5
114
Let’s speak now about cloning farm animals. Cloning may be used to improve some characteristics of farmed animals in food production. Due to the high cost of cloning, this technique would mainly be used to produce cloned animals which will reproduce with non-cloned animals. Their offspring would then be used to produce meat and milk of higher quality. However, critics have raised questions about ethics of animal cloning.
QB5b Have you ever heard of animal cloning in food production before? Yes 1 No 2 ASK QB6b IF "YES", CODE 1 IN QB5b - OTHERS GO TO QB7b QB6b Have you ever…? (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Yes, frequently
Yes, occasionally
Yes, only
once or twice
No, never DK
1 Talked about animal cloning in food production with anyone before today
1 2 3 4 5
2 Searched for information about
animal cloning in food production 1 2 3 4 5
115
ASK ALL IN SPLIT B QB7b For each of the following statements regarding animal cloning in food production please
tell me if you agree or disagree with it. (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Totally
agree Tend to agree
Tend to disagree
Totally disagree
DK
1 Animal cloning in food production is good for the (NATIONALITY) economy
1 2 3 4 5
2 Animal cloning in food production is not good for you and your family
1 2 3 4 5
3 Animal cloning in food production helps people in developing countries
1 2 3 4 5
4 Animal cloning in food production
is safe for future generations 1 2 3 4 5
5 Animal cloning in food production benefits some people but puts others at risk
1 2 3 4 5
6 Animal cloning in food production
is fundamentally unnatural 1 2 3 4 5
7 Animal cloning in food production
makes you feel uneasy 1 2 3 4 5
8 Animal cloning in food production is safe for your health and your family’s health
1 2 3 4 5
9 Animal cloning in food production
does no harm to the environment 1 2 3 4 5
10 Animal cloning in food production
should be encouraged 1 2 3 4 5
ASK QB5a TO QB10a ONLY TO SPLIT A - OTHERS GO TO QB8b
Let’s speak now about regenerative medicine which is a new field of medicine and clinical applications that focuses on the repairing, replacing or growing of cells, tissues, or organs.
QB5a Stem cell research involves taking cells from human embryos that are less than 2 weeks
old. They will never be transplanted into a woman’s body but are used to grow new cells which then can be used to treat diseases in any part of the body. Would you say that...?
(READ OUT – ONE ANSWER ONLY) You fully approve and do not think that special laws are necessary 1 You approve as long as this is regulated by strict laws 2 You do not approve except under very special circumstances 3 You do not approve under any circumstances 4 DK 5
116
QB6a Now suppose scientists were able to use stem cells from other cells in the body, rather
than from embryos. Would you say that...? (READ OUT – ONE ANSWER ONLY) You fully approve and do not think that special laws are necessary 1 You approve as long as this is regulated by strict laws 2 You do not approve except under very special circumstances 3 You do not approve under any circumstances 4 DK 5 NEW QB7a Scientists can put human genes into animals that will produce organs and tissues for
transplant into humans, such as pigs for transplants or to replace pancreatic cells to cure diabetes. Would you say that...?
(READ OUT – ONE ANSWER ONLY) You fully approve and do not think that special laws are necessary 1 You approve as long as this is regulated by strict laws 2 You do not approve except under very special circumstances 3 You do not approve under any circumstances 4 DK 5 QB8a Scientists also work on gene therapy which involves treating inherited diseases by
intervening directly in the human genes themselves. Would you say that...? (READ OUT – ONE ANSWER ONLY) You fully approve and do not think that special laws are necessary 1 You approve as long as this is regulated by strict laws 2 You do not approve except under very special circumstances 3 You do not approve under any circumstances 4 DK 5 QB9a Regenerative medicine is not only about developing cures for people who are ill. It is also
looking into ways of enhancing the performance of healthy people, for example to improve concentration or to increase memory. Would you say that...?
(READ OUT – ONE ANSWER ONLY) You fully approve and do not think that special laws are necessary 1 You approve as long as this is regulated by strict laws 2 You do not approve except under very special circumstances 3 You do not approve under any circumstances 4 DK 5
117
QB10a Now I would like to know whether you agree or disagree with each of the following issues
regarding regenerative medicine. (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Totally
agree Tend to agree
Tend to disagree
Totally disagree
DK
1 Research involving human embryos should be forbidden, even if this means that possible treatments are not made available to ill people
1 2 3 4 5
2 It is ethically wrong to use human embryos in medical research even if it might offer promising new medical treatments
1 2 3 4 5
3 We have a duty to allow research that might lead to important new treatments, even when it involves the creation or use of human embryos
1 2 3 4 5
4 Should ethical and scientific viewpoints on regenerative medicine differ, the scientific viewpoint should prevail
1 2 3 4 5
5 Mixing animal and human genes is unacceptable even if it helps medical research for human health
1 2 3 4 5
6 You do not support developments in regenerative medicine if it only benefits rich people
1 2 3 4 5
7 Immediately after fertilisation the human embryo can already be considered to be a human being
1 2 3 4 5
8 Research on regenerative medicine should be supported, even though it will benefit only a few people
1 2 3 4 5
9 Research into regenerative medicine should go ahead, even if there are risks to future generations
1 2 3 4 5
118
ASK QB8b TO QB11b ONLY TO SPLIT B - OTHERS GO TO QB11a
Some European researchers think there are new ways of controlling common diseases in apples– things like scab and mildew. There are two new ways of doing this. Both mean that the apples could be grown with limited use of pesticides, and so pesticide residues on the apples would be minimal.
QB8b The first way is to artificially introduce a resistance gene from another species such as a
bacterium or animal into an apple tree to make it resistant to mildew and scab. For each of the following statements about this new technique please tell me if you agree or disagree.
(SHOW CARD WITH SCALE - SHOW PICTURE (Bacterium to apple) – ONE ANSWER PER LINE)
(READ OUT) Totally
agree Tend to agree
Tend to disagree
Totally disagree
DK
1 It is a promising idea 1 2 3 4 5
2 Eating apples produced using this
technique will be safe 1 2 3 4 5
3 It will harm the environment 1 2 3 4 5 4 It is fundamentally unnatural 1 2 3 4 5 5 It makes you feel uneasy 1 2 3 4 5 6 It should be encouraged 1 2 3 4 5 QB9b And which of the following statements is closest to your view?
Apples created by this technique would be like GM food and should be clearly identified with a special label 1
Apples created by this technique would be the same as ordinary apples and would not need special labelling 2
DK 3 QB10b The second way is to artificially introduce a gene that exists naturally in wild/ crab apples
which provides resistance to mildew and scab. For each of the following statements about this new technique please tell me if you agree or disagree.
(SHOW CARD WITH SCALE - SHOW PICTURE (Apple to apple) – ONE ANSWER PER LINE)
(READ OUT) Totally
agree Tend to agree
Tend to disagree
Totally disagree
DK
1 It will be useful 1 2 3 4 5 2 It will be risky 1 2 3 4 5 3 It will harm the environment 1 2 3 4 5 4 It is fundamentally unnatural 1 2 3 4 5 5 It makes you feel uneasy 1 2 3 4 5 6 It should be encouraged 1 2 3 4 5 QB11b And which of the following statements is closest to your view? (READ OUT – ONE ANSWER ONLY)
Apples created by this technique would be like GM food and should be clearly identified with a special label 1
Apples created by this technique would be the same as ordinary apples and would not need special labelling 2
DK 3
119
ASK QB11a TO QB16a ONLY TO SPLT A - OTHERS GO TO QB12b
Synthetic biology is a new field of research bringing together genetics, chemistry and engineering. The aim of synthetic biology is to construct completely new organisms to make new life forms that are not found in nature. Synthetic biology differs from genetic engineering in that it involves a much more fundamental redesign of an organism so that it can carry out completely new functions.
QB11a Before today, have you ever heard anything about synthetic biology? Yes 1 No 2 ASK QB12a IF "YES", CODE 1 IN QB11a - OTHERS GO TO QB13a1 QB12a Have you ever…? (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Yes, frequently
Yes, occasionally
Yes, only
once or twice
No, never DK
1 Talked about synthetic biology
with anyone before today 1 2 3 4 5
2 Searched for information about
synthetic biology 1 2 3 4 5
ASK ALL IN SPLIT A QB13a1 Suppose, there was a referendum about synthetic biology and you had to make up your
mind whether to vote for or against. Among the following, what would be the most important issue on which you would like to know more? Firstly?
QB13a2 And secondly?
QB13a3 And thirdly?
(SHOW CARD – ONE ANSWER PER COLUMN) (READ OUT) QB13a1 QB13a2 QB13a3 FIRSTLY SECONDLY THIRDLY What the scientific processes and techniques are 1 1 1 Who is funding the research and why 2 2 2 What the claimed benefits are 3 3 3 What the possible risks are 4 4 4 Who will benefit and who will bear the risks 5 5 5 What is being done to regulate and control synthetic biology 6 6 6
What is being done to deal with the social and ethical issues involved
7 7 7
Other (SPONTANEOUS) 8 8 8 None (SPONTANEOUS) 9 9 9 DK 10 10 10
120
QB14a Overall, what would you say about synthetic biology? (READ OUT – ONE ANSWER ONLY) You fully approve and do not think that special laws are necessary 1 You approve as long as this is regulated by strict laws 2 You do not approve except under very special circumstances 3 You do not approve under any circumstances 4 DK 5
Let’s speak now about biofuels. Biofuels are made from crops like maize and sugar cane that are turned into ethanol and biodiesel for airplanes, cars and lorries. Unlike oil, biofuels are renewable, would reduce greenhouse gas emissions and make the European Union less dependent on imported oil. Critics, however, say that these biofuels take up precious agricultural land and may lead to higher food prices in the European Union and food shortages in the developing world.
QB15a To what extent do you think these biofuels should be encouraged or not be encouraged? (READ OUT – ONE ANSWER ONLY) Should definitely be encouraged 1 Should probably be encouraged 2 Should probably not be encouraged 3 Should definitely not be encouraged 4 DK 5
Now, scientists are working on more sustainable biofuels. These can be made from plant stems and leaves - the things we don’t eat, or from trees and algae. With these second generation biofuels, there is no longer the need to use food crops.
QB16a To what extent do you think these sustainable biofuels should be encouraged or not be
encouraged? (READ OUT – ONE ANSWER ONLY) Should definitely be encouraged 1 Should probably be encouraged 2 Should probably not be encouraged 3 Should definitely not be encouraged 4 DK 5 ASK QB12b TO QB18b ONLY TO SPLIT B - OTHERS GO TO QB19
And now thinking about biobanks for biomedical research: These are collections of biological materials (such as blood and/or tissues) and personal data (medical records, lifestyle data) from large numbers of people. Using biobanks, researchers will try to identify the genetic and environmental factors in diseases, to improve prevention, diagnosis and treatment. Participation in biobanks is voluntary. Critics, however, raise questions about privacy, confidentiality and commercial interests regarding the biobanks and about who is going to regulate them.
QB12b Before today, have you ever heard anything about biobanks? Yes 1 No 2
121
ASK QB13b IF "YES", CODE 1 IN QB12b - OTHERS GO TO QB14b QB13b Have you ever…? (SHOW CARD WITH SCALE – ONE ANSWER PER LINE)
(READ OUT) Yes, frequently
Yes, occasionally
Yes, only
once or twice
No, never DK
1 Talked about biobanks with
anyone before today 1 2 3 4 5
2 Searched for information about
biobanks 1 2 3 4 5
ASK ALL IN SPLIT B QB14b In a hospital doctors ask the patient to sign a form giving permission to carry out an
operation – this is called ‘informed consent’ and it is also required of medical researchers who do research involving members of the public. When a scientist does research on data in a biobank, what do you think about the need for this kind of permission? Researchers should…
(READ OUT - ONE ANSWER ONLY) Not need to ask for permission 1 Ask for permission only once 2 Ask for permission for every new piece of research 3 DK 4 DO NOT ASK QB15b2 IF "NONE" OR "DK", CODE 9-10 IN QB15b1 QB15b1 Biobanks will follow up participants over long periods of time. And many biobanks will
work with industrial companies to develop new medicines. Who do you think should be primarily responsible for protecting the public interest? Firstly?
QB15b2 And secondly?
(SHOW CARD – ONE ANSWER PER COLUMN) (READ OUT) QB15b1 QB15b2 FIRSTLY SECONDLY Medical doctors 1 1 Researchers 2 2 Public institutions (universities, hospitals) 3 3 National governments 4 4 Ethics committees 5 5
International organisations such as the European Union or World Health Organisation
6 6
National Data Protection Authorities 7 7 Other (SPONTANEOUS) 8 8 None (SPONTANEOUS) 9 9 DK 10 10
122
QB16b Would you be willing to provide information about yourself to a biobank? (READ OUT – ONE ANSWER ONLY) Yes, definitely 1 Yes, probably 2 No, probably not 3 No, never 4 DK 5 QB17b In order to understand the causes of diseases researchers need as much information as
possible about the people in the biobank. Would you personally be concerned or reluctant about the collection of any of the following types of data and materials from you?
(SHOW CARD – READ OUT – MULTIPLE ANSWERS POSSIBLE) Blood samples 1, Tissue collected during medical operations 2, Your genetic profile 3, Medical record from your doctor 4, Lifestyle (what you eat, how much exercise you take, etc.) 5, Other (SPONTANEOUS) 6, None (SPONTANEOUS) 7, DK 8, QB18b Some countries in the European Union have one or more biobanks. Do you think the
sharing and exchange of personal data and biological materials tissue across Member States should be encouraged?
(READ OUT – ONE ANSWER ONLY) Yes, definitely 1 Yes, probably 2 No, probably not 3 No, definitely not 4 DK 5
123
ASK ALL QB19 For each of the following people and groups, do you think they are doing a good job for
society or not doing a good job for society? (ONE ANSWER PER LINE)
(READ OUT – ROTATE) Doing a good
job for society
Not doing a good job for society
DK
1 Newspapers, magazines and television which report on
biotechnology 1 2 3
2 Industries which develop new products with biotechnology 1 2 3 3 University scientists who conduct research in biotechnology 1 2 3 4 Consumer organisations which test biotechnological products 1 2 3 5 Environmental groups who campaign about biotechnology 1 2 3
6 (NATIONALITY) Government making laws about
biotechnology 1 2 3
7 Retailers who ensure our food is safe 1 2 3
8 The European Union making laws about biotechnology for all
EU Member States 1 2 3
9 Ethics committees who consider the moral and ethical
aspects of biotechnology 1 2 3
10 Religious leaders who say what is right and wrong in the
development of biotechnology 1 2 3
11 Medical doctors 1 2 3 ASK QB20a TO QB22a ONLY TO SPLIT A - OTHERS GO TO QB20b QB20a Which of the following views is closest to your own? (READ OUT – ONE ANSWER ONLY) Decisions about synthetic biology should be based primarily on scientific evidence 1
Decisions about synthetic biology should be based primarily on the moral and ethical issues 2
DK 3 QB21a Which of the following views is closest to your own? (READ OUT – ONE ANSWER ONLY) Decisions about synthetic biology should be based mainly on the advice of experts 1
Decisions about synthetic biology should be based mainly on what the majority of people in a country thinks 2
DK 3 QB22a Which of the following views is closest to your own? (READ OUT – ONE ANSWER ONLY) Synthetic biology should be tightly regulated by Government 1 Synthetic biology should be allowed to operate in the market place like a business 2 DK 3 ASK QB20b TO QB22b ONLY TO SPLIT B - OTHERS GO TO QB23
124
QB20b Which of the following views is closest to your own? (READ OUT – ONE ANSWER ONLY) Decisions about animal cloning should be based primarily on scientific evidence 1
Decisions about animal cloning should be based primarily on the moral and ethical issues 2
DK 3 QB21b Which of the following views is closest to your own? (READ OUT – ONE ANSWER ONLY) Decisions about animal cloning should be based mainly on the advice of experts 1
Decisions about animal cloning should be based mainly on what the majority of people in a country thinks 2
DK 3 QB22b Which of the following views is closest to your own? (READ OUT – ONE ANSWER ONLY) Animal cloning should be tightly regulated by Government 1 Animal cloning should be allowed to operate in the market place like a business 2 DK 3 ASK ALL QB23 Which of the following views is closest to your own? (READ OUT – ONE ANSWER ONLY)
The Government should take responsibility to ensure that new technologies benefit everyone 1
It is up to people to seek out the benefits from new technologies themselves 2 DK 3 QB24 And which of the following do you think is most important? (READ OUT – ONE ANSWER ONLY) Protecting freedom of speech and human rights 1 Fighting crime and terrorism 2 DK 3 QB25 And which of the following do you think is most important? (READ OUT – ONE ANSWER ONLY) Having strong European companies to compete in global markets 1 Reducing economic inequalities among people in the European Union 2 DK 3
125
QB26 And which of the following do you think is most important? (READ OUT – ONE ANSWER ONLY)
To halt climate change and global warming we will all have to rethink our ways of living even if it means lower economic growth in (OUR COUNTRY) 1
Technology will find a way to stop climate change and global warming so that we can maintain our way of life and have economic growth 2
DK 3 QB27 To what extent do you think your view on climate change and global warming is shared in
(OUR COUNTRY)? (READ OUT – ONE ANSWER ONLY) Everyone shares my views 1 A lot of people share my views 2 A few people share my views 3 No one shares my views 4 DK 5 QB28 Do you think (OUR COUNTRY) will adopt policies in line with your view on this matter? (READ OUT – ONE ANSWER ONLY) Yes, definitely 1 Yes, probably 2 No, probably not 3 No, definitely not 4 DK 5 QB29 Overall how strongly would you say you feel about issues concerning biotechnology that
we have been talking about in this survey? (READ OUT – ONE ANSWER ONLY) Extremely strongly 1 Very strongly 2 Somewhat strongly 3 Not at all strongly 4 DK 5 QB30 Does/Did any of your family have a job or a university qualification in natural science,
technology or engineering (for instance, physics, chemistry, biology, medicine)? (READ OUT – MULTIPLE ANSWERS POSSIBLE) Yes, your father 1, Yes, your mother 2, Yes, another member of your family 3, No, no one in your family 4, DK 5,
126
QB31 Have you ever studied natural science, technology or engineering: at school, in college, in
the university or anywhere else? (READ OUT – ONE ANSWER ONLY) Yes, at the university 1 Yes, in college 2 yes, at school 3 Yes, elsewhere 4 No, you have never studied any of these 5 DK 6 QB32 Which of these statements comes closest to your beliefs? (SHOW CARD - READ OUT - ONE ANSWER ONLY) You believe there is a God 1 You believe there is some sort of spirit or life force 2 You don’t believe there is any sort of spirit, God or life force 3 DK 4 EB63.1 QB2 QB33 Do you consider yourself to be…? (DO NOT READ - SHOW CARD - PRECODED LIST - ONE ANSWER ONLY) Catholic 1 Orthodox 2 Protestant 3 Other Christian 4 Jewish 5 Muslim 6 Sikh 7 Buddhist 8 Hindu 9 Atheist 10 Non believer\Agnostic 11 Other (SPONTANEOUS) 12 DK 13 EB71.2 D44 QB34 Apart from weddings or funerals, about how often do you attend religious services? (SHOW CARD - READ OUT - ONE ANSWER ONLY) More than once a week 1 Once a week 2 About once a month 3 About each 2 or 3 month 4 Only on special holy days 5 About once a year 6 Less often 7 Never 8 DK 9
127
Annex 2
Eurobarometer on Biotechnology and the Life Sciences, 2010 (73.1)
Descriptive statistics
128
Take
n di
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NS
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and
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ctor
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esea
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and
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Ana
lysi
s’. T
he S
PECI
AL E
URO
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R
n°34
1 (‘L
ife S
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and
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echn
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is p
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f wav
e 73
.1 a
nd c
over
s th
e po
pula
tion
of th
e re
spec
tive
natio
nalit
ies
of th
e Eu
rope
an U
nion
Mem
ber St
ates
, res
iden
t in
each
of
the
Mem
ber
Stat
es a
nd a
ged
15 y
ears
and
ove
r. T
he E
URO
BARO
MET
ER 7
3.1
has
also
bee
n co
nduc
ted
in t
wo
cand
idat
e co
untr
ies
(Cro
atia
and
Tur
key)
and
in
Switz
erla
nd, I
cela
nd a
nd N
orw
ay. I
n th
ese
coun
trie
s, th
e su
rvey
cov
ers
the
natio
nal p
opul
atio
n of
citi
zens
and
the
popu
latio
n of
citi
zens
of a
ll th
e Eu
rope
an U
nion
Mem
ber
Stat
es th
at a
re r
esid
ents
in th
ese
coun
trie
s an
d ha
ve a
suf
ficie
nt c
omm
and
of th
e na
tiona
l lan
guag
es to
ans
wer
the
ques
tionn
aire
. The
bas
ic s
ampl
e de
sign
app
lied
in a
ll st
ates
is a
mul
ti-st
age,
ran
dom
(pr
obab
ility
) on
e. I
n ea
ch c
ount
ry,
a nu
mbe
r of
sam
plin
g po
ints
was
dra
wn
with
pro
babi
lity
prop
ortio
nal t
o po
pula
tion
size
(fo
r a
tota
l co
vera
ge o
f the
cou
ntry
) an
d to
pop
ulat
ion
dens
ity. I
n or
der t
o do
so,
the
sam
plin
g po
ints
wer
e dr
awn
syst
emat
ical
ly fr
om e
ach
of th
e ‘a
dmin
istr
ativ
e re
gion
al u
nits
’, af
ter
stra
tific
atio
n by
indi
vidu
al u
nit a
nd ty
pe o
f are
a. T
hey
thus
repr
esen
t the
who
le te
rrito
ry o
f the
cou
ntrie
s su
rvey
ed a
ccor
ding
to th
e EU
ROST
AT N
UTS
II (or
equ
ival
ent)
and
ac
cord
ing
to th
e di
strib
utio
n of
the
resi
dent
pop
ulat
ion
of th
e re
spec
tive
natio
nalit
ies
in te
rms
of m
etro
polit
an, u
rban
and
rura
l are
as. I
n ea
ch o
f the
sel
ecte
d sa
mpl
ing
poin
ts,
a st
artin
g ad
dres
s w
as d
raw
n, a
t ran
dom
. Fur
ther
add
ress
es (
ever
y N
th a
ddre
ss)
wer
e se
lect
ed b
y st
anda
rd ‘r
ando
m r
oute
’ pro
cedu
res,
from
the
initi
al a
ddre
ss. I
n ea
ch
hous
ehol
d, th
e re
spon
dent
was
dra
wn,
at r
ando
m (f
ollo
win
g th
e ‘c
lose
st b
irthd
ay ru
le’).
All
inte
rvie
ws
wer
e co
nduc
ted
face
-to-
face
in p
eopl
e's
hom
es a
nd in
the
appr
opria
te
natio
nal l
angu
age.
As
far as
the
data
cap
ture
is c
once
rned
, CAP
I (C
ompu
ter A
ssis
ted
Pers
onal
Int
ervi
ew) w
as u
sed
in th
ose
coun
trie
s w
here
this
tech
niqu
e w
as a
vaila
ble.
Fo
r eac
h co
untr
y a
com
paris
on b
etw
een
the
sam
ple
and
the
univ
erse
was
car
ried
out.
The
Uni
vers
e de
scrip
tion
was
der
ived
from
Eur
osta
t pop
ulat
ion
data
or f
rom
nat
iona
l st
atis
tics
offic
es. F
or a
ll co
untr
ies
surv
eyed
, a n
atio
nal w
eigh
ting
proc
edur
e, u
sing
mar
gina
l and
inte
rcel
lula
r wei
ghtin
g, w
as c
arrie
d ou
t bas
ed o
n th
is U
nive
rse
desc
riptio
n. In
al
l cou
ntrie
s, g
ende
r, a
ge, r
egio
n an
d si
ze o
f loc
ality
wer
e in
trod
uced
in th
e ite
ratio
n pr
oced
ure.
For
inte
rnat
iona
l wei
ghtin
g (i.
e. E
U a
vera
ges)
, TN
S O
pini
on &
Soc
ial a
pplie
s th
e of
ficia
l pop
ulat
ion
figur
es a
s pr
ovid
ed b
y EU
ROST
AT o
r nat
iona
l sta
tistic
off
ices
. The
tota
l pop
ulat
ion
figur
es fo
r inp
ut in
this
pos
t-w
eigh
ting
proc
edur
e ar
e lis
ted
abov
e.
Read
ers
are
rem
inde
d th
at s
urve
y re
sults
are
est
imat
ions
, the
acc
urac
y of
whi
ch, e
very
thin
g be
ing
equa
l, re
sts
upon
the
sam
ple
size
and
upo
n th
e ob
serv
ed p
erce
ntag
e.
With
sam
ples
of ab
out
1,00
0 in
terv
iew
s, t
he r
eal p
erce
ntag
es v
ary
with
in t
he fol
low
ing
conf
iden
ce li
mits
:
129
130
Euro
baro
met
er 7
3.1
Bio
tech
nolo
gy a
nd th
e lif
e sc
ienc
es
Cod
eboo
k
CO
UN
TRY
CO
DES
USE
D IN
TAB
LES
BEBe
lgiu
mD
KD
enm
ark
DE
Ger
man
yG
RG
reec
eES
Spai
nFI
Finl
and
FRFr
ance
IEIre
land
ITIta
lyLU
Luxe
mbo
urg
NL
Net
herla
nds
AT
Aus
tria
PTPo
rtuga
lSE
Swed
enU
KU
nite
d Ki
ngdo
mC
YC
ypru
sC
ZC
zech
Rep
ublic
EEEs
toni
aH
UH
unga
ryLV
Latv
iaLT
Lith
uani
aM
TM
alta
PLPo
land
SK
Slo
vaki
aSI
Slov
enia
BGBu
lgar
iaR
OR
oman
iaTR
Turk
eyIS
Icel
and
HR
C
roat
iaC
HSw
itzer
land
NO
Nor
way
131
qb1
Sola
r en
ergy
exp_
sola
rB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OPo
sitiv
e ef
fect
85.2
95.9
92.6
92.3
87.7
94.3
89.2
89.1
80.5
89.9
93.6
88.7
81.3
92.1
85.4
90.8
86.7
82.6
85.3
76.0
58.3
87.1
81.2
87.7
82.0
87.9
79.1
70.1
74.4
90.2
91.4
93.6
86.6
No
effe
ct6.
52.
64.
11.
62.
24.
15.
02.
07.
73.
05.
08.
54.
14.
96.
52.
96.
67.
29.
910
.012
.5.8
6.2
6.6
4.7
1.8
3.8
6.6
22.8
2.1
6.1
4.9
5.3
Neg
ativ
e ef
fect
6.7
.81.
95.
04.
5.8
2.1
2.3
5.7
5.2
.61.
78.
21.
53.
73.
25.
44.
73.
26.
917
.67.
07.
63.
39.
71.
97.
18.
41.
93.
51.
4.5
4.1
DK
1.6
.71.
31.
15.
6.8
3.6
6.7
6.2
1.9
.81.
26.
31.
54.
53.
11.
35.
51.
67.
211
.55.
15.
02.
43.
68.
510
.014
.9.9
4.3
1.0
1.0
4.1
Com
pute
rs a
nd In
form
atio
n Te
chno
logy
exp_
com
pute
rB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OPo
sitiv
e ef
fect
74.6
87.9
76.0
80.1
84.9
82.3
67.8
85.5
72.2
75.6
80.7
66.0
74.5
80.8
84.9
74.8
79.1
80.7
79.5
73.2
61.9
91.2
78.6
80.9
72.4
83.5
71.0
65.1
91.2
78.8
68.5
84.1
77.1
No
effe
ct9.
96.
06.
05.
52.
38.
19.
53.
210
.05.
58.
319
.58.
36.
74.
47.
66.
44.
111
.24.
75.
52.
95.
37.
28.
21.
83.
56.
96.
74.
115
.05.
56.
7N
egat
ive
effe
ct13
.44.
111
.312
.35.
66.
717
.44.
210
.113
.98.
410
.27.
98.
36.
210
.312
.810
.27.
616
.425
.32.
110
.610
.113
.75.
916
.011
.01.
612
.411
.75.
010
.5D
K2.
12.
16.
72.
17.
22.
95.
37.
17.
75.
02.
64.
39.
34.
24.
57.
31.
65.
11.
75.
77.
33.
95.
51.
85.
78.
89.
417
.0.5
4.7
4.8
5.4
5.8
Bio
tech
nolo
gy a
nd g
enet
ic e
ngin
eeri
ng
exp_
biot
ech
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Posi
tive
effe
ct53
.962
.842
.651
.165
.069
.455
.047
.952
.152
.953
.434
.946
.071
.855
.965
.065
.477
.061
.156
.944
.646
.051
.362
.252
.937
.943
.149
.879
.348
.948
.573
.252
.9N
o ef
fect
13.2
8.8
6.6
6.2
3.2
9.0
6.3
3.4
9.0
9.0
11.5
11.9
7.9
6.9
6.6
3.0
9.0
4.6
15.3
7.1
5.4
1.8
7.1
9.0
7.9
3.7
6.6
7.4
16.6
6.6
16.6
4.1
7.2
Neg
ativ
e ef
fect
24.5
21.3
32.8
23.1
8.6
14.5
18.5
14.2
15.2
25.4
25.5
40.7
11.0
13.8
16.3
6.5
17.0
8.3
10.5
20.8
23.9
9.4
19.2
19.1
25.0
22.5
18.4
14.5
2.1
28.2
21.0
11.4
19.6
DK
8.3
7.1
18.0
19.6
23.2
7.1
20.3
34.4
23.7
12.7
9.6
12.5
35.1
7.5
21.2
25.4
8.6
10.1
13.1
15.2
26.1
42.8
22.4
9.8
14.3
35.9
31.9
28.3
2.0
16.4
14.0
11.3
20.3
Spac
e ex
plor
atio
n
exp_
spac
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OPo
sitiv
e ef
fect
46.3
45.9
41.5
64.7
57.1
46.6
36.3
33.9
50.2
31.7
32.3
37.8
45.4
42.6
39.2
54.6
62.6
62.3
55.3
61.2
52.1
33.4
55.6
64.3
52.7
67.6
51.1
49.6
25.2
48.1
28.7
45.6
46.8
No
effe
ct29
.839
.735
.015
.017
.337
.437
.724
.825
.233
.146
.940
.517
.342
.335
.518
.924
.018
.833
.517
.816
.227
.419
.621
.624
.15.
911
.211
.969
.320
.147
.736
.728
.7N
egat
ive
effe
ct19
.09.
812
.914
.98.
410
.015
.916
.212
.324
.814
.912
.913
.28.
916
.48.
910
.710
.05.
612
.313
.08.
711
.610
.015
.95.
915
.413
.23.
821
.517
.06.
513
.0D
K4.
94.
510
.65.
417
.25.
910
.125
.012
.410
.45.
98.
824
.26.
18.
917
.62.
78.
85.
68.
718
.730
.513
.24.
17.
320
.722
.325
.41.
710
.36.
611
.111
.5
Nuc
lear
ene
rgy
exp_
nucl
ear
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Posi
tive
effe
ct37
.230
.629
.722
.537
.348
.438
.936
.134
.225
.535
.216
.927
.753
.852
.440
.558
.053
.543
.541
.739
.828
.646
.156
.337
.649
.235
.139
.720
.328
.132
.834
.838
.8N
o ef
fect
17.8
24.6
9.1
4.9
5.9
16.9
12.9
9.5
9.7
11.2
19.3
13.4
9.7
14.5
8.3
7.0
10.2
8.1
18.4
8.8
7.5
8.7
5.8
7.6
10.5
3.4
5.7
8.8
45.5
9.2
17.8
12.9
9.8
Neg
ativ
e ef
fect
41.0
40.2
49.9
66.4
42.7
28.8
38.3
32.1
40.4
56.0
39.4
61.3
39.6
24.7
26.8
40.0
27.1
30.2
28.1
38.3
33.2
35.9
32.3
30.5
44.7
24.6
37.3
22.0
31.1
51.2
40.1
37.9
38.8
DK
4.1
4.6
11.2
6.3
14.1
5.9
9.9
22.4
15.7
7.2
6.1
8.4
23.0
7.0
12.5
12.5
4.8
8.2
9.9
11.2
19.5
26.7
15.8
5.6
7.1
22.8
21.9
29.5
3.1
11.5
9.3
14.4
12.6
I am
goi
ng to
rea
d ou
t a li
st o
f are
as w
here
new
tech
nolo
gies
are
cur
rent
ly d
evel
opin
g. F
or e
ach
of th
ese,
do
you
thin
k it
will
hav
e a
posi
tive,
a n
egat
ive
or n
o ef
fect
on
our
way
of l
ife in
the
next
20
year
s?
