Societal appreciation of energy security. Volume 2, Long-term security (EE, NL and PT).
Date: 2019-01-14
Institutions: Joint Research Centre
Subjects: Estonia, Netherlands, Portugal, consumer survey, data collection, electrical energy, electricity supply, energy consumption, energy supply, household consumption, research report, sample survey, security of supply

Societal appreciation of energy 
security  

Volume 2: Long-term security 
(EE, NL and PT) 

Giaccaria S., Bouman T., Longo A., 
Efthimiadis T. 

2018 

EUR 29512 EN 

This  publication  is  a  Science  for  Policy  report  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science  and  knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does 
not  imply  a  policy  position  of  the  European  Commission.  Neither  the  European  Commission  nor  any  person  acting  on  behalf  of  the 
Commission is responsible for the use that might be made of this publication. 

Contact information 
Name: Tilemahos EFTHIMIADIS 
Address: Westerduinweg 3, 1755 LE Petten, the Netherlands 
Email: tilemahos.efthimiadis@ec.europa.eu  
Tel.: +31 22456 5003 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC112729 

EUR  29512 EN 

PDF 

978-92-79-98283-5 

ISSN 1831-9424 

Doi:10.2760/05174 

Luxembourg: Publications Office of the European Union, 2018 

© European Union, 2018 

The reuse policy of the European Commission is implemented by Commission Decision 2011/833/EU of 12 December 2011 on the reuse 
of Commission documents (OJ L 330, 14.12.2011, p. 39). Reuse is authorised, provided the source of the document is acknowledged and 
its original meaning or message is not distorted. The European Commission shall not be liable for any consequence stemming from the 
reuse. For any use or reproduction of photos or other material that is not owned by the EU, permission must be sought directly from the 
copyright holders. 

How to cite this report: Giaccaria S., Bouman T., Longo A., Efthimiadis T., Societal appreciation of energy security: Volume 2: Long-
term security (EE, NL and PT), EUR 29512 EN, Publications Office of the European Union, Luxembourg, 2018, ISBN 
978-92-79-98283-5, doi:10.2760/05174 

All content © European Union 2018 

Societal appreciation of energy security. Volume 2: Long-term security (EE, NL and PT). 

The report presents the results of a multi-country survey providing qualitative and quantitative information on the level of appreciation of 
customers  and  of  regulators,  suppliers  and  other  key  actors  involved  in  electricity  markets.  The  survey  was  conducted  in  Estonia,  the 
Netherlands and Portugal. 

Contents 

Foreword ................................................................................................................ 1 

Acknowledgements ..................................................................................................... 2 

Executive summary ..................................................................................................... 3 

1 

Introduction and background ...................................................................................... 4 

2  Profiles of residential energy users ............................................................................... 6 

2.1  General demographics ....................................................................................... 6 

2.2  Household characteristics .................................................................................... 7 

2.2.1 

Number of persons living in the residence .......................................................... 7 

2.2.2 

Size of residence ...................................................................................... 7 

2.2.3 

Area of residence ..................................................................................... 7 

2.3  Energy-related profile of households ....................................................................... 9 

3  Experiences with interruptions in energy supply ............................................................... 12 

3.1 

Inconveniences caused by interruptions .................................................................. 12 

3.2  Personal values ............................................................................................. 12 

3.2.1 

Scale construction .................................................................................. 15 

4  Needs for additional reliability and affordability of energy supply ........................................... 20 

5  Experienced planned and unplanned interruptions ............................................................. 22 

6  Measures to improve energy security ........................................................................... 25 

6.1  Willingness to reduce energy use ......................................................................... 25 

6.2 

Investments in energy technologies ....................................................................... 25 

6.2.1  Willingness to invest ................................................................................ 25 

6.2.2 

Acceptability of measures to improve energy security .......................................... 25 

6.2.3 

Acceptability of measures to improve energy security .......................................... 27 

7  Long term EU strategy for energy security: a monetary assessment ........................................ 28 

7.1  Contingent Valuation: model and econometric analyses ................................................ 28 

7.1.1 

The contingent scenario ............................................................................ 28 

7.1.2 

Statistical Models of the Contingent Valuation Responses ...................................... 29 

7.1.3 

Internal validity of the WTP responses ............................................................ 29 

8  Conclusions ....................................................................................................... 35 

List of abbreviations and definitions ................................................................................ 40 

List of figures ......................................................................................................... 41 

List of tables .......................................................................................................... 42 

Annex A: Questionnaire to households .......................................................................... 43 

Annex B: Technical Description of the phases of the fieldwork from SSI..................................... 59 

i 

 
 Foreword 

This report  was developed in the framework of the joint DG Energy  – Joint Research Centre project entitled 
Societal Appreciation of Security of Energy Supply (SASOS).  

Other publications in the series Societal appreciation of energy security: 

  Volume 1: Value of lost load – households (EE, NL and PT) 
  Volume 2: Long-term security (EE, NL and PT) 
  Volume 3: Non-residential actors (EE and NL) 
  Volume 4: Value of Lost Load - Greece 

1 

 
Acknowledgements 

We are grateful to our colleagues Ana Raquel Tibúrcio Castanho, Hugo Calisto, Hana  Gerbelová and those in 
the  European  Commission's  Directorate-General  for  Translation  for  linguistic  competence  and  the  help  in 
refining the questionnaire. Thanks also to Anca Costescu for the suggestions on some mathematical aspects, 
to Ádám Szolyak and Nicola Zaccarelli for their invaluable support, the contractor who conducted survey (SSI 
S.A.), but most of all to the respondents for their time and willingness to participate. Finally, we would like to 
express our gratitude to Efthymios Manitsas, Savvas Politis and Sylke Schnepf who reviewed this document. 
The authors are solely responsible for remaining errors 

Authors 

Sergio Giaccaria, European Commission, DG JRC 

Thijs Bouman, University of Groningen 

Alberto Longo, Queen's University of Belfast 

Tilemahos Efthimiadis, European Commission, DG JRC 

2 

 
Executive summary 

The study offers an overview on a survey that investigates energy security and its implications from different 
angles. The survey was conducted in Estonia, the Netherlands and Portugal. The data covers subjects such as 
the context in which energy users live, the technological components and energy sources used for satisfying 
needs  of  energy  services.  After  profiling  respondents’  energy  use,  the  survey  describes  the  way  in  which 
energy users appreciate security of energy supply. The analysis provides insights on the type and importance 
of inconveniences occurring in case of interruptions of electricity supply. The study investigates the level of 
openness of energy users to the transitions that are likely to affect the energy system, and their acceptance 
of  strategies  for  securing  energy  supply.  The  study  offers  a  monetary  evaluation  of  the  economic  benefits 
that the respondent assigned to the improvements of long term security of energy supply.  

Main findings 

The  responses show  that  the  fuel  mix  for  supporting  space  heating  and cooling, cooking  and water heating 
differs across countries. Thus, the inconveniences from disruptions of electricity supply can substantially differ 
among the three countries. 

We also find that individual values significantly contribute to explain, alongside more common socioeconomic 
and  demographic  individual  traits,  the  propensity  of  residential  energy  users  towards  smart  home 
technologies  and  acceptance  of  strategies  to  improve  security  of  energy  supply.  The  respondents  did  not 
express a strong or enthusiastic support to the potential innovations typical of enhanced power grids (smart 
metering,  storage,  diffused  generation,  and  systems  for  direct  load  control),  but  they  indicated  acceptance 
toward goals of securing energy supply, and for adjusting their behaviours to reduce energy consumption. 

The  study  also  offers  empirical  evidence  of  the  appreciation  of  citizens  for  additional  security  of  energy 
supply.  Respondents  have  been  asked  to  evaluate  their  willingness  to  support  a  EU  roadmap  for  energy 
security.  Estimated  benefits  of  a  long-term  strategy  for  energy  security  differ  by  country.  In  particular,  the 
willingness-to-pay  through  increases  in  the  energy  bill  is  39.04  euros  in  Estonia,  50.97  euros  in  the 
Netherlands, and 42.14 euros in Portugal.  

Related and future JRC work 

The study builds upon a previous JRC report that offers the economic assessment of the Value of Lost Load 
for the three countries (SASOS, Volume I), which focused on how households value improvements or losses in 
the short-term security of the supply of electricity. This report (Volume II) emphasises long-term appreciation, 
always from the perspective of residential energy users. A forthcoming third volume will investigate more the 
meaning  of  energy  security,  comparing  preferences  of  the  households  with  those  of  industrial,  commercial 
customers,  regulators,  and  retailer  electricity  suppliers.  In  this  way,  we  will  explore  to  which  extent  the 
convergence among different market/societal actors may support various components of energy security. 

Quick guide 

The  study  attempts  to  bridge  many  methodological  domains  from  different  disciplines,  connecting  them  to 
the  theme  of  energy  security.  Statistics,  economics and  social  psychology  have  been  closely  supporting  the 
various  steps  of  the  implementation  of  the  study, from the  design  of  the  questionnaire  to  the  fieldwork  of 
collection of data through interviews, and in the quantitative analyses. For the monetary evaluation, the study 
involves methods for design of choice experiments and econometric analyses. An original trait of the study is 
the  attempt  to  conjugate  an  economic  approach  with  the  application  of  social  psychology  and  behavioural 
sciences. 

3 

 
1 

Introduction and background 

The concept of energy security has an increasing importance for the development of modern energy systems. 
In  the  EU  framework,  energy  security,  sustainability  and  competitiveness  have  been  endorsed  as  key  long-
term  goals  for  the  ongoing  transition  of  the  energy  system.  The  present  report  aims  at  providing  some 
empirical  evidence  as  to  the  perception  of  the  topic  'energy  security';  from  a  socio-economic  perspective. 
Investments in energy security can require substantial funds, e.g. for physical infrastructures, while the public 
acceptance and their perceptions/knowledge about the topic may be low, or uncertain. To address this issue, 
the  report  captures  quantitative  and  qualitative  information  from  survey  data  collected  within  three  EU 
countries:  Estonia  (EE),  the  Netherlands  (NL)  and  Portugal  (PT).  The  study  proposes  a  set  of  methodologies 
develop  for  observing  public  preferences  over  a  comprehensive  set  of  aspects  targeting  a  long-term 
perspective on energy security.  

At an academic and practitioners' level, a unified theory defining the meaning of 'energy security' or 'security 
of  energy  supply'  is  a  challenging  task.  Different  interdependent  technological,  institutional  and  social 
systems evolve in defining the needs and the general concept of security. Different disciplines have developed 
specialized  measurement  approaches  for  this  topic,  for  example  the  use  of  quantitative  indicators  of  the 
vulnerability  of  physical  energy  networks  (Badea,  Rocco,  Tarantola,  &  Bolado-Lavin,  2011).  Systemic 
approaches to qualify security over long-term scenarios have been devised (Scheepers, Seebregts, De Jong, & 
Maters,  2006;  Jansen  &  Seebregts,  2010)  also  with  the  purpose  of  modelling  the  behaviour  of  energy 
systems, as in the case of the Global Energy Assessment model (Cherp, et al., 2012) or the Model of Short-
term  Energy  Security  (IEA  International  Energy  Agency;  Jewell, Jessica, 2011).  Analyses dealing  with  energy 
security implicitly need to define what is a concern for security, and "whether such a concern is significant is 
between  facts  and  perceptions"  (Cherp  &  Jewell,  2010).  While  applied  measurement  approaches  tend  to 
describe the concerns for security more in terms of facts, as measurable properties of systems, in this study 
the emphasis is shifted radically on the perceptions of final energy users and on the subjective components 
that shape the societal needs of security, and its appreciation. 

The three countries (EE, NL and PT) were selected for the survey on residential energy users, as they are in 
different situations regarding experienced reliability of energy supply, per capita income and carbon intensity 
of the economy (Figure 1). In a previous study (Longo, Giaccaria, Bouman, & Efthimiadis, 2018) survey data 
were used to inform an assessment of the Value of Lost Load for the three countries, as a proxy of the value 
of  the  short  term  damage  from  disruption  of  energy  supplies.  Two  further  volumes  are  in  preparation:  one 
develops  for  EE,  NL  and  PT  an  exploration  of  the  preferences  of  Distribution  System  Operators,  energy 
retailers, industrial  and  commercial  consumers, and  consumers' associations  on  policy  measures  for  energy 
security; while the other will regard an integrated study only for Greece. 

Figure 1. Estonia, the Netherlands and Portugal by per capita gross domestic product and GHG emissions (Eurostat data 
2017) 

Sources: own elaborations on Eurostat data (2017) 

The  first  part  of  this  report  presents  a  profile  of  consumers.  A  comprehensive  set  of  information,  reported 
through descriptive analyses, illustrates beliefs, values, technological choices and habits  of the respondents. 
The purpose is to characterize with a wide set of quantitative and qualitative  variables how energy services 
are provided. This section also offers some insights on the way consumers see the future possible choices in a 
system in transition, given the past and current experience. We  further explore the respondents' experienced 

4 

 
 
level  of  insecurity  and  attitudes  toward  the  adoption  of  energy  technologies  to  improve  security  of  supply, 
with a special focus on electricity.  

A  set  of  drivers  of  the  preferences  over  energy  security  is  additionally  represented  by  personal  values, 
gathered through the survey data. Although all individuals share similar sets of values (Rokeach, 1973), they 
differ in the way they prioritize certain values over others.  Thus, individuals have their own value system, in 
which  different  values  are  hierarchically  ordered  based  on  the  importance  the  individual  attaches  to  each 
value  (Rokeach,  1973;  Schwartz,  1994,  2003,  2006;  Schwartz  et  al.,  2012).  These  value  systems  are  often 
used  to  characterize  individuals,  groups,  and  cultures.  The  study  searches  for  evidence,  of  statistical 
association among personal values and individuals’ behaviours, attitudes, and beliefs on energy security. 

The  report  is  focused  on  an  evaluation  of  the  perceived  benefits  from  a  long-term  EU  energy  security 
strategy. A Contingent Valuation exercise (CV) is presented to determine the monetary value residential users 
put on such long-term benefits.  

5 

 
2  Profiles of residential energy users 

The  perspective  of  the  residential  energy  users  is  quantitatively  and  qualitatively  gathered  through  survey 
data, gathered in October 2017. The sample for the online survey was drawn at random by Survey Sampling 
International (SSI S.A.) from an online panel of adults. The panel size was 22,000 for EE; 265,000 for NL and 
60,000 for PT. 

Quotas were set to ensure that the resulting sample was broadly representative of the target populations. The 
target population include those individuals having some responsibility in the choice of purchasing energy for 
residential uses. We decide to focus on the older than 25(1). Younger respondents were excluded to focus on 
preferences  of  those  who  are  usually  the  ones  purchasing  security  as  embedded  within  their  choices  as 
customers (e.g. energy contract) or voters. Younger citizens are less likely to have a say or responsibility with 
regard to energy, and less familiarity with purchase decisions under income constraints2.  

The  approach  is  similar  to  other  multicountry  studies,  such  as  the  pan-European  survey  developed  in  the 
framework of the FP7 project SESAME(3) and two studies by London Economics tendered by DG Justice on the 
functioning  of  the  retail  electricity  market  (4).  In  the  questionnaire  for  the  online  interview  (see  Annex  A), 
respondents were asked to describe themselves, their household, and their energy consumption pattern.  

2.1  General demographics  

For  the  three  countries  (EE,  NL  and  PT)  the  differences  between  the  target  and  the  obtained  number  of 
interviews are reported in Table 1. The differences remained within the 5 % deviation threshold. In Figures 2 
and 3 we report the frequencies of age for the sampled data. 

Table 1. Count of data (collected in the fieldwork versus target sample) 

Estonia 

male 

female 

the Netherlands 

male 

female 

Portugal 

male 

female 

25-34 
35-44 
45-54 
55-64 
65+ 
25-34 
35-44 
45-54 
55-64 
65+ 
25-34 
35-44 
45-54 
55-64 
65+ 
25-34 
35-44 
45-54 
55-64 
65+ 
25-34 
35-44 
45-54 
55-64 
65+ 
25-34 
35-44 
45-54 
55-64 
65+ 

target 
105 
105 
95 
84 
84 
105 
95 
95 
175 
179 
95 
105 
116 
95 
116 
95 
105 
116 
95 
137 
95 
105 
105 
84 
116 
95 
105 
105 
95 
158 

obtained 
76 
104 
96 
86 
77 
105 
96 
95 
183 
125 
91 
101 
115 
91 
112 
91 
101 
112 
91 
133 
96 
106 
106 
85 
118 
96 
108 
105 
97 
142 

diff 
-2.58 % 
-0.09 % 
0.09 % 
0.18 % 
-0.62 % 
0.00 % 
0.09 % 
0.00 % 
0.71 % 
-4.81 % 
-0.37 % 
-0.37 % 
-0.09 % 
-0.37 % 
-0.37 % 
-0.37% 
-0.37 % 
-0.37 % 
-0.37% 
-0.37 % 
0.05 % 
0.05 % 
0.05 % 
0.05 % 
0.09 % 
0.05 % 
0.14 % 
0.00 % 
0.09 % 
-0.75 % 

Source: SSI S.A. 

Figure 2. Sample distribution of age (%) 

(1) Demographic groups by age and gender quotas are defined through the most updated national statistical information available in the 

three countries. 

(2) This last condition is suggested to improve the evaluation of willingness-to-pay to support a hypothetical strategy for energy security 

of the EU, the objective of the Section 3 of the study. 
(3) Documentation available at https://www.sesame-project.eu/  
(4) Documentation available at http://ec.europa.eu/newsroom/just/item-detail.cfm?item_id=533  

6 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Source: own elaborations 

Figure 3. Distribution of Age  

Source: own elaborations 

2.2  Household characteristics 

2.2.1  Number of persons living in the residence 

Single  occupancy  households  represent  23  %  in  Estonia,  28  %  in  the  Netherlands  and  15  %  in  Portugal 
(Figure 4). 

2.2.2  Size of residence 

The size of the residence by country, and by size of household, is illustrated in Figure 5. We can see that the 
highest median is in PT, followed by NL. Residences inhabited by 6 or more persons are the only exception to 
this  ranking,  where  EE  has  a  higher  median  than  NL,  but  also  a  more  dispersed  distribution.  Data  are 
truncated at 500 square meters and houses 6 persons or more are grouped together, as their number in the 
sample is residual. 

2.2.3  Area of residence 

From Figure 6 we see that respondents stating that they live in a 'big city' represent 28 % of the sample in 
EE, 23 % in NL, and slightly higher 32 % in PT. For 'suburbs and outskirts of a big city' we find 14 % of the 
sample in EE, 17 % in NL, and 25 % in PT. Respondents declaring to live in a 'town or small city' are 28 % in 
EE, 35 % in NL, and 33 % in PT. For the 'country village' the shares are 18 %, 22 % and 8 % respectively for 

7 

 
 
 
EE, NL and PT. Last, 'farm homes in countryside' represents 12 %, 3 % and 2 %, respectively for EE, NL and 
PT.  We  consider  the  first  three  categories  as  more  densely  populated  areas,  and  the  last  two  as  sparsely 
populated.  

Figure 4. Number of persons living in the residence 

Source: own elaborations 

Figure 5. Boxplots of the size of the house in square meters, categories by country and number of persons living in the 
house 

Source: own elaborations 

8 

 
 
 
 
 
Figure 6. Share of responses from different territorial contexts  

Source: own elaborations 

2.3  Energy-related profile of households 

The monthly expenditures for the supply of different fuels are reported in Figure 7. Respondents further were 
asked to indicate the main energy fuel they used to heat their residence, cook, and heat water. Depending on 
the fuel, these energy behaviours are interrupted (or not) in case of a blackout electricity outage. For example, 
gas boilers often require electricity to function, whereas heating using wood often does not require electricity. 

Respondents clearly differed on the main energy fuel used to heat (cool) the house, which is always a large 
share of households’ energy consumption (Figure 8): 

in EE, the main fuel to heat the house is wood (49 %), followed by electricity (15 %), and gas (10 %) 
in NL the main fuel is gas (76%), followed by electricity (17 %), and wood (3 %), and  
in PT the main fuel is electricity (56 %) followed by wood (21 %) and then gas (16 %).  

 
 
 
Accordingly,  most  respondents  in  NL  and  PT  are  not  able  to  heat  their  residencies  when  the  supply  of 
electricity is interrupted. 

For cooking and heating water, respondents in NL and PT relied mostly on gas, followed by electricity (Figures 
9 and 10). In EE, electricity  was  used as the  main fuel  for cooking  and heating water,  followed by  gas  and 
wood. 

9 

 
 
Figure 7. Monthly expenditure for the supply of different fuels (euros). On the right, boxplots for the amount of the family; 
on the left, boxplots of per capita values 

y
t
i
c
i
r
t
c
e
E

l

s
a
G

l

a
r
u
t
a
N

r
e
t
a
W

l

y
t
i
c
i
r
t
c
e
E
&
s
a
G

i

s
s
a
m
o
B
d
o
o
W

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 8: Main fuel used for space heating/cooling in the house 

Figure 9. Man fuel used for Cooking 

Figure 10. Main fuel used for water heating 

Source: own elaborations 

11 

 
 
 
 
3  Experiences with interruptions in energy supply 

3.1 

Inconveniences caused by interruptions 

Respondents also choose from a pre-defined list the activities that they would be unable to do in the case of 
a  power cut  (Figure  11).  Indoor lighting  is clearly  the  most  chosen  activity  in  the  three  countries.  Electricity 
seems to be more necessary for the Netherlands  for warming/cooling rooms, washing dishes, talking on the 
telephone, cleaning floors, and having a bath/shower, which corresponds to the earlier mentioned household 
energy  consumption  profile.  On  the  contrary,  EE  is  less  dependent  from  electricity,  especially  for  warming 
rooms (see Figure 8).  

In  the  survey,  we  asked  respondents  to  indicate  which  inconvenience  they  would  generally  consider  as 
relevant in the case of blackout (Figure 12) where we found that respondents are generally most concerned 
about  spoiled  food  due  in  the  fried  or  freezer.  In  addition,  for  respondents  in  NL,  the  inability  to  heat  the 
house  was  also  considered  an  important  inconvenience.  Furthermore,  compared  to  the  other  countries, 
respondents  in  EE  were  relatively  concerned  about  being  unable  to  recharge  their  mobile  telephones,  and 
respondents in PT were relatively concerned about being trapped in an elevator. 

3.2  Personal values 

As  mentioned,  individuals  have  their  own  value  system,  in  which  different  values  are  hierarchically  ordered 
based on the importance the individual attaches to each value (Rokeach, 1973; Schwartz, 1994, 2003, 2006; 
Schwartz et al., 2012). These value systems are often used to characterize individuals, groups, and cultures. In 
addition, values are  generally seen as important and stable underlying  predictors of individuals’ behaviours, 
attitudes, and beliefs. 

Personal  values  could  thereby  provide  critical  insights  for  policymakers  (Jans,  Bouman,  &  Fielding,  2018; 
Lopes, Antunes, & Martins, 2012; Steg, Bolderdijk, Keizer, & Perlaviciute, 2014; Steg, Dreijerink, & Abrahamse, 
2005; Steg, Perlaviciute, & Van Der Werff, 2015; Steg, Shwom, & Dietz, 2018; Van der Werff & Steg, 2015). In 
the context of the current report, values could provide insights on why individuals prioritize certain dimensions 
of  energy  security  over  others,  and  why  individuals  are  willing-to-pay  (WTP)  for  energy  security  or  accept 
lower  levels  of  energy  security.  Moreover,  a  better  understanding  of  personal  values  could  help  with 
understanding  evaluations  of  the  benefits  of  energy  security,  both  in  the  short  and  in  the  long  term.  This 
information is crucial for understanding citizens’ choices, attitudes, beliefs, and behaviours, and provides key 
insights on how individuals could be approached, and which types of feedback, information or incentives are 
likely  to  be  effective  (Abrahamse  &  Steg,  2009;  Abrahamse,  Steg,  Vlek,  &  Rothengatter,  2005;  Bolderdijk, 
Gorsira, Keizer, & Steg, 2013). 

In the value literature, values are measured by 57 items  (Schwartz, 1994; Schwartz et al., 2012), which are 
then  categorized  in  more  specific  value  clusters  (Schwartz,  1994,  2003;  Schwartz  et  al.,  2012).  However, 
environmental  and  energy  research  typically  employ  a  shortened  and  adapted  version  of  this  measure 
(Bouman,  Steg,  &  Kiers,  2018;  De  Groot  &  Steg,  2008;  Steg,  Perlaviciute,  Van  Der  Werff,  &  Lurvink,  2014; 
Stern  et  al.,  1998),  consisting  of  16  (SVS)  or  17  (E-PVQ)  value  items  that  identify  four  value  clusters  that 
steadily and consistently predict energy behaviours, attitudes and beliefs. Those four values are: 

• 

• 

• 

• 

Altruistic values, e.g. a concern for the welfare and fair treatment of others 

Biospheric values, e.g. a concern for nature and the environment 

Egoistic values, e.g. a concern for personal resources and power 

Hedonic values, e.g. a concern for pleasure, comfort and reducing effort 

12 

 
Figure 11. Activities that cannot be done at home in absence of electricity 

Source: own elaborations 

13 

 
 
Figure 12. Inconveniences from blackouts classified by respondents as "more relevant" 

Source: own elaborations 

14 

 
 
 
Each of these values represent a different motive to invest (or not) in energy security and engage (or not) in a 
sustainable  energy  transition.  For  example,  altruistic  and  biospheric  values  are  likely  to  positively  correlate 
with  WTP  for  energy  security  if  investments  in  a  more  secure  energy  system  are  associated  with 
environmental (e.g. larger penetration of renewables, locally generated energy) and societal (e.g. secure and 
stable  supply  of  energy)  benefits.  Similarly,  hedonic  values  might  positively  correlate  with  WTP  when 
investments are perceived to reduce inconveniences related to loss of load, unless such policies or measures 
are  perceived  as  inconvenient  (e.g.  require  a  lot  of  effort).  For  egoistic  values,  this  relationship  is  more 
ambiguous. On the one hand, investments in energy security could be associated with financial costs, which 
would  suggest  a  negative  correlation  between  egoistic  values  and  WTP  and  a  positive  correlation  with 
willingness-to-accept  (WTA)  compensations.  On  the  other  hand,  investments  in  energy  security  could  be 
associated  with  economic  benefits  and  independence  of  other  countries  and  power,  which  would  suggest  a 
positive correlation between egoistic values and WTP.  

Having a better understanding of these values could help policymakers in multiple ways (e.g. Bolderdijk et al., 
2013;  Leijten  et  al.,  2014;  Van  Den  Broek  et  al.,  2017).  Firstly,  based  on  individual  or  group-level  value 
systems  indicating  how  value  clusters  are  hierarchically  ordered,  one  could  select  and  tailor  information, 
feedback  and  incentives  that  correspond  to  those  values  that  are  most  strongly  endorsed.  For  instance, 
information highlighting sustainable benefits of energy security might be effective when biospheric values are 
strongly  endorsed,  but  ineffective  when  other  values  (e.g.  egoistic)  are  more  prominent  and  activated. 
Secondly,  when  one  knows  the  importance  attached  to  each  value,  one  could  predict  which  barriers  and 
opportunities policies might face and act accordingly. For example, knowing that egoistic values are strongly 
endorsed could indicate that individuals are unlikely to spend money (i.e. barriers), unless they get financial or 
power  benefits  in  return  (i.e.  opportunity).  Lastly,  identifying  correlations  between  values  and  key  energy 
behaviours,  attitudes  and  beliefs,  provide  insights  in  which  individuals  are  (un)likely  to  accept  and  adopt 
certain policies enabling policymakers to identify early adopters and laggards. 