Cou
ntry
Cou
ntry
EU27
EU27
EU27
EU27
Cou
ntry
EU27
Cou
ntry
Cou
ntry
132
Nan
otec
hnol
ogy
exp_
nano
tech
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Posi
tive
effe
ct44
.561
.543
.437
.142
.458
.145
.527
.236
.150
.851
.629
.829
.263
.339
.946
.357
.954
.144
.248
.931
.919
.733
.846
.042
.128
.628
.532
.546
.438
.747
.359
.441
.1N
o ef
fect
16.4
11.2
7.1
10.1
3.3
12.5
7.6
4.9
12.6
7.8
9.4
16.6
7.2
8.0
8.1
3.3
10.8
7.1
18.3
6.7
4.8
3.4
8.8
15.6
10.2
3.1
5.3
8.0
26.5
7.9
12.7
5.7
8.6
Neg
ativ
e ef
fect
14.0
8.7
12.6
21.2
7.7
6.9
8.2
10.2
10.8
17.0
9.5
24.8
10.7
4.6
5.4
8.3
10.4
5.8
6.4
10.8
8.0
1.2
9.8
14.3
15.1
8.2
13.3
11.2
3.1
19.1
10.3
5.7
10.0
DK
25.1
18.7
37.0
31.6
46.6
22.5
38.7
57.7
40.5
24.4
29.5
28.8
52.8
24.1
46.6
42.0
20.8
33.0
31.1
33.6
55.3
75.7
47.7
24.1
32.7
60.1
52.9
48.4
23.9
34.3
29.7
29.3
40.3
Win
d en
ergy
exp_
win
dB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OPo
sitiv
e ef
fect
85.1
96.0
91.0
91.0
85.3
92.0
81.1
89.3
74.1
86.6
89.1
86.3
79.5
86.5
84.3
89.4
84.8
84.4
85.7
84.8
82.4
88.1
84.0
85.5
88.0
84.5
78.0
59.7
75.9
86.5
88.4
91.8
84.1
No
effe
ct7.
02.
74.
11.
22.
26.
08.
51.
18.
95.
17.
09.
04.
98.
86.
7.4
10.5
7.4
10.0
7.9
6.8
1.0
3.8
8.1
4.6
1.9
4.1
7.4
22.2
3.1
7.3
4.7
5.9
Neg
ativ
e ef
fect
5.9
.93.
53.
02.
81.
26.
12.
05.
94.
43.
53.
05.
33.
14.
41.
23.
73.
22.
82.
82.
92.
25.
33.
23.
61.
34.
17.
71.
25.
12.
22.
44.
3D
K2.
0.4
1.4
4.9
9.7
.94.
27.
611
.03.
9.4
1.6
10.2
1.6
4.6
9.0
1.0
5.1
1.5
4.4
7.9
8.7
6.9
3.2
3.9
12.2
13.9
25.1
.75.
32.
11.
25.
7
Bra
in a
nd c
ogni
tive
enha
ncem
ent
exp_
brai
nB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OPo
sitiv
e ef
fect
59.9
66.0
55.8
63.8
73.2
72.6
81.2
43.6
66.8
51.4
54.4
23.1
50.3
15.6
55.4
73.9
64.0
70.5
69.5
54.5
60.1
45.5
29.3
61.8
37.4
55.3
44.7
59.7
65.1
67.8
14.5
85.0
58.7
No
effe
ct14
.520
.514
.57.
73.
012
.78.
54.
710
.111
.819
.614
.97.
115
.211
.23.
012
.36.
616
.711
.66.
65.
410
.012
.514
.13.
75.
77.
329
.26.
116
.57.
710
.5N
egat
ive
effe
ct13
.05.
111
.510
.73.
08.
03.
99.
710
.514
.48.
052
.47.
537
.310
.73.
310
.54.
16.
213
.29.
61.
818
.317
.132
.78.
311
.38.
92.
011
.057
.71.
710
.9D
K12
.68.
518
.217
.920
.86.
76.
442
.012
.622
.418
.19.
635
.131
.922
.719
.813
.218
.87.
520
.723
.847
.342
.48.
715
.732
.738
.324
.13.
715
.011
.45.
619
.9
Cou
ntry
EU27
EU27
EU27
Cou
ntry
Cou
ntry
133
qb2a
- sp
lit b
allo
t A
Hav
e yo
u ev
er h
eard
of g
enet
ical
ly m
odifi
ed (o
r G
M) f
oods
bef
ore?
hear
d_gm
food
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Hea
rd73
.786
.794
.679
.773
.993
.185
.680
.284
.784
.293
.467
.959
.190
.889
.080
.176
.479
.574
.190
.083
.549
.381
.269
.491
.379
.269
.967
.690
.392
.589
.096
.383
.6N
ot h
eard
26.3
13.3
5.4
20.3
26.1
6.9
14.4
19.8
15.3
15.8
6.6
32.1
40.9
9.2
11.0
19.9
23.6
20.5
25.9
10.0
16.5
50.7
18.8
30.6
8.7
20.8
30.1
32.4
9.7
7.5
11.0
3.7
16.4
qb3a
[IF Y
ES] H
ave
you
ever
talk
ed a
bout
GM
food
with
any
one
befo
re to
day?
talk
ed_g
mfo
odB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, f
requ
ently
5.9
8.8
16.0
7.7
4.0
8.1
13.6
6.2
7.8
19.4
11.6
10.1
4.5
11.1
9.2
4.3
3.7
7.2
3.5
9.1
15.0
4.3
5.3
3.3
9.0
6.3
4.6
8.7
14.8
14.7
20.1
7.8
9.5
Yes
, occ
asio
nally
32.6
42.2
45.4
38.3
29.4
37.7
37.8
24.8
44.2
37.3
38.3
43.0
29.2
40.6
29.6
20.3
26.6
34.5
23.3
38.7
45.9
31.2
24.3
29.8
43.2
32.4
37.2
21.6
40.8
38.3
38.9
41.9
36.4
Yes
, onl
y on
ce o
r 17
.122
.016
.529
.523
.423
.513
.519
.021
.916
.319
.229
.131
.928
.517
.923
.622
.522
.533
.022
.515
.213
.419
.033
.124
.426
.325
.014
.822
.322
.120
.921
.920
.1N
o, n
ever
44.4
27.0
21.7
24.4
42.9
30.7
35.1
48.9
25.1
25.7
30.4
16.4
33.7
19.8
42.8
51.3
46.4
35.5
40.3
29.4
23.7
50.7
50.3
32.9
23.4
33.4
31.7
52.8
22.1
23.3
19.7
27.8
33.4
DK
.3.3
1.1
.91.
3.5
1.3
.8.6
.5.8
.3.2
.2.4
1.2
1.0
1.6
1.5
2.2
1.6
.4.6
.5
[IF Y
ES] H
ave
you
ever
sea
rche
d fo
r in
form
atio
n ab
out G
M fo
od?
info
sear
ch_g
mfo
odB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, f
requ
ently
2.5
5.0
6.6
5.2
2.1
4.9
7.1
3.6
4.9
11.3
5.4
4.6
2.9
4.0
3.2
3.3
2.9
4.2
3.1
4.4
9.0
3.9
4.5
2.1
5.4
2.2
3.7
3.6
7.1
7.4
12.4
2.6
4.7
Yes
, occ
asio
nally
22.1
15.1
20.7
24.2
14.5
21.7
22.9
15.1
22.9
22.5
20.5
24.4
13.3
24.4
12.5
16.9
11.1
17.9
8.9
18.9
23.7
20.5
12.1
19.7
18.6
9.9
16.1
9.4
18.8
21.8
25.0
16.9
18.2
Yes
, onl
y on
ce o
r 10
.115
.016
.624
.414
.216
.57.
47.
217
.710
.711
.417
.127
.220
.810
.514
.415
.618
.316
.416
.311
.98.
215
.517
.915
.015
.018
.512
.422
.011
.516
.321
.014
.5N
o, n
ever
65.4
64.9
55.9
45.8
69.0
56.7
62.5
72.5
54.0
54.2
62.7
53.9
56.3
50.8
73.0
65.0
70.5
59.4
71.7
60.2
55.1
66.9
67.6
59.7
61.0
70.3
59.6
71.4
52.1
56.1
46.3
59.3
62.1
DK
.1.4
.3.2
1.6
.51.
3.3
.7.5
.2.2
.3.4
.3.5
2.6
2.2
3.1
3.3
.2.4
qb4a
For
each
of t
he fo
llow
ing
issu
es r
egar
ding
GM
food
ple
ase
tell
me
if yo
u ag
ree
or d
isag
ree
with
it.
GM
food
is g
ood
for
the
(NA
TIO
NA
LITY
) eco
nom
y
gmfo
od_e
cono
my
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
5.0
9.9
8.6
4.6
9.2
3.5
4.2
3.2
3.5
2.4
6.6
2.9
4.5
8.6
7.4
4.1
1.5
5.3
3.0
3.2
7.0
7.9
4.6
3.6
2.7
4.4
4.1
4.2
7.2
5.1
4.7
5.5
5.9
Tend
to a
gree
29.2
37.8
24.0
16.1
31.4
17.5
20.4
17.6
23.4
16.5
34.3
16.2
24.9
23.8
35.2
13.8
31.4
20.6
21.9
18.1
21.9
19.9
14.9
26.7
15.3
12.2
20.9
5.1
25.4
9.8
19.0
24.9
24.9
Tend
to d
isag
ree
33.4
26.7
31.9
27.3
18.9
40.6
29.4
21.6
30.9
32.0
29.2
31.5
25.5
28.0
25.8
26.2
36.6
32.8
29.1
32.8
19.9
17.0
32.0
36.7
36.5
27.1
22.1
15.7
38.0
23.1
28.7
24.6
28.5
Tota
lly d
isag
ree
18.7
12.4
25.5
43.0
10.2
27.9
27.7
20.4
24.2
33.4
14.9
39.0
8.9
28.6
9.9
33.5
18.2
28.3
29.1
35.9
34.2
15.5
25.4
17.0
41.6
35.0
24.0
56.8
23.4
53.8
34.6
25.5
21.8
DK
13.6
13.2
10.1
8.9
30.4
10.5
18.2
37.2
18.0
15.7
15.0
10.4
36.2
11.1
21.7
22.4
12.3
13.0
17.0
9.9
16.9
39.7
23.1
16.0
4.0
21.2
28.8
18.3
6.0
8.2
13.0
19.5
18.9
GM
food
s is
not
goo
d fo
r yo
u an
d yo
ur fa
mily
gmfo
od_f
amily
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
20.2
28.9
44.0
61.0
15.4
31.2
29.2
14.5
28.4
35.0
16.4
36.2
16.0
34.1
10.3
59.3
17.1
31.3
29.6
49.7
46.5
8.4
26.3
19.0
44.5
27.0
25.5
48.4
29.1
44.0
31.0
29.3
27.1
Tend
to a
gree
30.9
31.0
25.2
17.1
27.9
28.2
25.5
24.9
30.7
25.0
26.3
24.2
33.3
22.1
30.1
14.1
26.6
28.4
26.1
29.8
17.9
27.7
26.6
32.7
26.2
20.6
16.8
9.4
29.9
16.7
23.4
30.2
26.7
Tend
to d
isag
ree
29.0
24.1
13.4
6.7
18.1
22.8
17.9
17.0
17.9
18.2
35.0
15.5
19.6
20.5
29.9
8.8
30.1
15.9
21.8
10.3
9.7
16.2
15.9
26.8
15.6
12.7
17.6
8.8
25.8
12.9
19.9
18.8
19.5
Tota
lly d
isag
ree
11.1
6.4
7.3
9.4
11.4
10.6
9.2
8.9
11.9
9.2
9.3
16.9
5.0
11.9
8.4
10.7
13.9
11.5
9.3
3.4
14.9
14.4
15.1
9.4
10.8
19.2
17.7
16.3
8.5
19.4
17.1
7.6
10.6
DK
8.7
9.6
10.2
5.8
27.2
7.2
18.1
34.7
11.0
12.6
13.0
7.2
26.0
11.5
21.3
7.1
12.3
12.9
13.2
6.8
11.1
33.3
16.2
12.2
2.8
20.5
22.4
17.1
6.7
6.9
8.7
14.1
16.0
Let’s
spe
ak n
ow a
bout
gen
etic
ally
mod
ified
(GM
) foo
d m
ade
from
pla
nts
or m
icro
-org
anis
ms
that
hav
e be
en c
hang
ed b
y al
teri
ng th
eir
gene
s. F
or e
xam
ple
a pl
ant m
ight
hav
e its
gen
es m
odifi
ed to
mak
e it
resi
stan
t to
a pa
rtic
ular
pla
nt
dise
ase,
to im
prov
e its
food
qua
lity
or to
hel
p it
grow
fast
er.
EU27
EU27
Cou
ntry
Cou
ntry
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
EU27
134
GM
food
hel
ps p
eopl
e in
dev
elop
ing
coun
trie
s
gmfo
od_d
evel
opin
gB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee14
.619
.710
.86.
512
.011
.47.
09.
69.
33.
514
.710
.47.
99.
317
.513
.711
.010
.28.
86.
910
.69.
611
.38.
911
.06.
15.
75.
819
.810
.38.
215
.510
.9Te
nd to
agr
ee38
.445
.129
.920
.334
.335
.531
.133
.024
.827
.940
.726
.028
.041
.541
.623
.137
.037
.333
.434
.531
.829
.927
.535
.927
.420
.722
.04.
746
.126
.930
.334
.031
.6Te
nd to
dis
agre
e20
.914
.721
.629
.013
.927
.320
.610
.525
.827
.121
.828
.121
.015
.518
.715
.822
.120
.524
.325
.017
.59.
118
.026
.828
.016
.718
.013
.924
.721
.023
.218
.120
.5To
tally
dis
agre
e15
.89.
624
.632
.813
.816
.720
.89.
520
.623
.012
.123
.49.
024
.35.
117
.311
.114
.018
.520
.019
.011
.815
.19.
226
.617
.019
.952
.64.
429
.527
.317
.717
.2D
K10
.410
.913
.211
.426
.09.
220
.537
.519
.518
.610
.712
.234
.29.
317
.130
.018
.717
.915
.113
.621
.139
.628
.119
.17.
039
.534
.423
.05.
012
.311
.114
.719
.7
GM
food
is s
afe
for
futu
re g
ener
atio
ns
gmfo
od_s
afe
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
5.7
6.9
2.9
1.9
5.8
2.8
1.3
3.7
4.0
2.3
4.0
5.6
3.4
2.5
6.0
3.2
3.5
4.3
2.4
2.5
2.2
3.1
4.4
3.1
3.9
2.9
3.7
4.1
5.5
4.9
4.0
4.6
3.9
Tend
to a
gree
26.6
26.3
12.6
8.4
23.5
17.7
9.6
19.0
19.1
13.9
30.1
16.2
22.0
7.2
25.3
5.1
27.8
17.0
18.8
5.8
6.0
12.9
12.6
26.5
16.5
8.3
9.9
3.6
26.5
10.8
10.8
19.7
17.3
Tend
to d
isag
ree
31.1
34.2
34.2
24.6
20.8
40.7
30.6
17.3
29.4
33.9
30.0
26.6
29.0
29.1
28.3
23.4
32.3
28.8
34.0
30.5
20.7
19.7
30.0
34.2
32.6
22.7
22.9
12.3
37.3
26.8
32.4
29.3
29.1
Tota
lly d
isag
ree
23.8
22.5
38.1
56.9
20.5
27.5
40.5
14.9
26.1
35.0
16.5
40.8
12.6
50.9
10.6
47.3
19.1
33.7
27.6
52.3
55.0
19.7
31.1
18.7
39.2
41.2
33.1
63.5
22.6
45.0
36.8
28.0
28.9
DK
12.8
10.2
12.2
8.2
29.4
11.3
18.0
45.2
21.4
14.9
19.5
10.9
33.0
10.2
29.7
21.1
17.3
16.3
17.1
8.9
16.1
44.5
21.9
17.6
7.8
24.9
30.5
16.6
8.1
12.5
15.9
18.4
20.8
GM
food
ben
efits
som
e pe
ople
but
put
s ot
hers
at r
isk
gmfo
od_u
nequ
alB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee13
.926
.537
.824
.820
.019
.314
.910
.517
.121
.516
.422
.912
.29.
613
.231
.68.
313
.722
.826
.264
.310
.819
.715
.440
.427
.534
.129
.913
.522
.530
.527
.521
.4Te
nd to
agr
ee42
.445
.036
.834
.634
.639
.028
.432
.734
.030
.546
.538
.433
.227
.041
.830
.342
.931
.235
.634
.919
.223
.535
.742
.035
.134
.129
.911
.937
.933
.432
.137
.235
.7Te
nd to
dis
agre
e19
.313
.112
.316
.914
.724
.916
.411
.017
.017
.916
.817
.515
.316
.815
.96.
423
.619
.617
.616
.53.
310
.815
.922
.110
.07.
38.
213
.227
.714
.413
.612
.115
.2To
tally
dis
agre
e12
.97.
16.
316
.46.
17.
420
.86.
412
.915
.05.
79.
64.
831
.05.
37.
18.
917
.99.
914
.62.
410
.210
.54.
88.
07.
16.
724
.911
.017
.313
.16.
810
.1D
K11
.58.
36.
97.
224
.69.
319
.439
.419
.015
.114
.611
.634
.515
.723
.824
.616
.417
.614
.27.
810
.844
.718
.215
.56.
524
.021
.120
.110
.012
.310
.716
.417
.6
GM
food
is fu
ndam
enta
lly u
nnat
ural
gmfo
od_u
nnat
ural
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
38.8
53.4
40.0
71.5
37.5
46.6
44.9
26.1
37.0
45.7
37.5
44.8
25.1
59.1
29.6
69.6
38.1
42.2
42.7
55.9
58.8
27.1
36.9
34.2
59.1
37.5
31.3
53.6
31.3
55.0
44.1
53.7
39.0
Tend
to a
gree
29.6
26.9
28.7
17.9
29.5
31.1
28.8
26.4
33.1
28.8
29.7
33.1
31.8
20.0
34.6
21.0
36.3
30.9
30.1
24.9
21.2
27.4
37.6
41.2
24.2
30.6
24.8
9.8
34.0
25.4
29.9
26.2
30.7
Tend
to d
isag
ree
18.8
12.2
19.0
4.7
14.0
13.0
10.7
12.0
15.8
12.6
21.4
12.7
15.9
12.2
17.1
3.2
17.2
11.7
14.7
10.5
6.4
11.3
9.2
14.5
8.3
6.6
11.6
9.1
24.5
6.3
14.5
10.9
14.5
Tota
lly d
isag
ree
5.7
2.6
5.9
1.4
5.6
4.6
5.6
4.6
7.0
6.3
8.3
2.8
4.7
5.0
6.2
.43.
04.
74.
63.
23.
13.
55.
82.
54.
75.
57.
411
.95.
07.
16.
75.
95.
7D
K7.
14.
96.
54.
513
.34.
710
.030
.97.
16.
63.
16.
622
.53.
712
.55.
85.
310
.67.
95.
410
.430
.610
.57.
53.
719
.824
.915
.65.
16.
14.
83.
310
.1
GM
food
mak
es y
ou fe
el u
neas
y
gmfo
od_u
neas
yB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee22
.531
.650
.368
.322
.535
.125
.920
.125
.431
.826
.845
.115
.934
.821
.364
.521
.528
.618
.346
.456
.614
.434
.120
.553
.728
.223
.751
.222
.947
.636
.231
.830
.7Te
nd to
agr
ee30
.338
.127
.620
.328
.828
.329
.327
.931
.328
.731
.332
.634
.524
.228
.420
.434
.032
.428
.425
.922
.625
.734
.538
.025
.427
.529
.311
.029
.929
.129
.733
.629
.6Te
nd to
dis
agre
e25
.914
.412
.45.
322
.721
.919
.115
.620
.716
.123
.713
.319
.420
.126
.77.
229
.317
.822
.316
.37.
512
.813
.226
.813
.37.
214
.59.
724
.16.
915
.117
.218
.8To
tally
dis
agre
e12
.411
.56.
21.
714
.211
.313
.85.
912
.37.
814
.83.
26.
915
.311
.4.8
8.5
11.7
8.9
3.9
4.1
7.5
7.9
6.3
5.1
7.4
10.3
11.3
21.4
10.2
12.3
13.4
10.3
DK
8.9
4.5
3.6
4.3
11.8
3.5
12.0
30.4
10.4
15.6
3.4
5.8
23.3
5.6
12.1
7.1
6.7
9.5
22.1
7.5
9.2
39.6
10.3
8.4
2.4
29.7
22.1
16.8
1.8
6.3
6.6
4.1
10.5
Cou
ntry
Cou
ntry
EU27
EU27
Cou
ntry
EU27
EU27
Cou
ntry
Cou
ntry
EU27
135
GM
food
is s
afe
for
your
hea
lth a
nd y
our
fam
ily’s
hea
lth
gmfo
od_h
ealth
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
7.5
5.5
4.5
3.4
5.1
5.7
3.9
2.5
5.3
5.6
5.3
4.3
4.2
6.0
8.1
2.9
4.1
5.8
2.7
4.2
3.7
2.1
7.3
4.2
3.7
1.6
2.3
5.2
7.1
4.2
4.5
6.2
5.2
Tend
to a
gree
22.9
16.8
13.2
5.3
20.3
15.9
12.0
18.2
16.4
14.4
32.2
13.2
21.2
11.4
25.4
5.1
26.3
17.4
19.8
9.0
4.7
11.8
15.1
23.0
11.7
8.4
9.8
3.9
24.7
7.2
10.9
20.2
17.0
Tend
to d
isag
ree
33.5
40.5
28.6
18.1
21.0
32.8
26.3
20.1
27.8
21.2
28.2
27.1
26.2
27.7
25.3
17.9
30.0
27.4
27.4
25.6
19.2
21.6
26.6
34.5
28.5
23.7
19.2
12.2
32.9
21.1
27.4
29.3
26.3
Tota
lly d
isag
ree
25.4
29.1
44.8
66.6
26.1
36.6
36.0
19.7
34.7
41.9
17.8
46.2
21.5
43.3
13.6
65.1
24.4
34.6
32.3
52.5
59.8
21.8
33.4
23.6
49.7
44.7
42.7
61.3
28.4
58.1
41.9
32.6
33.1
DK
10.7
8.2
8.9
6.6
27.5
9.0
21.8
39.5
15.9
16.9
16.6
9.2
26.9
11.6
27.7
8.9
15.2
14.8
17.9
8.8
12.6
42.8
17.6
14.6
6.4
21.6
26.1
17.3
6.9
9.4
15.3
11.7
18.5
GM
food
doe
s no
har
m to
the
envi
ronm
ent
gmfo
od_e
nviro
nmen
tB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee7.
13.
44.
53.
45.
65.
43.
92.
55.
94.
13.
24.
74.
12.
55.
12.
07.
25.
14.
96.
85.
34.
16.
34.
610
.25.
15.
23.
94.
17.
34.
24.
95.
0Te
nd to
agr
ee23
.421
.116
.010
.322
.120
.711
.514
.819
.215
.518
.916
.823
.78.
419
.99.
733
.925
.527
.016
.18.
716
.819
.431
.017
.812
.315
.73.
717
.412
.211
.613
.018
.2Te
nd to
dis
agre
e38
.537
.629
.129
.920
.134
.028
.618
.530
.826
.135
.625
.321
.432
.829
.824
.226
.727
.727
.428
.324
.115
.424
.433
.727
.723
.219
.314
.441
.221
.830
.929
.027
.8To
tally
dis
agre
e17
.923
.635
.344
.213
.328
.235
.615
.122
.630
.117
.738
.211
.440
.614
.937
.413
.618
.316
.833
.738
.814
.022
.710
.932
.125
.622
.356
.925
.340
.933
.226
.724
.6D
K13
.114
.315
.112
.238
.911
.820
.449
.121
.424
.224
.614
.939
.415
.730
.326
.818
.623
.423
.915
.123
.149
.727
.319
.812
.333
.937
.421
.112
.117
.720
.126
.524
.4
The
deve
lopm
ent o
f GM
food
sho
uld
be e
ncou
rage
d
gmfo
od_e
ncou
rage
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
6.4
5.4
4.3
2.4
5.5
7.5
2.4
2.7
3.3
3.5
6.7
4.0
2.8
5.0
7.0
1.2
5.5
4.8
4.9
2.7
3.2
2.8
5.9
4.9
3.8
4.2
3.6
2.7
8.7
3.5
5.7
7.7
4.6
Tend
to a
gree
19.4
23.5
15.7
6.9
21.4
19.6
11.4
18.6
16.6
13.5
20.6
17.4
22.0
21.2
28.4
6.9
29.6
19.3
22.0
10.3
6.5
16.9
18.6
27.3
16.2
6.4
7.7
2.5
29.0
8.3
12.2
19.1
18.2
Tend
to d
isag
ree
34.8
30.2
27.4
24.3
21.9
30.8
28.2
16.1
31.6
23.2
37.8
24.6
25.7
27.7
27.0
23.9
32.5
29.5
28.6
28.2
21.5
19.3
29.8
37.6
26.7
26.7
22.0
11.9
34.6
20.4
30.5
28.3
27.9
Tota
lly d
isag
ree
30.3
30.4
44.7
58.2
27.0
32.7
42.8
20.5
32.1
49.0
25.0
45.3
17.3
38.6
17.5
50.4
17.3
32.6
29.4
52.2
55.3
22.7
27.0
15.3
49.1
41.8
38.8
61.8
23.8
57.1
41.0
33.5
33.1
DK
9.1
10.5
7.9
8.1
24.2
9.3
15.2
42.0
16.4
10.9
9.8
8.7
32.2
7.5
20.0
17.6
15.1
13.8
15.0
6.6
13.4
38.3
18.6
14.9
4.3
20.8
27.9
21.1
3.9
10.7
10.6
11.3
16.3
Cou
ntry
Cou
ntry
EU27
EU27
EU27
Cou
ntry
136
Split
bal
lot B
qb2b
Hav
e yo
u ev
er h
eard
of n
anot
echn
olog
y be
fore
?
hear
d_na
note
chB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OH
eard
41.0
77.1
64.7
44.7
32.1
73.3
53.9
33.4
37.3
56.5
61.2
47.0
20.9
74.8
47.5
36.7
58.7
46.6
47.0
52.0
35.5
21.7
30.7
34.5
45.5
30.6
25.5
25.3
59.5
45.4
75.7
77.7
46.3
Not
hea
rd59
.022
.935
.355
.367
.926
.746
.166
.662
.743
.538
.853
.079
.125
.252
.563
.341
.353
.453
.048
.064
.578
.369
.365
.554
.569
.474
.574
.740
.554
.624
.322
.353
.7
qb3b
[IF Y
ES] H
ave
you
ever
talk
ed a
bout
nan
otec
hnol
ogy
with
any
one
befo
re to
day?
talk
ed_n
anot
ech
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Yes
, fre
quen
tly2.
99.
29.
94.
23.
15.
17.
43.
44.
25.
16.
93.
33.
34.
33.
03.
31.
32.
03.
74.
34.
13.
62.
73.
15.
44.
03.
52.
07.
73.
77.
56.
35.
6Y
es, o
ccas
iona
lly21
.630
.727
.729
.914
.427
.021
.518
.637
.521
.625
.528
.135
.019
.122
.415
.914
.821
.914
.920
.726
.220
.217
.218
.624
.328
.023
.29.
025
.421
.530
.230
.824
.5Y
es, o
nly
once
or
18.8
27.3
21.4
35.4
28.0
16.5
15.4
24.0
18.9
14.8
18.5
39.9
26.8
28.4
16.0
22.8
25.8
25.4
25.6
25.0
19.7
17.2
21.1
36.8
26.2
25.0
16.0
17.2
26.2
25.5
27.8
23.7
21.1
No,
nev
er56
.732
.840
.430
.554
.551
.555
.753
.438
.357
.049
.128
.134
.947
.357
.558
.056
.950
.755
.850
.149
.459
.158
.541
.444
.142
.252
.170
.540
.747
.934
.338
.948
.2D
K.6
.61.
11.
5.6
.91.
11.
2.5
.5.8
5.2
1.4
1.4
.2.3
.6
[IF Y
ES] H
ave
you
ever
sea
rche
d fo
r in
form
atio
n ab
out n
anot
echn
olog
y?
info
sear
ch_n
anot
ech
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Yes
, fre
quen
tly2.
56.
95.
73.
63.
04.
45.
73.
82.
45.
04.
12.
57.
63.
31.
73.
41.
12.
21.
52.
42.
93.
63.
42.
35.
21.
74.
82.
03.
24.
84.
73.
83.
9Y
es, o
ccas
iona
lly16
.115
.915
.416
.910
.317
.518
.013
.122
.416
.918
.113
.220
.913
.09.
510
.98.
112
.015
.014
.517
.418
.58.
616
.113
.817
.916
.94.
415
.612
.117
.917
.015
.0Y
es, o
nly
once
or
17.7
16.9
13.5
22.0
10.7
13.9
10.3
14.0
20.0
12.8
16.3
24.8
22.0
15.5
9.0
14.7
20.9
16.4
16.0
19.2
14.5
10.9
18.9
24.5
12.8
19.4
11.9
10.6
15.4
16.5
14.9
19.5
14.5
No,
nev
er63
.660
.464
.657
.476
.064
.366
.169
.155
.263
.961
.559
.548
.367
.878
.771
.069
.969
.467
.563
.965
.267
.067
.857
.268
.259
.863
.780
.865
.765
.062
.059
.466
.1D
K.8
1.5
1.3
.41.
11.
21.
22.
72.
21.
6.4
.3.5
qb4b
For
each
of t
he fo
llow
ing
stat
emen
ts r
egar
ding
nan
otec
hnol
ogy
plea
se te
ll m
e if
you
agre
e or
dis
agre
e w
ith it
.N
anot
echn
olog
y is
goo
d fo
r th
e (N
ATI
ON
ALI
TY) e
cono
my
nano
tech
_eco
nom
yB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee8.
09.
519
.013
.79.
513
.59.
25.
96.
07.
812
.75.
74.
615
.27.
514
.313
.512
.59.
13.
110
.44.
96.
45.
010
.78.
05.
411
.36.
511
.514
.512
.410
.0Te
nd to
agr
ee42
.740
.838
.237
.128
.245
.534
.821
.934
.631
.040
.929
.027
.236
.136
.536
.042
.639
.741
.020
.330
.315
.630
.342
.938
.630
.524
.613
.540
.133
.936
.933
.834
.6Te
nd to
dis
agre
e19
.619
.511
.818
.110
.119
.414
.85.
815
.419
.117
.117
.610
.513
.211
.97.
915
.412
.914
.827
.913
.213
.09.
121
.718
.110
.08.
013
.822
.913
.710
.117
.213
.1To
tally
dis
agre
e5.
84.
85.
610
.47.
54.
57.
63.
09.
06.
95.
410
.04.
77.
45.
36.
44.
75.
95.
111
.86.
38.
83.
83.
914
.13.
54.
814
.04.
513
.45.
16.
46.
4D
K23
.925
.425
.420
.744
.817
.133
.663
.535
.035
.223
.937
.753
.028
.138
.835
.423
.829
.029
.936
.939
.957
.750
.426
.518
.548
.057
.247
.426
.027
.533
.530
.236
.0
Nan
otec
hnol
ogy
is n
ot g
ood
for
you
and
your
fam
ily
nano
tech
_fam
ilyB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee8.
59.
08.
418
.49.
65.
06.
82.
610
.55.
26.
510
.67.
57.
36.
711
.07.
45.
77.
55.
28.
53.
94.
95.
715
.05.
64.
210
.93.
110
.96.
94.
87.
9Te
nd to
agr
ee25
.024
.319
.826
.116
.421
.919
.314
.421
.121
.915
.425
.023
.317
.618
.019
.727
.718
.616
.911
.917
.116
.116
.528
.028
.518
.113
.712
.017
.820
.113
.719
.619
.3Te
nd to
dis
agre
e36
.633
.931
.525
.618
.244
.128
.216
.522
.928
.237
.818
.316
.629
.527
.626
.733
.731
.534
.133
.224
.814
.422
.831
.827
.817
.614
.514
.446
.523
.927
.334
.226
.3To
tally
dis
agre
e8.
311
.212
.410
.214
.611
.911
.36.
314
.314
.412
.111
.64.
717
.07.
48.
76.
011
.411
.512
.912
.28.