Values  are  measured  on  a  scale  consisting  of  17  items  (in  randomized  order),  which  is  an  adapted  and 
shortened version of the Schwartz Value Survey (Schwartz, 1994, 2003, 2012; Steg, Perlaviciute, et al., 2014; 
Stern et al., 1998) and has been validated in earlier research  (Bouman et al., 2018; Schwartz, 2003, 2012). 
The value scale uses  17 short verbal portraits of another person  – gender– matched  to the participant  – in 
which  a  value  is  described  that  is  important  to  this  person  (e.g.  'It  is  important  to  her  to  enjoy  life’s 
pleasures.').  Respondents  are  asked  to  indicate  the  extent  to  which  each  portrayed  person  is  like  the 
respondent herself or himself, ranging from 1 (entirely not like me) to 7 (entirely like me). 

3.2.1  Scale construction 

At  first,  we  computed  the  value  clusters  out  of  the  single  items  and  inspected  whether  the  items  loaded 
correctly  on  the  corresponding  value  clusters.  For  this  we  used  the  Oblique  Multiple  Group  method  (OMG 
method;  e.g.  Guttman,  1952;  Nunnally,  1978;  Stuive,  2007)  of  confirmatory  factor  analyses,  as  well  as 
multidimensional scaling to visualize the value clusters. The OMG method was applied in three steps:  

First, the mean score was calculated for each value scale: 

Altruistic values = 

∑(𝑄𝐴1𝑎−𝑒)
5

Biospheric values = 

∑(𝑄𝐴1𝑓−𝑖)
4

Hedonic values = 

∑(𝑄𝐴1𝑗−𝑙)
3

Egoistic values = 

∑(𝑄𝐴1𝑚−𝑞)
5

Second, for each value item we calculated its correlation with each of the composite value scales, while the 
correlation  between  an  item  and  the  scale  to  which  it  theoretically  belongs  is  being  corrected  for  'self-
correlation'.  

Third, we checked whether the corrected correlation between an item and the scale to which it was supposed 
to belong was stronger than its correlation with the other scales, which would confirm that the item loads on 
the right cluster (see Nunnally, 1978). 

From the application of the OMG method, we find that the mean value scores show a clear general pattern 
(see also Table 2 and Figure 13). Overall, respondents assigned the highest scores on altruistic values (Mtotal = 
5.64, SDtotal = 1.02), followed by hedonic values (Mtotal = 5.54, SDtotal = 1.08) and biospheric values (Mtotal = 5.38, 

15 

 
 
 
 
 
SDtotal  =  1.22),  while  scoring  clearly  lower  on  egoistic  values  (Mtotal  =  3.84,  SDtotal  =  1.17).  PTscored 
comparatively high on all values, whereas respondents in NL endorsed hedonic values relatively strongly and 
biospheric values relatively weakly. 

After performing the OMG method, we used multidimensional scaling to plot the items on the theorized two-
dimensional  scale  (illustrated  in  Figure  7).  For  this  purpose,  we  used  the  PROXSCAL  program  in  SPSS, 
employing 20 random starting configurations as well as the classical Torgerson starting configuration, and set 
the  convergence  values at  .0001,  and fixed  the  maximum number  of  iterations  to  100.  We  only  report  two 
dimensional solutions, as these portray the structure well enough, i.e. dispersion accounted for (DAF) around 
95 %. From the 21 differently started analyses, we selected the ones with lowest stress.  

Table 2. Scores of components of values 

Estonia 

the Netherlands 

Portugal 

.29 
.29 
.33 
.30 

.78 
.74 
.77 
.72 

Bio  Hed  Ego  Alt 

Bio  Hed  Ego  Alt 

.36 
.41 
.29 
.21 
.30 

.42 
.40 
.36 
.40 
.43 

.53 
.54 
.52 
.48 
.55 

.07 
.02 
.15 
.06 
.08 

.40 
.47 
.31 
.39 
.47 

.46 
.49 
.46 
.48 
.47 

.08 
.05 
.14 
.03 
.09 

.46 
.57 
.49 
.38 
.50 

.66 
.71 
.63 
.57 
.67 

.66 
.69 
.61 
.59 
.64 

Altruistic 
Equal 
social justice 
taking care 
peace 
helpful 
Biospheric 
pollution 
protection 
respect 
Unity 
Hedonic 
pleasure / fun  .27 
.31 
enjoying life 
self-indulgent  .45 
Egoistic 
social power 
authority 
influential 
wealth 
ambitious 

Bio  Hed  Ego 
Alt 
Cronhach’s α = .775  Cronhach’s α = .840  Cronhach’s α = .835 
.58 
.00 
.66 
.01 
.59 
.14 
.37 
.05 
.60 
.11 
Cronhach’s α = .886  Cronhach’s α = .905  Cronhach’s α = .873 
.82  .32 
.05 
.58 
.80  .31 
.13 
.54 
.72  .48 
.06 
.60 
.81  .33 
.55 
.08 
Cronhach’s α = .696  Cronhach’s α = .833  Cronhach’s α = .738 
.24 
.21 
.22 
Cronhach’s α = .766  Cronhach’s α = .824  Cronhach’s α = .786 
.59 
.01 
.04 
.59 
.04 
.05 
.63 
-.10  -.07 
.49 
.18 
.24 
.52 
.15 
.17 

.72 
.25 
.65  -.02  -.01  .10 
.73  -.09  -.04  .04 
.46 
.31 
.55 
.31 

.10 
.02 
.00 
.26 
.19 
Source: own elaborations 

.67 
.06 
.62 
.02 
.63  -.01 
.35 
.26 
.42 
.06 

.12 
.07 
.05 
.25 
.07 

.24 
.13 
.12 
.28 
.41 

.77 
.67 
.73 
.75 

.08 
.07 
.06 
.09 

.52 
.50 
.57 
.54 

.58 
.54 
.60 
.58 

.40 
.39 
.40 
.36 

.16 
.18 
.05 
.13 

.53 
.54 
.48 

.69 
.70 
.68 

.58 
.64 
.48 

.30 
.30 
.15 

.20 
.28 
.36 

.41 
.49 
.46 

.35 
.38 
.32 

.18 
.10 
.10 

.40 
.39 
.47 

.34 
.36 
.41 

.19 
.10 

.20 
.11 

.12 

.12 

16 

 
 
 
 
 
Figure 13. Sum of the scores for the altruistic, biospheric, hedonic and egoistic values  

Source: own elaborations 

Figure 14. Correlation among the values components  

Source: own elaborations 

17 

altruisticaltruisticbiosphericbiospherichedonichedonicegoisticegoisticaltruisticbiospherichedonicegoistic.1-.15.2-.25.35-.4.45-.5.65-.71 
 
 
 
 
Figure 15. Two-way frequency tables, in colours. The vertical axes are reversed with larger values downwards 

Biospheric 

Hedonic 

Egoistic 

Altruistic 

Biospheric 

Hedonic 

Source: own elaborations 

The raw scores and computed mean scores for each value could be used as predictor variables in regression 
analyses, MDS and factor analyses. However, if one is interested in only one value as a predictor or in group-
mean comparisons, one could also choose to use the centred value score to correct for scale use biases (this 
choice  depends on the assumptions one  has about scale use and the exact  question one wants to answer). 
Centred  value  score  can  be  computed  by  following  these  steps  (Schwartz,  2003;  Schwartz  et  al.,  2012):  1) 
compute the mean score for each value (as indicated above), 2) compute for each individual the mean score 
across all 17 items (i.e. MRAT), and 3) subtract the MRAT from each of the four mean value scores (computed 
as indicated above). The current analyses presented below are based on the mean scores and not the MRAT. 

The Figure 16 indicates that items which are theoretically assumed to relate to each other (i.e., because they 
aim to measure the same construct), indeed closely relate to each other. That is, based on theory, the items 
with  the  same  colour  should  be  located  close  to  each  other  and  form  clusters/clouds,  which  is  mostly  the 
case. Accordingly, it confirms our clustering into egoistic, hedonic, biospheric and altruistic values. Moreover, it 
suggests that, at least on one dimension, the egoistic cluster differs quite strongly from the other clusters.  

18 

r1r5r9r13r17r21r25r29c1c5c9c13c17c21c250-11-33-99-2020-3535-240.001r1r5r9r13r17r21r25r29c1c4c7c10c13c16c190-11-22-55-1313-2828-3939-168.001r1r5r9r13r17r21r25r29c1c5c9c13c17c21c25c290-11-22-55-99-1414-1919-34.001r1r5r9r13r17r21r25c1c4c7c10c13c16c190-11-22-44-77-1515-2929-4444-184.001r1r5r9r13r17r21r25c1c5c9c13c17c21c25c290-11-22-33-66-1010-1717-2222-40.001r1r4r7r10r13r16r19c1c5c9c13c17c21c25c290-11-22-44-88-1515-2222-2929-36.001 
 
 
 
 
 
 
 
 
 
 
Figure 16. Two-dimensional multidimensional scaling for the PVQ value scale for each country.  

1.5

0

[CELLRAN
GE] 
[CELLRAN
[CELLRAN
GE] 
GE] 

[CELLRAN
[CELLRAN
[CELLRAN
GE]  [CELLRAN
GE] 
GE] 
[CELLRAN
GE] 

[CELLRAN
GE] 
[CELLRAN
GE] 
[CELLRAN
[CELLRAN
GE] 
GE] 
GE] 
[CELLRAN
[CELLRAN
[CELLRAN
GE] 
GE] 
GE] 

[CELLRAN
GE] 

[CELLRAN
GE] 

-1.5

-1.5

0

1.5

Estonia 

the Netherlands 

Portugal 

Notes:  PVQ  items  (e.g.,  Alt1)  are  plotted  on  a  two-dimensional  space,  where  the  distance  between  the  items  represents  their  resemblance.  The  closer  two  items  are  located  to  each  other,  the 
stronger their resemblance. Pre-defined theory-based clusters are indicated in colours (egoistic cluster = red, hedonic cluster = orange, biospheric cluster = green, altruistic cluster = blue), and are 
supported by our data (i.e., items belonging to the same cluster are located close to each other). Item abbreviations (e.g. Alt1) correspond to the ordering of the items as presented in Appendix A (e.g. 
first item measuring altruistic values). 

Source: own elaborations 

19 

 
 
 
 
4  Needs for additional reliability and affordability of energy supply 

As previously mentioned, energy security is not a monolithic concept. From an epistemological point of view, 
there is a major difference if one just refers to the analysis of physical and technological dimension, checking 
for  the  vulnerabilities  of  energy  systems  to  assess  risks  of  interruptions  or  shortages,  or  if  we  accept  to 
consider subjective components of society around the meaning of "security" when speaking of energy (Cherp, 
Defining energy security takes more than asking around, 2012). Making reference to two specific dimensions 
of  energy  security,  reliability  and  affordability,  consumers  have  been  asked  to  choose  to  which  level  of 
importance they agreed regarding the following statement: 

"How  important  do  you  think  it  is  to  increase  the  reliability  and  the  affordability  of  energy  supply 
implementing the EU Energy Security Strategy?" 

Figure 17. Shares of respondents by level of importance assigned to improved reliability and affordability 

Source: own elaborations 

Estonian and Portuguese respondents agreed with the statement that the need to increase the two issues are 
"Very Important" in approximately 60 % of responses. Netherlands on the contrary limit to 35 % the share of 
respondents with specific concerns for additional reliability and affordability.  

Across  countries,  most  respondents  rated  energy  security  as  highly  important  for  themselves,  their  family, 
their country  of  residence,  the  European  Union,  and future  generations  (see  Figures  18  and  19).  In  general, 
respondents  from  NL  attached  slightly  less  importance  to  energy  security  compared  to  the  other  countries, 
perhaps  due  to  fact  that  the  country  has  traditionally  been  an  energy  exporter  (given  the  indigenous 
production  of  natural  gas)  with  reliable  infrastructure  (further  detail  on  measured  continuity  of  supply  is 
discussed in the next paragraph).  

In  explaining  for  whom  enhancing  reliability  and  affordability  is  expected  to  count,  respondent  tend  to 
prioritize  the  protection  of  their  inner  circles  (them,  the  family),  Future  generations  are  emphasized  are 
meaningful receivers of the benefits of additional security (see Figures 18 and 19). 

20 

 
 
 
 
Figure 18. Respondents agreeing with the statement "reliability and affordability of energy supply are very important 
for…" 

Source: own elaborations 

Figure 19. Respondents agreeing with the statement "reliability and affordability of energy supply are not important for…" 

Source: own elaborations 

21 

 
 
 
5  Experienced planned and unplanned interruptions 

The  questionnaire  included  an  introductory  statement  to  clarify  the  distinction  between  planned  power cuts 
(e.g.  due  to  planned  maintenance  and  known  in  advance)  and  unplanned  (e.g.  due  to  unforeseen 
malfunctioning and/or shortage of supplies). 

54 % of respondents in EE recall the last planned power cut occurring in the last 12 months. The same figure 
amounts  respectively  14  %  for  NL  and  11  %  for  the  PT  (Figure  20).  Regarding  the  frequency  of  planned 
power  cuts,  55  %  of  the  NL  sample  recalled  just  one, while  the  majority  of  the  EE  and  PT  recalled  several 
more (see Figure 21). In all the three countries, the duration of the longest planned interruption is perceived to 
have lasted less than four hours, despite a non-negligible share of the respondents (15 % in NL and PT, 18 % 
in EE) declared to have suffered a planned blackout during more than 4 hours (see Figure 22).  

Figure 20. Experienced of planned blackouts 

Source: own elaborations 

Figure 21. Frequency of planned blackouts stated by respondents 

Source: own elaborations 

22 

 
 
 
 
 
Figure 22. Duration of the longest planned power cut during the last 12 months 

Source: own elaborations 

Figure 23. Last unplanned power cut  

Source: own elaborations 

Figure 24. Thinking about the last 12 months how many times have you experienced an unplanned power cut? 

Source: own elaborations 

The  number  of  unplanned  events  experienced  by  respondents  also  varied  across  countries.  For  most 
respondents in EE and PT, the last recalled unplanned interruption took place in the last 12 months (44 % and 
39 % respectively) or in the last 1 to 5 years (21 % for both). For respondents in  NL, this was considerably 
longer ago (17 % in the last year, 24 % in the last 1 to 5 years). Similarly, most respondents in EE last had a 

23 

 
 
 
 
planned  power  cut  during  the  past  year  (54  %),  whereas  the  respondents  in  NL  and  EE  did  not  recall  the 
occurrence of the last planned power cut (52 % and 61 %, respectively).  

When looking at the number of times respondents experienced a power cut in the last 12 months, one could 
also observe that power cuts are more frequent in EE and PT than in NL. When respondents in NL experienced 
a power cut, this was almost always only one time in the last 12 months, whereas in  EE and PT power cuts 
were,  in  general, experienced  more  frequently.  In  all  countries,  power  outages  mostly  lasted  shorter than  4 
hours. For unplanned power cuts, Figure 24 reports the frequencies, and Figure 25 report the duration of the 
events stated by respondents.  

The  Council  of  European  Energy  Regulators  (CEER)  provides  some  benchmarks  of  the  performances  of 
country-wide energy systems in terms of continuity of electricity supply and some measured indexes can be 
broadly  compared  to  the  perceptions  reported  by  the  participants  to  the  survey.  In  particular,  the  System 
Average Interruption Duration Index (SAIDI) and the System Average Interruption Frequency Index (SAIFI) are 
calculated to represent the presence of blackouts on the system. Continuity at a customer level is defined by 
the  Customer  Average  Interruption  Duration  Index  (CAIDI)  and  the  Customer  Average  Interruption  Duration 
Index (CAIFI). 

Compare observed interruptions of supply with the survey data, we  find the share of respondents which are 
likely  to  have  provided  an  accurate  description  of  the  actual  duration  of  the  experienced  power  cut.  Some 
caution  should  be  placed  on  the  interpretation  of  this  comparison,  as  respondents  may  have  indeed  faced 
localised  blackouts  lacks,  while  SAIFI  and  SAIDI  refer  to  performance  of  the  whole  system.  Furthermore, 
respondents may not have been aware of planned interruptions. 

According to SAIDI, the planned interruption customers suffered in EE was 73.75 minutes, thus, at least 19 % 
of Estonian sample may  have  substantially overstated the duration of the planned interruption. This  is also 
valid for NL, where the planned SAIDI in 2016 was 6.28 minutes (while 15 % stated more than 4 hours) and 
in PT, where planned SAIDI was 1.91 minutes and 15.1 % of respondents declared more than 4 hours. 

Unplanned  SAIDI  is  also  used  to  check  potential  biased  perceptions.  Comparing  CEER  data  with  shares  in 
Figure 25 we see that for the EE sample a share of 21 % of respondents declaring a duration from 3 to 10 
hours, while the  sum of all the unplanned including exceptional events captured by  unplanned SAIDI is 148 
minutes. For NL unplanned SAIDI is 21 minutes and 68 % of respondents declaring a duration of more than 
one hour for the longest event. For Portugal the total unplanned SAIDI is 75.74 minutes  with a 13 % stating 
three or more hours for the longest event. 

Figure 25. Duration of the longest unplanned blackout 

Source: own elaborations 

24 

 
 
6  Measures to improve energy security 

6.1  Willingness to reduce energy use 

Respondents rated on a 7-point scale (from 1 completely disagree to 7 completely agree) whether they were 
willing to reduce their energy consumption. In general, respondents in Estonia (M = 5.05, SD = 1.69) and in the 
Netherlands (M = 5.13, SD = 1.62) agreed with the statement to reduce their energy consumption, whereas 
respondents in Portugal strongly agreed with this statement (M = 6.14, SD = 1.26).  

Regression  analyses  revealed  that  standard  demographic  variables  did  not  explain  the  variance  in  energy 
savings in the three countries.  However,  adding personal values increased the explained variance  by  4 % in 
EE, 8 % in NL, and 4 % in PT. Biospheric values positively related to willingness to reduce energy use in  EE 
and NL. In addition, in NL, altruistic values are positively related to the willingness to reduce energy use, while 
egoistic values are negatively related. 

6.2 

Investments in energy technologies 

6.2.1  Willingness to invest 

For  different  technologies  respondents  were  asked  to  indicate  whether  they  owned  the  technology  or  were 
planning to buy the technology for household use. Except for smart meters, the adoption of all technologies is 
quite low across countries, less than 15 % (Figure 26), and the intention to buy such technologies in future 
was also quite low in EE and NL, but a bit higher in PT. Still for almost every technology, the majority of the 
respondents indicated to not own the technology and also not having the intention to purchase it in the near 
future.  

6.2.2  Acceptability of measures to improve energy security 

Acceptability of four different methods to improve energy security were measured, i.e. centralized fossil fuels, 
decentralized  renewables,  manual  energy  matching,  and  automated  energy  matching.  For  each  method, 
respondents  had  to  answer  two  items  on  7-point  scales  (from  1  very  unacceptable  and  very  negative  to  7 
very acceptable and very positive), for which the means are: 

 
centralized fossil fuels, M = 3.62, SD = 1.71, r = .81;  
 
decentralized renewables, M = 5.68, SD = 1.30, r = .74;  
  manual energy matching, M = 5.39, SD = 1.27, r = .70;  
 

automated energy matching, M = 4.77, SD = 1.60, r = .80.  

When looking at specific solutions to increase energy security, more sustainable options (i.e. energy matching, 
decentralized  energy  production  from  renewables)  were  seen  as  more  acceptable  and  more  positive  than 
unsustainable  options  (i.e.  centralized  energy  production  from  nuclear,  coal,  and  gas).  In  addition, 
manual/decentralized options were preferred over automatization, indicating that people prefer some kind of 
control over the solutions.  

25 

 
 
 
Figure 26. Attitudes of respondents toward the adoptions of energy technology options 

Photovoltaic panels 

Solar thermal 

Woodchip heater 

Micro wind 

Electric vehicles 

Automated load control 

Smart meters

Source: own elaborations 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6.2.3  Acceptability of measures to improve energy security 

Regression  analyses  with  only  demographic  variables as  predictors  explained  between  1  %  and 5  %  of  the 
variance in acceptability of single ways to improve energy security. However, the addition of personal values 
improved all modes (see R2s in Table 10).  

Across  all  countries,  biospheric  values  were  positively  related  to  acceptability  of  decentralized  renewables, 
manual  energy  matching  and  automated  energy  matching,  and  negatively  related  to  acceptability  of 
centralized  fossil  energy.  Accordingly,  stronger  biospheric  values  are  indicative  for  more  support  for 
sustainable  ways  to  improve  energy  security,  and  less  support  for  unsustainable  ways  to  improve  energy 
security.  In  addition,  in  the  Netherlands  and  Portugal,  altruistic  values  positively  related  to  acceptability  of 
decentralized renewable energy and manual energy matching.  

Egoistic values positively related to acceptability of centralized fossil energy in all countries, positively related 
to acceptability of automated energy matching in the Netherlands, and negatively related to acceptability of 
decentralized  renewable  energy  in  all  countries.  Although  some  demographic  variables were  also  related  to 
acceptability  of  certain  ways  to  improve  energy  security,  there  was  no  clear  or  consistent  pattern  across 
countries or variables (Table 5). 

Table 3. Personal values and acceptance of technological options of the power system.  

Centralized fossil energy 

Decentralized renewable energy 

EE 

NL 

PT 

EE 

NL 

PT 

Income 

Age 

Gender 

-0.05 

 0.01 

-0.17*** 

Education 

-0.08** 

Hh size 

Hedonic  

Egoisitc  

-0.06 

-0.03 

 0.07** 

-0.01 

 0.06* 

 0.09** 

 0.03 

-0.06 

 0.01 

0.07** 

-0.02 

-0.03 

-0.12*** 

-0.19*** 

 0.07* 

0.10*** 

 0.14*** 

-0.07* 

-0.11*** 

-0.11*** 

 0.07** 

 0.00 

 0.09** 

 0.03 

 0.02 

 0.05 

-0.02 

-0.02 

 0.02 

-0.03 

 0.05 

 0.09** 

 0.16*** 

 0.22*** 

 0.17*** 

-0.07** 

-0.15*** 

-0.09*** 

Altruistic  

 0.05 

 0.02 

 0.04 

 0.08 

 0.18*** 

 0.14*** 

Biospheric  

-0.13*** 

-0.23*** 

-0.17*** 

 0.20*** 

 0.23*** 

 0.15*** 

R2 

 0.07*** 

 0.10*** 

 0.09*** 

 0.11*** 

 0.17*** 

 0.13*** 

Note. Boldfaced coefficients are discussed in text, they represent coefficients with p-value below .001 ro that approached .001. 
*** p < .01, ** p < .05, * p < .10 

Manual energy matching 

Automated energy matching 

EE 

NL 

PT 

EE 

NL 

PT 

Income 

Age 

Gender 

Education 

Hh size 

Hedonic  

Egoisitc  

Altruistic  

 0.05 

 0.00 

 0.01 

-0.01 

-0.04 

 0.05 

-0.05 

 0.05 

 0.09*** 

 0.01 

 0.12*** 

 0.11*** 

 0.12*** 

 0.07* 

-0.05 

 0.04 

 0.00 

 0.03 

 0.00 

 0.02 

 0.00 

 0.04 

 0.03 

-0.08*** 

-0.06* 

 0.14*** 

 0.17*** 

-0.14*** 

-0.04 

-0.04 

-0.04 

 0.01 

 0.05 

 0.04 

-0.04 

 0.04 

 0.04 

-0.09** 

 0.13*** 

 0.06 

 0.02 

 0.06 

-0.07** 

 0.01 

 0.06* 

 0.05 

 0.01 

 0.02 

Biospheric  

 0.27*** 

 0.24*** 

 0.20*** 

 0.14*** 

 0.15*** 

 0.14*** 

R2 

 0.11*** 

 0.15*** 

 0.14*** 

 0.05*** 

 0.07*** 

 0.05*** 

Note. Boldfaced coefficients are discussed in text, they represent coefficients with p-value below .001 ro that approached .001. 
*** p < .01, ** p < .05, * p < .10 

Source: own elaborations 

27 

 
 
 
 
 
 
7  Long term EU strategy for energy security: a monetary assessment 

7.1  Contingent Valuation: model and econometric analyses 

7.1.1  The contingent scenario 

Contingent Valuation (CV) is a technique belonging to the family of stated preference methods. CV makes use 
of  statements  and  a  baseline  reference,  usually  provided  through  interviews  by  a  statistical  sample  of 
respondents, and is used to define a monetary value typically for non-market goods and services, or for those 
that  are  not  yet  on  a  market.  CV  section  is  used  in  this  project  to  elicit  society’s  WTP  for  the  long-term 
security of energy supply5.  

Respondents facing the CV section are first informed that the EU currently imports more than half of all the 
energy  it  consumes,  with  specific  data  on  imports  of  crude  oil  (more  than  90  %)  and  natural  gas  (66  %). 
Respondents are made aware that the total import bill is more than €1 billion per day. Given this information, 
respondents are asked how important it is for them, their families, the country they reside in, the EU, and for 
future  generations  to  have  reliable  and  affordable  energy  supply.  This  question  has  a  double  objective:  To 
elicit  whether  respondents’  WTP  for  the  long-term  security  of  energy  supply  is  driven  by  individualistic  or 
altruistic motivations, and to maintain the attention of respondents  during the description of the contingent 
scenario.  

Respondents are then told of a "EU Energy Security Strategy" describes a roadmap to 2030 that EU member 
states  would  need  to  follow  to  increase  their energy  security, that  is,  to  have  more  reliable  and affordable 
energy and be less dependent on imports of energy. In particular, the strategy aims to reduce imports of oil 
by 3 %, gas by 14 % and coal by 12 % compared to the business as usual scenario by 2030. Respondents are 
also  informed  that  these  goals  will  be  achieved  by  saving  energy,  producing  more  local  renewable  energy, 
making it easier to transport gas and electricity around Europe, finding different ways and routes to import 
energy,  building  good  relationships  with  suppliers  and  distributers,  and  having  common  goals  when 
negotiating  with  other  countries.  To  elicit  respondents’  value  of  the  long-term  energy  security,  we  inform 
them that the implementation  of the strategy will require an increase in energy prices now for EU  member 
states to be able to undertake all the investments needed for a more reliable and affordable energy in the 
future. If the strategy is not implemented, sudden prolonged energy disruptions could occur in the future, as 
well as very large fluctuations in electricity prices. We then ask respondents how important they think it is to 
increase the reliability and affordability of energy supply implementing the EU Energy Security Strategy. 

Next,  following  the  'double  bounded'  CV  approach  (  Alberini, Rosato, Longo, &  Zanatta, 2005), responds are 
presented with two closed-ended CV questions where they are asked whether they would be willing to support 
the implementation of the EU Energy Security Strategy given an annual increase in their electricity bill for the 
next  five  years.  The  initial  bid  values  vary  across  respondents,  with  one  quarter  of  respondents  being 
allocated to each of the following initial bid values: €10, €20, €50, and €100. If a respondent accepts to pay 
the initial electricity bill increase, he/she is asked whether he/she would pay a higher amount (€20, €50, €100, 
and €200); if not, the follow-up bid is lower than the initial bid (€5, €10, €20, and €50). Table 6 describes the 
bid values design. 

(5)This differs from report I where instead of a CV approach, we presented a discrete choice experiment eliciting 

respondents’ WTP and WTA for variations in the frequency and duration of power cuts. 

28 

 
 
 
                                           
Table 4. The bid design of the contingent valuation 

Initial value (€) 

If 'yes' at the initial value (€) 

If 'no' or 'don’t know' at the initial 
value (€) 

10 

20 

50 

100 

Source: own elaborations 

20 

50 

100 

200 

5 

10 

20 

50 

We then enquire respondents to choose between the following options their motivation for supporting, or not, 
the energy security strategy:  

the reliability and the affordability of energy are important;  
I cannot afford to pay more for my electricity bill;  
I am not interested in the reliability and the affordability of energy supply;  
I don’t believe that the Energy Security Strategy can be implemented;  
I don’t believe the hypothetical scenario of an increase in electricity bill;  

 
 
 
 
 
  Other, such as the government and industry, should pay for the Energy Security Strategy.  