47.
28.
510
.29.
310
.712
.98.
219
.015
.119
.711
.0D
K21
.621
.727
.919
.841
.117
.034
.460
.331
.130
.328
.134
.547
.928
.640
.334
.025
.232
.829
.936
.837
.457
.348
.626
.018
.549
.457
.049
.924
.326
.037
.121
.835
.5
Cou
ntry
EU27
Cou
ntry
EU27
EU27
Cou
ntry
Cou
ntry
And
now
thin
king
abo
ut n
anot
echn
olog
y: N
anot
echn
olog
y in
volv
es w
orki
ng w
ith a
tom
s an
d m
olec
ules
to m
ake
new
par
ticle
s th
at a
re u
sed
in c
osm
etic
s to
mak
e be
tter
anti-
agin
g cr
eam
s, s
unta
n oi
ls fo
r be
tter
prot
ectio
n ag
ains
t ski
n ca
ncer
and
cle
anin
g flu
ids
to m
ake
the
hom
e m
ore
hygi
enic
. Des
pite
thes
e be
nefit
s, s
ome
scie
ntis
ts a
re c
once
rned
abo
ut th
e un
know
n an
d po
ssib
ly n
egat
ive
effe
cts
of n
ano
part
icle
s in
the
body
and
in th
e en
viro
nmen
t
EU27
EU27
Cou
ntry
137
Nan
otec
hnol
ogy
help
s pe
ople
in d
evel
opin
g co
untr
ies
nano
tech
_dev
elop
ing
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
5.4
6.2
4.7
14.7
7.0
8.3
5.3
4.1
7.1
6.5
5.4
7.3
3.8
9.4
5.0
19.3
6.8
10.8
11.8
9.6
10.0
4.5
7.9
4.7
9.6
6.9
5.8
8.4
2.8
8.2
4.1
9.4
6.4
Tend
to a
gree
28.1
32.5
22.6
33.1
25.7
34.1
22.4
19.8
26.0
23.2
23.1
21.1
21.3
28.5
29.9
35.6
34.2
33.0
40.5
33.7
28.2
14.5
28.9
36.4
30.4
24.4
19.9
12.0
34.7
30.8
23.4
27.8
26.2
Tend
to d
isag
ree
30.5
23.2
27.2
19.1
14.5
27.8
21.7
11.0
20.1
27.7
25.6
19.8
16.7
16.7
16.9
7.2
21.3
14.2
14.1
12.7
12.9
14.4
9.6
23.1
22.8
8.7
9.4
14.2
29.5
16.1
23.4
20.8
19.1
Tota
lly d
isag
ree
13.5
14.1
17.9
11.0
11.4
11.0
15.4
3.2
12.1
5.9
18.4
14.5
6.2
14.6
8.0
5.8
7.2
6.7
6.5
5.7
6.1
9.4
2.4
5.5
16.7
4.7
6.9
13.2
8.6
16.6
15.1
14.7
11.4
DK
22.4
24.0
27.6
22.1
41.3
18.9
35.1
61.8
34.8
36.6
27.4
37.3
52.0
30.9
40.3
32.0
30.6
35.2
27.2
38.3
42.7
57.2
51.3
30.3
20.5
55.4
58.0
52.2
24.4
28.4
33.9
27.3
36.9
Nan
otec
hnol
ogy
is s
afe
for
futu
re g
ener
atio
ns
nano
tech
_saf
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee4.
45.
66.
09.
87.
15.
76.
33.
56.
87.
54.
37.
22.
72.
55.
17.
18.
45.
810
.55.
25.
51.
65.
05.
311
.33.
77.
58.
62.
17.
75.
28.
36.
1Te
nd to
agr
ee33
.333
.120
.026
.826
.943
.219
.017
.028
.826
.525
.018
.919
.711
.226
.427
.043
.930
.537
.724
.021
.67.
623
.540
.226
.017
.420
.713
.136
.628
.117
.625
.124
.7Te
nd to
dis
agre
e29
.230
.227
.125
.513
.921
.826
.29.
516
.525
.327
.421
.118
.432
.914
.413
.514
.420
.416
.825
.217
.315
.911
.823
.621
.517
.28.
413
.023
.120
.623
.525
.819
.6To
tally
dis
agre
e10
.47.
713
.113
.68.
06.
416
.14.
59.
24.
88.
816
.57.
525
.05.
07.
04.
96.
36.
17.
69.
19.
95.
35.
615
.78.
36.
914
.77.
613
.015
.210
.69.
8D
K22
.823
.433
.824
.344
.022
.932
.365
.538
.735
.934
.536
.351
.728
.349
.245
.428
.537
.029
.038
.046
.565
.054
.325
.425
.553
.356
.550
.630
.530
.738
.430
.239
.8
Nan
otec
hnol
ogy
bene
fits
som
e pe
ople
but
put
s ot
hers
at r
isk
nano
tech
_une
qual
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
14.1
20.6
21.8
17.0
11.6
9.0
16.1
3.7
9.5
14.1
17.4
17.0
9.8
15.8
8.3
21.9
7.2
13.1
14.4
17.2
24.1
5.6
12.1
7.9
23.8
12.0
13.2
11.8
8.9
14.9
19.9
20.1
13.8
Tend
to a
gree
42.7
46.1
39.3
41.5
28.6
44.2
45.2
20.2
31.6
38.0
41.5
36.9
24.9
39.8
37.7
33.8
37.5
35.5
40.1
34.3
29.7
26.2
29.1
41.1
38.2
29.8
23.3
12.8
35.9
32.9
37.3
39.6
35.9
Tend
to d
isag
ree
19.8
11.7
12.4
16.0
11.7
23.6
6.2
10.8
16.5
14.3
13.9
14.5
10.7
8.8
12.2
9.1
22.2
15.4
17.8
13.3
8.6
7.8
8.8
20.9
14.3
7.8
6.8
13.2
20.8
15.1
10.1
12.3
12.2
Tota
lly d
isag
ree
4.5
3.4
3.3
5.3
6.9
4.2
5.1
2.6
7.9
4.9
3.2
3.0
4.2
5.8
2.9
2.0
4.6
6.5
5.4
3.2
3.2
2.4
2.3
4.2
4.2
2.3
4.9
10.2
3.8
9.6
4.9
5.9
4.6
DK
18.9
18.2
23.2
20.2
41.2
19.0
27.3
62.7
34.5
28.8
24.0
28.5
50.4
29.8
38.8
33.1
28.5
29.4
22.3
32.0
34.4
57.9
47.6
25.9
19.5
48.0
51.8
52.0
30.6
27.6
27.9
22.2
33.6
Nan
otec
hnol
ogy
is fu
ndam
enta
lly u
nnat
ural
nano
tech
_unn
atur
alB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee20
.222
.413
.630
.521
.110
.926
.54.
114
.916
.919
.220
.519
.622
.011
.822
.410
.511
.312
.919
.824
.411
.910
.112
.137
.77.
811
.217
.58.
623
.919
.819
.616
.5Te
nd to
agr
ee33
.430
.920
.028
.022
.832
.136
.020
.828
.142
.226
.824
.421
.524
.625
.917
.331
.125
.131
.523
.824
.925
.523
.841
.528
.424
.015
.811
.432
.332
.429
.824
.226
.1Te
nd to
dis
agre
e23
.822
.928
.820
.016
.129
.59.
314
.719
.311
.324
.421
.69.
518
.121
.321
.234
.723
.526
.016
.89.
57.
517
.820
.09.
317
.19.
510
.229
.414
.714
.819
.319
.6To
tally
dis
agre
e6.
78.
111
.74.
510
.310
.74.
34.
77.
86.
511
.36.
22.
316
.36.
26.
03.
410
.09.
34.
83.
11.
73.
73.
97.
14.
44.
09.
24.
76.
211
.419
.37.
4D
K16
.015
.825
.817
.129
.716
.823
.955
.729
.723
.218
.327
.347
.019
.034
.833
.120
.330
.120
.334
.738
.053
.444
.622
.517
.546
.859
.651
.725
.022
.724
.217
.730
.5
Nan
otec
hnol
ogy
mak
es y
ou fe
el u
neas
y
nano
tech
_une
asy
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
10.2
11.8
16.7
26.2
10.6
8.1
11.7
6.6
6.9
9.9
14.1
18.1
9.8
5.9
9.0
20.9
4.0
5.8
5.1
7.8
13.0
5.2
8.3
7.9
24.9
4.4
5.7
14.0
1.6
17.0
13.3
5.7
10.7
Tend
to a
gree
26.0
22.6
21.3
31.6
18.5
19.5
18.3
18.6
22.3
24.2
19.2
32.3
21.1
16.9
22.7
24.4
19.4
16.0
17.3
15.7
18.6
15.0
19.4
23.4
27.4
14.6
14.7
9.5
16.2
27.5
20.8
17.7
20.7
Tend
to d
isag
ree
33.7
29.8
28.0
26.0
24.6
32.4
26.8
16.4
24.1
21.9
29.2
21.9
17.3
19.5
24.5
24.5
41.7
29.6
33.9
27.0
23.5
11.6
24.8
37.5
20.7
20.1
14.3
13.1
38.5
20.0
28.5
25.9
25.7
Tota
lly d
isag
ree
12.5
22.2
17.4
4.0
24.9
27.6
22.7
6.3
18.0
19.7
23.6
9.6
5.7
40.9
14.5
12.4
17.1
24.9
15.5
16.6
12.0
7.0
9.2
10.2
14.4
9.9
10.2
10.8
33.9
15.3
20.1
37.5
17.1
DK
17.7
13.6
16.6
12.3
21.5
12.5
20.5
52.1
28.7
24.4
13.9
18.0
46.1
16.9
29.2
17.8
17.8
23.7
28.2
32.9
32.9
61.1
38.3
21.0
12.7
50.9
55.1
52.6
9.8
20.2
17.4
13.1
25.9
Cou
ntry
EU27
EU27
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
EU27
Cou
ntry
138
Nan
otec
hnol
ogy
is s
afe
for
your
hea
lth a
nd y
our
fam
ily’s
hea
lth
nano
tech
_hea
lthB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee3.
85.
36.
56.
05.
35.
53.
31.
03.
45.
84.
14.
71.
210
.73.
53.
84.
85.
87.
56.
93.
75.
35.
22.
66.
32.
72.
45.
63.
25.
85.
711
.24.
5Te
nd to
agr
ee27
.929
.919
.623
.922
.842
.112
.616
.224
.616
.526
.920
.716
.516
.724
.923
.442
.531
.032
.324
.817
.910
.323
.738
.425
.518
.115
.29.
835
.421
.617
.824
.222
.3Te
nd to
dis
agre
e35
.632
.731
.128
.214
.423
.928
.111
.119
.932
.927
.125
.218
.327
.817
.719
.517
.620
.819
.220
.819
.710
.911
.727
.225
.916
.611
.712
.424
.320
.228
.826
.121
.9To
tally
dis
agre
e9.
910
.412
.920
.812
.68.
318
.96.
212
.39.
410
.017
.912
.513
.15.
416
.05.
88.
37.
310
.212
.89.
25.
66.
820
.39.
58.
818
.06.
622
.813
.512
.811
.4D
K22
.821
.729
.921
.144
.920
.337
.165
.639
.835
.431
.931
.551
.531
.748
.537
.429
.334
.133
.737
.446
.064
.253
.825
.022
.053
.161
.954
.130
.629
.534
.325
.839
.9
Nan
otec
hnol
ogy
does
no
harm
to th
e en
viro
nmen
t
nano
tech
_env
ironm
enB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee2.
44.
84.
87.
73.
34.
42.
81.
14.
23.
12.
55.
22.
04.
53.
13.
67.
45.
88.
14.
34.
03.
14.
04.
29.
43.
42.
96.
2.7
5.7
5.0
6.7
3.9
Tend
to a
gree
23.3
22.9
17.2
22.7
18.8
33.7
13.7
15.5
21.5
24.2
15.9
21.1
14.3
13.5
16.0
17.9
39.2
25.2
30.1
19.3
14.0
8.7
20.9
33.2
20.8
18.0
15.6
6.6
15.8
20.3
12.5
14.9
18.9
Tend
to d
isag
ree
39.8
34.7
30.0
28.1
17.1
29.6
28.2
9.6
18.9
21.4
33.6
23.3
18.3
33.4
20.9
17.4
19.2
23.3
21.5
26.5
20.7
12.4
15.5
30.4
25.9
14.9
9.8
14.5
38.7
19.9
26.3
28.9
23.0
Tota
lly d
isag
ree
11.0
10.6
13.0
16.0
9.9
7.0
13.8
4.7
10.5
12.0
10.5
14.5
7.3
13.0
5.6
10.1
5.2
6.4
6.0
9.3
9.3
5.5
5.9
6.3
18.2
6.5
7.4
16.0
5.6
19.6
15.6
10.4
9.9
DK
23.5
27.1
35.0
25.4
50.9
25.3
41.5
69.1
45.0
39.3
37.5
35.9
58.1
35.5
54.5
51.1
29.0
39.2
34.2
40.6
51.9
70.3
53.7
25.9
25.7
57.2
64.4
56.7
39.2
34.6
40.6
39.2
44.2
Nan
otec
hnol
ogy
shou
ld b
e en
cour
aged
nano
tech
_enc
oura
geB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee7.
715
.712
.68.
98.
417
.26.
84.
66.
28.
69.
86.
12.
016
.39.
59.
815
.210
.112
.211
.47.
35.
56.
57.
09.
66.
65.
68.
816
.09.
611
.421
.38.
8Te
nd to
agr
ee35
.732
.133
.029
.428
.647
.234
.017
.228
.728
.131
.327
.322
.133
.828
.837
.242
.634
.339
.633
.229
.314
.230
.139
.332
.221
.620
.79.
452
.427
.332
.832
.530
.7Te
nd to
dis
agre
e25
.423
.218
.827
.811
.613
.117
.06.
617
.725
.925
.222
.315
.318
.615
.76.
614
.717
.217
.615
.913
.78.
58.
222
.923
.210
.47.
911
.711
.815
.618
.017
.116
.2To
tally
dis
agre
e9.
67.
010
.313
.110
.65.
410
.14.
111
.26.
810
.313
.98.
26.
55.
77.
62.
76.
36.
76.
87.
69.
54.
45.
815
.98.
46.
914
.61.
913
.48.
18.
58.
7D
K21
.721
.925
.320
.840
.817
.132
.267
.536
.230
.723
.330
.452
.324
.940
.438
.824
.832
.223
.932
.642
.062
.250
.724
.919
.053
.058
.955
.417
.934
.229
.720
.535
.6
EU27
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
139
qb5b
Hav
e yo
u ev
er h
eard
of a
nim
al c
loni
ng in
food
pro
duct
ion
befo
re?
hear
d_an
imal
clon
ing
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Hea
rd74
.681
.387
.484
.573
.784
.476
.561
.462
.878
.686
.871
.158
.587
.080
.872
.771
.267
.476
.470
.056
.653
.569
.565
.974
.770
.053
.954
.841
.978
.575
.573
.874
.9N
ot h
eard
25.4
18.7
12.6
15.5
26.3
15.6
23.5
38.6
37.2
21.4
13.2
28.9
41.5
13.0
19.2
27.3
28.8
32.6
23.6
30.0
43.4
46.5
30.5
34.1
25.3
30.0
46.1
45.2
58.1
21.5
24.5
26.2
25.1
qb6b
[IF Y
ES] H
ave
you
ever
talk
ed a
bout
ani
mal
clo
ning
in fo
od p
rodu
ctio
n w
ith a
nyon
e be
fore
toda
y?
talk
ed_a
nim
alcl
onin
gB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, f
requ
ently
4.0
9.0
12.8
3.3
4.0
4.6
5.1
2.1
5.5
6.6
9.3
3.9
1.9
5.6
3.9
2.2
1.4
2.2
2.6
1.5
2.9
4.6
2.7
3.3
5.0
.6.6
2.4
4.9
9.1
10.3
5.0
5.8
Yes
, occ
asio
nally
19.0
36.0
37.5
37.8
20.0
31.8
29.0
18.2
36.7
35.8
30.9
38.3
32.0
34.8
26.0
17.6
23.4
25.5
14.5
25.2
26.3
28.4
17.2
23.3
34.9
26.9
22.3
9.2
27.4
28.9
37.0
38.6
28.9
Yes
, onl
y on
ce o
r 20
.926
.620
.429
.531
.126
.516
.128
.425
.022
.022
.231
.828
.929
.720
.433
.929
.921
.835
.521
.820
.09.
026
.330
.426
.929
.825
.417
.428
.825
.028
.325
.023
.8N
o, n
ever
54.9
28.2
29.1
29.2
44.4
37.1
49.4
49.0
31.1
34.0
36.9
25.4
36.5
29.2
49.5
45.4
44.8
50.2
47.4
50.4
49.9
57.9
53.1
43.0
32.9
40.6
47.8
68.0
38.9
36.8
24.5
30.3
40.8
DK
1.2
.2.1
.2.5
.32.
31.
71.
6.6
.6.6
.7.2
1.0
.4.3
1.2
.9.8
.32.
23.
83.
1.2
1.1
.7
[IF Y
ES] H
ave
you
ever
talk
ed a
bout
ani
mal
clo
ning
in fo
od p
rodu
ctio
n w
ith a
nyon
e be
fore
toda
y?
info
sear
ch_a
nim
alcl
onB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, f
requ
ently
2.0
3.6
3.0
2.4
1.3
3.3
3.2
1.3
2.0
2.7
3.5
2.9
1.0
1.6
3.7
1.7
.9.9
.7.8
1.6
2.4
1.1
1.1
3.0
.3.8
.81.
94.
53.
81.
32.
4Y
es, o
ccas
iona
lly8.
611
.113
.618
.711
.215
.710
.97.
622
.520
.313
.611
.711
.313
.48.
88.
89.
88.
87.
68.
912
.712
.410
.213
.213
.08.
710
.23.
74.
715
.115
.011
.512
.6Y
es, o
nly
once
or
11.9
14.9
14.0
28.0
9.9
19.7
8.3
9.6
14.5
14.3
14.2
18.9
19.0
17.9
6.5
14.3
13.8
9.7
15.6
12.1
9.6
8.0
11.2
14.6
17.0
12.8
15.0
7.6
7.8
10.4
16.6
11.3
12.4
No,
nev
er77
.070
.169
.150
.677
.361
.177
.679
.060
.162
.768
.765
.568
.166
.880
.874
.275
.580
.276
.177
.274
.577
.277
.271
.166
.776
.070
.484
.885
.569
.563
.975
.572
.2D
K.4
.2.3
.2.3
.12.
51.
01.
0.6
.4.2
1.0
.31.
01.
6.3
.32.
33.
73.
1.5
.7.4
.5
qb7b
For
each
of t
he fo
llow
ing
stat
emen
ts r
egar
ding
ani
mal
clo
ning
in fo
od p
rodu
ctio
n pl
ease
tell
me
if yo
u ag
ree
or d
isag
ree
with
it.
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n is
goo
d fo
r th
e (N
ATI
ON
ALI
TY) e
cono
my
anim
alcl
onin
g_ec
onom
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
3.5
8.4
6.2
6.3
5.9
3.8
3.0
1.1
3.6
1.4
5.0
1.2
2.3
5.3
4.8
3.9
3.4
3.5
2.3
1.4
4.1
1.2
2.3
2.5
1.9
5.0
3.2
9.9
2.5
3.6
3.2
6.5
4.2
Tend
to a
gree
18.8
30.0
20.1
21.8
26.2
15.3
12.3
14.3
17.7
9.0
21.7
11.2
22.4
18.9
23.7
17.2
25.4
18.5
20.9
10.4
18.3
17.3
9.7
22.9
12.3
17.5
14.2
13.6
13.4
8.5
10.6
21.0
18.7
Tend
to d
isag
ree
38.6
27.7
27.2
27.5
18.6
35.6
30.6
25.5
27.6
32.2
30.4
32.9
24.8
22.2
30.8
21.0
36.1
28.7
33.2
31.9
25.0
20.8
31.5
41.9
27.2
27.1
18.3
20.4
27.2
16.5
26.9
22.6
28.1
Tota
lly d
isag
ree
30.2
19.7
35.4
35.9
18.6
38.7
42.5
26.6
34.5
46.2
33.9
48.0
18.7
43.6
24.2
32.2
24.3
34.3
29.3
38.6
28.0
19.8
31.2
23.6
52.3
27.1
32.0
29.3
50.9
62.0
53.4
36.0
31.7
DK
9.0
14.2
11.1
8.4
30.7
6.5
11.5
32.5
16.6
11.3
9.1
6.6
31.8
10.1
16.5
25.7
10.7
14.9
14.3
17.7
24.5
40.9
25.4
9.1
6.3
23.3
32.3
26.8
6.0
9.3
5.9
13.9
17.2
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n is
not
goo
d fo
r yo
u an
d yo
ur fa
mily
anim
alcl
onin
g_fa
mily
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
23.3
26.2
42.8
48.6
18.9
32.9
37.5
20.8
29.8
31.4
26.7
42.1
22.3
42.8
18.2
57.6
22.6
27.6
27.7
31.4
28.1
13.6
26.8
17.3
46.7
24.6
22.3
31.7
41.8
44.5
43.4
34.8
29.8
Tend
to a
gree
31.8
34.8
26.5
26.3
26.7
27.6
28.2
28.9
26.7
28.0
23.2
27.4
30.5
20.0
31.8
18.3
28.1
24.3
30.9
27.2
22.3
27.9
24.6
32.2
23.5
23.9
17.5
14.7
20.7
19.8
21.6
25.2
27.0
Tend
to d
isag
ree
27.0
20.7
11.6
12.1
14.3
20.0
11.7
11.8
15.6
15.7
26.6
12.2
11.7
16.8
20.6
8.8
27.5
19.3
20.7
18.5
13.5
15.6
14.3
28.1
13.8
16.9
15.6
14.9
24.5
8.3
11.6
18.2
15.9
Tota
lly d
isag
ree
9.7
7.1
7.2
7.7
13.8
11.9
10.5
7.3
16.0
14.8
12.2
13.3
9.2
10.6
8.8
7.3
10.6
10.6
7.4
6.2
14.6
6.8
11.2
14.0
12.3
10.2
18.2
14.8
5.6
18.7
14.4
8.2
11.1
DK
8.2
11.2
11.9
5.2
26.4
7.6
12.2
31.1
11.9
10.0
11.4
5.0
26.3
9.7
20.6
8.0
11.2
18.1
13.4
16.6
21.5
36.1
23.0
8.5
3.6
24.5
26.4
23.9
7.3
8.7
9.0
13.6
16.2
EU27
Cou
ntry
Cou
ntry
EU27
EU27
Cou
ntry
Cou
ntry
EU27
Cou
ntry
EU27
Let’s
spe
ak n
ow a
bout
clo
ning
farm
ani
mal
s. C
loni
ng m
ay b
e us
ed to
impr
ove
som
e ch
arac
teri
stic
s of
farm
ed a
nim
als
in fo
od p
rodu
ctio
n. D
ue to
the
high
cos
t of c
loni
ng, t
his
tech
niqu
e w
ould
mai
nly
be u
sed
to p
rodu
ce c
lone
d an
imal
s w
hich
will
rep
rodu
ce w
ith n
on-c
lone
d an
imal
s. T
heir
offs
prin
g w
ould
then
be
used
to p
rodu
ce m
eat a
nd m
ilk o
f hig
her
qual
ity.
How
ever
, cri
tics
have
rai
sed
ques
tions
abo
ut e
thic
s of
ani
mal
clo
ning
.
140
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n he
lps
peop
le in
dev
elop
ing
coun
trie
s
anim
alcl
onin
g_de
velo
pB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee7.
79.
47.
19.
211
.56.
63.
83.
96.
02.
95.
74.
94.
34.
17.
39.
98.
59.
97.
77.
86.
24.
24.
66.
46.
98.
04.
712
.48.
46.
63.
510
.46.
6Te
nd to
agr
ee28
.831
.921
.431
.426
.428
.018
.927
.022
.520
.223
.020
.626
.921
.232
.727
.735
.631
.428
.131
.227
.518
.717
.929
.623
.726
.618
.111
.525
.820
.118
.233
.724
.2Te
nd to
dis
agre
e30
.826
.523
.727
.017
.330
.425
.516
.326
.231
.527
.435
.423
.019
.022
.416
.523
.620
.427
.922
.317
.715
.225
.533
.127
.712
.718
.920
.125
.618
.121
.717
.323
.9To
tally
dis
agre
e23
.118
.336
.325
.117
.624
.836
.213
.026
.634
.033
.427
.614
.344
.318
.115
.916
.418
.520
.621
.217
.116
.220
.316
.434
.213
.521
.424
.032
.239
.248
.425
.425
.8D
K9.
713
.811
.57.
327
.210
.115
.739
.818
.711
.310
.511
.531
.511
.419
.529
.915
.919
.915
.717
.531
.445
.631
.714
.47.
539
.136
.932
.08.
216
.18.
213
.219
.5
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n is
saf
e fo
r fu
ture
gen
erat
ions
anim
alcl
onin
g_sa
feB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee2.
83.
22.
42.
24.
91.
51.
61.
03.
71.
63.
76.
11.
22.
94.
01.
73.
84.
63.
83.
03.
21.
12.
03.
83.
43.
73.
58.
01.
73.
42.
63.
33.
1Te
nd to
agr
ee18
.121
.710
.713
.622
.912
.85.
914
.815
.511
.213
.910
.515
.86.
218
.110
.526
.719
.019
.410
.25.
712
.010
.920
.015
.312
.111
.97.
620
.09.
08.
217
.814
.1Te
nd to
dis
agre
e40
.537
.330
.226
.419
.740
.029
.217
.526
.335
.534
.230
.730
.524
.429
.619
.831
.131
.936
.231
.723
.616
.630
.740
.427
.327
.021
.921
.830
.119
.526
.728
.328
.6To
tally
dis
agre
e28
.925
.044
.950
.124
.337
.749
.622
.235
.340
.934
.042
.220
.358
.021
.142
.523
.824
.426
.737
.338
.421
.330
.723
.346
.426
.829
.935
.935
.854
.351
.332
.434
.6D
K9.
612
.711
.87.
628
.38.
013
.744
.619
.110
.714
.210
.432
.28.
627
.225
.414
.620
.013
.817
.829
.249
.025
.712
.57.
630
.432
.726
.712
.413
.911
.318
.219
.6
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n be
nefit
s so
me
peop
le b
ut p
uts
othe
rs a
t ris
k
anim
alcl
onin
g_un
equa
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
13.3
19.2
37.1
24.4
18.5
23.7
12.5
7.1
16.5
19.9
22.6
23.4
15.2
8.9
14.4
32.1
10.4
15.4
21.8
24.8
41.9
8.9
18.0
13.3
38.4
26.2
26.5
24.4
11.7
25.5
27.1
25.3
20.5
Tend
to a
gree
40.8
41.9
33.4
44.2
33.5
39.0
31.0
31.0
29.7
34.8
34.2
40.8
32.2
27.5
37.4
36.4
35.1
35.9
40.2
36.1
26.9
29.3
32.4
45.9
33.6
34.6
28.6
18.9
28.2
30.6
32.5
35.4
33.7
Tend
to d
isag
ree
21.6
18.9
10.6
14.3
12.2
18.3
12.9
10.7
19.4
18.0
17.9
15.5
13.5
16.0
19.2
7.6
26.3
15.0
19.9
12.2
6.3
6.7
13.4
22.5
11.7
8.3
6.4
12.8
23.2
10.7
10.9
14.4
14.8
Tota
lly d
isag
ree
16.4
11.0
8.4
9.6
9.5
11.1
27.3
7.8
16.7
18.1
12.6
10.9
7.1
31.9
7.4
6.1
9.1
15.5
8.3
10.2
4.6
6.8
10.9
8.9
9.9
5.2
11.4
13.6
22.5
17.0
17.2
10.1
12.8
DK
7.9
8.9
10.5
7.5
26.3
7.9
16.3
43.4
17.7
9.2
12.8
9.3
32.0
15.6
21.6
17.8
19.0
18.2
9.8
16.6
20.4
48.3
25.3
9.4
6.4
25.8
27.2
30.3
14.3
16.2
12.3
14.9
18.1
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n is
fund
amen
tally
unn
atur
al
anim
alcl
onin
g_un
natu
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
56.4
63.9
59.9
64.0
44.1
59.4
63.6
34.4
38.7
51.9
67.0
52.5
32.8
80.5
43.9
70.4
47.5
44.2
54.6
56.8
49.4
26.0
46.5
42.1
64.5
32.8
34.2
37.6
61.3
61.7
68.4
74.3
50.6
Tend
to a
gree
25.3
25.2
24.1
23.8
23.8
25.2
21.2
27.6
28.9
31.3
17.0
32.3
30.0
8.1
31.6
14.1
29.1
27.0
26.0
23.4
26.8
33.4
27.9
35.9
20.1
30.3
21.9
15.6
24.9
21.2
22.1
13.2
25.7
Tend
to d
isag
ree
8.9
5.4
6.5
6.6
9.8
6.6
3.4
9.0
13.5
8.1
7.9
7.4
10.9
4.6
9.7
5.0
14.7
12.0
10.3
6.5
3.7
5.0
6.7
11.6
6.3
8.8
9.1
8.7
6.7
3.5
2.6
6.9
8.4
Tota
lly d
isag
ree
4.5
1.8
3.6
2.2
6.7
4.6
5.6
2.7
8.4
3.5
5.1
4.2
3.8
4.0
4.5
.42.
56.
34.
52.
94.
42.
65.
35.
55.
95.
69.
712
.52.
66.
94.
72.
85.
4D
K4.
93.
75.
83.
515
.64.
26.
126
.310
.45.
23.
03.
522
.52.
810
.310
.16.
110
.44.
610
.415
.733
.013
.54.
83.
222
.525
.125
.64.
56.
72.
22.
89.
9
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n m
akes
you
feel
une
asy
anim
alcl
onin
g_un
easy
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
36.3
40.8
58.7
62.4
21.7
39.2
39.9
27.6
27.5
38.0
50.1
56.9
26.5
58.2
37.5
65.6
30.2
29.2
31.6
48.0
41.6
23.6
38.1
30.2
58.3
28.6
23.9
32.4
50.1
54.7
55.9
36.3
38.9
Tend
to a
gree
31.5
34.8
23.9
27.5
34.6
31.0
27.1
30.6
29.5
35.4
24.8
26.1
31.1
21.9
28.2
20.5
30.6
27.4
33.7
24.9
30.0
23.8
28.0
34.2
27.1
28.2
27.0
17.0
24.5
21.9
26.9
29.7
28.3
Tend
to d
isag
ree
19.4
11.4
8.2
7.5
16.6
16.7
14.8
9.9
18.6
11.9
14.6
7.9
13.7
8.6
14.4
6.5
24.1
16.7
18.3
12.2
10.1
10.7
15.1
22.8
8.9
13.1
13.1
11.3
14.1
6.1
7.2
12.6
14.2
Tota
lly d
isag
ree
7.8
8.7
5.3
.714
.49.
512
.63.
911
.99.
18.
35.
76.
78.
610
.23.
38.
213
.66.
44.
63.
73.
15.
86.
94.
16.
112
.013
.47.
89.
37.
117
.19.
1D
K4.
94.
33.
81.
912
.83.
75.
627
.912
.55.
52.
23.
422
.02.
79.
74.
16.
813
.010
.010
.314
.738
.712
.95.
91.
623
.923
.926
.03.
58.
12.
94.
39.
5
Cou
ntry
EU27
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
EU27
Cou
ntry
EU27
141
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n is
saf
e fo
r yo
ur h
ealth
and
you
r fa
mily
’s h
ealth
anim
alcl
onin
g_he
alth
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
5.3
5.3
2.4
4.1
1.7
4.1
4.2
1.1
2.7
5.6
3.1
3.7
2.0
7.2
3.2
3.3
3.0
9.7
5.0
4.0
2.5
.33.
43.
42.
43.
63.
94.
47.
64.
55.
06.
53.
2Te
nd to
agr
ee16
.319
.711
.011
.318
.212
.95.
910
.116
.515
.117
.312
.014
.311
.217
.36.
024
.522
.319
.114
.66.
29.
010
.118
.611
.211
.29.
58.
321
.05.
77.
415
.413
.5Te
nd to
dis
agre
e37
.334
.925
.324
.021
.736
.325
.819
.322
.923
.727
.823
.128
.122
.827
.917
.329
.527
.626
.123
.625
.221
.026
.437
.524
.820
.819
.218
.422
.014
.621
.927
.725
.5To
tally
dis
agre
e30
.027
.946
.554
.528
.637
.743
.925
.939
.342
.933
.853
.928
.245
.024
.960
.726
.323
.933
.838
.942
.726
.334
.429
.353
.731
.337
.140
.938
.163
.851
.433
.837
.0D
K11
.112
.214
.86.