The  answers  to  this  question  allows  us  to  identify  'protest'  respondents,  that  is,  respondents  that  do  not 
engage with the CV scenarios, either because they do not believe the hypothetical scenario, or because they 
deem that other parties should pay for the Strategy. When we analyse the data, we remove protesters from 
the analysis, as is common practice (Longo, Hoyos, & Markandya, 2012). 

7.1.2  Statistical Models of the Contingent Valuation Responses  

To obtain estimates of mean and median WTP for the proposed policy, we assume that WTP is distributed as 
a Weibull with scale  and shape parameter .6 Respondents’ answers to the initial and follow-up payment 
questions can be combined to form intervals around the respondent’s WTP, and to estimate  and  using the 
method of maximum likelihood.  
Given our assumptions, the log likelihood function of the sample is: 

where  WTPL  and  WTPU  denote  the  lower  and  upper  bounds  of  the  interval  around  the  respondent’s  WTP 
amount,  and  i  denotes  the  individual  respondent7.  Mean  WTP  is  equal  to 
,  whereas  median 

WTP is 

. 

7.1.3 

Internal validity of the WTP responses 

After WTP responses have been collected through the survey, it is important to test for internal validity, that 
is, to estimate models of WTP that relate the respondents’ WTP amounts to the individual characteristics of 
the respondents and to specific characteristics of the survey.  

Firstly,  we  should  expect  that  the  percentage  of  respondents’  WTP  the  initial  amount  decreases  for  larger 
amounts (Haab & Mc Connell, 2002). Secondly, using the econometric model described above, we explore how 
respondents’ heterogeneity affects WTP using the underlying regression equation: 

6 We work with the Weibull distribution because Weibull variates are defined on the positive semi-axis and have 

a flexible shape parameter.  

7  The  estimates  based  on  the  specific  likelihood  function  are  often  referred  to  as  'double-bounded'  in  the  CV 
literature (Hanemann, Loomis, & Kanninen, 1991), under the implicit assumption that respondents refer to 
the same underlying WTP amount when answering both payment questions.  

29 

niUiLiWTPWTPL1)/(exp()/(exp(loglog)1/1(/1)]5.0ln([iiiWTPz* 
 
 
  
 
 
  
                                           
where  WTP*  represents  the  WTP  amount,8  and  z  is  an  m1  vector  of  individual  characteristics  of  the 
respondents,  is a  vector of unknown coefficients, and   is the error term. Specifically, we explore whether 
WTP  varies  with  respondents’  socio-economic  characteristics  and  attitudes  towards  the  environment,  long 
term security of energy supply and power outages.  

The  analysis of  the  CV  data  on  society’s WTP  for the  long  term  energy  security  strategy  is reported  in  this 
section using the data from the clean sample, that is, after removing the answers from respondents who did 
not  engage  with  the  hypothetical  scenario.  Firstly, we  report  in  Table  7  the  percentage  of  respondents  WTP 
the  initial  bid  offered  them,  which  decreases  with  the  increase  in  the  bid  level,  as  theory  predicts.  For 
example, in PT, of the 198 people that were offered the initial bid of €10, 53 % of them were WTP. As the bid 
increases, the percentage of respondents WTP decreases. At the bid level of €100, only 24 % of respondents 
were  WTP.  The  results  are  quite  similar  across  the  three  countries.  The  initial  result  shows  that  our 
questionnaire is robust and provides policy relevant information.  

Table 5. Percentage of people WTP the initial bids 

Initial bid 

€10 

€20 

€50 

€100 

Total respondents 

Number of respondents 
presented the bid 

198 

189 

187 

183 

757 

Percentage of "yes" 

53.03 

43.39 

28.34 

23.5 

Number of respondents 
presented the bid 

202 

210 

208 

200 

820 

Percentage of "yes" 

55.94 

40.48 

27.88 

23.5 

Number of respondents 
presented the bid 

207 

193 

204 

200 

804 

Percentage of "yes" 

49.28 

46.11 

31.37 

26.5 

Portugal 

Estonia 

The Netherlands 

Source: own elaborations 

Next, we report the results of the econometric models using the interval data model. Table 8 shows the mean 
WTP for each country and for all the countries pooled together. Mean WTP is to €39 for  EE, €42 for PT and 
€51 for NL. These results show that the NL has the highest WTP, as one would expect given the higher GDP of 
the country compared to the other two countries.  

To test for the internal validity of the data, we augment the model by adding socio-economic variables and 
variables that control for the location of where the respondents live. Table 9 shows that older respondents are 
WTP more for the implementation of the energy security strategy, as the coefficient for AGE is positive and 
statistically significant at the 5 % level for PT and at the 10 % level for EE, but not statistically significant for 
NL. 

8 WTP is unobserved if we assume that a respondent’s WTP lies between the amount stated by the respondent 

and the next higher amount.  

30 

 
 
 
 
 
 
 
                                           
Table 6. Mean WTP 

M WTP 

SE 

n 

Source: own elaborations 

All countries 

Estonia 

the Netherlands 

Portugal 

43.77 

39.04 

50.97 

42.14 

1.63 

2381 

2.22 

820 

3.68 

804 

2.76 

757 

Female  respondents  have  a  lower  WTP  in  all  three  countries.  The  size  of  the  household  (HSIZE)  is  only 
statistically significant for EE, where larger households have a lower WTP.  

To capture the effect of income, we included two variables in the model, the logarithm of income (LICNOME) 
and INCMISS, which is a dummy variable equal to one for respondents who did not report their income. This 
second variable is not statistically significant for EE and NL (while we had no respondents who did not report 
their income in PT), indicating that the WTP of people who reported their income is not different to the WTP of 
respondents  who  did  not  report  this  information.  LINCOME  is  positive  and  significant  in  all  our  samples, 
indicating a strong effect of income on WTP: the richer our respondents, the more they are willing to pay. This 
particular result emphasizes the internal validity of our data. The following four variables - BIGCITY, VILLAGE, 
COUNTRYSIDE, TOWN - capture any geographical difference in WTP across respondents (the reference dummy 
being SUBURBS). We find that WTP is not affected by where respondents live, in any of the countries.  

Table 7. Double bounded estimates for the Weibull distribution: effect of socio economic and location variables 

Estonia 

Portugal 

The Netherlands 

Coefficient 

Standard 
Error 

Coefficient 

Standard 
Error 

Coefficient 

Standard 
Error 

-1.1205 

0.0069 

-0.2655 

-0.095 

0.4862 

0.707 

0.0039 

0.1087 

0.0458 

0.0659 

1.1257 

53.6275 

0.1709 

0.1793 

0.1999 

0.1651 

0.0521 

0.0283 

0.0036 

-0.1667 

-0.0413 

0.0128 

1.3565 

0.7372 

-1051.04 

2124.08 

820 

-1.1123 

0.0084 

-0.3073 

0.0021 

0.4265 

0.0000 

0.1199 

0.0764 

-0.2696 

-0.0058 

1.466 

0.6821 

-966.95 

1953.89 

757 

0.7676 

0.0043 

0.1209 

0.0548 

0.0776 

-2.7839 

0.0049 

-0.2674 

-0.0507 

0.6079 

0.9276 

0.0045 

0.1262 

0.0611 

0.0839 

. 

1.3396 

24.2747 

0.1573 

0.2439 

0.4566 

0.1585 

0.0591 

0.0275 

0.1995 

0.2014 

0.4139 

0.1797 

0.0637 

0.0247 

0.114 

-0.1616 

0.4745 

-0.0857 

1.6043 

0.6233 

-1013.52 

2049.04 

804 

intercept 

AGE 

FEMALE 

HSIZE 

LINCOME 

INCMISS 

BIGCITY 

VILLAGE 

COUNTRYSIDE 

TOWN 

SCALE 

Weibull shape 

Loglikelihood 

AIC 

observations 

Source: own elaborations 

We then add a set of variables to capture the importance of energy security on respondents’ WTP. We use the 
answers to the question 'Thinking about energy security for your country of residence in the next five years, 
how important is it for you …?' (Table 4). These variables range between 1 and 5, with one indicating that the 
dimension  of  energy  security  captured  by  that  specific  definition  is  extremely  not  important  and  5  if  it  is 
extremely  important.  As  we  found  a  large  correlation  between  the  responses  given  to  the  different 
dimensions of energy security, we only use five definitions of energy security in the model. Therefore, QA2_1 
captures the effect of having a secure supply of oil, gas, coal and uranium. This variable is not statistically 
significant in any of our models. Also the next variable, QA2_5, which explains the effect of having affordably 
priced energy services, is not statistically significant at the 5 % level in any of our models. The provision of 
clean water, expressed by Q2A_5, is positive and significant in the Netherlands, and in Estonia, indicating that 

31 

 
 
 
 
it  is  an  important  determinant  of  WTP.  The  remaining  two  variables  QA2_15  and  QA2_16  capture  the 
importance of minimizing the impact of climate change and reducing greenhouse gas emissions respectively. 
Only the latter is positive and statistically significant for EE and PT. 

Table 8. Double bounded estimates for the Weibull distribution: effect of importance of energy security on WTP 

Estonia 

Portugal 

The Netherlands 

Coefficient 

Standard 
Error 

Coefficient 

Standard 
Error 

Coefficient 

Standard 
Error 

intercept 

-2.5197*** 

  0.9261 

-0.6183 

 0.9253 

-3.875*** 

  1.0104 

AGE 

 0.0042 

  0.004 

 0.0084** 

 0.0043 

-0.0015 

  0.0048 

FEMALE 

-0.3167*** 

  0.1099 

-0.3132*** 

 0.1218 

-0.2844** 

  0.1267 

HSIZE 

-0.094** 

  0.0455 

-0.0019 

 0.0546 

-0.0496 

  0.0607 

LINCOME 

 0.4579*** 

  0.0657 

 0.4275*** 

 0.0778 

 0.5894*** 

  0.0832 

INCMISS 

 1.177 

57.3961 

 0.0000 

 . 

 1.1621 

BIGCITY 

-0.0475 

  0.1719 

 0.0743 

 0.1582 

 0.1559 

VILLAGE 

-0.1803 

  0.178 

 0.0342 

 0.2451 

-0.1629 

COUNTRYSIDE 

-0.0693 

  0.1973 

-0.2684 

 0.4562 

 0.43 

TOWN 

QA2_1 

QA2_5 

-0.0315 

 0.0411 

  0.1638 

-0.0113 

 0.1582 

-0.1055 

  0.0558 

 0.046 

 0.0596 

-0.0035 

-0.2086** 

  0.1068 

-0.0907 

 0.1174 

-0.1428 

24.9865 

  0.2002 

  0.2009 

  0.4128 

  0.1792 

  0.0627 

  0.0959 

QA2_13 

 0.3586** 

  0.1511 

-0.2255 

 0.1839 

 0.2591** 

  0.1227 

QA2_15 

-0.0474 

  0.0996 

-0.1575 

 0.1527 

 0.0639 

QA2_16 

 0.2632*** 

  0.0994 

 0.3385** 

 0.1742 

 0.1789 

  0.1416 

  0.1397 

Scale 

 1.3346*** 

  0.0513 

 1.4582*** 

 0.0589 

 1.5891*** 

  0.0629 

Weibull shape 

 0.7493*** 

  0.0288 

 0.6858*** 

 0.0277 

 0.6293*** 

  0.0249 

Loglikelihood 

AIC 

observations 

Source: own elaborations 

-1039 

2111.46 

820 

-964 

1958.61 

757 

-1003 

2039.53 

804 

To further investigate whether WTP is explained by respondents’ experience with and attitudes towards power 
outages and by the  regret they may experience if they  will be restricted in the  use of  energy in the future, 
should  the  "EU  Energy  Security  Strategy"  not  be  implemented,  we  added  four  additional  variables  to  the 
model: 

  UNPLDURM3, which captures the impact on WTP of respondents who suffered unplanned power outages 
lasting more than three hours, is positive and significant only for EE. Respondents in NL are WTP less for 
the implementation of the strategy the more they agree with the statement 'I want to reduce my energy 
consumption',  as  shown  by  the  negative  and  statistically  significant  coefficient  for  Q56_56.  This  also 
means that respondents in NL who do not want to reduce their consumption of energy are willing-to-pay 
more for the long-term security of energy supply.  
The  next  two  variables  Q56_57  and  Q56_58  capture  the  regret  that  respondents  would  suffer  if  the 
security  strategy  were  not  to  be  implemented.  In  particular,  Q56_57  shows  that  the  WTP  for 
guaranteeing the long-term energy security increases with respondents agreeing with the statement  'If I 
don’t support the implementation of the EU Energy Security Strategy, and then I will be restricted with the 
use of energy, I will later wish that I had' for all countries.  

 

  Q56_57 captures the effect of more personal regret, Q56_58 shows the effect of a more altruistic form 
of  regret:  'If  I  don’t  support  the  implementation  of  the  security  strategy,  and  then  my  family  will  be 
restricted  with  the  use  of  energy, I  will  later feel  bad for  my  family'.  The  coefficients  estimate  for  this 
latter  variable  are  strongly  positive  and  significant  for  PT  and  NL,  showing  that  in  these  countries 

32 

 
 
 
respondents  are  particularly  concerned  about  the  effect  of  long  term  energy  security  for  their  families 
and not only for themselves.  