129
.78.
920
.243
.518
.512
.718
.07.
327
.413
.826
.812
.716
.816
.615
.919
.023
.443
.525
.711
.37.
933
.030
.327
.911
.211
.414
.216
.620
.8
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n do
es n
o ha
rm to
the
envi
ronm
ent
anim
alcl
onin
g_en
viron
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
5.1
8.2
5.9
4.3
6.6
4.7
3.7
1.2
4.8
3.6
6.3
6.5
2.2
6.6
5.3
3.4
6.5
8.1
6.9
4.2
5.0
.55.
43.
08.
94.
74.
47.
92.
88.
85.
28.
65.
3Te
nd to
agr
ee23
.725
.515
.412
.721
.220
.512
.811
.720
.515
.821
.014
.218
.714
.322
.211
.239
.522
.926
.819
.411
.613
.613
.026
.617
.713
.013
.010
.019
.811
.012
.915
.918
.1Te
nd to
dis
agre
e36
.433
.325
.131
.418
.135
.422
.519
.222
.728
.628
.031
.622
.325
.524
.516
.524
.525
.025
.725
.422
.915
.121
.734
.725
.217
.916
.018
.231
.219
.222
.824
.123
.7To
tally
dis
agre
e20
.617
.632
.740
.013
.627
.333
.316
.327
.530
.525
.230
.715
.236
.115
.831
.411
.518
.416
.427
.529
.913
.026
.421
.334
.920
.324
.533
.230
.839
.339
.725
.425
.1D
K14
.215
.420
.911
.540
.412
.027
.751
.724
.521
.419
.517
.041
.617
.432
.137
.418
.025
.524
.323
.530
.657
.733
.514
.413
.344
.142
.030
.715
.521
.719
.426
.027
.7
Ani
mal
clo
ning
in fo
od p
rodu
ctio
n sh
ould
be
enco
urag
ed
anim
alcl
onin
g_en
cour
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
2.4
3.3
2.5
2.6
4.3
3.8
1.6
1.0
3.1
3.5
2.0
3.1
2.5
1.2
6.2
.84.
03.
93.
81.
72.
5.3
2.6
3.6
1.4
3.7
2.0
5.4
1.1
1.9
3.5
4.2
3.2
Tend
to a
gree
14.5
14.9
8.0
9.7
19.1
13.3
3.8
9.6
16.7
6.4
12.5
10.0
14.6
8.4
14.5
10.7
24.9
15.3
20.7
9.8
6.5
11.8
9.8
19.0
14.5
11.2
9.9
7.3
13.3
4.8
7.0
11.5
12.2
Tend
to d
isag
ree
33.8
28.0
22.1
29.3
19.3
29.6
24.5
21.4
24.3
25.9
28.4
26.9
28.7
18.5
33.4
22.6
34.1
26.8
26.0
26.9
22.9
14.2
25.9
38.0
23.0
22.8
19.1
21.9
23.1
16.1
17.9
25.2
25.5
Tota
lly d
isag
ree
42.1
45.5
60.0
51.6
31.8
45.3
60.5
29.0
39.4
53.8
51.1
52.9
24.5
67.6
32.3
46.2
26.3
37.6
40.1
49.1
44.7
29.7
40.1
28.4
57.2
33.8
39.3
34.5
56.2
64.6
67.6
51.1
44.5
DK
7.2
8.4
7.4
6.8
25.4
8.0
9.6
39.0
16.4
10.4
6.0
7.1
29.6
4.3
13.6
19.7
10.7
16.4
9.5
12.5
23.3
44.0
21.5
11.0
3.8
28.5
29.7
30.9
6.3
12.7
4.1
7.9
14.7
EU27
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
142
Split
bal
lot A
qb5a
Let’s
spe
ak n
ow a
bout
reg
ener
ativ
e m
edic
ine
whi
ch is
a n
ew fi
eld
of m
edic
ine
and
clin
ical
app
licat
ions
that
focu
ses
on th
e re
pair
ing,
rep
laci
ng o
r gr
owin
g of
cel
ls, t
issu
es, o
r or
gans
.
stem
cell_
embr
yoni
cB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OFu
lly a
ppro
ve a
nd d
o no
t thi
nk th
at s
peci
al
law
s ar
e ne
cess
ary
13.3
12.7
9.2
8.1
16.0
11.8
11.7
5.8
16.0
12.4
10.5
5.9
5.4
7.0
14.6
5.3
8.0
9.5
10.5
10.5
11.5
5.5
12.9
9.2
2.1
8.7
12.4
11.0
9.4
8.6
7.3
8.2
12.1
Appr
ove
as lo
ng a
s th
is is
regu
late
d by
st
rict l
aws
59.9
62.8
38.7
53.2
57.4
53.0
59.1
49.1
48.5
45.0
63.0
31.8
58.1
63.7
60.9
45.7
43.2
57.5
56.4
45.7
43.0
43.0
36.5
38.6
45.5
42.1
47.2
31.2
66.5
39.8
47.4
64.2
50.6
Do
not a
ppro
ve
exce
pt u
nder
ver
y sp
ecia
l ci
rcum
stan
ces
16.7
15.8
26.5
20.3
11.1
18.2
15.7
16.4
18.3
20.4
14.3
26.5
16.4
21.3
9.0
20.7
25.7
11.2
18.7
22.0
18.6
12.1
16.7
25.4
23.7
21.0
10.5
12.0
16.9
22.8
24.7
17.2
17.3
Do
not a
ppro
ve u
nder
an
y ci
rcum
stan
ces
8.7
6.1
22.0
14.7
9.6
12.8
7.8
16.1
10.4
15.8
10.0
31.3
10.2
5.6
10.0
20.7
21.1
10.6
11.0
14.4
16.4
24.4
21.2
23.2
24.9
9.4
10.0
18.9
5.5
18.7
16.4
7.9
13.5
DK
1.3
2.5
3.6
3.7
5.9
4.2
5.8
12.5
6.8
6.4
2.2
4.5
9.9
2.4
5.4
7.5
1.9
11.1
3.3
7.4
10.5
15.0
12.7
3.7
3.7
18.7
19.9
26.9
1.7
10.1
4.2
2.5
6.5
qb6a
Now
sup
pose
sci
entis
ts w
ere
able
to u
se s
tem
cel
ls fr
om o
ther
cel
ls in
the
body
, rat
her
than
from
em
bryo
s. W
ould
you
say
that
...?
stem
cell_
none
mbr
yon
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Fully
app
rove
and
do
not t
hink
that
spe
cial
la
ws
are
nece
ssar
y16
.114
.312
.98.
018
.617
.614
.89.
716
.514
.315
.27.
66.
814
.817
.46.
911
.412
.07.
212
.714
.05.
616
.912
.74.
37.
513
.410
.911
.48.
710
.611
.914
.7
Appr
ove
as lo
ng a
s th
is is
regu
late
d by
st
rict l
aws
59.9
66.2
46.7
62.7
59.8
60.9
59.1
56.6
52.1
56.7
68.6
39.3
57.0
67.1
63.3
47.4
43.2
54.3
63.0
47.7
44.2
53.9
36.8
49.2
48.6
46.1
44.9
34.5
70.0
45.3
52.8
73.2
53.9
Do
not a
ppro
ve
exce
pt u
nder
ver
y sp
ecia
l ci
rcum
stan
ces
15.4
12.7
22.7
16.2
8.8
11.4
11.9
11.8
17.5
12.1
9.7
28.6
16.6
12.1
7.0
26.9
30.9
12.4
19.6
20.7
19.9
13.2
15.8
21.2
24.1
17.3
9.5
11.4
11.0
20.9
20.4
9.4
15.0
Do
not a
ppro
ve u
nder
an
y ci
rcum
stan
ces
6.7
4.3
13.2
9.2
6.8
5.2
7.7
10.0
8.1
8.6
3.9
20.9
6.9
2.8
5.9
9.5
12.5
8.7
6.4
11.2
11.8
9.9
16.5
11.5
20.6
8.5
7.7
15.7
4.0
13.8
11.4
2.6
9.2
DK
1.9
2.5
4.5
3.9
6.1
4.9
6.5
11.9
5.8
8.3
2.5
3.8
12.7
3.2
6.3
9.3
2.0
12.6
3.8
7.6
10.1
17.5
14.0
5.4
2.5
20.5
24.6
27.6
3.6
11.4
4.7
2.8
7.2
EU27
Stem
cel
l res
earc
h in
volv
es ta
king
cel
ls fr
om h
uman
em
bryo
s th
at a
re le
ss th
an 2
wee
ks o
ld. T
hey
will
nev
er b
e tr
ansp
lant
ed in
to a
wom
an’s
bod
y bu
t are
use
d to
gro
w n
ew c
ells
whi
ch th
en c
an b
e us
ed to
trea
t dis
ease
s in
any
par
t of
the
body
. Wou
ld y
ou s
ay th
at...
?
EU27
Cou
ntry
Cou
ntry
143
qb7a
xeno
_org
ans
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Fully
app
rove
and
do
not t
hink
that
spe
cial
la
ws
are
nece
ssar
y13
.411
.47.
14.
914
.78.
011
.66.
215
.311
.97.
69.
16.
810
.312
.74.
78.
911
.49.
58.
712
.06.
011
.510
.93.
17.
214
.19.
37.
76.
810
.19.
411
.2
Appr
ove
as lo
ng a
s th
is is
regu
late
d by
st
rict l
aws
57.7
60.2
39.9
38.4
52.0
39.6
52.6
53.5
43.0
52.1
58.8
28.0
53.1
58.5
52.5
35.2
42.3
44.7
54.3
41.5
35.8
45.4
36.3
44.7
41.7
42.0
39.7
38.1
60.6
39.1
41.5
61.5
46.5
Do
not a
ppro
ve
exce
pt u
nder
ver
y sp
ecia
l ci
rcum
stan
ces
13.8
18.2
24.7
25.2
11.8
24.1
16.0
11.2
21.2
17.2
18.3
27.3
17.1
17.5
12.5
21.4
29.1
15.8
19.5
21.6
19.8
10.5
20.0
25.4
22.4
20.0
12.5
13.5
21.1
21.8
20.8
18.6
18.5
Do
not a
ppro
ve u
nder
an
y ci
rcum
stan
ces
12.4
8.7
24.0
26.9
14.2
23.2
14.3
19.4
13.9
14.2
12.8
32.5
11.5
10.3
16.9
32.4
17.8
17.6
13.5
20.6
22.3
21.7
20.1
15.5
31.0
14.2
13.5
18.1
9.3
23.1
21.9
8.9
17.2
DK
2.6
1.5
4.3
4.5
7.2
5.0
5.5
9.7
6.6
4.7
2.4
3.1
11.4
3.4
5.4
6.2
2.0
10.5
3.3
7.7
10.0
16.4
12.2
3.5
1.7
16.5
20.1
21.0
1.3
9.2
5.7
1.6
6.7
qb8a
Scie
ntis
ts a
lso
wor
k on
gen
e th
erap
y w
hich
invo
lves
trea
ting
inhe
rite
d di
seas
es b
y in
terv
enin
g di
rect
ly in
the
hum
an g
enes
them
selv
es. W
ould
you
say
that
...?
gene
_the
rapy
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Fully
app
rove
and
do
not t
hink
that
spe
cial
la
ws
are
nece
ssar
y13
.69.
94.
87.
315
.010
.414
.25.
813
.912
.77.
84.
67.
710
.215
.510
.76.
813
.68.
614
.311
.59.
413
.810
.62.
47.
113
.610
.18.
09.
76.
98.
511
.4
Appr
ove
as lo
ng a
s th
is is
regu
late
d by
st
rict l
aws
63.0
57.3
38.5
59.7
62.3
56.3
57.0
57.1
56.0
53.9
64.3
32.5
60.1
60.9
58.0
52.8
42.7
53.4
59.0
50.5
38.7
49.2
37.7
50.3
47.3
48.0
46.5
39.0
61.6
44.4
42.6
66.5
52.1
Do
not a
ppro
ve
exce
pt u
nder
ver
y sp
ecia
l ci
rcum
stan
ces
13.2
21.7
30.7
20.4
9.9
17.9
14.8
14.0
17.0
15.8
17.2
35.5
15.3
19.3
10.6
23.3
34.0
12.3
20.8
19.7
21.4
9.8
16.4
22.4
23.7
18.2
12.4
12.2
22.8
21.4
25.9
17.2
18.1
Do
not a
ppro
ve u
nder
an
y ci
rcum
stan
ces
7.4
8.7
21.1
8.6
5.4
10.6
6.8
11.0
7.9
11.0
7.9
22.4
6.4
5.5
7.4
7.7
13.8
10.2
7.4
9.6
18.3
12.6
17.1
13.2
23.6
9.6
7.3
14.0
4.6
14.6
18.4
5.4
10.8
DK
2.7
2.4
4.9
3.9
7.4
4.8
7.2
12.0
5.2
6.7
2.8
5.0
10.5
4.0
8.5
5.5
2.7
10.4
4.3
5.8
10.1
18.9
15.0
3.4
2.9
17.2
20.2
24.7
3.1
9.9
6.3
2.3
7.5
qb9a
rege
nera
tive_
med
icin
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OFu
lly a
ppro
ve a
nd d
o no
t thi
nk th
at s
peci
al
law
s ar
e ne
cess
ary
11.3
8.3
5.4
7.4
17.3
8.6
10.8
4.6
13.5
13.4
6.4
6.4
9.8
11.9
13.7
8.1
11.9
16.9
5.4
16.4
19.3
5.2
14.3
13.1
5.2
9.5
13.8
10.9
10.8
10.7
6.8
8.0
11.3
Appr
ove
as lo
ng a
s th
is is
regu
late
d by
st
rict l
aws
48.5
42.6
34.2
55.0
54.3
43.4
42.8
53.6
45.8
39.5
43.8
32.6
57.8
44.4
50.2
50.0
36.2
47.7
47.6
47.9
41.9
47.2
38.5
47.9
47.9
42.9
45.2
39.5
50.3
43.2
27.4
50.4
44.4
Do
not a
ppro
ve
exce
pt u
nder
ver
y sp
ecia
l ci
rcum
stan
ces
21.2
26.2
29.5
21.4
9.2
22.4
21.0
15.5
18.1
15.4
29.2
27.7
13.3
25.6
14.9
28.4
31.0
15.0
20.1
15.4
15.5
12.5
16.1
22.9
16.9
18.0
11.7
11.5
26.4
21.8
25.8
21.2
19.7
Do
not a
ppro
ve u
nder
an
y ci
rcum
stan
ces
16.5
18.9
26.0
11.4
11.3
21.0
19.3
12.6
15.6
25.1
18.8
29.7
8.8
13.9
14.5
8.5
18.8
8.8
23.5
11.9
13.0
16.0
15.6
13.2
27.9
11.0
9.6
13.6
11.4
14.6
35.2
17.4
17.2
DK
2.5
4.0
4.8
4.7
7.9
4.6
6.1
13.7
6.9
6.7
1.9
3.6
10.3
4.3
6.7
5.0
2.1
11.7
3.4
8.4
10.2
19.1
15.4
2.9
2.2
18.6
19.6
24.5
1.2
9.6
4.8
3.1
7.4
Scie
ntis
ts c
an p
ut h
uman
gen
es in
to a
nim
als
that
will
pro
duce
org
ans
and
tissu
es fo
r tr
ansp
lant
into
hum
ans,
suc
h as
pig
s fo
r tr
ansp
lant
s or
to r
epla
ce p
ancr
eatic
cel
ls to
cur
e di
abet
es. W
ould
you
say
that
...?
EU27
Cou
ntry
Cou
ntry
Cou
ntry
Reg
ener
ativ
e m
edic
ine
is n
ot o
nly
abou
t dev
elop
ing
cure
s fo
r pe
ople
who
are
ill.
It is
als
o lo
okin
g in
to w
ays
of e
nhan
cing
the
perf
orm
ance
of h
ealth
y pe
ople
, for
exa
mpl
e to
impr
ove
conc
entr
atio
n or
to in
crea
se m
emor
y. W
ould
you
EU27
EU27
144
qb10
aN
ow I
wou
ld li
ke to
kno
w w
heth
er y
ou a
gree
or
disa
gree
with
eac
h of
the
follo
win
g is
sues
reg
ardi
ng r
egen
erat
ive
med
icin
e.
Res
earc
h in
volv
ing
hum
an e
mbr
yos
shou
ld b
e fo
rbid
den,
eve
n if
this
mea
ns th
at p
ossi
ble
trea
tmen
ts a
re n
ot m
ade
avai
labl
e to
ill p
eopl
e
forb
id_e
mbr
yo_r
esea
rcB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee8.
214
.023
.723
.610
.311
.913
.214
.912
.319
.215
.425
.38.
67.
810
.124
.012
.012
.115
.619
.215
.215
.214
.918
.324
.914
.515
.323
.95.
824
.218
.79.
014
.7Te
nd to
agr
ee24
.724
.525
.229
.921
.221
.919
.218
.425
.734
.818
.434
.931
.020
.116
.716
.719
.020
.526
.525
.817
.022
.026
.730
.928
.619
.120
.318
.318
.917
.822
.316
.422
.7Te
nd to
dis
agre
e43
.233
.031
.031
.830
.242
.937
.731
.532
.826
.442
.225
.532
.032
.140
.730
.846
.131
.735
.631
.935
.029
.431
.733
.825
.926
.925
.424
.345
.628
.529
.736
.334
.1To
tally
dis
agre
e18
.424
.513
.67.
627
.815
.520
.714
.316
.811
.620
.57.
010
.134
.524
.010
.116
.213
.715
.012
.114
.311
.912
.89.
414
.912
.313
.113
.225
.014
.519
.032
.218
.1D
K5.
43.
96.
57.
110
.57.
89.
320
.912
.48.
03.
67.
318
.35.
68.
618
.46.
821
.97.
211
.018
.521
.513
.97.
55.
727
.226
.020
.34.
615
.010
.36.
110
.4
It is
eth
ical
ly w
rong
to u
se h
uman
em
bryo
s in
med
ical
rese
arch
eve
n if
it m
ight
offe
r pr
omis
ing
new
med
ical
trea
tmen
ts
ethi
cs_e
mbr
yo_r
esea
rB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee11
.614
.924
.529
.812
.017
.310
.416
.312
.721
.617
.627
.19.
611
.412
.336
.715
.413
.018
.223
.219
.818
.716
.423
.132
.116
.514
.530
.39.
624
.320
.614
.015
.9Te
nd to
agr
ee28
.228
.327
.631
.020
.527
.528
.724
.028
.032
.520
.734
.930
.124
.220
.030
.728
.326
.131
.431
.126
.429
.630
.537
.630
.128
.727
.022
.321
.423
.528
.420
.526
.5Te
nd to
dis
agre
e40
.729
.628
.226
.432
.334
.632
.727
.730
.029
.838
.024
.832
.028
.436
.116
.137
.629
.234
.126
.525
.822
.725
.627
.523
.222
.421
.618
.843
.327
.425
.534
.130
.7To
tally
dis
agre
e15
.823
.111
.95.
324
.213
.117
.011
.117
.97.
220
.56.
610
.033
.022
.64.
911
.111
.410
.69.
210
.48.
810
.65.
39.
96.
914
.39.
422
.812
.517
.228
.316
.2D
K3.
64.
17.
77.
411
.07.
511
.120
.911
.48.
93.
26.
618
.33.
09.
111
.67.
520
.45.
710
.017
.520
.217
.06.
54.
625
.522
.519
.12.
912
.38.
23.
110
.7
We
have
a d
uty
to a
llow
res
earc
h th
at m
ight
lead
to im
port
ant n
ew tr
eatm
ents
, eve
n w
hen
it in
volv
es th
e cr
eatio
n or
use
of h
uman
em
bryo
s
duty
_em
bryo
_res
earc
hB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee12
.021
.011
.710
.919
.615
.911
.011
.814
.99.
212
.47.
08.
720
.218
.29.
812
.012
.210
.27.
711
.03.
813
.17.
212
.810
.89.
315
.221
.68.
112
.021
.713
.7Te
nd to
agr
ee41
.634
.028
.130
.043
.341
.939
.830
.435
.629
.532
.930
.941
.835
.842
.532
.444
.735
.240
.428
.129
.322
.929
.837
.429
.933
.734
.521
.945
.430
.528
.038
.736
.2Te
nd to
dis
agre
e29
.627
.227
.428
.213
.425
.125
.522
.022
.130
.226
.827
.120
.925
.617
.518
.521
.518
.124
.827
.218
.722
.923
.529
.927
.518
.520
.822
.621
.522
.326
.118
.822
.7To
tally
dis
agre
e12
.012
.324
.723
.612
.510
.611
.814
.214
.321
.823
.627
.18.
815
.211
.922
.813
.511
.816
.723
.819
.728
.212
.517
.725
.713
.612
.318
.57.
627
.025
.315
.515
.7D
K4.
85.
48.
17.
411
.26.
511
.921
.513
.09.
24.
38.
019
.73.
39.
916
.58.
322
.67.
913
.321
.422
.221
.07.
94.
223
.423
.121
.93.
912
.18.
75.
211
.7
Shou
ld e
thic
al a
nd s
cien
tific
vie
wpo
ints
on
rege
nera
tive
med
icin
e di
ffer,
the
scie
ntifi
c vi
ewpo
int s
houl
d pr
evai
l
scie
nce_
vs_e
thic
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee13
.913
.312
.89.
620
.96.
89.
94.
211
.77.
85.
47.
38.
615
.710
.413
.413
.816
.114
.912
.916
.05.
814
.98.
415
.710
.97.
223
.25.
18.
47.
312
.012
.2Te
nd to
agr
ee35
.931
.629
.430
.134
.332
.529
.921
.237
.333
.622
.033
.037
.533
.430
.428
.249
.535
.243
.338
.833
.817
.934
.834
.129
.729
.030
.520
.333
.834
.926
.533
.132
.6Te
nd to
dis
agre
e31
.830
.632
.531
.117
.331
.926
.224
.720
.632
.938
.533
.623
.526
.924
.726
.821
.620
.523
.820
.919
.422
.621
.035
.623
.524
.320
.518
.739
.623
.324
.227
.825
.4To
tally
dis
agre
e12
.418
.516
.817
.310
.719
.318
.412
.013
.813
.427
.714
.36.
118
.011
.720
.37.
06.
47.
012
.811
.923
.411
.411
.521
.411
.08.
612
.217
.217
.226
.818
.114
.0D
K6.
06.
08.
511
.916
.89.
415
.737
.916
.612
.26.
411
.924
.46.
122
.911
.38.
121
.811
.014
.718
.930
.318
.010
.49.
724
.833
.325
.74.
316
.115
.29.
015
.8
Cou
ntry
Cou
ntry
EU27
Cou
ntry
EU27
EU27
EU27
Cou
ntry
145
Mix
ing
anim
al a
nd h
uman
gen
es is
una
ccep
tabl
e ev
en if
it h
elps
med
ical
res
earc
h fo
r hu
man
hea
lth
mix
_gen
esB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee22
.822
.028
.734
.720
.932
.527
.525
.228
.529
.426
.734
.415
.822
.525
.648
.725
.728
.028
.233
.833
.819
.535
.626
.536
.620
.221
.236
.326
.537
.033
.825
.227
.0Te
nd to
agr
ee26
.626
.327
.032
.728
.425
.525
.223
.325
.931
.520
.930
.633
.124
.325
.117
.528
.326
.728
.824
.722
.626
.126
.633
.626
.424
.321
.517
.228
.322
.426
.025
.326
.3Te
nd to
dis
agre
e35
.033
.428
.118
.124
.427
.323
.521
.623
.023
.036
.323
.223
.630
.728
.216
.431
.621
.627
.320
.718
.517
.414
.027
.018
.826
.417
.813
.931
.016
.319
.329
.925
.0To
tally
dis
agre
e11
.614
.68.
96.
414
.88.
213
.48.
312
.67.
49.
34.
77.
218
.312
.17.
75.
98.
57.
711
.012
.414
.78.
45.
013
.97.
510
.912
.711
.410
.111
.013
.711
.0D
K4.
03.
77.
28.
211
.56.
510
.321
.79.
98.
76.
97.
120
.34.
29.
09.
78.
615
.28.
09.
812
.722
.215
.47.
94.
321
.528
.620
.02.
814
.29.
95.
810
.7
You
do n
ot s
uppo
rt d
evel
opm
ents
in r
egen
erat
ive
med
icin
e if
it on
ly b
enef
its r
ich
peop
le
ineq
ualit
y_re
gen_
med
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
50.0
63.0
63.9
57.0
56.8
55.2
50.7
29.1
40.3
50.8
70.0
50.2
36.5
69.4
47.3
75.8
52.3
43.2
55.1
35.2
42.3
30.6
43.5
43.5
68.5
40.5
27.7
41.6
62.5
58.5
57.0
68.9
50.8
Tend
to a
gree
26.7
21.3
12.8
25.0
21.5
25.5
28.3
25.6
27.9
22.3
14.3
29.8
26.7
9.7
22.5
8.6
27.7
23.9
26.8
26.6
26.1
29.7
29.5
32.1
15.6
26.3
23.6
15.7
17.7
16.0
19.8
10.3
23.0
Tend
to d
isag
ree
14.2
6.4
6.8
10.8
8.5
10.1
6.0
12.8
15.8
10.3
7.5
13.4
17.8
6.1
11.0
4.0
12.5
10.2
10.6
18.5
11.3
10.2
8.9
14.6
5.9
11.1
15.8
9.6
12.3
8.4
9.1
7.7
10.2
Tota
lly d
isag
ree
7.1
6.1
11.8
2.0
6.4
3.4
6.9
9.2
8.6
10.7
6.5
3.7
3.1
11.0
11.7
4.1
2.8
8.4
4.6
10.5
7.6
14.3
5.2
4.5
7.8
5.7
11.7
11.9
6.4
7.1
9.5
9.2
8.2
DK
2.0
3.2
4.7
5.1
6.7
5.8
8.1
23.3
7.4
5.9
1.6
2.9
15.9
3.7
7.5
7.5
4.8
14.3
2.9
9.2
12.7
15.1
12.9
5.2
2.3
16.4
21.2
21.3
1.2
10.1
4.6
4.0
7.8
Imm
edia
tely
afte
r fe
rtili
satio
n th
e hu
man
em
bryo
can
alr
eady
be
cons
ider
ed to
be
a hu
man
bei
ng
embr
yo_h
uman
bein
gB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee29
.425
.642
.747
.221
.423
.930
.324
.124
.729
.030
.136
.418
.614
.019
.971
.421
.226
.841
.942
.734
.839
.227
.134
.243
.621
.826
.339
.017
.737
.937
.917
.028
.8Te
nd to
agr
ee31
.923
.023
.432
.427
.522
.329
.131
.529
.329
.222
.733
.540
.721
.125
.112
.627
.023
.428
.421
.822
.732
.926
.632
.224
.127
.426
.915
.323
.922
.525
.318
.427
.0Te
nd to
dis
agre
e25
.624
.219
.79.
020
.427
.817
.814
.321
.320
.827
.220
.217
.823
.627
.07.
834
.220
.219
.518
.318
.64.
619
.121
.618
.016
.313
.013
.036
.715
.115
.523
.520
.9To
tally
dis
agre
e10
.021
.28.
52.
518
.215
.914
.05.
213
.610
.717
.94.
95.
535
.615
.2.8
13.0
10.4
6.1
7.4
10.9
5.1
10.4
5.4
10.8
6.7
7.0
12.1
20.0
11.0
12.6
34.7
12.5
DK
3.1
6.0
5.8
9.0
12.5
10.1
8.8
24.8
11.2
10.4
2.1
5.0
17.4
5.7
12.8
7.3
4.5
19.2
4.1
9.8
13.1
18.2
16.7
6.6
3.5
27.8
26.8
20.6
1.7
13.4
8.7
6.3
10.9
Res
earc
h on
reg
ener
ativ
e m
edic
ine
shou
ld b
e su
ppor
ted,
eve
n th
ough
it w
ill b
enef
it on
ly a
few
peo
ple
bene
fits_
rege
n_m
edic
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
13.8
18.6
11.7
7.4
10.8
8.3
12.8
7.2
9.0
5.7
13.1
10.3
7.5
14.6
15.2
8.4
4.2
10.5
9.3
7.7
12.7
12.9
12.6
4.7
13.9
4.7
6.3
18.1
9.5
8.0
11.7
13.4
11.2
Tend
to a
gree
39.4
34.4
26.9
29.3
35.3
35.7
37.5
30.0
27.5
33.8
39.1
30.5
32.9
30.5
37.5
32.7
33.8
31.7
35.3
32.1
34.5
19.6
32.0
35.6
24.9
25.7
22.8
17.8
59.7
17.9
24.4
32.7
32.2
Tend
to d
isag
ree
27.2
25.5
26.3
31.7
19.5
26.9
21.2
20.0
30.7
23.5
26.8
23.4
23.4
22.3
25.1
21.1
33.3
22.9
24.3
27.3
22.7
24.4
22.2
32.6
23.6
24.5
24.7
17.0
17.7
20.7
29.6
21.9
25.1
Tota
lly d
isag
ree
15.2
16.6
27.5
24.6
25.6
20.5
17.8
12.1
22.1
27.9
15.0
28.9
17.1
26.7
9.3
24.1
20.2
17.7
24.0
21.1
13.5
23.7
11.7
20.5
33.6
19.1
21.9
24.7
10.3
39.1
26.3
26.5
19.9
DK
4.4
5.0
7.5
7.1
8.7
8.5
10.7
30.8
10.7
9.1
6.1
7.0
19.1
5.9
12.8
13.7
8.6
17.3
7.1
11.9
16.5
19.4
21.5
6.5
4.1
25.9
24.4
22.4
2.7
14.4
7.9
5.5
11.6
Res
earc
h in
to r
egen
erat
ive
med
icin
e sh
ould
go
ahea
d, e
ven
if th
ere
are
risk
s to
futu
re g
ener
atio
ns
risks
_reg
en_m
edic
ine
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
6.9
6.9
3.6
3.2
5.8
6.4
4.1
3.3
6.5
5.6
4.4
6.6
3.3
5.9
6.1
5.1
5.0
3.6
9.2
5.0
4.6
1.9
7.6
5.3
6.5
1.9
4.9
14.3
2.9
3.7
5.3
6.1
5.3
Tend
to a
gree
31.5
24.7
16.0
14.6
22.4
31.1
22.0
16.6
26.2
21.5
32.6
16.3
27.7
22.9
24.2
18.9
42.3
14.8
40.8
8.9
12.5
13.4
24.1
35.7
21.0
14.4
15.4
18.0
34.2
12.0
12.3
19.3
23.0
Tend
to d
isag
ree
37.6
35.7
31.5
35.0
24.7
25.8
35.9
24.9
29.7
33.8
36.7
30.7
26.9
30.7
35.4
28.3
30.6
22.9
25.2
34.0
30.3
29.6
28.2
35.4
30.8
33.4
25.9
19.1
40.5
27.6
30.2
34.1
31.2
Tota
lly d
isag
ree
18.6
25.9
39.9
41.1
34.7
29.2
24.3
22.2
24.8
28.1
18.8
42.0
20.1
34.7
18.3
35.1
12.2
43.2
14.7
41.3
37.5
35.0
20.6
13.8
35.7
28.9
27.5
25.5
17.1
40.5
42.9
32.0
27.3
DK
5.4
6.8
9.1
6.2
12.5
7.4
13.8
33.0
12.9
11.0
7.5
4.5
22.0
5.9
15.9
12.6
9.9
15.6
10.1
10.7
15.0
20.1
19.6
9.8
6.1
21.4
26.2
23.1
5.3
16.3
9.4
8.6
13.3
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
EU27
EU27
146
Split
bal
lot B
qb8b
It is
a p
rom
isin
g id
ea
trans
appl
e_pr
omis
ing
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
14.9
15.2
14.7
7.9
16.6
15.1
10.8
6.0
7.4
7.3
21.4
7.2
5.7
16.3
14.6
7.8
12.6
9.6
12.2
9.7
11.4
11.7
9.5
12.1
7.8
9.4
10.8
13.9
14.3
11.6
10.3
22.6
12.3
Tend
to a
gree
41.2
32.5
24.6
20.6
31.7
34.7
31.7
35.1
32.4
30.6
37.5
30.4
40.5
28.2
37.1
22.5
36.5
30.3
36.1
25.7
28.6
33.7
32.1
35.1
26.8
25.5
26.7
17.6
40.3
19.5
24.3
31.4
31.5
Tend
to d
isag
ree
23.1
21.2
24.8
29.2
22.0
25.1
18.6
18.8
24.6
27.2
14.8
28.5
23.5
20.6
18.4
20.8
26.8
23.2
29.1
24.8
19.3
14.1
25.5
28.9
28.2
20.2
20.3
13.5
23.2
19.7
27.9
17.7
22.5
Tota
lly d
isag
ree
16.6
25.9
26.3
36.9
18.2
19.5
27.3
16.8
18.5
27.0
20.3
22.3
16.8
30.2
20.7
22.9
16.3
23.8
18.7
29.0
22.6
17.4
14.8
18.9
33.5
16.4
18.1
26.3
17.9
41.0
28.1
22.0
21.5
DK
4.2
5.2
9.6
5.4
11.5
5.6
11.6
23.4
17.1
7.9
5.9
11.6
13.5
4.7
9.3
26.0
7.9
13.1
4.0
10.9
18.1
23.0
18.1
5.0
3.7
28.6
24.1
28.8
4.3
8.2
9.4
6.3
12.1
Eatin
g ap
ples
pro
duce
d us
ing
this
tech
niqu
e w
ill b
e sa
fe
trans
appl
e_sa
feB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee8.