Table 9. Double bounded estimates for the Weibull distribution: effect of experience with unplanned power outages and 
regret if the EU Energy Strategy is not implemented on WTP 

Estonia 

Portugal 

The Netherlands 

Coefficient 

Standard 
Error 

Coefficient 

Standard 
Error 

Coefficient 

Standard 
Error 

intercept 

-2.6888*** 

  0.9051 

-0.7123 

AGE 

 0.0037 

  0.0039 

 0.0077* 

FEMALE 

-0.2909*** 

  0.1093 

-0.2662** 

HSIZE 

-0.0633 

  0.0457 

-0.022 

0.8999 

0.0041 

0.1152 

0.053 

-3.6952*** 

  0.9719 

 0.0013 

  0.0047 

-0.2272* 

  0.122 

-0.0552 

  0.0576 

LINCOME 

 0.4359*** 

  0.0645 

 0.3182*** 

0.0748 

 0.5394*** 

  0.0809 

INCMISS 

 0.9393 

63.3097 

 0 

BIGCITY 

-0.1003 

  0.1708 

 0.1235 

VILLAGE 

-0.2034 

  0.1776 

-0.0487 

COUNTRYSIDE 

-0.2067 

  0.2006 

-0.2425 

TOWN 

QA2_1 

QA2_5 

-0.095 

-0.0017 

  0.162 

-0.0355 

  0.0558 

-0.0265 

-0.1758* 

  0.104 

 0.019 

QA2_13 

 0.382*** 

  0.1467 

-0.2544 

QA2_15 

-0.0588 

  0.098 

-0.1068 

QA2_16 

 0.2018** 

  0.0995 

 0.2155 

UNPLDURM3 

 0.3163* 

  0.1803 

 0.0759 

Q56_56 

-0.0478 

  0.0347 

-0.0273 

 . 

0.1477 

0.2366 

0.4308 

0.1484 

0.0579 

0.1116 

0.1746 

0.1465 

0.163 

0.2369 

0.0472 

 0.925 

 0.123 

-0.1208 

 0.4729 

-0.1107 

-0.0238 

29.4954 

  0.192 

  0.1926 

  0.3941 

  0.1717 

  0.0598 

-0.2404*** 

  0.091 

 0.257** 

 0.0276 

 0.1075 

-0.044 

  0.1151 

  0.1372 

  0.1349 

  0.2977 

-0.0697* 

  0.0383 

Q56_57 

 0.1894*** 

  0.0382 

 0.1907*** 

0.0707 

 0.0995* 

  0.0536 

Q56_58 

 0.0000 

  0.037 

 0.1618** 

0.0742 

 0.2323*** 

  0.0539 

Scale 

 1.3058*** 

  0.0494 

 1.3441*** 

0.0536 

 1.508*** 

  0.0602 

Weibull shape 

 0.7658*** 

  0.029 

 0.744*** 

0.0297 

 0.6631*** 

  0.0265 

Loglikelihood 

AIC 

observations 

Source: own elaborations 

-1017 

2074.395 

820 

-910 

1858.046 

757 

-976 

1993.448 

804 

The next model explores the effect of personal values on people’s WTP. We add four variables, Hed, Ego, Alt 
and  Bio  to  capture  the  effect  of  endorsing  hedonic  values  (i.e.  concern  for  pleasure,  comfort  and  reducing 
effort),  egoistic  values  (i.e.  concern  for  personal  resources  and  power),  altruistic  values  (i.e.  concern  for  the 
welfare  and  fair  treatment  of  others)  and  biospheric  values  (i.e.  concern  for  nature  and  the  environment) 
respectively.  These  variables  range  between  1  and  7,  with  1  indicating  that  a  person  weakly  endorses  that 
particular value and 7 strongly endorses that value.  

The results show that for EE and PT these additional variables do not help explaining WTP. However, for the 
Netherlands,  Hed  and  Ego  are  negative  and  significant,  and  Alt  is  positive  and  significant,  indicating  that  a 
high score for hedonic and egoistic values decrease WTP, while a high score for altruistic values increase WTP. 

33 

 
 
 
 
 
Table 10. Double bounded estimates for the Weibull distribution: Effect of personal values on WTP 

Estonia 

The Netherlands 

Portugal 

Coefficient 

Standard 
Error 

Coefficient 

Standard 
Error 

Coefficient 

Standard Error 

intercept 

-2.3305*** 

  0.9214 

-0.4887 

0.9308 

-3.4296*** 

  1.0151 

AGE 

FEMALE 

HSIZE 

LINCOME 

INCMISS 

BIGCITY 

VILLAGE 

COUNTRYSIDE 

TOWN 

QA2_1 

QA2_5 

QA2_13 

QA2_15 

QA2_16 

UNPLDURM3 

Q56_56 

Q56_57 

Q56_58 

HED 

EGO 

ALT 

BIO 

Scale 

 0.0029 

  0.0041 

 0.0079* 

0.0042 

-0.0022 

  0.0048 

-0.297*** 

  0.1103 

-0.2674** 

0.1175 

-0.3223*** 

  0.1245 

-0.0684*** 

  0.0456 

-0.0157 

0.0533 

-0.0448 

  0.0572 

 0.4475 

 1.0243 

-0.0857 

-0.1987 

-0.1965 

-0.0832 

 0.0158 

-0.1691 

  0.0647 

 0.3195*** 

0.0754 

 0.54*** 

  0.08 

64.2416 

 0 

. 

 1.2374 

30.7846 

  0.1716 

 0.1393 

0.1486 

 0.1161 

  0.1905 

  0.1779 

-0.0316 

0.2379 

-0.1466 

  0.1918 

  0.2011 

-0.2534 

0.4309 

 0.3923 

  0.3916 

  0.1623 

-0.0232 

0.1493 

-0.1455 

  0.1704 

  0.0574 

-0.0183 

0.0584 

-0.0117 

  0.0596 

  0.1048 

 0.0215 

0.1116 

-0.2215** 

  0.0908 

 0.3676** 

  0.1466 

-0.2592 

0.1751 

 0.283** 

  0.1155 

-0.0508 

  0.0979 

-0.1072 

0.1463 

 0.0029 

  0.1367 

 0.2367** 

  0.1014 

 0.2385 

0.1668 

 0.001 

  0.1376 

 0.3052 

-0.0446 

  0.1805 

 0.0942 

0.2385 

-0.0141 

  0.2952 

  0.0345 

-0.0253 

0.0476 

-0.0891** 

  0.0388 

 0.1921*** 

  0.0385 

 0.1986*** 

0.0715 

 0.0835 

  0.0532 

 0.0016 

-0.0232 

-0.0744 

 0.0001 

-0.0503 

  0.0372 

 0.1597** 

0.0747 

 0.2519*** 

  0.0544 

  0.0605 

 0.0053 

0.0675 

-0.1223* 

  0.0716 

  0.0543 

-0.026 

0.0512 

-0.1147** 

  0.0558 

  0.0799 

-0.0793 

0.0935 

 0.177* 

  0.0931 

  0.0621 

 0.0187 

0.0775 

 0.0952 

  0.0708 

 1.3012*** 

  0.0492 

 1.3424*** 

0.0537 

 1.4895*** 

  0.0595 

Weibull shape 

 0.7685*** 

  0.0291 

 0.7449*** 

0.0298 

 0.6714*** 

  0.0268 

Loglikelihood 

AIC 

observations 

Source: own elaborations 

-1014 

2077.962 

820 

-909 

1864.738 

757 

-970 

1989.491 

804 

This latest model specification is also the preferred model for M: in terms of goodness-of-fit, as shown by the 
Akaike  Information  Criterion  (AIC),  but  not  for  the  other  two  countries  for  which  the  previous  model  is  the 
preferred one.  

34 

 
 
 
 
8  Conclusions 

The survey data showed differences among the three samples regarding the residence and the choices over 
energy technologies. For example, the main energy source for space heating is wood biomass in Estonia (50 
% of the sample), natural gas in the Netherlands (77 %), and electricity in Portugal (56 %). 

Regarding the expenditures for different types of fuels, the data  indicates the importance of natural gas for 
space heating, which is also reflected in the higher levels of expenditure for the fuel (especially in NL). On the 
contrary, the dominant use of electricity from PT respondents does not correspond to a higher expenditure. 

The  most  'rural'  energy  users  have  been  found  in  EE  and  the  least  in  PT.  The  highest  share  of  consumers 
resident in a highly urbanized area of a big city was in fact found in the sample for PT (with only 7 % living in 
rural areas of sparse houses), while in NL and EE the share of rural respondents was respectively 29 % and 
23 %. 

The fuel mix for residential energy users emerging from the survey data exhibit some differences with regard 
to  the  electrification  of  heating  and  cooling,  i.e.  very  low  in  EE  and  very  high  in  PT.  Counterintuitively, 
electricity plays a key role for cooking and warming water in EE, while natural gas appears crucial for NL for 
space heating, cooking and water heating.  

Consequently to the highest level of penetration of electricity, respondents from  NL stated the most energy 
services  that  could  not  be  supplied  in  case  of  a  blackout,  e.g.  indoor  lighting,  cleaning  floors,  cooking, 
showering, talking on the phone, using internet, warming rooms, washing clothes and washing dishes.  

Respondents  in  EE  declared  to  be  most  adverse  to  the  damages  of  food  in  fridges/refrigerators,  and  the 
inability  to  recharge  mobile  phones.  The  respondents  from  PT  were  also  concerned  on  being  trapped  in 
elevators, telecommunications interruptions, the loss of data from computers and damages to appliances. 

The  section  of  the  questionnaire  used  to  collect  data  on  the  values  of  respondents  (altruistic,  egoistic, 
biospheric and hedonic) was used to inform the statistical analysis of the preferences. We tested the role of 
values as predictors of: 

 

 

the willingness of respondents to invest in energy technologies  

the acceptability of strategies to improve security of energy supply  

The  survey  placed  emphasis  to  the  reliability  and  affordability  of  energy  supplies,  asking  respondents  to 
assess the importance of securing these two aspects for them their family, their country, other EU countries 
and the future generations. Our results suggest that the concerns with this respect appear more limited in the 
NL  sample.  A  special  concern  for  future  generations  has  been  stated  mainly  by  the  sample  from  PT. 
Respondents in EE highlighted the relevance for their direct circle, with a minor role of the country wide and 
EU benefits.  

The study also compared the perceptions of the residential customers regarding power cuts with actual data, 
showing that the planned interruptions  may have been  remarkably overstated in duration by 15  – 18  % of 
respondents.  For  the  unplanned  blackouts  the  gap  between  the  perception  and  measurements  is  sensibly 
higher in the case of the respondents in NL, largely overestimating the experienced insecurity of the last 12 
months. However, these discrepancies may also be due to localised blackouts. 

In  the  survey  the  respondents  have  been  presented  with  possible  strategies  to  improve  the  environmental 
sustainability  and  security  of  electricity  supply,  via  the  integration  of  renewables,  electric  mobility,  storage 
and other smart home technologies. This exercise aimed at describe the openness of energy users to future 
evolution  of  the  distribution  networks  and  the  level  of  aversion/acceptance  of  a  role  of  prosumer.  The 
landscape  emerging  from  the  analysis  suggest  that  the  openness  to  the  energy  technologies  is  country 
specific and relatively low. The acceptance of smart meters is even and high in the three countries, while the 
openness  to  electric  mobility  is  of  particular  interest  in  Portugal.  A  further  analysis  has  been  devised  to 
explore  the  relationship  between  the  willingness  to  invest  in  these  technological  options,  and  individual 
characteristics of respondents. Two different econometric approaches were applied to test the robustness of 
the results: an ordered logit and a negative binomial model. More traditional individual characteristic as Age, 
gender,  education  and  family  size  play  a  key  role  in  explaining  the  acceptance  toward  these  energy 
technologies.  

Personal  values  also  contribute  to  improve  the  explanatory  power  of  the  models.  The  openness  to  the 
technological  options  is  related  ina  special  way  to  the  interest  for  the  environmental  implications,  as  the 
Biospheric component of personal values plays a key role in terms of size and sign of the coefficient, and of 

35 

 
statistical  significance.  The  orientation  toward  an  environmental  awareness  is  not  the  only  component 
resulting  statistically  associated  to  the  openness  to  the  investments  in  these  technologies.  Ceteris  paribus, 
respondents  with  a  higher  interest  for  their  own  wellbeing  and  utility  (hedonic  and  egoistic)  are  also 
associated to a higher openness, while the altruistic component does not explain the stated/current choices of 
respondents.  

Testing  whether  the  same  drivers  also  explain  the  acceptability  and  orientation  to  support  strategies  for 
improving the security of energy supply on a long run horizon, it emerged the trade-off between egoistic and 
biospheric  values.  While  the  first  are  positively  associated  to  higher acceptance  of  centralized  fossil  energy 
production,  the  latter  are  clearly  associated  to  integration  of  decentralized  small  scale  renewables.  The 
willingness to reshape the consumption behaviour, for example voluntarily rescheduling the usage of energy 
demanding  appliances  to  limit  the  demand  and  improve  the  performances  of  the  grid  is  shown  to  be 
positively correlated (for the three countries) with biospheric and altruistic values. The same result holds for 
the automated load control options, under which the network operator may limit the capacity allocated to the 
user. 

Finally, the study offers an estimation of the perceived monetary benefits of a long-term strategy for energy 
security, in  the  form  of  an  application  of  contingent  valuation.  The  respondent  to  the  survey  have  provided 
their  own  evaluation  of  a  hypothetical  strategy  as  a  willingness  to  support  through  an  increase  in  their 
electricity bill for the implementation of such a strategy. 

The  perceived  benefit  of  a  long-term  strategy  for  energy  security  differs  by  country.  More  specifically,  the 
willingness-to-pay  through  increases  in  the  energy  bill  is  39.04  euros  in  Estonia,  50.97  euros  in  the 
Netherlands, and 42.14 euros in Portugal. 

36 

 
 
 
References 

Abrahamse, W., & Steg, L. (2009). How do socio-demographic and psychological factors relate to households’ 

direct and indirect energy use and savings? Retrieved from http://www.elsevier.com/copyright 

Abrahamse,  W.,  Steg,  L.,  Vlek,  C.,  &  Rothengatter,  T.  (2005).  A  review  of  intervention  studies  aimed  at 
conservation.  Journal  of  Environmental  Psychology,  25(3),  273–291. 

household  energy 
doi:10.1016/j.jenvp.2005.08.002 

Abrate, G., Bruno, C., Erbetta, F., Fraquelli, G., & Lorite-Espejo, A. (2016). A choice experiment on the willingness 

of households to accept power outages. Utilities Policy(43), 151-164. 