17.
07.
74.
47.
68.
53.
63.
15.
83.
010
.57.
13.
49.
67.
93.
99.
74.
38.
03.
55.
53.
95.
17.
13.
95.
46.
68.
56.
66.
46.
717
.66.
6Te
nd to
agr
ee28
.627
.721
.212
.724
.226
.512
.421
.128
.215
.130
.517
.828
.419
.426
.99.
932
.623
.129
.917
.620
.517
.522
.926
.116
.615
.616
.414
.041
.416
.613
.425
.722
.7Te
nd to
dis
agre
e35
.533
.030
.031
.625
.832
.229
.918
.625
.130
.821
.735
.029
.329
.723
.224
.326
.831
.629
.333
.123
.920
.925
.834
.032
.023
.822
.816
.424
.524
.229
.922
.127
.2To
tally
dis
agre
e19
.121
.927
.244
.821
.322
.730
.318
.322
.334
.517
.424
.316
.727
.015
.831
.716
.823
.220
.034
.729
.619
.717
.819
.736
.824
.722
.229
.912
.741
.530
.216
.023
.0D
K8.
710
.413
.96.
521
.110
.123
.839
.018
.616
.619
.915
.822
.114
.326
.230
.114
.117
.912
.711
.120
.437
.928
.413
.010
.730
.632
.031
.214
.711
.319
.818
.620
.4
It w
ill h
arm
the
envi
ronm
ent
trans
appl
e_en
viro
nme
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
11.6
18.8
20.2
34.7
14.8
19.1
22.8
11.8
13.3
27.8
12.4
21.6
10.3
25.1
13.5
23.7
9.8
14.5
7.7
23.4
19.7
5.6
14.5
10.3
32.2
11.0
12.0
24.4
10.7
29.1
17.9
17.2
16.5
Tend
to a
gree
30.7
31.1
26.6
36.2
27.5
34.3
29.1
22.4
21.7
33.5
23.1
33.2
29.4
33.8
27.3
19.1
24.5
30.3
27.2
27.3
22.6
23.1
26.0
35.7
33.3
27.3
22.2
16.9
27.4
27.0
32.6
24.2
26.9
Tend
to d
isag
ree
36.6
30.9
25.4
15.0
22.2
27.9
18.2
20.0
26.9
17.9
36.7
22.2
22.6
18.9
26.0
13.7
39.7
26.4
32.7
26.1
22.6
19.3
25.4
33.3
20.2
16.0
19.6
14.4
40.7
17.0
19.4
21.4
24.8
Tota
lly d
isag
ree
10.7
8.9
12.7
5.6
10.6
8.3
7.1
7.9
12.6
4.7
12.8
6.9
9.7
7.4
10.1
5.3
11.5
9.6
16.3
8.1
11.7
8.9
6.1
8.9
4.8
8.6
10.9
11.5
8.4
13.1
10.8
15.4
10.2
DK
10.4
10.2
15.0
8.5
24.9
10.4
22.8
37.9
25.4
16.1
14.9
16.1
27.9
14.7
23.2
38.2
14.5
19.3
16.0
15.2
23.4
43.1
27.9
11.7
9.6
37.2
35.4
32.8
12.9
13.9
19.3
21.8
21.7
It is
fund
amen
tally
unn
atur
al
trans
appl
e_un
natu
ral
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
36.2
54.9
40.9
56.3
39.6
41.3
54.2
24.7
30.2
50.7
48.0
37.5
29.0
67.4
36.2
60.7
37.5
34.5
39.2
49.9
39.1
21.5
31.0
30.1
56.6
22.7
19.8
32.8
32.0
48.8
50.8
58.0
38.9
Tend
to a
gree
39.5
30.1
31.3
29.6
32.6
31.6
28.5
32.5
32.4
34.2
26.0
35.8
36.9
15.7
34.5
19.7
34.9
32.0
33.0
27.5
33.9
39.6
42.1
47.0
29.2
33.4
27.1
16.2
36.1
29.5
27.6
19.1
32.5
Tend
to d
isag
ree
16.0
7.7
15.2
7.4
13.6
19.8
7.0
12.1
18.9
7.0
15.5
15.8
14.7
8.3
15.0
8.9
19.6
15.1
17.8
10.2
8.7
13.1
13.4
13.6
8.2
12.5
15.5
10.0
22.8
9.3
10.7
12.0
14.0
Tota
lly d
isag
ree
5.5
4.3
7.5
3.3
7.9
4.9
3.1
5.4
8.9
3.4
6.6
4.0
5.6
4.7
5.6
.84.
34.
87.
04.
06.
39.
12.
76.
12.
76.
68.
010
.13.
97.
07.
66.
66.
0D
K2.
82.
95.
23.
46.
32.
47.
125
.29.
54.
73.
87.
013
.73.
98.
79.
93.
713
.62.
98.
412
.016
.610
.83.
23.
324
.729
.630
.95.
35.
43.
34.
38.
5
EU27
Cou
ntry
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
Som
e Eu
rope
an r
esea
rche
rs th
ink
ther
e ar
e ne
w w
ays
of c
ontr
ollin
g co
mm
on d
isea
ses
in a
pple
s– th
ings
like
sca
b an
d m
ildew
. The
re a
re tw
o ne
w w
ays
of d
oing
this
. Bot
h m
ean
that
the
appl
es c
ould
be
grow
n w
ith li
mite
d us
e of
pe
stic
ides
, and
so
pest
icid
e re
sidu
es o
n th
e ap
ples
wou
ld b
e m
inim
al.
The
first
way
is to
art
ifici
ally
intr
oduc
e a
resi
stan
ce g
ene
from
ano
ther
spe
cies
suc
h as
a b
acte
rium
or
anim
al in
to a
n ap
ple
tree
to m
ake
it re
sist
ant t
o m
ildew
and
sca
b. F
or e
ach
of th
e fo
llow
ing
stat
emen
ts a
bout
this
new
tech
niqu
e pl
ease
tell
me
if yo
u ag
ree
or d
isag
ree.
EU27
147
It m
akes
you
feel
une
asy
trans
appl
e_un
easy
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
21.7
39.6
41.3
53.2
20.6
27.4
28.3
23.1
20.2
37.9
33.3
36.7
21.9
33.1
29.2
48.9
18.9
26.5
23.8
39.0
31.4
21.0
24.4
21.3
47.4
18.3
16.0
30.5
20.7
41.8
37.7
27.2
28.2
Tend
to a
gree
32.8
30.2
29.4
31.8
33.2
32.7
27.1
28.9
26.4
31.4
20.2
38.6
34.3
25.7
29.3
31.3
33.1
25.2
30.4
26.5
27.9
34.4
36.5
36.2
29.2
28.4
25.6
14.6
25.3
29.4
31.0
24.6
29.7
Tend
to d
isag
ree
28.4
17.4
15.7
9.4
25.1
24.0
20.7
17.0
28.2
15.0
27.9
14.2
21.6
17.6
22.7
10.6
29.9
18.5
25.7
19.2
15.9
12.8
23.4
30.3
16.4
16.8
18.6
12.4
37.3
12.0
13.3
18.4
21.9
Tota
lly d
isag
ree
12.7
8.0
10.1
3.3
16.7
13.8
17.1
6.4
12.5
9.5
16.5
4.9
8.7
20.3
11.5
2.5
11.4
17.3
13.7
6.8
11.3
7.4
6.0
9.6
4.6
8.7
11.9
10.3
15.8
10.7
14.0
25.0
12.0
DK
4.4
4.7
3.5
2.2
4.5
2.1
6.7
24.7
12.7
6.2
2.1
5.6
13.5
3.4
7.3
6.8
6.8
12.5
6.5
8.5
13.4
24.3
9.8
2.6
2.3
27.9
27.9
32.2
.96.
24.
04.
78.
2
It sh
ould
be
enco
urag
ed
trans
appl
e_en
cour
age
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
5.6
8.2
8.6
3.5
7.8
11.4
4.4
3.4
5.4
5.6
7.8
6.1
3.4
6.4
9.8
4.4
8.1
5.8
8.7
4.2
5.4
5.9
6.8
6.3
4.3
4.3
5.0
8.0
4.5
5.9
5.4
12.7
6.9
Tend
to a
gree
28.9
21.0
15.9
13.1
23.6
21.2
19.3
20.0
24.5
9.5
29.2
17.8
26.3
18.5
25.2
13.4
31.3
17.3
28.6
17.4
15.7
19.7
22.4
29.6
16.6
16.4
15.7
11.3
31.5
14.4
16.0
21.8
21.6
Tend
to d
isag
ree
29.4
26.5
28.4
30.2
21.9
33.3
22.5
15.8
27.6
23.6
25.3
27.8
27.2
21.9
27.9
22.2
29.9
31.4
24.9
26.9
25.1
18.7
24.5
34.6
28.1
22.2
16.6
15.5
31.6
17.8
25.4
22.0
25.7
Tota
lly d
isag
ree
28.7
33.5
40.6
47.8
30.7
26.2
39.4
25.3
22.3
52.7
31.9
35.1
21.1
47.0
25.4
33.0
20.1
29.7
28.9
41.0
33.4
24.1
23.4
23.1
44.0
26.7
30.3
30.6
27.4
49.0
44.9
32.4
31.2
DK
7.3
10.9
6.5
5.5
16.1
7.9
14.4
35.5
20.2
8.6
5.7
13.2
22.0
6.2
11.8
26.9
10.6
15.8
8.9
10.6
20.3
31.5
22.9
6.4
7.1
30.5
32.4
34.7
5.0
13.0
8.4
11.0
14.6
qb9b
And
whi
ch o
f the
follo
win
g st
atem
ents
is c
lose
st to
you
r vi
ew?
trans
appl
e_la
bel
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Thes
e ap
ples
wou
ld
be th
e sa
me
as
ordi
nary
app
les
and
wou
ld n
ot n
eed
spec
ial l
abel
ling
11.7
8.5
8.8
5.4
10.5
9.1
7.0
7.1
12.6
10.6
14.2
13.2
15.1
8.0
8.1
.810
.39.
914
.57.
012
.52.
010
.49.
53.
77.
910
.36.
410
.06.
710
.111
.49.
8
Thes
e ap
ples
wou
ld
be li
ke G
M fo
od a
nd
shou
ld b
e cl
early
id
entif
ied
with
a
spec
ial l
abel
84.1
89.1
87.6
92.6
84.9
89.2
85.9
69.2
75.4
87.6
83.5
77.8
72.2
89.5
87.3
95.9
86.1
78.4
82.4
90.4
79.5
91.7
81.2
87.1
93.8
76.2
70.6
61.0
88.7
81.5
84.8
85.2
83.3
DK
4.2
2.4
3.6
2.0
4.7
1.8
7.1
23.7
12.0
1.7
2.3
9.0
12.6
2.6
4.6
3.3
3.6
11.7
3.1
2.6
8.0
6.3
8.4
3.4
2.5
15.9
19.1
32.6
1.3
11.9
5.1
3.5
6.9
Cou
ntry
EU27
Cou
ntry
EU27
Cou
ntry
EU27
148
qb10
b
It w
ill b
e us
eful
cisa
pple
_use
ful
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
22.7
31.5
26.1
19.5
23.0
37.1
19.2
14.1
11.2
13.5
34.4
12.3
13.9
40.4
27.0
28.3
27.8
30.3
27.8
31.6
28.9
22.2
22.9
22.5
19.1
15.6
17.4
18.4
29.6
22.9
21.4
52.7
22.4
Tend
to a
gree
48.3
44.5
37.9
42.5
42.0
37.6
41.4
44.7
38.6
45.0
41.1
48.9
46.4
34.2
45.5
40.5
44.8
38.9
44.8
37.3
36.7
40.9
37.2
49.2
38.0
36.5
35.7
19.3
46.4
28.9
37.4
30.1
40.8
Tend
to d
isag
ree
14.4
11.3
15.1
21.7
13.9
11.7
13.3
10.1
19.9
21.6
11.2
20.5
17.6
6.8
10.6
6.8
14.6
12.5
16.6
15.0
9.9
10.7
15.3
16.5
18.1
13.2
12.4
11.8
12.7
17.2
14.6
4.9
14.6
Tota
lly d
isag
ree
11.5
8.1
12.6
10.6
9.8
8.1
14.8
7.1
13.3
15.6
9.4
10.2
7.4
13.4
7.5
5.0
5.9
5.8
5.9
9.4
9.2
7.3
7.2
7.6
19.0
9.8
9.5
22.6
4.5
21.8
17.3
4.5
10.6
DK
3.0
4.7
8.3
5.7
11.3
5.5
11.4
24.1
17.1
4.3
3.9
8.1
14.8
5.2
9.4
19.5
6.8
12.5
5.0
6.6
15.4
18.9
17.4
4.1
5.9
25.0
25.0
27.9
6.8
9.2
9.3
7.7
11.7
It w
ill b
e ri
sky
cisa
pple
_ris
kyB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee9.
59.
416
.412
.718
.513
.516
.09.
215
.317
.18.
115
.211
.310
.89.
47.
710
.56.
59.
412
.511
.75.
812
.110
.121
.58.
810
.531
.122
.021
.016
.77.
913
.5Te
nd to
agr
ee26
.328
.021
.922
.834
.519
.529
.130
.328
.736
.615
.732
.030
.225
.930
.212
.823
.314
.527
.525
.821
.626
.128
.134
.430
.122
.219
.018
.851
.924
.123
.320
.027
.0Te
nd to
dis
agre
e44
.437
.735
.339
.823
.641
.825
.324
.523
.226
.543
.133
.429
.728
.733
.433
.342
.337
.839
.535
.629
.022
.530
.740
.426
.927
.230
.311
.510
.923
.430
.229
.931
.0To
tally
dis
agre
e13
.016
.816
.617
.712
.518
.112
.78.
410
.28.
420
.19.
510
.528
.413
.621
.615
.625
.117
.218
.618
.211
.010
.510
.413
.413
.713
.810
.22.
620
.016
.127
.113
.8D
K6.
78.
19.
87.
010
.97.
116
.927
.522
.611
.513
.09.
918
.36.
213
.424
.68.
416
.16.
37.
519
.634
.718
.64.
78.
128
.126
.428
.312
.611
.513
.615
.014
.7
It w
ill h
arm
the
envi
ronm
ent
cisa
pple
_env
ironm
ent
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
8.6
6.9
12.0
14.2
10.1
10.0
12.6
6.2
9.8
15.4
7.9
15.7
6.1
8.2
6.8
8.1
5.6
5.5
4.6
8.2
10.0
4.6
11.6
8.5
22.8
7.4
10.1
25.0
2.9
16.2
12.3
5.5
10.0
Tend
to a
gree
24.8
20.5
19.0
21.5
22.9
18.2
23.5
18.0
20.4
32.2
16.2
26.3
26.0
20.1
19.7
10.2
15.7
13.2
14.5
20.5
14.3
16.5
18.3
21.4
24.4
15.0
14.5
16.8
12.6
20.4
18.6
12.7
20.0
Tend
to d
isag
ree
44.8
41.5
31.5
33.8
26.2
43.4
29.7
29.7
31.5
25.6
42.9
36.2
29.2
32.4
35.9
28.1
44.8
35.5
41.0
33.8
30.6
29.2
36.7
48.3
28.2
26.5
29.3
14.1
55.3
28.1
31.5
29.2
33.3
Tota
lly d
isag
ree
14.5
21.9
20.5
22.3
16.8
21.9
14.7
10.6
13.5
10.6
20.7
9.3
13.6
26.2
18.4
20.6
23.9
30.3
30.6
23.8
22.1
11.9
12.6
14.2
15.7
20.2
15.5
12.1
18.6
22.0
20.1
33.2
17.3
DK
7.3
9.3
17.0
8.2
24.0
6.5
19.6
35.4
24.8
16.2
12.3
12.5
25.1
13.1
19.1
33.0
10.0
15.6
9.3
13.8
23.0
37.9
20.9
7.7
8.9
30.8
30.5
32.0
10.6
13.3
17.6
19.4
19.4
It is
fund
amen
tally
unn
atur
al
cisa
pple
_unn
atur
alB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee22
.835
.721
.423
.229
.415
.431
.712
.426
.130
.032
.722
.324
.330
.917
.315
.017
.712
.019
.016
.818
.49.
618
.316
.840
.811
.912
.431
.114
.328
.029
.824
.223
.2Te
nd to
agr
ee37
.831
.825
.527
.030
.822
.933
.231
.728
.040
.625
.133
.130
.025
.430
.721
.429
.421
.025
.927
.826
.835
.033
.643
.329
.221
.121
.519
.327
.128
.630
.322
.329
.2Te
nd to
dis
agre
e27
.017
.429
.429
.421
.039
.219
.722
.627
.116
.625
.931
.125
.419
.829
.633
.435
.230
.434
.027
.725
.523
.725
.730
.217
.926
.325
.911
.441
.822
.619
.423
.726
.4To
tally
dis
agre
e9.
111
.516
.715
.912
.217
.78.
29.
68.
69.
414
.07.
49.
120
.314
.117
.513
.824
.619
.019
.915
.112
.99.
76.
77.
816
.014
.49.
713
.414
.416
.226
.112
.5D
K3.
23.
67.
14.
56.
64.
77.
123
.810
.23.
42.
36.
211
.23.
68.
412
.73.
812
.02.
17.
814
.218
.812
.82.
94.
424
.625
.828
.53.
56.
44.
33.
78.
8
EU27
EU27
Cou
ntry
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
The
seco
nd w
ay is
to a
rtifi
cial
ly in
trod
uce
a ge
ne th
at e
xist
s na
tura
lly in
wild
/ cra
b ap
ples
whi
ch p
rovi
des
resi
stan
ce to
mild
ew a
nd s
cab.
For
eac
h of
the
follo
win
g st
atem
ents
abo
ut th
is n
ew te
chni
que
plea
se te
ll m
e if
you
agre
e or
149
It m
akes
you
feel
une
asy
cisa
pple
_une
asy
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Tota
lly a
gree
14.0
19.2
22.5
22.4
15.7
12.8
18.1
11.3
16.5
20.5
19.7
21.2
13.5
11.7
13.1
14.9
7.6
6.9
10.0
12.3
13.4
7.3
14.3
9.8
30.2
8.7
10.5
27.1
9.3
25.1
24.1
8.7
16.2
Tend
to a
gree
27.1
27.0
21.8
24.3
32.8
16.1
22.1
28.5
28.3
32.0
19.4
30.6
26.8
16.9
21.2
23.0
24.7
13.5
19.1
20.8
19.2
31.7
26.7
27.0
28.9
19.3
18.9
19.8
12.1
23.0
20.5
18.4
24.2
Tend
to d
isag
ree
37.9
32.0
30.0
34.1
26.9
38.4
26.6
27.0
28.7
25.9
33.7
32.8
31.9
24.8
35.9
31.3
41.4
29.2
38.4
30.7
29.9
27.5
33.7
46.9
22.8
26.2
24.7
12.3
48.5
26.1
27.8
23.4
30.9
Tota
lly d
isag
ree
17.5
16.9
20.2
16.0
21.0
30.1
27.3
10.4
13.3
16.0
24.3
10.4
14.4
43.5
21.8
21.2
22.2
36.7
27.1
27.8
24.3
12.0
15.3
14.3
15.0
19.4
19.6
10.3
28.0
17.9
22.0
46.4
20.3
DK
3.5
4.9
5.5
3.2
3.7
2.6
5.8
22.8
13.2
5.7
2.9
4.9
13.3
3.1
7.9
9.6
4.2
13.7
5.4
8.4
13.1
21.5
10.0
2.0
3.1
26.4
26.3
30.5
2.0
7.9
5.6
3.1
8.3
It sh
ould
be
enco
urag
ed
cisa
pple
_enc
oura
geB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
tally
agr
ee12
.117
.517
.217
.79.
628
.111
.610
.46.
69.
315
.38.
59.
321
.917
.122
.421
.121
.621
.823
.719
.112
.216
.714
.516
.213
.613
.19.
510
.716
.714
.133
.614
.1Te
nd to
agr
ee36
.533
.928
.035
.334
.636
.230
.530
.128
.322
.532
.737
.936
.331
.637
.132
.940
.635
.143
.835
.428
.727
.033
.344
.227
.729
.127
.413
.546
.424
.325
.625
.232
.4Te
nd to
dis
agre
e27
.720
.823
.922
.216
.816
.017
.812
.027
.227
.024
.421
.719
.918
.020
.010
.220
.916
.617
.317
.716
.510
.516
.523
.322
.611
.512
.117
.423
.517
.020
.917
.220
.4To
tally
dis
agre
e16
.118
.423
.215
.522
.412
.126
.913
.617
.332
.921
.220
.015
.222
.012
.46.
97.
19.
810
.614
.813
.516
.412
.110
.124
.714
.614
.924
.412
.629
.330
.012
.818
.2D
K7.
79.
47.
79.
316
.57.
713
.233
.920
.78.
26.
411
.919
.36.
613
.427
.710
.316
.96.
68.
422
.233
.821
.47.
98.
831
.232
.535
.16.
912
.89.
411
.214
.9
qb11
bA
nd w
hich
of t
he fo
llow
ing
stat
emen
ts is
clo
sest
to y
our
view
?
cisa
pple
_lab
elB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTh
ese
appl
es w
ould
be
the
sam
e as
or
dina
ry a
pple
s an
d w
ould
not
nee
d sp
ecia
l lab
ellin
g
17.0
19.6
24.2
24.0
14.0
40.0
14.3
11.8
16.5
12.9
25.1
26.9
22.9
29.8
18.8
15.3
28.1
34.7
37.1
28.6
29.8
5.8
21.8
23.4
6.5
25.6
19.3
10.7
29.0
22.1
18.2
32.8
20.4
Thes
e ap
ples
wou
ld
be li
ke G
M fo
od a
nd
shou
ld b
e cl
early
id
entif
ied
with
a
spec
ial l
abel
79.1
77.9
70.3
72.2
81.7
57.6
78.9
66.2
70.7
84.8
72.6
64.7
64.0
67.1
75.1
80.2
68.3
55.0
59.5
68.1
61.0
86.6
68.7
73.3
91.4
57.5
63.5
62.7
68.0
68.9
76.6
64.5
72.1
DK
3.9
2.5
5.5
3.8
4.2
2.4
6.8
22.1
12.8
2.3
2.3
8.4
13.1
3.1
6.0
4.5
3.6
10.2
3.4
3.3
9.2
7.6
9.6
3.3
2.1
17.0
17.2
26.7
2.9
9.0
5.3
2.7
7.5
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
EU27
150
Split
bal
lot A
qb11
aB
efor
e to
day,
hav
e yo
u ev
er h
eard
any
thin
g ab
out s
ynth
etic
bio
logy
?
hear
d_sy
nbio
logy
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Hea
rd17
.314
.917
.815
.318
.128
.112
.021
.912
.626
.919
.915
.316
.522
.620
.716
.211
.817
.720
.422
.218
.519
.415
.020
.321
.616
.020
.59.
514
.728
.229
.223
.916
.9N
ot h
eard
82.7
85.1
82.2
84.7
81.9
71.9
88.0
78.1
87.4
73.1
80.1
84.7
83.5
77.4
79.3
83.8
88.2
82.3
79.6
77.8
81.5
80.6
85.0
79.7
78.4
84.0
79.5
90.5
85.3
71.8
70.8
76.1
83.1
qb12
a[IF
YES
] Hav
e yo
u ev
er ta
lked
abo
ut s
ynth
etic
bio
logy
with
any
one
befo
re to
day?
talk
ed_s
ynbi
olog
yB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, f
requ
ently
3.1
4.8
1.9
4.3
3.4
2.9
2.8
2.4
5.3
2.2
4.4
10.6
4.8
1.0
7.5
2.5
1.4
3.4
4.4
2.2
2.1
3.3
1.3
4.7
3.4
7.0
5.1
5.9
4.4
2.8
3.8
Yes
, occ
asio
nally
28.8
7.2
28.5
15.3
19.0
26.6
14.9
18.4
37.3
30.6
23.1
38.0
26.3
15.0
16.6
16.1
10.2
14.3
15.9
12.3
22.4
19.9
12.4
26.5
29.1
27.1
26.7
4.5
1.9
20.1
21.7
20.9
22.3
Yes
, onl
y on
ce o
r 17
.830
.318
.736
.220
.718
.920
.733
.230
.119
.622
.627
.121
.918
.322
.838
.827
.428
.730
.124
.823
.413
.720
.824
.522
.517
.822
.112
.335
.825
.421
.822
.122
.6N
o, n
ever
50.3
57.6
50.9
44.2
56.9
50.3
61.7
43.1
25.4
47.6
49.9
23.0
44.6
63.8
49.8
42.6
61.0
53.6
49.5
62.0
52.0
63.0
61.9
47.6
43.8
51.7
46.0
69.8
57.1
47.0
52.1
53.5
50.0
DK
1.3
2.9
1.8
1.3
2.4
1.9
3.3
.91.
31.
75.
26.
51.
5.7
1.3
[IF Y
ES] H
ave
you
ever
sea
rche
d fo
r in
form
atio
n ab
out s
ynth
etic
bio
logy
?
info
sear
ch_s
ynbi
olog
yB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, f
requ
ently
2.5
4.0
.42.
93.
51.
11.
48.
41.
04.
46.
12.
04.
32.
51.
43.
85.
21.
02.
14.
31.
33.
42.
91.
77.
02.
33.
81.
93.
1Y
es, o
ccas
iona
lly17
.212
.312
.017
.617
.413
.112
.716
.717
.914
.729
.918
.09.
07.
918
.78.
85.
811
.99.
513
.916
.09.
317
.215
.08.
47.
41.
710
.316
.413
.810
.212
.6Y
es, o
nly
once
or
12.5
12.5
13.6
24.7
7.7
17.5
13.7
23.2
15.5
9.9
9.2
12.8
24.5
10.5
9.1
18.6
16.9
17.1
20.4
13.5
18.3
5.8
15.4
23.1
10.7
13.5
21.4
15.3
17.1
16.7
13.4
8.2
13.7
No,
nev
er67
.883
.573
.860
.471
.163
.371
.762
.159
.571
.371
.750
.157
.577
.778
.660
.272
.873
.362
.475
.265
.676
.971
.058
.371
.075
.365
.969
.672
.761
.169
.179
.870
.3D
K.7
2.1
1.2
.8.1
.91.
33.
76.
53.
6.3
EU27
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
151
Split
bal
lot A
qb13
a
Firs
tly?
synb
iolo
gy_i
nfo1
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Wha
t the
sci
entif
ic
proc
esse
s an
d te
chni
ques
are
14.9
10.7
13.3
13.5
15.0
26.4
13.4
18.7
12.8
25.7
14.2
15.7
15.6
9.7
14.8
13.0
45.6
20.6
13.0
22.0
10.4
15.2
11.5
9.9
27.3
38.4
17.5
11.0
13.3
15.3
22.8
18.0
15.1
Who
is fu
ndin
g th
e re
sear
ch a
nd w
hy11
.34.
06.
38.
97.
07.
412
.611
.511
.510
.83.
47.
77.
45.
95.
08.
45.
24.
97.
45.
03.
57.
58.
97.
84.
95.
47.
52.
93.
39.
36.
36.
37.
9
Wha
t the
cla
imed
be
nefit
s ar
e10
.815
.728
.624
.525
.022
.615
.615
.625
.316
.18.
528
.425
.915
.817
.624
.66.
09.
022
.412
.732
.125
.719
.023
.916
.623
.123
.717
.828
.123
.516
.317
.021
.3
Wha
t the
pos
sibl
e ris
ks a
re30
.129
.421
.532
.822
.419
.828
.719
.321
.717
.623
.324
.230
.323
.124
.433
.519
.926
.923
.818
.917
.523
.424
.918
.418
.413
.821
.913
.419
.524
.918
.024
.923
.7
Who
will
ben
efit
and
who
will
bea
r the
ris
ks15
.113
.011
.410
.47.
110
.96.
68.
78.
86.
426
.07.
35.
516
.78.
77.
814
.913
.69.
810
.99.
48.
39.
922
.510
.86.
98.
29.
512
.312
.18.
517
.110
.0
Wha
t is
bein
g do
ne
to re
gula
te a
nd
cont
rol s
ynth
etic
bi
olog
y
4.1
9.7
3.7
1.0
3.1
1.7
7.0
6.5
5.8
6.9
7.3
5.7
2.3
12.3
7.1
2.0
2.8
4.5
8.5
4.7
5.9
2.8
3.5
5.7
4.9
3.1
2.3
1.8
8.3
5.0
9.9
6.7
5.1
Wha
t is
bein
g do
ne
to d
eal w
ith th
e so
cial
an
d et
hica
l iss
ues
invo
lved
5.1
10.8
2.9
2.5
3.7
6.4
2.7
6.0
2.3
3.3
9.8
2.7
.87.
73.
85.
0.9
2.7
2.0
4.4
1.0
.72.
63.
66.
2.8
2.7
3.6
7.9
1.9
4.1
4.6
3.4
Oth
er
(SPO
NTA
NEO
US)
.8.3
.9.2
1.1
.2
.2
.4
.9.4
.2
.2
.3.7
.2
.3
.6
.3.2
Non
e (S
PON
TAN
EOU
S)3.
51.
43.
42.
15.
51.
42.
21.
22.
71.
71.
13.
01.
81.
04.
45.
11.
31.
63.
59.
25.
41.
15.
62.
05.
3.4
1.5
1.4
1.2
1.5
2.2
1.2
3.3
DK
4.1
5.1
9.0
4.3
10.2
3.3
11.1
12.5
9.3
11.5
5.3
5.2
10.3
7.6
14.2
.53.
215
.39.
212
.214
.515
.314
.15.
85.
08.
114
.438
.55.
96.
511
.43.
810
.1
Supp
ose,
ther
e w
as a
ref
eren
dum
abo
ut s
ynth
etic
bio
logy
and
you
had
to m
ake
up y
our
min
d w
heth
er to
vot
e fo
r or
aga
inst
. Am
ong
the
follo
win
g, w
hat w
ould
be
the
mos
t im
port
ant i
ssue
on
whi
ch y
ou w
ould
like
to k
now
mor
e?
EU27
Cou
ntry
152
And
sec
ondl
y?
synb
iolo
gy_i
nfo2
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Wha
t the
sci
entif
ic
proc
esse
s an
d te
chni
ques
are
10.5
7.1
8.5
5.7
6.8
10.6
8.9
10.4
10.0
9.3
6.8
9.4
9.9
9.1
7.3
5.6
14.9
11.3
7.1
10.9
6.6
6.9
10.7
7.7
9.6
6.9
9.1
6.3
5.7
7.9
10.8
9.5
8.7
Who
is fu
ndin
g th
e re
sear
ch a
nd w
hy9.
27.
49.
25.
86.
06.
78.
710
.18.
613
.44.
38.
910
.08.
410
.58.
07.
17.
04.
811
.55.
64.
411
.110
.25.
66.
810
.810
.45.
211
.66.
39.
38.
6
Wha
t the
cla
imed
be
nefit
s ar
e13
.722
.823
.430
.224
.826
.316
.119
.824
.520
.417
.221
.329
.417
.517
.224
.013
.516
.625
.423
.921
.425
.422
.820
.121
.230
.325
.820
.119
.919
.923
.821
.021
.7
Wha
t the
pos
sibl
e ris
ks a
re26
.827
.725
.034
.131
.929
.431
.524
.931
.430
.222
.930
.723
.922
.925
.234
.229
.228
.534
.022
.739
.732
.328
.128
.625
.235
.432
.727
.227
.330
.023
.523
.228
.7
Who
will
ben
efit
and
who
will
bea
r the
ris
ks19
.812
.717
.018
.514
.714
.813
.113
.711
.211
.119
.418
.914
.316
.717
.119
.322
.516
.913
.617
.214
.616
.112
.619
.020
.512
.511
.222
.314
.718
.616
.918
.215
.1
Wha
t is
bein
g do
ne
to re
gula
te a
nd
cont
rol s
ynth
etic
bi
olog
y
14.0
9.5
10.5
2.9
7.8
6.4
17.0
13.3
10.0
11.8
15.8
6.2
6.0
14.5
12.6
3.8
7.2
10.0
11.8
8.2
7.1
7.6
7.4
9.9
10.1
6.1
5.8
6.5
13.8
6.4
9.0
11.0
10.6
Wha
t is
bein
g do
ne
to d
eal w
ith th
e so
cial
an
d et
hica
l iss
ues
invo
lved
5.2
12.2
5.6
2.5
4.0
4.6
3.0
6.4
3.8
3.6
11.8
3.8
3.7
10.1
7.7
5.1
5.0
5.4
2.8
4.0
3.7
4.5
5.0
4.4
6.5
1.7
2.8
6.0
13.0
4.8
9.0
5.7
5.1
Oth
er
(SPO
NTA
NEO
US)
.6
1.6
.6
.1
.8
.5.2
.2
.2
.2
.5
.3
.2.4
.2.2
Non
e (S
PON
TAN
EOU
S).2
.2.4
.2.6
.1.4
.5.5
.5
.2
.1.9
.3
.4.2
.6.5
.2
.8
1.
0
.31.
3.5
DK
.5
.2
1.9
.61.
1.9
.5.2
2.4
.41.
6
.23.
8.3
1.1
.52.
71.
8
.2
.71.
2
.6
.5.8
And
thir
dly?
synb
iolo
gy_i
nfo3
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Wha
t the
sci
entif
ic
proc
esse
s an
d te
chni
ques
are
9.7
7.2
8.5
12.5
12.3
8.7
7.7
8.9
11.9
8.5
9.9
9.5
11.6
12.6
11.4
13.2
8.8
12.2
11.0
10.3
7.4
8.8
13.6
7.2
10.7
7.4
9.1
12.8
10.8
9.4
7.8
8.7
10.3
Who
is fu
ndin
g th
e re
sear
ch a
nd w
hy9.
28.
87.
710
.59.
27.
29.
511
.310
.09.
77.
68.
39.
07.
512
.37.
37.
87.
49.
510
.77.
211
.110
.89.
911
.57.
015
.45.
58.
89.
77.
79.
39.
7
Wha
t the
cla
imed
be
nefit
s ar
e14
.512
.514
.815
.712
.813
.612
.315
.616
.314
.213
.114
.515
.912
.812
.311
.315
.515
.214
.218
.013
.917
.111
.215
.211
.215
.611
.713
.911
.116
.011
.319
.013
.7
Wha
t the
pos
sibl
e ris
ks a
re16
.813
.817
.714
.218
.818
.215
.615
.217
.621
.215
.814
.820
.116
.013
.816
.821
.117
.818
.719
.117
.317
.916
.620
.117
.319
.616
.815
.518
.216
.314
.717
.716
.9
Who
will
ben
efit
and
who
will
bea
r the
ris
ks16
.118
.918
.826
.021
.925
.120
.517
.821
.417
.118
.127
.117
.914
.215
.826
.822
.517
.218
.016
.227
.421
.124
.422
.018
.122
.523
.319
.516
.221
.319
.115
.620
.3
Wha
t is
bein
g do
ne
to re
gula
te a
nd
cont
rol s
ynth
etic
bi
olog
y
18.4
19.1
21.3
10.2
12.4
12.6
20.3
16.2
13.9
16.8
15.2
16.4
15.1
20.5
19.5
6.2
13.3
15.1
18.9
13.6
15.2
12.0
15.2
14.5
14.7
16.4
9.5
12.7
19.1
14.9
21.1
16.2
16.7
Wha
t is
bein
g do
ne
to d
eal w
ith th
e so
cial
an
d et
hica
l iss
ues
invo
lved
14.0
14.4
10.5
9.9
8.0
10.3
9.5
10.3
7.6
9.8
17.4
8.4
7.2
15.5
10.7
17.0
10.0
8.2
8.3
9.7
7.4
8.7
6.2
11.0
14.1
8.1
10.1
14.5
15.4
10.4
15.7
11.4
9.7
Oth
er
(SPO
NTA
NEO
US)
1.1
.3
1.
51.
4
.1.2
1.4
1.0
.5
.2
.4
.3
.8
.4
.5
1.
9.8
.3.7
.3.1
.4
Non
e (S
PON
TAN
EOU
S).3
1.3
.4.8
.91.
52.
6
.2
.6
.4.3
2.6
.3.6
.6.2
.7
1.
3
1.3
.7
.4
.5
1.4
1.0
DK
3.
6.3
2.
11.
32.
04.
6.9
1.3
1.3
.32.
8.4
1.7
.8.4
5.9
1.2
1.7
3.3
3.0
.7
.83.
41.
44.
3
1.4
1.8
.41.
3
Cou
ntry
EU27
Cou
ntry
EU27
153
Men
tione
d (fi
rst,
seco
nd o
r thi
rd)
Wha
t the
sci
entif
ic p
roce
sses
and
tech
niqu
es a
re
synb
iolo
gy_p
roce
ssB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t, se
cond
or t
hird
)33
.524
.028
.230
.530
.944
.827
.735
.332
.041
.229
.733
.134
.229
.429
.930
.868
.239
.728
.738
.521
.628
.130
.823
.745
.351
.432
.622
.428
.631
.138
.935
.231
.4
Not
men
tione
d66
.576
.071
.869
.569
.155
.272
.364
.768
.058
.870
.366
.965
.870
.670
.169
.231
.860
.371
.361
.578
.471
.969
.276
.354
.748
.667
.477
.671
.468
.961
.164
.868
.6
Who
is fu
ndin
g th
e re
sear
ch a
nd w
hy
synb
iolo
gy_f
undi
ngB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t, se
cond
or t
hird
)28
.219
.121
.124
.219
.620
.628
.329
.927
.730
.914
.523
.523
.920
.523
.322
.919
.416
.719
.822
.213
.720
.226
.326
.320
.118
.029
.312
.516
.428
.918
.423
.923
.7
Not
men
tione
d71
.880
.978
.975
.880
.479
.471
.770
.172
.369
.185
.576
.576
.179
.576
.777
.180
.683
.380
.277
.886
.379
.873
.773
.779
.982
.070
.787
.583
.671
.181
.676
.176
.3
Wha
t the
cla
imed
ben
efits
are
synb
iolo
gy_b
enef
itsB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t, se
cond
or t
hird
)36
.848
.762
.067
.556
.460
.540
.146
.061
.246
.236
.761
.265
.343
.541
.357
.933
.634
.956
.945
.460
.360
.946
.156
.545
.665
.155
.138
.256
.956
.446
.554
.751
.8
Not
men
tione
d63
.251
.338
.032
.543
.639
.559
.954
.038
.853
.863
.338
.834
.756
.558
.742
.166
.465
.143
.154
.639
.739
.153
.943
.554
.434
.944
.961
.843
.143
.653
.545
.348
.2
Wha
t the
pos
sibl
e ris
ks a
re
synb
iolo
gy_r
isks
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst,
seco
nd o
r thi
rd)
70.4
68.0
58.8
78.1
64.8
65.0
69.3
53.8
64.7
62.1
59.4
66.1
68.5
58.7
55.8
81.6
67.9
64.8
69.8
51.6
63.0
65.0
60.5
63.2
56.4
64.0
63.4
39.0
61.9
67.5
50.9
63.4
63.0
Not
men
tione
d29
.632
.041
.221
.935
.235
.030
.746
.235
.337
.940
.633
.931
.541
.344
.218
.432
.135
.230
.248
.437
.035
.039
.536
.843
.636
.036
.661
.038
.132
.549
.136
.637
.0
Who
will
ben
efit
and
who
will
bea
r th
e ris
ks
synb
iolo
gy_e
quity
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst,
seco
nd o
r thi
rd)
48.2
42.3
42.7
52.0
37.5
48.9
35.4
35.7
37.4
30.9
61.0
49.5
33.4
44.9
35.2
51.3
57.8
41.4
37.2
36.9
42.9
38.9
39.2
60.4
45.2
39.0
36.9
34.5
41.0
48.7
39.6
49.0
40.4
Not
men
tione
d51
.857
.757
.348
.062
.551
.164
.664
.362
.669
.139
.050
.566
.655
.164
.848
.742
.258
.662
.863
.157
.161
.160
.839
.654
.861
.063
.165
.559
.051
.360
.451
.059
.6
Wha
t is
bein
g do
ne to
reg
ulat
e an
d co
ntro
l syn
thet
ic b
iolo
gy
synb
iolo
gy_r
egul
ate
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst,
seco
nd o
r thi
rd)
34.0
36.2
31.4
13.3
19.9
19.8
39.1
31.8
26.8
31.8
36.1
26.4
20.5
44.2
32.9
11.4
22.2
24.8
35.2
21.7
23.7
19.0
21.4
28.3
27.0
23.7
15.1
13.2
38.9
24.5
35.8
32.3
28.6
Not
men
tione
d66
.063
.868
.686
.780
.180
.260
.968
.273
.268
.263
.973
.679
.555
.867
.188
.677
.875
.264
.878
.376
.381
.078
.671
.773
.076
.384
.986
.861
.175
.564
.267
.771
.4
EU27
EU27
Cou
ntry
- la
bels
Cou
ntry
Cou
ntry
EU27
Cou
ntry
- la
bels
EU27
EU27
Cou
ntry
EU27
Cou
ntry
154
Wha
t is
bein
g do
ne to
dea
l with
the
soci
al a
nd e
thic
al is
sues
invo
lved
synb
iolo
gy_e
thic
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t, se
cond
or t
hird
)22
.835
.616
.914
.113
.720
.513
.520
.312
.314
.937
.013
.910
.231
.118
.525
.915
.213
.811
.715
.19.
911
.511
.517
.924
.59.
813
.515
.934
.315
.825
.420
.616
.1
Not
men
tione
d77
.264
.483
.185
.986
.379
.586
.579
.787
.785
.163
.086
.189
.868
.981
.574
.184
.886
.288
.384
.990
.188
.588
.582
.175
.590
.286
.584
.165
.784
.274
.679
.483
.9
Oth
er (S
PON
TAN
EOU
S)
synb
iolo
gy_o
ther
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst,
seco
nd o
r thi
rd)
1.3
.6
2.
21.
4
.2.2
1.2
1.3
.9.2
.2
.3.4
1.2
.4
.6.3
.2.3
.9
1.8
.5.3
.71.
0.6
.5
Not
men
tione
d98
.799
.410
0.0
100.
097
.898
.610
0.0
99.8
99.8
98.8
98.7
99.1
99.8
99.8
100.
099
.799
.698
.899
.610
0.0
99.4
99.7
99.8
99.7
99.1
100.
098
.299
.599
.799
.399
.099
.499
.5
Non
e (S
PON
TAN
EOU
S)
synb
iolo
gy_n
one
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
No
issu
e (S
PON
TAN
EOU
S)4.
02.
74.
13.
16.
72.
94.
81.
63.
31.
72.
13.
02.
41.
37.
25.
52.
22.
43.
910
.25.
81.
16.
82.
07.
2.4
2.9
1.6
1.2
1.5
2.8
3.7
4.5
(som
e is
sue
men
tione
d)96
.097
.395
.996
.993
.397
.195
.298
.496
.798
.397
.997
.097
.698
.792
.894
.597
.897
.696
.189
.894
.298
.993
.298
.092
.899
.697
.198
.498
.898
.597
.296
.395
.5
DK
(SPO
NTA
NEO
US)
synb
iolo
gy_D
KB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OD
K4.
15.
19.
04.
310
.23.
311
.112
.59.
311
.55.
35.
210
.37.
614
.2.5
3.2
15.3
9.2
12.2
14.5
15.3
14.1
5.8
5.0
8.1
14.4
38.5
5.9
6.5
11.4
3.8
10.1
(som
e no
n-D
K
resp
onse
)95
.994
.991
.095
.789
.896
.788
.987
.590
.788
.594
.794
.889
.792
.485
.899
.596
.884
.790
.887
.885
.584
.785
.994
.295
.091
.985
.661
.594
.193
.588
.696
.289
.9
qb14
aO
vera
ll, w
hat w
ould
you
say
abo
ut s
ynth
etic
bio
logy
?
synb
iolo
gy_a
ppro
veB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OFu
lly a
ppro
ve a
nd d
o no
t thi
nk th
at s
peci
al
law
s ar
e ne
cess
ary
4.2
1.7
2.7
2.2
5.9
4.9
3.2
1.7
3.7
3.9
1.9
2.5
3.5
2.9
2.8
.42.
25.
93.
92.
63.
82.
13.
81.
81.
41.
95.
06.
81.
82.
34.
12.
03.
4
Appr
ove
as lo
ng a
s th
is is
regu
late
d by
st
rict l
aws
47.3
44.3
26.6
40.2
38.5
31.6
44.0
39.2
36.2
38.6
37.0
31.1
48.0
36.2
41.7
35.1
31.3
40.9
44.8
31.5
31.2
32.3
28.5
37.9
33.4
27.2
36.7
24.4
32.3
34.6
32.7
45.8
36.4
Do
not a
ppro
ve
exce
pt u
nder
ver
y sp
ecia
l ci
rcum
stan
ces
21.5
21.0
30.8
22.6
14.9
27.1
17.3
13.2
16.0
12.9
23.0
29.8
14.3
27.9
20.7
15.9
34.2
13.1
23.8
22.5
19.5
11.4
18.1
28.9
17.9
21.0
12.2
12.0
32.8
22.2
25.3
25.9
20.9
Do
not a
ppro
ve u
nder
an
y ci
rcum
stan
ces
15.1
21.4
20.8
26.3
11.4
24.5
16.5
7.4
18.8
19.0
20.7
22.5
9.8
16.7
14.7
33.6
15.3
16.9
8.8
18.6
14.7
19.1
18.2
12.8
36.9
13.5
11.4
13.8
21.8
22.0
14.1
13.5
16.8
DK
12.0
11.6
19.1
8.7
29.2
11.8
19.1
38.5
25.3
25.6
17.4
14.1
24.4
16.3
20.0
15.0
17.0
23.3
18.7
24.9
30.8
35.2
31.4
18.6
10.5
36.4
34.7
43.0
11.3
18.9
23.9
12.7
22.5
Cou
ntry
Cou
ntry
Cou
ntry
EU27
EU27
EU27
Cou
ntry
EU27
Cou
ntry
EU27
155
qb15
aTo
wha
t ext
ent d
o yo
u th
ink
thes
e bi
ofue
ls s
houl
d be
enc
oura
ged
or n
ot b
e en
cour
aged
?
first
gen_
biof
uel_
enco
uB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OS
houl
d de
finite
ly b
e en
cour
aged
34.5
49.7
32.3
31.8
38.4
33.7
24.9
36.9
32.9
32.3
36.7
30.0
28.2
33.5
30.6
51.7
38.8
44.9
40.7
50.4
47.0
15.2
39.2
44.7
35.1
24.7
37.5
18.2
21.9
42.4
25.0
16.1
33.5
Shou
ld p
roba
bly
be
enco
urag
ed41
.736
.132
.137
.137
.642
.741
.236
.233
.132
.036
.146
.146
.240
.743
.227
.839
.238
.443
.837
.234
.531
.340
.843
.438
.741
.530
.415
.444
.437
.828
.047
.637
.9
Shou
ld p
roba
bly
not
be e
ncou
rage
d14
.79.
317
.218
.09.
515
.717
.26.
213
.415
.416
.111
.610
.917
.012
.110
.014
.48.
77.
86.
75.
517
.99.
57.
913
.211
.79.
86.
524
.010
.622
.122
.113
.3
Sho
uld
defin
itely
not
be
enc
oura
ged
7.0
2.9
13.6
7.3
5.4
5.7
10.9
4.6
7.4
12.6
8.4
6.7
2.4
5.7
3.7
3.2
4.4
4.6
3.9
2.2
2.5
17.0
2.3
.78.
66.
24.
216
.17.
85.
221
.69.
77.
1
DK
2.1
2.0
4.8
5.8
9.1
2.1
5.7
16.1
13.2
7.7
2.7
5.6
12.3
3.0
10.3
7.3
3.3
3.5
3.8
3.5
10.4
18.6
8.3
3.4
4.5
15.9
18.0
43.8
1.9
3.9
3.3
4.4
8.2
qb16
aTo
wha
t ext
ent d
o yo
u th
ink
thes
e su
stai
nabl
e bi
ofue
ls s
houl
d be
enc
oura
ged
or n
ot b
e en
cour
aged
?
sust
aina
ble_
biof
uel_
enB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OS
houl
d de
finite
ly b
e en
cour
aged
53.1
74.8
53.1
48.0
51.8
58.4
52.0
44.6
42.1
41.9
65.6
38.7
37.3
60.5
52.8
75.1
50.0
54.6
54.1
57.2
57.6
41.4
47.4
49.8
58.6
43.8
44.3
21.5
63.8
53.4
37.5
58.7
50.5
Shou
ld p
roba
bly
be
enco
urag
ed34
.120
.829
.934
.130
.636
.330
.834
.331
.232
.326
.443
.342
.730
.131
.514
.436
.834
.036
.833
.930
.135
.038
.842
.630
.836
.326
.916
.328
.835
.133
.532
.132
.1
Shou
ld p
roba
bly
not
be e
ncou
rage
d9.
12.
07.
17.
95.
94.
17.
93.
78.
815
.44.
610
.39.
25.
34.
73.
28.
85.
64.
44.
52.
04.
64.
44.
35.
34.
16.
87.
33.
64.
714
.05.
96.
5
Sho
uld
defin
itely
not
be
enc
oura
ged
1.7
.25.
74.
63.
8.9
3.6
1.2
6.6
5.1
1.7
4.4
1.9
1.2
3.4
.42.
92.
62.
31.
21.
27.
71.
9.8
2.6
3.6
3.2
11.8
2.3
2.3
10.6
1.2
3.8
DK
1.9
2.1
4.1
5.4
7.9
.25.
816
.211
.35.
31.
73.
38.
82.
97.
77.
01.
53.
12.
43.
19.
211
.47.
62.
42.
712
.118
.843
.21.
54.
44.
42.
27.
0
Cou
ntry
EU27
EU27
Let’s
spe
ak n
ow a
bout
bio
fuel
s. B
iofu
els
are
mad
e fr
om c
rops
like
mai
ze a
nd s
ugar
can
e th
at a
re tu
rned
into
eth
anol
and
bio
dies
el fo
r ai
rpla
nes,
car
s an
d lo
rrie
s. U
nlik
e oi
l, bi
ofue
ls a
re r
enew
able
, wou
ld r
educ
e gr
eenh
ouse
gas
em
issi
ons
and
mak
e th
e Eu
rope
an U
nion
less
dep
ende
nt o
n im
port
ed o
il. C
ritic
s, h
owev
er, s
ay th
at th
ese
biof
uels
take
up
prec
ious
agr
icul
tura
l lan
d an
d m
ay le
ad to
hig
her
food
pri
ces
in th
e Eu
rope
an U
nion
and
food
sho
rtag
es in
the
deve
lopi
ng w
orld
.
Now
, sci
entis
ts a
re w
orki
ng o
n m
ore
sust
aina
ble
biof
uels
. The
se c
an b
e m
ade
from
pla
nt s
tem
s an
d le
aves
- th
e th
ings
we
don’
t eat
, or
from
tree
s an
d al
gae.
With
thes
e se
cond
gen
erat
ion
biof
uels
, the
re is
no
long
er th
e ne
ed to
use
fo
od c
rops
.
Cou
ntry
156
Split
bal
lot B
qb12
bB
efor
e to
day,
hav
e yo
u ev
er h
eard
any
thin
g ab
out b
ioba
nks?
hear
d_bi
oban
ksB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OH
eard
28.0
40.2
28.8
39.3
55.1
62.7
24.4
31.4
30.9
52.4
44.0
18.4
19.2
74.7
34.2
44.0
48.7
67.6
31.4
45.8
34.7
23.1
28.5
33.6
52.1
27.8
31.5
15.5
80.3
50.3
42.0
64.5
34.3
Not
hea
rd72
.059
.871
.260
.744
.937
.375
.668
.669
.147
.656
.081
.680
.825
.365
.856
.051
.332
.468
.654
.265
.376
.971
.566
.447
.972
.268
.584
.519
.749
.758
.035
.565
.7
qb13
b[IF
YES
] Hav
e yo
u ev
er ta
lked
abo
ut b
ioba
nks
with
any
one
befo
re to
day?
talk
ed_b
ioba
nks
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Yes
, fre
quen
tly3.
95.
47.
45.
01.
85.
53.
12.
14.
53.
67.
13.
910
.84.
47.
11.
9.3
3.6
3.5
.93.
42.
32.
12.
02.
93.
316
.66.
05.
72.
94.
3Y
es, o
ccas
iona
lly20
.521
.422
.527
.520
.420
.522
.426
.838
.220
.523
.427
.831
.018
.917
.311
.212
.221
.211
.312
.217
.523
.219
.619
.927
.622
.222
.111
.141
.523
.919
.424
.822
.6Y
es, o
nly
once
or
19.5
18.8
19.5
31.6
27.2
19.2
18.5
24.6
22.0
14.5
19.7
34.7
32.0
25.6
17.2
38.5
22.8
27.7
27.9
21.8
23.9
10.2
16.5
31.4
26.2
29.4
20.2
17.6
22.0
26.4
24.5
25.3
21.9
No,
nev
er56
.154
.349
.834
.650
.554
.756
.042
.434
.760
.349
.633
.626
.350
.957
.948
.464
.047
.557
.364
.658
.063
.259
.745
.643
.445
.652
.866
.920
.042
.849
.846
.750
.6D
K.8
1.4
4.1
.61.
0.2
.2.4
.7.5
.62.
01.
0.8
2.8
2.0
1.1
.9.6
.4.6
[IF Y
ES] H
ave
you
ever
sea
rche
d fo
r in
form
atio
n ab
out b
ioba
nks?
info
sear
ch_b
ioba
nks
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Yes
, fre
quen
tly3.
23.
94.
23.
71.
04.
63.
3.6
2.4
4.9
4.4
1.2
4.1
1.8
3.1
1.9
2.0
2.3
.4.5
3.4
1.9
1.0
2.0
3.2
.41.
63.
31.
92.
6Y
es, o
ccas
iona
lly8.
19.
38.
18.
47.
811
.610
.311
.225
.315
.410
.713
.921
.08.
76.
56.
58.
55.
76.
06.
710
.318
.56.
512
.510
.18.
98.
38.
016
.014
.47.
77.
510
.4Y
es, o
nly
once
or
10.4
12.5
10.1
23.6
10.5
12.6
8.7
10.0
12.8
8.1
10.1
18.7
16.5
10.5
6.7
18.3
11.7
10.7
12.0
11.7
10.2
4.2
11.2
21.7
12.8
12.8
10.8
6.2
9.0
13.5
11.0
9.8
10.9
No,
nev
er78
.373
.977
.363
.780
.771
.277
.773
.459
.568
.674
.266
.358
.478
.983
.073
.279
.481
.679
.880
.279
.073
.980
.464
.274
.574
.376
.884
.774
.669
.677
.480
.775
.8D
K.4
.3.7
4.8
3.0
.7.2
.7.4
1.0
.5.5
4.0
.91.
1.8
.6.3
qb14
b
biob
anks
_con
sent
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Ask
for p
erm
issi
on fo
r ev
ery
new
pie
ce o
f re
sear
ch69
.051
.574
.884
.366
.854
.575
.159
.357
.967
.956
.869
.762
.663
.165
.173
.670
.665
.677
.771
.365
.773
.361
.467
.573
.475
.555
.056
.059
.868
.970
.759
.466
.9
Ask
for p
erm
issi
on
only
onc
e21
.724
.615
.411
.717
.131
.711
.922
.020
.617
.333
.117
.619
.126
.421
.412
.816
.618
.913
.712
.316
.213
.922
.122
.619
.29.
313
.26.
732
.314
.114
.926
.518
.3
No
need
to a
sk fo
r pe
rmis
sion
6.1
16.4
2.9
2.2
9.1
9.9
6.3
6.4
6.8
11.0
6.9
4.3
3.7
6.1
8.1
9.4
6.5
9.5
4.4
6.4
4.2
4.5
3.5
5.3
3.1
1.8
7.0
9.0
6.5
6.0
5.8
10.8
6.0
DK
3.2
7.5
7.0
1.8
7.0
3.8
6.8
12.3
14.7
3.9
3.2
8.3
14.5
4.4
5.4
4.2
6.3
6.1
4.2
10.0
13.8
8.4
13.0
4.6
4.4
13.4
24.8
28.3
1.4
11.0
8.6
3.3
8.9
Cou
ntry
Cou
ntry
In a
hos
pita
l doc
tors
ask
the
patie
nt to
sig
n a
form
giv
ing
perm
issi
on to
car
ry o
ut a
n op
erat
ion
– th
is is
cal
led
‘info
rmed
con
sent
’ and
it is
als
o re
quir
ed o
f med
ical
res
earc
hers
who
do
rese
arch
invo
lvin
g m
embe
rs o
f the
pub
lic. W
hen
a sc
ient
ist d
oes
rese
arch
on
data
in a
bio
bank
, wha
t do
you
thin
k ab
out t
he n
eed
for
this
kin
d of
per
mis
sion
? R
esea
rche
rs s
houl
d…
EU27
Cou
ntry
EU27
EU27
Cou
ntry
EU27
And
now
thin
king
abo
ut b
ioba
nks
for
biom
edic
al r
esea
rch:
The
se a
re c
olle
ctio
ns o
f bio
logi
cal m
ater
ials
(suc
h as
blo
od a
nd/o
r tis
sues
) and
per
sona
l dat
a (m
edic
al r
ecor
ds, l
ifest
yle
data
) fro
m la
rge
num
bers
of p
eopl
e. U
sing
bio
bank
s,
rese
arch
ers
will
try
to id
entif
y th
e ge
netic
and
env
iron
men
tal f
acto
rs in
dis
ease
s, to
impr
ove
prev
entio
n, d
iagn
osis
and
trea
tmen
t. Pa
rtic
ipat
ion
in b
ioba
nks
is v
olun
tary
. Cri
tics,
how
ever
, rai
se q
uest
ions
abo
ut p
riva
cy, c
onfid
entia
lity
and
com
mer
cial
inte
rest
s re
gard
ing
the
biob
anks
and
abo
ut w
ho is
goi
ng to
reg
ulat
e th
em.
157
qb15
b
Firs
tly?
biob
anks
_pub
licin
tere
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
edic
al d
octo
rs30
.814
.314
.835
.232
.316
.825
.736
.725
.133
.714
.826
.445
.07.
925
.135
.620
.431
.224
.731
.222
.341
.931
.127
.621
.131
.334
.428
.110
.932
.419
.516
.025
.2R
esea
rche
rs16
.811
.86.
018
.616
.418
.312
.37.
122
.411
.68.
48.
913
.27.
28.
811
.028
.822
.821
.225
.219
.36.
917
.830
.418
.624
.020
.88.
47.
220
.57.
05.
814
.4P
ublic
inst
itutio
ns
(uni
vers
ities
, ho
spita
ls)
9.4
9.8
11.9
10.5
10.5
15.5
9.1
9.2
16.5
10.1
8.8
20.1
9.8
21.5
5.2
4.8
10.3
3.8
7.2
2.2
8.2
6.7
9.2
8.4
10.9
10.4
7.0
9.1
13.2
8.5
10.1
13.3
10.5
Nat
iona
l gov
ernm
ents
9.7
14.0
13.9
10.1
17.8
9.4
7.5
11.4
9.9
7.7
19.1
6.4
4.2
14.6
20.9
19.1
9.2
9.1
7.1
11.5
7.5
12.0
9.4
8.4
8.7
11.8
8.4
17.5
7.2
4.5
8.8
17.9
12.4
Eth
ics
com
mitt
ees
16.2
20.7
11.0
7.0
2.7
7.0
19.8
8.7
3.2
6.1
10.8
10.5
5.8
16.2
9.2
2.1
4.5
2.8
9.4
3.3
.22.
04.
83.
05.
22.
0.9
2.1
12.9
7.9
13.0
10.6
8.6
Inte
rnat
iona
l or
gani
satio
ns s
uch
as
the
Eur
opea
n U
nion
or
Wor
ld H
ealth
O
rgan
isat
ion
9.1
11.5
12.8
10.0
9.6
18.9
10.2
14.6
9.8
16.2
16.4
6.5
9.4
19.9
9.1
20.4
12.0
4.8
17.1
8.7
16.5
12.2
7.0
13.1
12.7
9.0
7.9
4.6
12.5
10.4
11.0
14.1
10.7
Nat
iona
l Dat
a P
rote
ctio
n Au
thor
ities
5.6
13.3
23.2
5.2
6.0
11.2
7.4
5.8
4.9
13.0
19.1
11.4
4.1
6.3
11.1
5.0
9.9
17.2
11.0
8.5
14.6
8.1
4.8
5.4
15.6
5.9
5.7
2.4
35.9
7.8
23.3
16.8
10.2
Oth
er
(SPO
NTA
NEO
US)
.5
.0.4
.2.8
.2.1
.2.3
.5.6
.3
.0
.41.
0
.2
.5
.7
.9
.3.5
.8.2
Non
e (S
PON
TAN
EOU
S).8
.21.
31.
7.9
.21.
2.2
1.1
.3
2.7
.2.2
1.1
.3
.51.
01.
01.
2.8
1.5
2.
9.8
.3.6
1.
5.7
1.2
1.0
DK
1.1
4.4
5.2
1.3
3.6
1.8
6.6
6.2
7.1
1.2
2.2
6.5
8.4
5.8
9.5
2.0
4.2
6.7
1.3
8.5
10.0
9.3
14.1
3.7
3.6
4.8
14.6
26.5
.36.
36.
13.
46.
8
And
sec
ondl
y?
biob
anks
_pub
licin
tere
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
edic
al d
octo
rs18
.716
.410
.917
.716
.111
.516
.317
.315
.716
.214
.318
.516
.48.
716
.521
.417
.921
.516
.625
.021
.617
.915
.821
.324
.215
.918
.017
.812
.917
.612
.38.
615
.5R
esea
rche
rs20
.912
.910
.723
.321
.018
.616
.019
.324
.222
.910
.710
.428
.112
.912
.717
.427
.124
.424
.326
.120
.215
.729
.226
.117
.528
.724
.621
.310
.019
.310
.611
.218
.7P
ublic
inst
itutio
ns
(uni
vers
ities
, ho
spita
ls)
15.0
16.0
19.5
15.9
15.3
18.5
14.1
14.4
22.6
16.9
16.7
20.2
14.9
23.5
11.7
11.6
16.7
13.9
13.1
3.9
18.6
10.7
16.9
11.8
16.4
15.0
10.2
17.4
21.9
18.4
18.6
20.2
16.5
Nat
iona
l gov
ernm
ents
9.4
12.3
13.3
9.3
15.8
12.8
9.6
11.0
8.9
10.3
16.2
8.8
9.0
13.7
14.2
23.8
5.4
11.9
9.3
11.9
8.6
15.8
13.1
11.4
8.6
10.9
14.0
15.6
5.2
11.5
10.8
13.2
12.0
Eth
ics
com
mitt
ees
13.0
14.0
13.8
10.2
4.0
6.9
12.1
10.6
4.3
7.5
9.7
14.4
8.7
14.9
11.8
2.1
8.4
5.2
10.6
5.4
2.2
4.2
10.9
5.6
8.1
4.5
4.8
5.1
17.2
10.8
14.2
14.7
9.6
Inte
rnat
iona
l or
gani
satio
ns s
uch
as
the
Eur
opea
n U
nion
or
Wor
ld H
ealth
O
rgan
isat
ion
11.6
10.5
14.0
15.4
16.7
14.2
17.4
14.7
15.3
13.2
15.0
12.4
13.3
15.7
13.8
16.8
12.9
4.8
14.8
13.4
9.7
19.6
6.6
14.1
10.5
13.0
16.0
15.6
13.4
9.9
11.7
16.2
14.2
Nat
iona
l Dat
a P
rote
ctio
n Au
thor
ities
10.7
15.0
16.4
7.6
7.6
13.9
10.5
9.2
6.8
12.7
15.7
12.8
5.6
8.7
14.8
6.9
10.9
14.6
10.6
12.8
16.9
11.5
3.9
9.6
12.2
10.1
9.4
2.2
18.0
11.0
17.9
13.7
10.8
Oth
er
(SPO
NTA
NEO
US)
.2
.1
.4.6
.4.6
.2
.3
.2
.2
.2
.5
.1
.5.7
.5.5
.2
Non
e (S
PON
TAN
EOU
S).3
.2.5
.61.