Abrate, G., Bruno, C., Erbetta, F., Fraquelli, G., & Lorite-Espejo, A. (2016). A choice experiment on the willingness 

of households to accept power outages. Utilities Policy(43), 151-164. 

Alberini,  A.,  Rosato,  P.,  Longo,  A.,  &  Zanatta,  V.  (2005).  Information  and  willingness  to  pay  in  contingent 
valuation study: the alue of S.Erasmo in the lagoon of Venice. Journal of Environmental Planning and 
management, 48(2), 155-175. 

Badea, A., Rocco, C.  M., Tarantola, S., &  Bolado-Lavin,  R.  (2011).  Composite  indicators  for  security  of  energy 
supply using ordered weighted averaging. Reliability Engineering and System Safety, 96(6), 651-662. 

Bolderdijk,  J.  W.,  Gorsira,  M.,  Keizer,  K.,  &  Steg,  L.  (2013).  Values  determine  the  (in)effectiveness  of 
informational  interventions  in  promoting  pro-environmental  behavior.  PLoS  ONE,  8(12),  1–7. 
doi:10.1371/journal.pone.0083911 

Bouman,  T.,  Steg,  L.,  &  Kiers,  H.  A.  L.  (2018).  Measuring  Values  in  Environmental  Research:  A  Test  of  an 

Environmental Portrait Value Questionnaire. Article Submitted for Publication. 

Cherp, A. (2012). Defining energy security takes more than asking around. Energy Policy(48), 841-842. 

Cherp,  A.,  &  Jewell,  J.  (2010).  Measuring  energy  security:  From  universal  indicators  to  contextualized 
framework.  In  B.  Sovacool  (Ed.),  The  Routledge  Handbook  of  Energy  Security  (pp.  330-355). 
Routledge. 

Cherp, A., Adenikinju, A., Goldthau, A., Hughes, L., Jansen, J. C., Jewell, J., et al. (2012). Chapter-5 Energy and 
Security. In Global Energy Assessment: Toward a More Sustainable Future (pp. 325-384). Cambridge 
University  Press,  Cambridge,  UK  and  New  York,  NY,  USA  and  the  International  Institute  for  Applied 
Systems Analysis, Laxenburg. 

De Groot, J. I. M., & Steg, L. (2008). Value orientations to explain beliefs related to environmental significant 

behavior. Environment and Behavior, 40(3), 330–354. doi:10.1177/0013916506297831 

Durkheim, E. (1897). Suicide. Glencoe, Il.: Free Press. 

Feather, N. T. (1995). Values, valences, and choice: The influences of values on the perceived attractiveness 
and  choice  of  alternatives.  Journal  of  Personality  and  Social  Psychology,  68(6),  1135–1151. 
doi:10.1037/0022-3514.68.6.1135 

Guttman,  L.  (1952).  Multiple  group  methods  for  common-factor  analysis:  Their  basis,  computation,  and 

interpretation. Psychometrika, 17(2), 209–222. doi:10.1007/BF02288783 

Haab, M. C., & Mc Connell, K. E. (2002). Social norms and illicit behaviou an evolutionary model of compliance. 

Journal of environmental management, 66(1), 67-76. 

Hanemann, M., Loomis, J., & Kanninen, B. (1991). Statstical efficiency of double-bounded dichotomous choice 

contingent valuations. American journal of agricultural economics, 73(4), 1255-1263. 

IEA  International  Energy  Agency;  Jewell,  Jessica.  (2011).  The  IEA  Model  of  Short-term  Energy  Security 

(MOSES). International Energy Agency. 

Jans, L., Bouman, T., & Fielding, K. (2018). A Part of the Energy "In Crowd": Changing People’s Energy Behavior 
35–41. 
Power 

Energy  Magazine, 

Approaches. 

16(1), 

IEEE 

and 

via  Group-Based 
doi:10.1109/MPE.2017.2759883 

Jansen,  J.  C.,  &  Seebregts,  J.  (2010).  Long-term  energy  services  security:  What  is  it  and  how  can  it  be 

measured and valued? Energy Policy, 38(4), 1654-1664. 

37 

 
Leijten,  F.  R.  M.,  Bolderdijk,  J.  W.,  Keizer,  K.,  Gorsira,  M.,  van  der  Werff,  E.,  &  Steg,  L.  (2014).  Factors  that 
influence consumers’ acceptance of future energy systems: the effects of adjustment type, production 
level, and price. Energy Efficiency, 7(6), 973–985. doi:10.1007/s12053-014-9271-9 

Longo, A., Giaccaria, S., Bouman, T., & Efthimiadis, T. (2018). ., Societal appreciation of energy security: Volume 

1: Value of Lost Load – residential consumers (EE, NL and PT). Luxembourg: EUR XXXX EN. 

Longo,  A.,  Hoyos,  D.,  &  Markandya,  A.  (2012).  Willingness  to  pay  for  ancillary  benefits  of  climate  change 

mitigation. Environmental and resource economics, 51(1), 119-140. 

Longo, A., Giaccaria, S., Bouman, T., & Efthimiadis, T. (2018). ., Societal appreciation of energy security: Volume 

1: Value of Lost Load – residential consumers (EE, NL and PT). Luxembourg: EUR XXXX EN. 

Lopes, M. A. R., Antunes, C. H., & Martins, N. (2012).  Energy behaviours as promoters of energy efficiency: A 
review.  Renewable  and  Sustainable  Energy  Reviews,  16(6),  4095–4104. 

century 

21st 
doi:10.1016/j.rser.2012.03.034 

Nunnally, J. C. (1978). Psychometric theory. McGraw-Hill. 

Rokeach, M. (1973). The Nature of Human Values. New York, NY: Free Press. 

Scheepers, M., Seebregts, A., De Jong, J., & Maters, H. (2006). EU standards for Energy Security of Supply. ECN. 

Amsterdam: ECN / Clingendael INternational Energy Programme. 

Schultz,  W.,  &  Zelezny,  L.  (1999).  Values  as  predictors  of  environmental  attitudes:  Evidence  for  consistency 
255–265. 

Environmental 

Psychology, 

Journal 

19(3), 

14 

of 

across 
doi:10.1006/jevp.1999.0129 

countries. 

Schwartz, S. H. (1994). Are there universal aspects in the structure and content of human values?  Journal of 

Social Issues, 50(4), 19–45. 

Schwartz, S. H. (2003). A Proposal for Measuring Value Orientations across Nations. In Core ESS Questionnaire 
(pp. 
from 
https://www.europeansocialsurvey.org/docs/methodology/core_ess_questionnaire/ESS_core_questionnai
re_human_values.pdf 

259–319). 

Retrieved 

Schwartz,  S.  H.  (2006).  A  Theory  of  Cultural  Value  Orientations:  Explication  and  Applications.  Comparative 

Sociology, 5(2), 137–182. doi:10.1163/156913306778667357 

Schwartz, S. H. (2012). An overview of the Schwartz theory of basic values. Online Readings in Psychology and 

Culture, 2(1), 1–20. doi:10.9707/2307-0919.1116 

Schwartz, S. H., Cieciuch, J., Vecchione, M., Davidov, E., Fischer, R., Beierlein, C., … Konty, M. (2012). Refining the 
theory  of  basic  individual  values.  Journal  of  Personality  and  Social  Psychology,  103(4),  663–688. 
doi:10.1037/a0029393 

Sovacool, B., Valentine, S. V., Bambawale, M. J., Brown, M. A., de Fatima Cardoso, T., Nurbek, S., et al. (2012). 
Exploring  propositions  about  perceptions  of  energy  security:  An  international  survey.  Environmental 
Science and Policy, 44-64. 

Sovacool, B. K., Mukherjee, I., Drupady, I. M., & D’Agostino, A. L. (2011). Evaluating energy security performance 

from 1990 to 2010 for eighteen countries. Energy, 5846-5853. 

Steg, L., Bolderdijk, J. W., Keizer, K., & Perlaviciute, G. (2014). An Integrated Framework for Encouraging Pro-
environmental  Behaviour:  The  role  of  values,  situational  factors  and  goals.  Journal  of  Environmental 
Psychology, 38, 104–115. doi:10.1016/j.jenvp.2014.01.002 

Steg, L., Dreijerink, L., & Abrahamse, W. (2005). Factors influencing the acceptability of energy policies: A test 
of VBN theory. Journal of Environmental Psychology, 25(4), 415–425. doi:10.1016/j.jenvp.2005.08.003 

Steg, L., Perlaviciute, G., & Van Der Werff, E. (2015).  Understanding the  human dimensions of a sustainable 

energy transition. Frontiers in Psychology, 6. doi:10.3389/fpsyg.2015.00805 

Steg,  L.,  Perlaviciute,  G.,  Van  Der  Werff,  E.,  &  Lurvink,  J.  (2014).  The  significance  of  hedonic  values  for 
environmentally  relevant  attitudes,  preferences,  and  actions.  Environment  and  Behavior,  46(2),  163–
192. doi:10.1177/0013916512454730 

Steg,  L.,  Shwom,  R.,  &  Dietz,  T.  (2018).  What  Drives  Energy  Consumers?:  Engaging  People  in  a  Sustainable 

Energy Transition. IEEE Power and Energy Magazine, 16(1), 20–28. doi:10.1109/MPE.2017.2762379 

38 

 
Stern,  P.  C.,  Dietz,  T.,  &  Guagnano,  G.  A.  (1998).  A  brief  inventory  of  values.  Educational  and  Psychological 

Measurement, 58(6), 984–1001. doi:10.1177/0013164498058006008 

Stuive, I. (2007). A comparison of confirmatory factor analysis methods: oblique multiple group method versus 
from 

confirmatory 
http://www.rug.nl/research/portal/publications/a-comparison-of-confirmatory-factor-analysis-
methods(81618545-e619-4573-a903-7b5b6b63af4b)/export.html 

factor  method. 

Groningen. 

University 

Retrieved 

common 

of 

Van Den Broek, K., Bolderdijk, J. W., Steg, L., Van Den Broek, K., Bolderdijk, J. W., & Steg, L. (2017).  Individual 
differences  in  values  determine  the  relative  effectiveness  of  biospheric,  economic  and  combined 
appeals, 1–39. doi:10.1016/j.jenvp.2017.07.009 

Van  der  Werff,  E.,  &  Steg,  L.  (2015).  One  model  to  predict  them  all:  Predicting  energy  behaviours  with  the 
norm activation model. Energy Research & Social Science, 6, 8–14. doi:10.1016/j.erss.2014.11.002 

39 

 
 
List of abbreviations and definitions 

AIC 

BIC  

CAIDI 

CAIFI 

CV 

DAF 

EE 

kW 

PT 

RPL 

SAIDI 

SAIFI 

SP 

VoLL 

WTA 

WTP 

Akaike Information Criteria 

Bayesian Information Criteria 

Customer Average Interruption Duration Index 

Customer Average Interruption Frequency Index 

Contingent Valuation 

Dispersion accounted for 

Estonia 

Kilowatt 

Portugal 

Random Parameter Logit 

System Average Interruption Duration Index 

System Average interruption Frequency Index 

Stated Preferences 

Value of Lost Load 

Willingness-to-accept 

Willingness-to-pay 

40 

 
 
List of figures 

Figure 1. Estonia, the Netherlands and Portugal by per capita gross domestic product and GHG emissions 
(Eurostat data 2017) ........................................................................................................................................................................................................ 4 

Figure 2. Sample distribution of age (%) ............................................................................................................................................................. 6 

Figure 3. Distribution of Age ........................................................................................................................................................................................ 7 

Figure 4. Number of persons living in the residence .................................................................................................................................... 8 

Figure 5. Boxplots of the size of the house in square meters, categories by country and number of persons 
living in the house ............................................................................................................................................................................................................... 8 

Figure 6. Share of responses from different territorial contexts .......................................................................................................... 9 

Figure 7. Monthly expenditure for the supply of different fuels (euros). On the right, boxplots for the amount 
of the family; on the left, boxplots of per capita values ........................................................................................................................ 10 

Figure 8: Main fuel used for space heating/cooling in the house ..................................................................................................... 11 

Figure 9. Man fuel used for Cooking ................................................................................................................................................................... 11 

Figure 10. Main fuel used for water heating ................................................................................................................................................. 11 

Figure 11. Activities that cannot be done at home in absence of electricity ............................................................................ 13 

Figure 13. Sum of the scores for the altruistic, biospheric, hedonic and egoistic values ................................................. 17 

Figure 14. Correlation among the values components ........................................................................................................................... 17 

Figure 15. Two-way frequency tables, in colours. The vertical axes are reversed with larger values downwards
 .................................................................................................................................................................................................................................................... 18 

Figure 16. Two-dimensional multidimensional scaling for the PVQ value scale for each country. ............................ 19 

Figure 17. Shares of respondents by level of importance assigned to improved reliability and affordability ... 20 

Figure 18. Respondents agreeing with the statement "reliability and affordability of energy supply are very 
important for…" ................................................................................................................................................................................................................ 21 

Figure 19. Respondents agreeing with the statement "reliability and affordability of energy supply are not 
important for…" ................................................................................................................................................................................................................ 21 

Figure 20. Experienced of planned blackouts................................................................................................................................................ 22 

Figure 21. Frequency of planned blackouts stated by respondents ............................................................................................... 22 

Figure 22. Duration of the longest planned power cut during the last 12 months .............................................................. 23 

Figure 23. Last unplanned power cut ................................................................................................................................................................. 23 

Figure 24. Thinking about the last 12 months how many times have you experienced an unplanned power cut?
 .................................................................................................................................................................................................................................................... 23 

Figure 25. Duration of the longest unplanned blackout ......................................................................................................................... 24 

Figure 26. Attitudes of respondents toward the adoptions of energy technology options .............................................. 26 

41 

 
 
List of tables 

Table 1. Count of data (collected in the fieldwork versus target sample) ...................................................................................... 6 

Table 2. Scores of components of values........................................................................................................................................................ 16 

Table 3. Personal values and acceptance of technological options of the power system. .............................................. 27 

Table 4. The bid design of the contingent valuation ................................................................................................................................ 29 

Table 5. Percentage of people WTP the initial bids ................................................................................................................................... 30 

Table 6. Mean WTP ......................................................................................................................................................................................................... 31 

Table 7. Double bounded estimates for the Weibull distribution: effect of socio economic and location 
variables................................................................................................................................................................................................................................ 31 

Table 8. Double bounded estimates for the Weibull distribution: effect of importance of energy security on 
WTP .......................................................................................................................................................................................................................................... 32 

Table 9. Double bounded estimates for the Weibull distribution: effect of experience with unplanned power 
outages and regret if the EU Energy Strategy is not implemented on WTP ............................................................................. 33 

Table 10. Double bounded estimates for the Weibull distribution: Effect of personal values on WTP .................... 34 

42 

 
 
 
 
Annex A: Questionnaire to households 

Socio-demographics: this section is a generic example which is then adapted to the specific country  with the 
help of local experts, e.g. the classes of age or family size should fit the classification of national statistics if 
used for checking the match between the structure of the population and the one of the sample. 