31.
41.
3
.71.
2.6
1.1
2.5
.4.2
.5.4
.5
.6
1.
7.7
.8.5
1.3
1.1
.9
DK
.52.
4.9
1.
81.
62.
42.
81.
9.4
.91.
03.
4.7
1.9
.8
3.1
.31.
11.
34.
53.
0
1.8
2.1
.63.
6.5
1.0
2.2
.51.
6
EU27
Cou
ntry
EU27
Cou
ntry
Bio
bank
s w
ill fo
llow
up
part
icip
ants
ove
r lo
ng p
erio
ds o
f tim
e. A
nd m
any
biob
anks
will
wor
k w
ith in
dust
rial
com
pani
es to
dev
elop
new
med
icin
es.
Who
do
you
thin
k sh
ould
be
prim
arily
res
pons
ible
for
prot
ectin
g th
e pu
blic
inte
rest
?
158
Men
tione
d (fi
rst o
r sec
ond)
Med
ical
doc
tors
biob
anks
_doc
tors
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst o
r se
cond
)49
.130
.025
.052
.547
.728
.140
.752
.939
.549
.628
.843
.260
.016
.139
.956
.537
.551
.240
.953
.741
.558
.144
.448
.243
.746
.349
.741
.023
.848
.630
.924
.339
.5
Not
men
tione
d50
.970
.075
.047
.552
.371
.959
.347
.160
.550
.471
.256
.840
.083
.960
.143
.562
.548
.859
.146
.358
.541
.955
.651
.856
.353
.750
.359
.076
.251
.469
.175
.760
.5
Res
earc
hers
biob
anks
_res
earc
hers
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst o
r se
cond
)37
.324
.116
.041
.236
.536
.527
.025
.144
.634
.118
.818
.338
.919
.320
.128
.054
.645
.544
.948
.837
.321
.042
.455
.534
.951
.041
.724
.017
.138
.316
.916
.531
.7
Not
men
tione
d62
.775
.984
.058
.863
.563
.573
.074
.955
.465
.981
.281
.761
.180
.779
.972
.045
.454
.555
.151
.262
.779
.057
.644
.565
.149
.058
.376
.082
.961
.783
.183
.568
.3
Publ
ic in
stitu
tions
(uni
vers
ities
, hos
pita
ls)
biob
anks
_ins
titut
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t or
seco
nd)
24.1
25.1
30.1
25.9
25.1
33.7
22.1
22.7
37.2
26.8
25.1
38.5
23.4
43.5
15.7
16.2
26.3
16.6
20.0
5.7
24.6
16.4
23.4
19.8
26.2
24.5
15.6
21.8
35.1
25.4
27.4
32.5
25.7
Not
men
tione
d75
.974
.969
.974
.174
.966
.377
.977
.362
.873
.274
.961
.576
.656
.584
.383
.873
.783
.480
.094
.375
.483
.676
.680
.273
.875
.584
.478
.264
.974
.672
.667
.574
.3
Nat
iona
l gov
ernm
ents
biob
anks
_gov
ernm
ent
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst o
r se
cond
)19
.025
.726
.319
.232
.822
.016
.321
.718
.117
.834
.914
.412
.427
.533
.542
.414
.420
.116
.222
.215
.126
.220
.519
.416
.722
.120
.428
.812
.415
.118
.930
.623
.5
Not
men
tione
d81
.074
.373
.780
.867
.278
.083
.778
.381
.982
.265
.185
.687
.672
.566
.557
.685
.679
.983
.877
.884
.973
.879
.580
.683
.377
.979
.671
.287
.684
.981
.169
.476
.5
Ethi
cs c
omm
ittee
s
biob
anks
_eth
icsc
omm
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst o
r se
cond
)29
.034
.023
.916
.96.
513
.831
.018
.67.
213
.520
.323
.613
.830
.219
.84.
212
.57.
619
.88.
22.
25.
713
.98.
412
.76.
35.
05.
830
.017
.826
.224
.617
.4
Not
men
tione
d71
.066
.076
.183
.193
.586
.269
.081
.492
.886
.579
.776
.486
.269
.880
.295
.887
.592
.480
.291
.897
.894
.386
.191
.687
.393
.795
.094
.270
.082
.273
.875
.482
.6
Inte
rnat
iona
l org
anis
atio
ns s
uch
as th
e Eu
rope
an U
nion
or
Wor
ld H
ealth
Org
anis
atio
n
biob
anks
_int
_org
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t or
seco
nd)
20.5
21.6
25.9
24.9
25.6
32.8
26.2
28.4
23.9
29.2
31.1
17.8
21.5
34.6
21.4
36.9
24.3
9.3
31.6
20.8
25.1
29.9
12.5
26.7
22.5
21.3
21.5
16.0
25.8
19.5
21.8
29.6
23.8
Not
men
tione
d79
.578
.474
.175
.174
.467
.273
.871
.676
.170
.868
.982
.278
.565
.478
.663
.175
.790
.768
.479
.274
.970
.187
.573
.377
.578
.778
.584
.074
.280
.578
.270
.476
.2
EU27
Cou
ntry
EU27
Cou
ntry
EU27
Cou
ntry
EU27
Cou
ntry
EU27
EU27
Cou
ntry
Cou
ntry
159
Nat
iona
l Dat
a Pr
otec
tion
Aut
horit
ies
biob
anks
_dat
a_or
gsB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t or
seco
nd)
16.1
27.7
38.6
12.6
13.2
24.9
17.2
14.4
11.1
25.5
34.5
23.0
9.2
14.5
24.3
11.8
20.3
30.7
21.4
20.1
29.6
18.5
8.1
14.7
27.0
15.4
13.7
4.0
53.9
18.0
40.0
29.9
20.2
Not
men
tione
d83
.972
.361
.487
.486
.875
.182
.885
.688
.974
.565
.577
.090
.885
.575
.788
.279
.769
.378
.679
.970
.481
.591
.985
.373
.084
.686
.396
.046
.182
.060
.070
.179
.8
Oth
er (S
PON
TAN
EOU
S)
biob
anks
_oth
erB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t or
seco
nd)
.5.2
.1.4
.61.
0.6
.6.4
.3.5
.9
.5.0
.4
1.2
.2
.6
.5
.8
.51.
2
.3.9
1.3
.3
Not
men
tione
d99
.599
.899
.999
.699
.499
.099
.499
.499
.699
.799
.599
.110
0.0
99.5
100.
010
0.0
99.6
98.8
99.8
100.
099
.410
0.0
99.5
100.
099
.210
0.0
99.5
98.8
100.
099
.799
.198
.799
.7
Non
e (S
PON
TAN
EOU
S)
biob
anks
_non
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
ON
o ac
tor
(SPO
NTA
NEO
US)
1.0
.31.
82.
22.
11.
52.
3.2
1.1
.3.7
3.8
.71.
33.
4
.3.9
1.2
1.5
1.5
.82.
0
3.5
.81.
81.
1.8
2.1
2.0
2.2
1.9
(som
e ac
tor
men
tione
d)99
.099
.798
.297
.897
.998
.597
.799
.898
.999
.799
.396
.299
.398
.796
.610
0.0
99.7
99.1
98.8
98.5
98.5
99.2
98.0
100.
096
.599
.298
.298
.999
.297
.998
.097
.898
.1
DK
biob
anks
_DK
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
DK
1.1
4.4
5.2
1.3
3.6
1.8
6.6
6.2
7.1
1.2
2.2
6.5
8.4
5.8
9.5
2.0
4.2
6.7
1.3
8.5
10.0
9.3
14.1
3.7
3.6
4.8
14.6
26.5
.36.
36.
13.
46.
8(s
ome
non-
DK
re
spon
se)
98.9
95.6
94.8
98.7
96.4
98.2
93.4
93.8
92.9
98.8
97.8
93.5
91.6
94.2
90.5
98.0
95.8
93.3
98.7
91.5
90.0
90.7
85.9
96.3
96.4
95.2
85.4
73.5
99.7
93.7
93.9
96.6
93.2
qb16
bW
ould
you
be
will
ing
to p
rovi
de in
form
atio
n ab
out y
ours
elf t
o a
biob
ank?
biob
ank_
parti
cipa
teB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, d
efin
itely
16.1
33.8
11.0
4.0
18.8
24.4
17.9
12.3
14.4
15.2
20.9
4.2
15.6
39.9
14.9
28.5
9.6
26.1
12.6
5.5
8.0
13.2
9.8
6.3
10.7
6.6
9.7
11.0
53.8
15.1
17.6
36.2
14.4
Yes
, pro
babl
y35
.537
.630
.730
.530
.543
.529
.335
.633
.936
.638
.730
.536
.841
.734
.624
.337
.532
.728
.818
.924
.533
.029
.633
.734
.726
.423
.412
.638
.729
.629
.645
.931
.9N
o, p
roba
bly
not
25.6
16.0
25.2
23.8
17.7
18.6
16.8
17.2
18.8
22.8
23.7
31.1
20.0
11.2
21.5
11.4
30.4
21.2
29.9
29.7
22.9
18.7
26.7
31.0
26.7
27.5
18.7
16.5
4.4
22.1
22.3
11.5
21.8
No,
nev
er20
.59.
026
.236
.924
.69.
829
.422
.716
.617
.911
.826
.411
.63.
223
.620
.116
.716
.924
.639
.436
.117
.817
.623
.922
.718
.221
.226
.82.
618
.721
.24.
022
.1D
K2.
43.
66.
94.
78.
33.
76.
612
.216
.47.
54.
97.
715
.93.
95.
415
.65.
83.
14.
06.
48.
517
.216
.35.
15.
221
.427
.033
.2.5
14.5
9.3
2.4
9.7
EU27
EU27
EU27
Cou
ntry
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
Cou
ntry
160
qb17
b
Blo
od s
ampl
es
biob
anki
nfo_
bloo
dB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t or
seco
nd)
30.8
18.8
37.0
36.4
34.8
16.4
29.5
30.0
24.5
17.8
21.5
34.8
27.4
16.6
25.8
30.0
35.2
24.7
31.1
31.7
24.8
28.1
29.2
39.3
32.5
30.9
35.1
26.1
12.8
23.9
25.7
11.4
30.0
Not
men
tione
d69
.281
.263
.063
.665
.283
.670
.570
.075
.582
.278
.565
.272
.683
.474
.270
.064
.875
.368
.968
.375
.271
.970
.860
.767
.569
.164
.973
.987
.276
.174
.388
.670
.0
Tiss
ue c
olle
cted
dur
ing
med
ical
ope
ratio
ns
biob
anki
nfo_
tissu
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t or
seco
nd)
32.4
22.8
39.4
41.4
31.2
15.8
28.5
28.7
27.7
6.8
25.8
43.1
27.2
15.6
27.4
27.5
28.4
26.5
32.2
29.5
27.2
22.2
27.1
38.1
32.5
26.2
22.1
22.9
17.1
25.1
25.4
12.5
30.0
Not
men
tione
d67
.677
.260
.658
.668
.884
.271
.571
.372
.393
.274
.256
.972
.884
.472
.672
.571
.673
.567
.870
.572
.877
.872
.961
.967
.573
.877
.977
.182
.974
.974
.687
.570
.0
Your
gen
etic
pro
file
biob
anki
nfo_
gene
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t or
seco
nd)
32.7
27.2
47.7
42.4
32.4
27.1
35.3
27.9
26.3
19.5
36.7
41.1
23.0
24.5
31.1
34.6
40.3
24.9
40.3
31.8
31.2
22.1
31.5
44.2
41.9
23.5
24.1
22.1
18.1
33.8
36.0
28.8
34.1
Not
men
tione
d67
.372
.852
.357
.667
.672
.964
.772
.173
.780
.563
.358
.977
.075
.568
.965
.459
.775
.159
.768
.268
.877
.968
.555
.858
.176
.575
.977
.981
.966
.264
.071
.265
.9
Med
ical
rec
ord
from
you
r do
ctor
biob
anki
nfo_
reco
rds
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst o
r se
cond
)35
.927
.145
.932
.332
.828
.637
.235
.920
.121
.742
.638
.221
.127
.932
.930
.336
.126
.231
.035
.727
.431
.822
.541
.532
.419
.627
.317
.523
.726
.135
.436
.932
.6
Not
men
tione
d64
.172
.954
.167
.767
.271
.462
.864
.179
.978
.357
.461
.878
.972
.167
.169
.763
.973
.869
.064
.372
.668
.277
.558
.567
.680
.472
.782
.576
.373
.964
.663
.167
.4
Life
styl
e (w
hat y
ou e
at, h
ow m
uch
exer
cise
you
take
, etc
.)
biob
anki
nfo_
lifes
tyle
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Men
tione
d (fi
rst o
r se
cond
)26
.220
.538
.622
.225
.714
.324
.817
.515
.312
.933
.628
.611
.820
.222
.716
.423
.420
.418
.327
.021
.217
.717
.219
.325
.217
.119
.29.
116
.114
.525
.017
.824
.1
Not
men
tione
d73
.879
.561
.477
.874
.385
.775
.282
.584
.787
.166
.471
.488
.279
.877
.383
.676
.679
.681
.773
.078
.882
.382
.880
.774
.882
.980
.890
.983
.985
.575
.082
.275
.9
In o
rder
to u
nder
stan
d th
e ca
uses
of d
isea
ses
rese
arch
ers
need
as
muc
h in
form
atio
n as
pos
sibl
e ab
out t
he p
eopl
e in
the
biob
ank.
Wou
ld y
ou p
erso
nally
be
conc
erne
d or
rel
ucta
nt a
bout
the
colle
ctio
n of
any
of t
he fo
llow
ing
type
s of
da
ta a
nd m
ater
ials
from
you
?
EU27
Cou
ntry
Cou
ntry
EU27
EU27
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
161
Oth
er (S
PON
TAN
EOU
S)
biob
anki
nfo_
othe
rB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
entio
ned
(firs
t or
seco
nd)
1.5
.7.3
.42.
82.
3.2
1.5
.93.
9.5
2.1
1.4
1.8
.8.4
.2.2
.9
2.4
.9
3.
3
.9.9
.9.4
.21.
7.8
Not
men
tione
d98
.599
.399
.799
.697
.297
.799
.898
.599
.196
.199
.597
.998
.698
.299
.299
.699
.899
.899
.110
0.0
97.6
99.1
100.
010
0.0
96.7
100.
099
.199
.199
.199
.699
.898
.399
.2
Non
e (S
PON
TAN
EOU
S)
biob
anki
nfo_
none
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
No
info
rmat
ion
(SPO
NTA
NEO
US)
26.5
44.7
15.8
30.1
32.8
40.5
31.4
29.7
30.9
13.4
29.5
18.5
25.1
49.0
37.1
39.1
18.9
37.5
26.9
30.1
25.5
21.6
21.2
11.7
25.9
21.0
18.6
13.2
65.1
21.4
24.4
40.0
27.5
(som
e in
form
atio
n m
entio
ned)
73.5
55.3
84.2
69.9
67.2
59.5
68.6
70.3
69.1
86.6
70.5
81.5
74.9
51.0
62.9
60.9
81.1
62.5
73.1
69.9
74.5
78.4
78.8
88.3
74.1
79.0
81.4
86.8
34.9
78.6
75.6
60.0
72.5
DK
biob
anki
nfo_
DK
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
DK
2.7
6.8
7.2
4.3
7.1
4.5
5.8
14.6
11.7
4.0
2.9
9.1
14.7
4.2
9.4
12.5
7.7
11.8
3.5
8.4
16.2
29.1
17.9
4.7
6.3
27.8
25.9
37.0
.916
.011
.63.
59.
8(s
ome
non-
DK
re
spon
se)
97.3
93.2
92.8
95.7
92.9
95.5
94.2
85.4
88.3
96.0
97.1
90.9
85.3
95.8
90.6
87.5
92.3
88.2
96.5
91.6
83.8
70.9
82.1
95.3
93.7
72.2
74.1
63.0
99.1
84.0
88.4
96.5
90.2
qb18
bSo
me
coun
trie
s in
the
Euro
pean
Uni
on h
ave
one
or m
ore
biob
anks
. Do
you
thin
k th
e sh
arin
g an
d ex
chan
ge o
f per
sona
l dat
a an
d bi
olog
ical
mat
eria
ls ti
ssue
acr
oss
Mem
ber
Stat
es s
houl
d be
enc
oura
ged?
biob
ank_
inte
rnat
iona
lB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, d
efin
itely
25.6
30.6
18.0
12.0
27.2
19.8
27.2
13.9
14.2
25.1
25.3
6.4
18.8
21.6
16.4
51.6
16.9
18.6
16.8
13.5
20.9
20.4
11.8
17.0
19.0
8.5
13.7
12.1
29.3
20.9
23.1
25.1
18.7
Yes
, pro
babl
y42
.832
.225
.439
.233
.648
.730
.335
.342
.535
.529
.032
.938
.737
.132
.325
.141
.439
.539
.537
.738
.131
.538
.747
.141
.138
.831
.512
.039
.935
.325
.341
.934
.3N
o, p
roba
bly
not
16.3
18.6
20.8
19.0
13.6
18.2
13.6
15.2
14.9
17.5
20.4
25.5
16.1
18.9
21.1
6.8
22.8
17.6
19.6
20.0
13.1
10.5
16.1
18.1
15.8
10.4
10.8
12.3
16.9
13.2
21.0
16.9
17.1
No,
def
inite
ly n
ot10
.713
.526
.021
.814
.09.
216
.713
.910
.111
.419
.521
.65.
013
.618
.63.
97.
415
.213
.914
.511
.912
.85.
29.
113
.64.
67.
716
.29.
29.
318
.910
.415
.1D
K4.
75.
19.
88.
011
.64.
112
.121
.718
.310
.65.
813
.621
.38.
811
.512
.611
.69.
110
.314
.215
.924
.828
.28.
810
.537
.836
.347
.44.
721
.411
.85.
714
.8
EU27
EU27
Cou
ntry
EU27
Cou
ntry
Cou
ntry
Cou
ntry
EU27
162
All
resp
onde
nts
- no
split
bal
lot
qb19
For
each
of t
he fo
llow
ing
peop
le a
nd g
roup
s, d
o yo
u th
ink
they
are
doi
ng a
goo
d jo
b fo
r so
ciet
y or
not
doi
ng a
goo
d jo
b fo
r so
ciet
y?N
ewsp
aper
s, m
agaz
ines
and
tele
visi
on w
hich
rep
ort o
n bi
otec
hnol
ogy
good
job_
new
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OD
oing
a g
ood
job
for
soci
ety
75.3
71.5
62.1
77.3
66.9
88.4
52.4
53.1
55.1
83.9
80.7
75.8
58.7
79.5
46.4
84.3
83.4
51.5
75.4
87.2
80.5
54.8
71.7
86.2
65.8
79.4
82.5
47.9
78.9
82.6
64.0
57.9
63.8
Not
doi
ng a
goo
d jo
b fo
r soc
iety
18.3
19.8
18.6
17.4
19.6
9.1
39.1
18.8
20.3
9.9
12.5
14.3
16.4
11.1
33.2
8.6
11.3
34.1
14.7
6.8
8.8
19.6
11.6
10.0
26.0
8.6
7.5
15.1
19.0
11.1
22.8
28.5
20.9
DK
6.4
8.7
19.3
5.3
13.6
2.6
8.5
28.1
24.6
6.1
6.8
10.0
24.8
9.4
20.3
7.1
5.3
14.3
9.9
6.0
10.6
25.6
16.7
3.8
8.3
12.0
10.0
36.9
2.2
6.3
13.2
13.6
15.2
Indu
strie
s w
hich
dev
elop
new
pro
duct
s w
ith b
iote
chno
logy
good
job_
indu
stry
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Doi
ng a
goo
d jo
b fo
r so
ciet
y76
.268
.146
.349
.259
.579
.366
.345
.749
.867
.770
.059
.647
.271
.354
.773
.778
.258
.873
.277
.462
.746
.362
.981
.349
.545
.961
.940
.880
.172
.756
.556
.058
.2
Not
doi
ng a
goo
d jo
b fo
r soc
iety
15.2
18.4
24.0
39.6
19.6
13.4
21.0
17.9
19.7
19.0
15.6
20.2
15.4
12.6
15.7
11.9
10.3
20.8
14.6
11.7
16.3
15.6
13.4
10.1
40.3
20.0
10.9
18.3
14.2
15.8
19.1
20.4
18.6
DK
8.6
13.5
29.8
11.3
20.9
7.3
12.6
36.4
30.5
13.3
14.4
20.1
37.4
16.1
29.5
14.4
11.5
20.5
12.3
11.0
21.0
38.0
23.7
8.6
10.2
34.1
27.2
40.8
5.8
11.5
24.3
23.6
23.2
Uni
vers
ity s
cien
tists
who
con
duct
res
earc
h in
bio
tech
nolo
gy
good
job_
univ
ersi
tyB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OD
oing
a g
ood
job
for
soci
ety
88.7
88.0
72.9
81.0
79.3
95.6
86.3
59.5
67.2
82.5
89.9
74.5
67.5
88.3
70.7
87.4
87.6
79.7
87.6
89.5
82.3
61.6
76.2
88.4
78.3
65.8
69.3
47.3
97.1
84.9
78.4
73.6
76.7
Not
doi
ng a
goo
d jo
b fo
r soc
iety
7.3
5.2
7.5
12.6
7.7
2.3
5.7
10.5
13.2
10.0
3.4
12.5
5.8
1.7
6.7
4.6
4.7
6.7
5.9
3.2
5.7
5.9
6.6
6.6
14.2
11.1
9.0
14.2
1.0
8.3
6.2
11.1
7.7
DK
4.0
6.8
19.6
6.5
13.0
2.2
8.0
30.0
19.5
7.4
6.7
13.0
26.6
10.1
22.7
7.9
7.7
13.6
6.5
7.3
12.0
32.6
17.2
5.0
7.5
23.2
21.7
38.6
1.9
6.8
15.4
15.3
15.6
Con
sum
er o
rgan
isat
ions
whi
ch te
st b
iote
chno
logi
cal p
rodu
cts
good
job_
cons
umer
org
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Doi
ng a
goo
d jo
b fo
r so
ciet
y89
.886
.277
.971
.869
.687
.383
.550
.963
.679
.092
.480
.260
.684
.760
.781
.987
.358
.786
.083
.177
.957
.570
.183
.969
.253
.867
.041
.486
.972
.079
.370
.873
.1
Not
doi
ng a
goo
d jo
b fo
r soc
iety
6.9
6.3
5.6
19.6
11.2
9.8
9.2
13.5
14.3
11.3
4.4
10.5
8.6
5.3
11.4
7.6
5.9
20.2
7.1
7.4
8.6
8.1
10.4
9.8
22.5
17.9
9.4
15.4
8.9
17.0
9.3
11.8
9.9
DK
3.3
7.6
16.5
8.6
19.3
2.9
7.3
35.6
22.1
9.7
3.2
9.4
30.8
10.0
27.9
10.5
6.8
21.1
6.9
9.4
13.5
34.3
19.5
6.3
8.4
28.3
23.6
43.2
4.2
11.0
11.4
17.4
17.0
Envi
ronm
enta
l gro
ups
who
cam
paig
n ab
out b
iote
chno
logy
good
job_
envg
roup
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OD
oing
a g
ood
job
for
soci
ety
72.7
69.4
69.2
86.1
71.2
74.8
70.3
48.1
57.8
63.8
63.4
71.9
61.7
83.1
48.9
88.8
70.4
56.5
73.5
78.0
76.6
60.3
67.2
77.2
76.9
61.5
70.3
41.6
38.4
66.4
70.1
61.4
65.9
Not
doi
ng a
goo
d jo
b fo
r soc
iety
20.1
18.8
10.1
7.4
10.4
18.9
18.2
14.3
15.8
21.4
25.9
14.1
9.0
6.8
22.7
4.0
17.6
21.0
14.7
11.2
7.9
8.8
10.2
12.5
14.6
11.8
8.7
16.2
56.2
22.4
15.0
20.5
14.6
DK
7.2
11.8
20.7
6.4
18.4
6.3
11.5
37.6
26.4
14.8
10.7
14.0
29.3
10.1
28.4
7.3
12.0
22.6
11.8
10.7
15.5
30.9
22.5
10.3
8.5
26.6
20.9
42.2
5.4
11.2
15.0
18.0
19.5
EU27
EU27
Cou
ntry
Cou
ntry
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
EU27
163
(NA
TIO
NA
LITY
) Gov
ernm
ent m
akin
g la
ws
abou
t bio
tech
nolo
gy
good
job_
gove
rnm
ent
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Doi
ng a
goo
d jo
b fo
r so
ciet
y73
.863
.842
.269
.164
.386
.956
.332
.547
.568
.679
.563
.346
.076
.745
.479
.169
.136
.274
.265
.458
.451
.056
.375
.656
.748
.859
.840
.480
.966
.662
.346
.155
.2
Not
doi
ng a
goo
d jo
b fo
r soc
iety
17.6
23.5
24.8
18.9
16.2
8.0
26.5
23.8
19.0
16.9
9.0
20.2
20.9
9.7
22.8
6.0
17.3
36.1
13.4
21.0
20.5
11.8
15.2
15.0
31.1
25.9
13.1
16.4
14.1
20.9
18.5
33.4
19.9
DK
8.6
12.8
33.0
12.0
19.5
5.1
17.3
43.7
33.5
14.5
11.5
16.5
33.1
13.6
31.8
14.9
13.7
27.7
12.4
13.6
21.1
37.2
28.5
9.4
12.2
25.3
27.1
43.2
5.0
12.6
19.2
20.5
24.8
Ret
aile
rs w
ho e
nsur
e ou
r fo
od is
saf
e
good
job_
reta
ilers
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Doi
ng a
goo
d jo
b fo
r so
ciet
y80
.055
.258
.965
.359
.879
.264
.252
.351
.281
.169
.481
.754
.565
.164
.469
.377
.338
.978
.072
.767
.550
.652
.984
.352
.327
.963
.039
.779
.668
.668
.249
.361
.2
Not
doi
ng a
goo
d jo
b fo
r soc
iety
15.5
37.3
20.6
25.4
24.0
16.6
25.8
19.1
25.9
13.3
20.0
10.5
16.1
23.5
21.6
16.0
16.1
42.9
10.6
17.7
18.6
16.8
23.2
10.0
39.6
47.5
11.5
17.3
15.6
19.3
17.2
37.3
22.1
DK
4.4
7.6
20.4
9.3
16.2
4.2
10.0
28.6
22.9
5.6
10.6
7.8
29.3
11.3
14.0
14.7
6.6
18.2
11.3
9.6
13.9
32.6
24.0
5.7
8.1
24.6
25.6
43.0
4.7
12.1
14.6
13.3
16.7
The
Euro
pean
Uni
on m
akin
g la
ws
abou
t bio
tech
nolo
gy fo
r al
l EU
Mem
ber
Stat
es
good
job_
EU
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Doi
ng a
goo
d jo
b fo
r so
ciet
y78
.555
.245
.171
.671
.979
.964
.439
.556
.271
.979
.756
.456
.776
.741
.184
.179
.452
.284
.478
.273
.159
.564
.483
.267
.055
.568
.239
.478
.071
.554
.544
.760
.0
Not
doi
ng a
goo
d jo
b fo
r soc
iety
15.1
27.3
19.5
17.6
8.8
14.8
19.2
17.4
15.5
15.9
10.3
25.2
9.9
9.5
23.0
4.6
9.8
20.5
7.6
9.1
7.5
6.7
11.1
9.0
22.0
16.6
8.9
16.9
15.8
15.3
20.5
25.6
15.8
DK
6.4
17.5
35.4
10.8
19.3
5.3
16.4
43.1
28.3
12.2
10.0
18.4
33.4
13.8
35.9
11.2
10.8
27.4
8.0
12.7
19.4
33.8
24.5
7.8
11.0
27.9
22.9
43.7
6.2
13.3
25.0
29.7
24.3
Ethi
cs c
omm
ittee
s w
ho c
onsi
der
the
mor
al a
nd e
thic
al a
spec
ts o
f bio
tech
nolo
gy
good
job_
ethi
csco
mm
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OD
oing
a g
ood
job
for
soci
ety
77.0
71.9
55.9
78.0
60.5
83.2
62.3
41.1
52.7
66.6
81.1
66.6
55.0
69.0
50.9
78.3
77.5
48.2
80.5
75.5
66.1
54.2
64.1
81.9
70.6
54.6
62.9
39.1
89.2
74.5
63.1
60.6
61.0
Not
doi
ng a
goo
d jo
b fo
r soc
iety
15.9
13.9
14.6
12.8
18.5
10.2
21.0
15.9
19.2
19.9
11.2
15.7
8.2
13.5
18.3
7.6
11.8
22.2
9.2
13.4
13.1
6.3
12.0
10.2
18.1
14.3
10.3
15.5
7.9
12.6
18.1
17.3
15.9
DK
7.1
14.2
29.5
9.2
21.0
6.6
16.6
43.0
28.1
13.6
7.6
17.8
36.7
17.4
30.8
14.1
10.7
29.6
10.2
11.0
20.8
39.5
23.9
8.0
11.3
31.1
26.8
45.4
2.9
12.9
18.8
22.1
23.1
Rel
igio
us le
ader
s w
ho s
ay w
hat i
s ri
ght a
nd w
rong
in th
e de
velo
pmen
t of b
iote
chno
logy
good
job_
relig
ion
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Doi
ng a
goo
d jo
b fo
r so
ciet
y24
.614
.024
.761
.136
.813
.814
.325
.735
.125
.217
.737
.939
.58.
125
.163
.242
.519
.044
.252
.549
.650
.045
.557
.735
.424
.655
.036
.511
.944
.316
.210
.331
.1
Not
doi
ng a
goo
d jo
b fo
r soc
iety
67.9
75.9
43.2
27.2
45.8
79.3
75.0
30.0
35.1
59.4
73.4
40.3
23.0
81.0
46.8
24.5
41.7
51.6
38.4
30.1
27.0
15.5
29.3
31.5
50.5
39.4
15.3
17.8
84.8
41.1
63.7
71.6
45.9
DK
7.5
10.1
32.1
11.7
17.4
7.0
10.7
44.3
29.8
15.5
8.9
21.8
37.6
10.9
28.1
12.3
15.8
29.4
17.3
17.4
23.4
34.5
25.2
10.8
14.1
36.0
29.7
45.7
3.3
14.6
20.1
18.1
23.0
Med
ical
doc
tors
good
job_
doct
ors
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Doi
ng a
goo
d jo
b fo
r so
ciet
y90
.284
.280
.187
.989
.796
.182
.058
.271
.383
.393
.287
.674
.185
.780
.992
.291
.576
.891
.389
.984
.777
.177
.093
.782
.967
.878
.952
.398
.087
.479
.874
.681
.4
Not
doi
ng a
goo
d jo
b fo
r soc
iety
6.8
9.7
7.7
7.9
4.8
2.2
9.5
10.1
11.9
9.9
2.5
7.3
6.7
6.1
6.9
1.7
4.1
13.4
4.5
4.1
6.2
3.8
11.2
4.0
10.1
8.9
4.3
9.9
2.0
5.9
7.0
10.0
7.7
DK
3.0
6.0
12.3
4.3
5.5
1.7
8.4
31.6
16.9
6.7
4.4
5.1
19.2
8.2
12.2
6.1
4.4
9.8
4.3
6.0
9.1
19.1
11.8
2.3
7.0
23.3
16.8
37.8
6.
613
.115
.410
.8
EU27
EU27
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
EU27
164
Split
bal
lot A
qb20
aW
hich
of t
he fo
llow
ing
view
s is
clo
sest
to y
our
own?
D
ecis
ions
abo
ut s
ynth
etic
bio
logy
sho
uld
be b
ased
pri
mar
ily o
n…
synb
iolo
gy_s
cien
ceB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
Osc
ient
ific
evid
ence
64.6
46.5
34.3
46.2
59.5
55.0
58.8
37.3
57.8
58.6
51.4
44.0
48.2
59.4
55.3
39.8
62.6
54.4
68.7
60.8
58.4
29.9
51.4
61.6
42.4
41.3
56.0
34.5
39.9
52.3
42.4
53.2
52.4
the
mor
al a
nd e
thic
al
issu
es28
.046
.952
.345
.524
.540
.226
.932
.928
.722
.240
.344
.135
.331
.728
.750
.132
.531
.921
.830
.223
.443
.633
.031
.647
.339
.223
.223
.548
.238
.541
.736
.733
.7
DK
7.4
6.6
13.4
8.3
16.0
4.8
14.3
29.8
13.6
19.2
8.3
11.9
16.5
8.9
16.0
10.1
4.9
13.7
9.4
9.0
18.2
26.4
15.6
6.8
10.3
19.5
20.7
42.0
11.9
9.2
15.9
10.2
13.9
qb21
aW
hich
of t
he fo
llow
ing
view
s is
clo
sest
to y
our
own?
D
ecis
ions
abo
ut s
ynth
etic
bio
logy
sho
uld
be b
ased
mai
nly
on…
synb
iolo
gy_e
xper
tB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
Oth
e ad
vice
of e
xper
ts69
.558
.746
.054
.863
.673
.159
.841
.464
.855
.570
.449
.956
.061
.859
.458
.772
.960
.970
.654
.657
.057
.656
.168
.556
.346
.659
.334
.866
.557
.953
.466
.958
.8
wha
t the
maj
ority
of
peop
le in
a c
ount
ry
thin
ks24
.135
.741
.138
.623
.922
.526
.930
.124
.327
.221
.242
.526
.126
.727
.531
.422
.128
.022
.035
.024
.925
.330
.926
.533
.437
.322
.421
.924
.533
.431
.922
.229
.0
DK
6.3
5.7
12.9
6.6
12.5
4.4
13.3
28.5
11.0
17.3
8.4
7.6
18.0
11.6
13.2
9.9
4.9
11.1
7.4
10.4
18.1
17.1
13.0
5.0
10.3
16.2
18.3
43.3
9.0
8.7
14.8
10.8
12.2
qb22
aW
hich
of t
he fo
llow
ing
view
s is
clo
sest
to y
our
own?
Sy
nthe
tic b
iolo
gy s
houl
d be
…
synb
iolo
gy_m
arke
tB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
Otig
htly
regu
late
d by
G
over
nmen
t72
.979
.879
.088
.780
.684
.775
.665
.770
.774
.083
.078
.472
.283
.081
.988
.578
.180
.477
.470
.675
.977
.471
.183
.183
.077
.267
.548
.983
.579
.674
.483
.077
.0
allo
wed
to o
pera
te in
th
e m
arke
t pla
ce li
ke
a bu
sine
ss19
.615
.311
.15.
57.
09.
211
.29.
614
.410
.610
.013
.08.
98.
36.
93.
315
.08.
114
.717
.47.
13.
815
.311
.57.
67.
46.
911
.29.
111
.310
.59.
510
.7
DK
7.5
4.8
9.9
5.8
12.4
6.2
13.1
24.7
14.9
15.4
6.9
8.7
18.9
8.7
11.2
8.2
6.9
11.6
8.0
12.0
17.0
18.8
13.6
5.5
9.4
15.4
25.7
39.8
7.4
9.1
15.1
7.5
12.3
EU27
EU27
Cou
ntry
Cou
ntry
Cou
ntry
EU27
165
Split
bal
lot B
qb20
bW
hich
of t
he fo
llow
ing
view
s is
clo
sest
to y
our
own?
D
ecis
ions
abo
ut a
nim
al c
loni
ng s
houl
d be
bas
ed p
rim
arily
on…
anim
alcl
onin
g_sc
ienc
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
Osc
ient
ific
evid
ence
52.5
31.1
27.0
42.3
59.0
45.2
42.1
36.7
52.4
42.9
40.5
27.6
36.3
36.5
41.8
35.1
45.2
46.4
60.9
50.7
52.2
36.8
45.8
48.4
40.5
34.1
48.1
39.8
37.0
42.0
30.8
40.1
43.2
the
mor
al a
nd e
thic
al
issu
es42
.365
.559
.753
.629
.149
.248
.038
.134
.147
.149
.964
.644
.255
.645
.358
.349
.043
.234
.843
.235
.438
.440
.846
.851
.242
.627
.131
.556
.350
.759
.553
.244
.6
DK
5.2
3.3
13.3
4.1
11.8
5.6
9.8
25.2
13.5
10.0
9.6
7.7
19.5
7.9
12.9
6.6
5.8
10.4
4.3
6.0
12.4
24.8
13.4
4.8
8.3
23.3
24.8
28.7
6.7
7.3
9.7
6.7
12.2
qb21
bW
hich
of t
he fo
llow
ing
view
s is
clo
sest
to y
our
own?
D
ecis
ions
abo
ut a
nim
al c
loni
ng s
houl
d be
bas
ed m
ainl
y on
…
anim
alcl
onin
g_ex
pert
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
the
advi
ce o
f exp
erts
63.7
51.9
39.1
50.6
61.9
61.5
50.0
42.9
58.3
52.2
58.0
34.9
47.3
46.2
49.8
57.4
60.3
59.7
61.4
46.5
54.3
52.9
50.0
54.5
56.8
36.0
51.9
49.0
59.4
51.3
40.0
63.1
51.2
wha
t the
maj
ority
of
peop
le in
a c
ount
ry
thin
ks31
.743
.947
.545
.327
.533
.138
.638
.027
.837
.530
.458
.234
.743
.639
.036
.434
.829
.733
.746
.930
.630
.335
.041
.437
.548
.026
.024
.431
.142
.449
.425
.636
.8
DK
4.6
4.1
13.4
4.0
10.6
5.4
11.4
19.1
13.8
10.3
11.5
7.0
18.0
10.2
11.2
6.2
4.9
10.6
4.9
6.6
15.1
16.8
15.0
4.1
5.7
16.0
22.2
26.6
9.6
6.3
10.7
11.3
12.0
qb22
bW
hich
of t
he fo
llow
ing
view
s is
clo
sest
to y
our
own?
A
nim
al c
loni
ng s
houl
d be
…
anim
alcl
onin
g_m
arke
tB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
Otig
htly
regu
late
d by
G
over
nmen
t86
.792
.785
.693
.284
.890
.787
.275
.670
.882
.088
.279
.376
.793
.887
.796
.587
.687
.190
.878
.882
.778
.879
.690
.188
.079
.071
.561
.788
.983
.585
.190
.783
.3
allo
wed
to o
pera
te in
th
e m
arke
t pla
ce li
ke
a bu
sine
ss9.
04.
65.
24.
58.
76.
04.
26.
613
.76.
65.
011
.26.
62.
74.
11.
65.
81.
95.
912
.34.
73.
55.
85.
95.
17.
33.
713
.78.
09.
66.
33.
46.
6
DK
4.3
2.7
9.2
2.3
6.6
3.2
8.6
17.8
15.5
11.5
6.7
9.5
16.6
3.5
8.2
1.9
6.6
11.0
3.3
8.9
12.6
17.7
14.6
4.0
6.9
13.7
24.8
24.5
3.1
6.9
8.6
6.0
10.2
EU27
EU27
Cou
ntry
EU27
Cou
ntry
Cou
ntry
166
All
resp
onde
nts
- no
split
bal
lot
qb23
Whi
ch o
f the
follo
win
g vi
ews
is c
lose
st to
you
r ow
n?
new
tech
_ben
efits
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
The
Gov
ernm
ent
shou
ld ta
ke
resp
onsi
bilit
y to
en
sure
that
new
te
chno
logi
es b
enef
it ev
eryo
ne
80.6
73.4
79.3
82.2
86.1
77.0
74.2
72.5
75.1
78.2
85.5
72.9
76.0
56.5
81.0
78.4
81.9
76.6
78.0
67.6
67.2
85.7
54.8
80.1
80.3
77.7
58.3
67.7
53.9
75.3
69.7
79.0
75.6
It is
up
to p
eopl
e to
se
ek o
ut th
e be
nefit
s fro
m n
ew
tech
nolo
gies
th
emse
lves
15.7
22.3
12.6
14.6
8.7
19.2
16.8
12.0
15.1
15.6
10.9
20.6
10.5
35.3
12.5
18.9
12.9
15.9
17.9
24.3
20.2
9.7
29.7
17.2
13.8
18.3
23.2
12.1
41.3
17.8
21.6
15.1
16.0
DK
3.6
4.3
8.1
3.1
5.2
3.8
9.1
15.5
9.8
6.2
3.6
6.5
13.5
8.2
6.5
2.8
5.2
7.5
4.1
8.2
12.7
4.6
15.6
2.7
5.9
4.0
18.5
20.2
4.7
6.9
8.7
6.0
8.5
qb24
And
whi
ch o
f the
follo
win
g do
you
thin
k is
mos
t im
port
ant?
valu
es_f
reed
omB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OP
rote
ctin
g fre
edom
of
spee
ch a
nd h
uman
ri g
hts
52.9
50.0
56.1
56.8
57.7
59.8
59.2
51.1
48.6
60.8
66.5
56.6
54.4
65.8
41.0
59.2
49.8
43.4
42.2
50.2
52.9
56.8
51.6
46.9
56.7
29.5
47.0
55.5
62.8
40.8
63.6
57.9
52.4
Figh
ting
crim
e an
d te
rroris
m44
.045
.239
.941
.336
.235
.236
.540
.943
.336
.931
.339
.738
.030
.552
.640
.248
.150
.155
.145
.641
.838
.040
.750
.839
.466
.746
.633
.034
.355
.031
.636
.842
.3
DK
3.1
4.9
4.0
1.9
6.1
5.0
4.3
8.0
8.1
2.3
2.2
3.7
7.6
3.6
6.4
.62.
26.
52.
74.
25.
35.
27.
72.
33.
93.
86.
311
.52.
94.
24.
85.
35.
4
qb25
And
whi
ch o
f the
follo
win
g do
you
thin
k is
mos
t im
port
ant?
valu
es_e
con
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Hav
ing
stro
ng
Eur
opea
n co
mpa
nies
to
com
pete
in g
loba
l m
arke
ts40
.056
.533
.723
.126
.917
.632
.240
.634
.540
.544
.542
.727
.532
.035
.915
.937
.725
.127
.118
.926
.237
.731
.833
.234
.523
.527
.530
.325
.415
.126
.722
.432
.9
Red
ucin
g ec
onom
ic
ineq
ualit
ies
amon
g pe
ople
in th
e E
urop
ean
Uni
on56
.337
.956
.074
.267
.176
.760
.342
.855
.851
.550
.448
.363
.461
.846
.577
.058
.367
.069
.874
.463
.248
.156
.564
.561
.571
.657
.347
.568
.178
.265
.068
.757
.6
DK
3.8
5.6
10.3
2.7
6.0
5.8
7.5
16.6
9.7
8.0
5.1
9.1
9.1
6.1
17.6
7.1
4.0
7.8
3.1
6.7
10.6
14.2
11.6
2.2
4.1
4.9
15.2
22.3
6.5
6.7
8.3
8.9
9.6
EU27
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
167
qb26
And
whi
ch o
f the
follo
win
g do
you
thin
k is
mos
t im
port
ant?
valu
es_c
limat
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OTo
hal
t clim
ate
chan
ge a
nd g
loba
l w
arm
ing,
hav
e to
re
thin
k w
ays
of li
ving
ev
en if
it m
eans
low
er
econ
omic
gro
wth
64.1
64.3
79.9
70.6
68.8
83.4
65.2
56.2
59.9
71.2
66.4
71.2
57.0
71.2
58.2
63.8
58.0
51.8
62.5
45.4
51.3
35.9
52.6
59.2
77.9
53.4
48.9
63.6
65.2
63.8
73.3
64.3
64.3
Tech
nolo
gy w
ill s
top
clim
ate
chan
ge a
nd
glob
al w
arm
ing
so w
e ca
n m
aint
ain
our w
ay
of li
fe a
nd e
cono
mic
gr
owth
31.1
31.6
12.4
27.3
22.3
14.6
24.1
25.0
29.5
21.7
29.0
24.4
25.0
24.3
31.0
32.0
35.8
34.7
32.2
45.8
29.7
51.9
29.9
36.8
18.6
35.7
34.4
18.6
30.6
27.5
19.2
28.6
25.8
DK
4.8
4.1
7.7
2.2
9.0
2.0
10.7
18.8
10.6
7.1
4.5
4.4
18.0
4.5
10.8
4.2
6.2
13.4
5.3
8.8
19.0
12.2
17.5
4.0
3.6
10.9
16.7
17.9
4.2
8.7
7.5
7.1
9.9
qb27
To w
hat e
xten
t do
you
thin
k yo
ur v
iew
on
clim
ate
chan
ge a
nd g
loba
l war
min
g is
sha
red
in (O
UR
CO
UN
TRY)
?
clim
ate_
cons
ensu
sB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OEv
eryo
ne s
hare
s m
y vie
ws
6.0
2.2
2.4
11.6
3.9
2.5
3.7
3.0
5.9
5.4
1.9
4.8
4.9
1.2
4.0
6.5
3.5
2.3
4.3
2.6
4.6
4.3
4.1
4.8
2.8
4.4
2.9
9.7
.56.
23.
3.5
4.0
A lo
t of p
eopl
e sh
are
my
view
s63
.174
.158
.958
.852
.876
.348
.649
.558
.146
.269
.855
.135
.678
.452
.041
.165
.237
.755
.353
.742
.946
.344
.853
.051
.943
.934
.423
.247
.553
.454
.152
.354
.0
A fe
w p
eopl
e sh
are
my
view
s23
.720
.828
.619
.928
.116
.433
.514
.515
.134
.821
.329
.736
.214
.729
.737
.015
.843
.126
.230
.021
.916
.920
.331
.335
.716
.916
.423
.448
.322
.135
.642
.224
.9
No
one
shar
es m
y vie
ws
1.6
.1.9
3.6
.7.7
1.4
3.7
.72.
2.9
.63.
0.3
1.7
2.6
.74.
81.
81.
43.
41.
21.
4.8
1.8
2.6
5.1
12.6
.42.
1.8
.41.
4
DK
5.5
2.7
9.2
6.1
14.6
4.1
12.8
29.3
20.2
11.4
6.0
9.8
20.4
5.4
12.5
12.8
14.8
12.1
12.5
12.2
27.1
31.4
29.4
10.1
7.8
32.1
41.3
31.1
3.3
16.1
6.2
4.6
15.7
qb28
Do
you
thin
k (O
UR
CO
UN
TRY)
will
ado
pt p
olic
ies
in li
ne w
ith y
our
view
on
this
mat
ter?
clim
ate_
polic
yB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, d
efin
itely
6.4
8.0
3.4
13.3
8.3
10.9
10.2
4.7
5.0
10.5
4.3
9.0
4.3
8.5
4.4
22.3
4.8
6.0
5.9
1.6
2.6
8.0
4.6
4.5
3.1
5.6
5.5
8.9
6.6
5.3
12.0
6.8
6.0
Yes
, pro
babl
y50
.148
.036
.354
.038
.763
.740
.740
.542
.446
.046
.949
.532
.352
.237
.047
.635
.938
.148
.334
.235
.839
.034
.446
.244
.838
.133
.423
.854
.143
.648
.950
.240
.0N
o, p
roba
bly
not
28.9
35.5
36.5
19.2
25.1
17.0
25.5
17.1
23.5
24.0
36.5
26.2
24.9
29.0
37.8
13.5
30.4
30.2
25.0
36.5
21.5
14.7
22.9
29.6
27.8
16.1
15.9
17.9
30.7
23.2
21.6
29.3
28.2
No,
def
inite
ly n
ot6.
34.
213
.65.
38.
92.
27.
44.
06.
05.
35.
04.
211
.12.
96.
51.
211
.09.
54.
412
.18.
66.
03.
87.
511
.75.
210
.316
.54.
65.
35.
45.
77.
8D
K8.
34.
410
.28.
219
.16.
116
.233
.723
.214
.27.
411
.027
.37.
414
.315
.417
.816
.216
.515
.531
.532
.334
.312
.212
.635
.134
.932
.94.
122
.612
.18.
018
.1
qb29
Ove
rall
how
str
ongl
y w
ould
you
say
you
feel
abo
ut is
sues
con
cern
ing
biot
echn
olog
y th
at w
e ha
ve b
een
talk
ing
abou
t in
this
sur
vey?
feel
ings
_bio
tech
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Ext
rem
ely
stro
ngly
4.0
4.5
8.8
9.7
3.1
2.7
6.3
4.4
3.3
4.3
4.9
5.1
4.7
3.5
5.1
14.5
3.1
2.0
4.4
.43.
73.
43.
63.
46.
02.
81.
66.
62.
65.
65.
51.
05.
0Ve
ry s
trong
ly16
.530
.332
.833
.422
.729
.831
.621
.617
.919
.621
.431
.828
.821
.823
.033
.222
.77.
229
.23.
422
.322
.919
.837
.719
.28.
88.
614
.817
.019
.229
.211
.224
.3S
omew
hat s
trong
ly51
.849
.942
.944
.039
.760
.150
.137
.942
.846
.258
.238
.949
.258
.145
.337
.946
.736
.846
.643
.354
.241
.837
.544
.653
.645
.943
.030
.765
.446
.051
.756
.644
.9N
ot a
t all
stro
ngly
26.4
13.5
12.4
11.2
33.3
5.0
10.8
16.1
27.3
23.8
14.1
19.3
8.4
13.8
18.9
10.2
23.5
51.2
13.3
50.7
13.2
20.0
18.2
11.9
16.9
36.4
35.8
15.6
13.7
22.3
10.9
28.5
19.7
DK
1.1
1.8
3.1
1.7
1.2
2.3
1.3
20.0
8.7
6.1
1.5
4.8
9.0
2.7
7.8
4.1
4.0
2.8
6.5
2.3
6.6
11.8
20.9
2.4
4.2
6.0
11.1
32.2
1.3
6.9
2.7
2.7
6.1
Cou
ntry
EU27
Cou
ntry
Cou
ntry
Cou
ntry
EU27
EU27
EU27
168
qb30
Doe
s/D
id a
ny o
f you
r fa
mily
hav
e a
job
or a
uni
vers
ity q
ualif
icat
ion
in n
atur
al s
cien
ce, t
echn
olog
y or
eng
inee
ring
(for
inst
ance
, phy
sics
, che
mis
try,
bio
logy
, med
icin
e)?
Yes,
you
r fa
ther
fath
er_s
cien
ceB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OYe
s3.
33.
25.
41.
21.
03.
21.
52.
92.
72.
14.
14.
01.
28.
74.
21.
11.
74.
72.
52.
51.
71.
11.
32.
22.
21.
8.7
2.7
4.1
1.3
5.6
7.6
2.9
No
96.7
96.8
94.6
98.8
99.0
96.8
98.5
97.1
97.3
97.9
95.9
96.0
98.8
91.3
95.8
98.9
98.3
95.3
97.5
97.5
98.3
98.9
98.7
97.8
97.8
98.2
99.3
97.3
95.9
98.7
94.4
92.4
97.1
Yes,
you
r m
othe
r
mot
her_
scie
nce
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Yes
1.6
2.8
1.8
1.1
.82.
71.
31.
12.
2.9
1.1
2.3
2.7
3.0
3.2
.91.
14.
21.
73.
62.
4.9
2.4
2.7
2.6
2.3
.81.
33.
4.5
1.2
2.7
1.9
No
98.4
97.2
98.2
98.9
99.2
97.3
98.7
98.9
97.8
99.1
98.9
97.7
97.3
97.0
96.8
99.1
98.9
95.8
98.3
96.4
97.6
99.1
97.6
97.3
97.4
97.7
99.2
98.7
96.6
99.5
98.8
97.3
98.1
Yes,
ano
ther
mem
ber
of y
our
fam
ily
othe
rfam
ily_s
cien
ceB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OYe
s17
.126
.118
.812
.518
.720
.022
.222
.19.
428
.518
.111
.313
.830
.221
.321
.210
.719
.712
.111
.911
.219
.913
.416
.515
.79.
112
.213
.132
.811
.028
.137
.416
.9N
o82
.973
.981
.287
.581
.380
.077
.877
.990
.671
.581
.988
.786
.269
.878
.778
.889
.380
.387
.988
.188
.880
.186
.683
.584
.390
.987
.886
.967
.289
.071
.962
.683
.1
No,
no
one
in y
our
fam
ily
nofa
mily
_sci
ence
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
No
fam
ily m
embe
r78
.469
.675
.584
.679
.175
.075
.771
.382
.367
.178
.182
.978
.962
.072
.576
.586
.772
.484
.581
.782
.675
.081
.278
.781
.285
.284
.175
.262
.986
.866
.155
.778
.1S
ome
fam
ily m
embe
r21
.630
.424
.515
.420
.925
.024
.328
.717
.732
.921
.917
.121
.138
.027
.523
.513
.327
.615
.518
.317
.425
.018
.821
.318
.814
.815
.924
.837
.113
.233
.944
.321
.9
DK
dkfa
mily
_sci
ence
BE
DK
DE
GR
ES
FIFR
IEIT
LUN
LAT
PT
SE
UK
CY
CZ
EE
HU
LVLT
MT
PL
SK
SI
BG
RO
TRIS
HR
CH
NO
Don
't kn
ow1.
0.5
1.8
1.3
.6.6
.63.
54.
11.
9.3
.54.
21.
11.
01.
2.2
1.5
.11.
62.
93.
52.
4.6
.22.
62.
38.
0.5
.7.4
.91.
7S
ome
non-
DK
re
spon
se99
.099
.598
.298
.799
.499
.499
.496
.595
.998
.199
.799
.595
.898
.999
.098
.899
.898
.599
.998
.497
.196
.597
.699
.499
.897
.497
.792
.099
.599
.399
.699
.198
.3
qb31
(mod
ified
)H
ave
you
ever
stu
died
nat
ural
sci
ence
, tec
hnol
ogy
or e
ngin
eerin
g at
uni
vers
ity?
stud
y_sc
ienc
eB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OY
es, s
tudi
ed a
t un
iver
sity
11.9
5.6
9.6
8.2
8.0
12.1
11.9
7.4
5.0
5.4
7.0
3.8
5.2
9.4
9.6
6.0
3.4
14.5
2.7
14.4
8.8
5.1
6.3
5.8
8.6
8.7
4.4
4.2
11.6
6.3
7.3
7.8
8.0
No,
not
stu
died
at
univ
ersi
ty88
.194
.490
.491
.892
.087
.988
.192
.695
.094
.693
.096
.294
.890
.690
.494
.096
.685
.597
.385
.691
.294
.993
.794
.291
.491
.395
.695
.888
.493
.792
.792
.292
.0
Cou
ntry
Cou
ntry
EU27
EU27
Cou
ntry
EU27
Cou
ntry
Cou
ntry
EU27
Cou
ntry
EU27
EU27
169
qb32
Whi
ch o
f the
se s
tate
men
ts c
omes
clo
sest
to y
our b
elie
fs?
belie
vego
dB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OD
on't
belie
ve th
ere
is
any
sort
of s
pirit
, God
or
life
forc
e26
.924
.426
.74.
419
.122
.340
.26.
76.
024
.529
.812
.212
.134
.024
.62.
636
.728
.620
.011
.212
.21.
54.
813
.625
.415
.3.6
.818
.37.
411
.329
.220
.3
Belie
ve th
ere
is s
ome
sort
of s
pirit
or l
ife
forc
e31
.346
.924
.815
.920
.241
.426
.520
.219
.722
.239
.138
.115
.445
.233
.28.
544
.449
.733
.747
.936
.94.
113
.723
.136
.143
.17.
2.8
48.5
21.7
39.0
44.1
25.6
Belie
ve th
ere
is a
G
od37
.227
.744
.678
.958
.633
.127
.169
.874
.445
.727
.844
.069
.818
.337
.487
.616
.118
.244
.838
.146
.994
.279
.662
.732
.335
.592
.194
.530
.869
.044
.021
.651
.0
DK
4.6
1.0
3.8
.92.
13.
16.
23.
2
7.7
3.3
5.7
2.8
2.5
4.9
1.3
2.8
3.5
1.4
2.8
3.9
.21.
8.7
6.2
6.1
3.
92.
41.
95.
75.
13.
1
qb33
Do
you
cons
ider
you
rsel
f to
be…
?
deno
min
atio
nB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OC
atho
lic56
.51.
834
.0.2
67.8
.641
.282
.687
.167
.822
.278
.784
.11.
114
.12.
129
.62.
253
.423
.580
.095
.790
.066
.764
.0.1
6.2
3.
682
.440
.41.
246
.2O
rthod
ox.5
.21.
593
.61.
5.9
.11.
2.7
.6.1
1.3
.4.3
1.6
92.8
.313
.9.6
16.4
3.4
.9
.92.
483
.886
.2.1
.75.
8.8
.98.
4P
rote
stan
t1.
160
.929
.0.1
.370
.91.
41.
6.6
1.5
16.6
6.2
.640
.324
.9
.75.
67.
712
.5.3
1.1
.45.
1.7
.43.
0
49.6
.334
.838
.811
.4O
ther
Chr
istia
n4.
59.
63.
6.1
1.8
9.1
1.7
4.7
1.4
2.1
8.5
1.5
1.0
8.2
16.8
1.4
1.2
15.9
3.3
11.5
2.0
.5.7
6.8
.11.
02.
3
10.5
.43.
410
.44.
5Je
wis
h.1
.1.1
.2
.2.1
.2.2
.1
.2.1
.1.1
.3
.4
.1
.0
Mus
lim5.
21.
71.
9.2
.4.1
2.1
.2
.6.8
1.1
1.
12.
6
.2
.1
.3.6
3.0
5.1
.197
.2
.72.
5.7
1.2
Sikh
.1
.4
.1B
uddh
ist
.8.3
.3
.4.3
.9.1
.3
.9.1
1.
2.6
.3
.2.1
.1.1
.1.2
.4
.7
.7.4
Hin
du
.3
.1
.1
.1
.1
1.
4
.1
.2
.1.4
.2At
heis
t4.
96.
56.
62.
64.
53.
412
.92.
33.
35.
114
.9.6
2.5
12.3
6.2
.517
.718
.01.
43.
91.
1.3
1.6
9.5
13.2
1.8
.5.4
10.1
3.0
3.3
8.9
6.1
Non
bel
ieve
r/agn
ostic
20.4
15.6
18.6
2.5
18.3
8.0
28.7
4.4
2.6
15.2
27.7
6.2
9.6
28.9
24.4
1.1
42.6
11.1
18.6
24.8
7.5
1.0
4.1
8.1
10.9
2.9
.2.7
16.5
6.1
8.8
31.0
16.0
Oth
er
(SPO
NTA
NEO
US)
2.9
2.5
1.6
.42.
51.
63.
71.
41.
24.
27.
31.
9.2
5.0
1.9
1.6
.43.
2.1
.12.
2.7
.21.
23.
11.
01.
4.5
7.2
.44.
65.
32.
0
DK
3.1
.82.
4.3
2.4
5.1
7.2
1.4
2.8
2.6
.92.
31.
51.
35.
1.4
7.2
29.8
14.7
6.8
3.0
.22.
11.
72.
53.
9.2
.81.
4.7
.42.
03.
6
qb34
Apa
rt fr
om w
eddi
ngs
or fu
nera
ls, a
bout
how
ofte
n do
you
atte
nd r
elig
ious
ser
vice
s?
relig
ious
_atte
ndan
ceB
ED
KD
EG
RE
SFI
FRIE
ITLU
NL
ATP
TS
EU
KC
YC
ZE
EH
ULV
LTM
TP
LS
KS
IB
GR
OTR
ISH
RC
HN
OM
ore
than
onc
e a
wee
k2.
31.
11.
52.
22.
0.5
1.0
7.2
5.0
2.8
3.7
1.1
2.7
1.4
3.3
6.6
.4.8
1.4
1.2
1.1
27.0
5.7
12.4
2.7
.73.
017
.61.
44.
11.
92.
02.
8
Onc
e a
wee
k8.
12.
97.
513
.311
.61.
44.
432
.321
.79.
58.
29.
219
.63.
78.
817
.05.
11.
38.
03.
38.
245
.345
.427
.79.
93.
718
.312
.34.
317
.67.
03.
313
.6Ab
out o
nce
a m
onth
4.7
6.2
9.1
17.1
6.3
4.3
4.7
12.7
12.1
6.5
4.7
13.2
11.8
4.6
5.7
16.7
3.3
2.9
5.4
8.0
10.4
7.7
19.3
8.5
6.5
8.4
18.8
7.2
5.7
14.0
12.7
2.9
9.2
Abou
t eac
h tw
o or
th
ree
mon
ths
5.8
6.2
8.8
17.2
6.0
5.7
2.9
10.4
9.9
7.5
7.0
6.5
8.2
7.6
5.3
14.8
2.0
2.8
5.9
6.4
11.2
2.5
8.0
4.0
5.4
9.9
13.2
6.3
10.1
10.1
12.2
5.2
7.3
Onl
y on
spe
cial
hol
y da
ys13
.919
.819
.335
.214
.422
.913
.17.
424
.826
.28.
823
.924
.420
.66.
935
.58.
917
.218
.420
.737
.14.
98.
913
.121
.638
.520
.313
.722
.727
.717
.115
.516
.6
Abou
t onc
e a
year
7.1
18.2
13.3
4.6
3.8
18.6
10.3
6.9
6.4
12.8
11.3
9.1
4.9
12.2
9.3
4.7
5.0
18.6
8.7
16.8
9.9
3.6
2.5
4.9
6.1
6.8
4.6
2.8
17.9
7.2
13.5
13.8
8.3
Less
ofte
n17
.421
.111
.86.
314
.828
.49.
310
.610
.113
.49.
719
.18.
820
.213
.82.
113
.521
.320
.317
.813
.44.
02.
59.
818
.814
.717
.18.
618
.09.
513
.519
.012
.1N
ever
39.7
24.5
27.8
4.1
40.8
17.4
53.2
12.0
9.2
20.4
46.3
16.5
18.4
29.6
45.5
2.4
60.5
34.0
30.3
25.0
8.5
5.1
4.7
18.6
28.3
15.0
2.9
23.6
19.3
8.6
21.6
37.7
28.9
DK
.9.1
.9
.4.8
1.2
.7.8
.9.4
1.4
1.2
.11.
4.2
1.3
1.1
1.6
.7.3
.13.
0.9
.72.
21.
87.
9.6
1.1
.4.6
1.1
EU27
Cou
ntry
EU27
EU27
Cou
ntry
Cou
ntry
170
European Commission
EUR 24537 – Europeans and Biotechnology in 2010 Winds of change ?
Luxembourg: Publications Office of the European Union
2010 — 172 pp. — B5, 17,6 x 25 cm
ISBN 978-92-79-16878-9doi 10.2777/23393
Interested in European research?
Research*eu is our monthly magazine keeping you in touch with main developments (results, programmes, events, etc.). It is available in English, French, German and Spanish. A free sample copy or free subscription can be obtained from:
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EUROPEAN COMMISSIONDirectorate-General for Research
Directorate L – Science, Economy and SocietyUnit L.3 – Governance and Ethics
Contact: Lino Paula
European CommissionOffice SDME 07/80B-1049 Brussels
Tel. +32 2 29 63873Fax +32 2 29 84694E-mail: [email protected]
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Studies and reports
KI-N
A-24537-EN
-C
Europeans and Biotechnology in 2010
Winds of change?
This is the seventh in a series of Eurobarometer surveys on life sciences and biotechnology conducted in 1991, 1993, 1996, 1999, 2002, 2005 and 2010. This latest survey, carried out in February 2010, was based on a representative sample of 30,800 respondents from the 27 Member States, plus Croatia, Iceland, Norway, Switzerland and Turkey. Issues such as regenerative medicine, production of Genetically Modified Organisms (GMOs, both transgenic and cisgenic), biobanks, biofuels and other innovations such as nanotechnology and synthetic biology, in addition to broader issues such as the governance of science and the engagement of citizens, were investigated. These surveys provide an indication of the distribution of opinions and attitudes in the public at large and evidence of changes in these perceptions over time. To ensure the continuing independence and high reputation of this series of surveys, the Commission charged a team of social scientists throughout Europe with designing the questionnaire and analysing the responses.
Euro
pean
s an
d B
iote
chno
logy
in 2
010
- W
inds
of c
hang
e?