Section A: the respondent 

Please tell us about yourself: 

1. 

Level of education:  

 

 

 

 

 

Postgraduate  

Graduate 

Undergraduate  

Secondary  

Other 

Age: __ years old  

Gender:  

 

 

Male 

female  

Job role: __________________ 

How many people live in your household including you? 

What is the age composition of the household? 

2. 

3. 

4. 

5. 

Member 1 : 

□  0-18 □  19-30 

□  31-45 

□  46-60 

□  Over 60 

Member 2 : 

□  0-18 □  19-30 

□  31-45 

□  46-60 

□  Over 60 

Member 3 : 

□  0-18 □  19-30 

□  31-45 

□  46-60 

□  Over 60 

Member 4 : 

□  0-18 □  19-30 

□  31-45 

□  46-60 

□  Over 60 

Member 5 : 

□  0-18 □  19-30 

□  31-45 

□  46-60 

□  Over 60 

43 

 
 
 
Section B - Personal values 

1.  Below you will find brief descriptions of different persons. For each person we describe what is very important 
to  [him/her].  Please  read  each  description  carefully  and  indicate  how  much  this  person  is  like  you.  The 
meaning of the scores is as follows: 1 means that the person is entirely not like you, 7 means that the person 
is entirely like you. 

Try to distinguish as much as possible in your answering by using 
different scores. The person that is most like you should thus 
receive the highest score. The person that is the least like you, the 
lowest. 

a) 

It is important to [him/her] that every person has equal 
opportunities. 

b) 

It is important to [him/her] that every person is treated justly. 

c) 

It is important to [him/her] to take care of those who are worse 
off. 

d) 

It is important to [him/her] that there is no war or conflict. 

e) 

It is important to [him/her] to be helpful to others. 

f) 

It is important to [him/her] to protect the environment. 

g) 

It is important to [him/her] to be in unity with nature. 

h) 

It is important to [him/her] to respect nature. 

i) 

It is important to [him/her] to prevent environmental pollution. 

j) 

It is important to [him/her] to have fun. 

k) 

l) 

It is important to [him/her] to enjoy the life’s pleasures. 

It is important to [him/her] to do things [he/she] enjoys. 

m)  It is important to [him/her] to be influential. 

n) 

It is important to [him/her] to have control over others’ actions. 

o) 

It is important to [him/her] to have authority over others. 

p) 

It is important to [him/her] to work hard and be ambitious. 

q) 

It is important to [him/her] to have money and possessions. 

e
m
e
k
i
l

t
o
n

y
l
e
r
i
t
n
E

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

e
m
e
k
i
l

y
l
e
r
i
t
n
          E

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

2  3  4  5  6  7 

44 

 
 
 
 
 
 
 
 
 
 
 
Thinking about energy security for your country of residence in the next 
five years you, how important it is for you… 

t
n
a
t
r
o
p
m
n
u

i

y
l
e
m
e
r
t
x
E

t
a
h
w
e
m
o
S

t
n
a
t
r
o
p
m
n
u

i

r
e
h
t
i
e
N

2.  …To have a secure supply of oil, gas, coal and uranium 

3.  …To promote trade in energy products, technologies, and exports 

4.  …to minimize depletion of domestically available energy fuels 

5.  …to have stable, predictable, and clear price signals 

6.  …to have affordably priced energy services 

7.  …to have small scale, decentralized energy systems 

8. 

9. 

 …to have a low energy intensity (unit of energy required for unit of economic 
output)  

 …to conduct research and development an new and innovative energy 
technologies 

10.   …to assure equitable access to energy services to all its citizens 

11.   …to ensure transparency and participation in energy permitting, siting and 

decision making 

12.   …to inform consumers and promote social and community education about 

energy issues 

13.   …to minimize the destruction of forests and the degradation of land and soil  

14.   …to provide available and clean water  

15.   …to minimize air pollution 

16.   …To minimize the impact of climate change (i.e. adaptation) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

r
o
n

t
n
a
t
r
o
p
m
 

i

 

 

 

 

 

 

 

 

 

 

 

 

 

 

t
n
a
t
r
o
p
m
n
u

i

t
a
h
w
e
m
o
S

t
n
a
t
r
o
p
m
 

i

y
l
e
m
e
r
t
x
E

t
n
a
t
r
o
p
m
 

i

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

17.   …to reduce the greenhouse gas emissions (i.e. mitigation) 

 

 

 

 

 

45 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18. 
Given the sixteen dimensions of energy security discussed here, select the five that you think are the 
most  important  for  your  country  of  residence,  and  rank  them  from  1  (the  most  important)  to  5  (5th  most 
important), without allowing for ties. Please rank only 5 dimensions 

 
 
 
 
 
 
 
 

Bolstering trade 

Minimizing rate of depletion 

Secure supply of oil, gas, coal, and 
uranium 

 
 
 
Predictable and clear price signals   
 
Decentralization and small scale supply   
 
 

Affordably priced energy services 

Research and development 

Low energy intensity 

Equitable access 

Transparency and participation in siting and 
decision making 

Education and information 

Preservation of land 

Availability and quality of water 

Minimal air pollution 

Responding to climate change (adaptation) 

Reducing greenhouse gas emission 
(mitigation) 

19. 
residence in the next five years? Please enter below: 

Did we miss any dimension that you consider important for the energy security of your country of 

__________________________________________________________________________________________
__________________________________________________________________________________________
_____________ 

I don’t miss anything <go to Q21> 

<show Q20 on same page as Q19 in case respondents mentioned something in the open text box at Q19> 

20. 
important is the above dimension? 

When  you  think  about  energy  security  for  your  country  of  residence  in  the  next  five  years,  how 

 

 

 

 

 

Extremely important 

Somewhat important 

Neither important nor unimportant 

Somewhat unimportant 

Extremely unimportant 

46 

 
 
 
Section  B  -  Household  energy  use,  expenditures  and  perceived  risks  of  damages 
from power cuts 

21. 

Which of the following options best describes the area where you live? 

  A big city 

  The suburbs or outskirts of a big city 

  A town or a small city 

  A country village 

  A farm or home in the countryside 

22. 

What is approximately the size of your house (excluding garages attic and basement)?  

__________________ square meters 

23. 

Please indicate whether you own or you intend to purchase the following: 

I have 
this 

I do not have this 
but I intend to buy 
it in the near 
future 

I do not have this and 
have no intend of 
buying it in the near 
future 

I do 
not 
know 

Solar panels for electricity 

Electric/hybrid car 

Solar panels for heating 
water 

Woodchip heaters 

Micro wind generator 

Smart meters  

Applications to automatize 
operation of electric 
appliances at home 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

24. 

Which is the main fuel you use in your home to  

Electricity  Gas  Wood  Other (specify) 

Heat the house 

Cook 

Heat the water 

 

 

 

 

 

 

 

 

 

 

 

 

47 

 
 
 
 
 
 
 
25.  Which of the following activities cannot be done in your house in any way, if electricity is not 
available (in case you use gas to heat your home, heating is likely to stop as well without 
electricity) 

 Cooking 
 Indoor lighting 
 Using internet 
Others: 

 Warming rooms 
 Talk on the phone 
 Having a bath/shower 
_____________________ 

 Washing dishes 
 Washing clothes 
 Cleaning floors 
___________________ 

26.  A - Can you estimate how much you pay on a monthly basis for the following utilities? 

<answers are empty allowed> 

Electricity 

Gas 

Water 

Combined gas and electricity 

Wood 

euros 

_____ 

_____ 

_____ 

_____ 

_____ 

B - Could you tell when you pay for the following utilities? 

< empty answers allowed> 

Monthly 

 Every two 
months 

Every three 
months 

Every 4 
months 

Electricity 

Gas 

Water 

Combined gas and electricity 

Wood 

Other 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1)  In case of a power outage ("blackout"), what are the inconveniences that you are more 

concerned about? 

___________________________________________________________ 
question) 

(open 

48 

 
 
 
 
 
 
Section D: Planned power cuts  

Sometimes, the electricity network operator undertakes planned maintenance work on the network. When this 
happens, they will inform customers in advance of the planned power cut so that customers can adapt their 
activities accordingly and be prepared for the power cut. 

When, if at all, did you last have a planned power cut to your home? 

<If the answer is (Q27:1) "In the last 12 months" then go to question Q28. Otherwise go to Q30> 

In the last 12 months 
More than 1 year but less than 5 years ago 
More than 5 years but less than 10 years ago 
More than 10 years ago 
Do not recall having a planned power cut 
Had had a planned power cut but cannot recall when it took place 

1 
2 
3 
4 
5 
6 

Thinking about the last 12 months how many times have you experienced a planned power cut? 

<If the answer is (Q28:1) "Once" or (Q28:2) "Twice" or (Q28:3)"Three times" or (Q28:4)"More than three times" 
or (Q28:6)"Not sure" then go to Q29. If the answer is (Q28:5) "Never" go to Q30> 

Once 
Twice 
Three times 
More than three times 
Never 
Not sure 

1 
2 
3 
4 
5 
6 

In the last 12 months, what was the longest time you were without power during a planned power cut? 

Less than 4 hours 
More than 4 hours 
Not sure 

1 
2 
3 

49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section E: Unplanned power cuts  

Sometimes  the  electricity  network  suffers  an  unplanned  power  outage.  This  may  happen  because  of 
unpredictable damages, faults in the network. Customers cannot be informed in advance of an unplanned 
power outage.  

When, if at all, did you last have an unplanned power cut to your home? 

<If the answer is (Q30:1) "In the last 12 months" then go to the Q31. Otherwise go to infoQ33> 

In the last 12 months 
More than 1 year but less than 5 years ago 
More than 5 years but less than 10 years ago 
More than 10 years ago 
Do not recall having a unplanned power cut 
Had had an unplanned power cut but cannot recall when it took place 

1 
2 
3 
4 
5 
6 

Thinking about the last 12 months how many times have you experienced an unplanned power cut? 

<If the answer is (Q31:1) "Once" or (Q31:2) "Twice" or (Q31:3) "Three times" or (Q31:4) "More than three 
times" or (Q31:6) "Not sure" then go to Q32. If the answer is (Q31:5) "Never" then go to infoQ33> 

Once 
Twice 
Three times 
More than three times 
Never 

Not sure 

1 
2 
3 
4 

5 

6 

In the last 12 months, what was the longest time you were without power due to an unplanned power 
cut? 

Up to 1 hour 
1 to 3 hours 
3 to 10 hours 
More than 10 hours – please specify 

1 
2 
3 
4 

50 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section F: Hypothetical questions on electricity scenarios 

In this section, we will ask you to consider some hypothetical scenarios on power outages.  

The most recent data on power outages9 show that, under the current levels of investments in the electricity 
network, over the next five years we should expect to have 4 planned power outages lasting 10 hours and 10 
unplanned power outages lasting 10 hours.  

Suppose that no new investments are made to the electricity network during the next five years. As a result, 
the number and the duration of both planned and unplanned power outages will increase. Households will be 
compensated for the inconvenience caused through a reduction in their electricity bill.  

You  will  see  now  5  hypothetical  choice  cards,  with  each  card  showing  the  current  situation,  and  two 
alternative hypothetical scenarios of power outages in the next 5 years resulting in cost reductions during the 
same 5 year period. Before choosing your favourite option in each card, consider the effect that an increase in 
power outages would have on you and the discount on your electricity bill. 

Which option would you choose? 

 

 

€1 discount on electricity 
bill per year  

€3 discount on electricity 
bill per year  

27.  Choose your preferred option 

Number of planned power outages 
in the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power 
outages in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

28.   Choose your preferred option 

Number of planned power outages 
in the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power 
outages in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

Option A 

Option B 

6 

4 

Current 
situation 

4 

18 hours 

10 hours 

10 hours 

10 

10 

10 

15 hours 

18 hours 

10 hours 

No change 

 

Current 
situation 

4 

Option A 

Option B 

6 

5 

15 hours 

10 hours 

10 hours 

18 

10 

10 

18 hours 

15 hours 

10 hours 

€10 discount on 
electricity bill per year  

€3 discount on electricity 
bill per year  

No change 

 

Which option would you choose? 

 

 

51 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
 
 

29.  Choose your preferred option 

Number of planned power outages 
in the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power 
outages in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

30.  Choose your preferred option 

Number of planned power outages 
in the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power 
outages in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

Option A 

Option B 

4 

6 

Current 
situation 

4 

10 hours 

18 hours 

10 hours 

15 

18 

10 

15 hours 

10 hours 

10 hours 

Which option would you choose? 

 

 

€5 discount on electricity 
bill per year  

€20 discount on 
electricity bill per year  

No change 

 

Current 
situation 

4 

No change 

 

Current 
situation 

4 

Option A 

Option B 

5 

6 

18 hours 

10 hours 

10 hours 

10 

15 

10 

18 hours 

10 hours 

10 hours 

€5 discount on electricity 
bill per year  

€1 discount on electricity 
bill per year  

Which option would you choose? 

 

 

31.  Choose your preferred option 

Number of planned power outages 
in the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power 
outages in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

Option A 

Option B 

5 

4 

15 hours 

18 hours 

10 hours 

15 

10 

10 

10 hours 

15 hours 

10 hours 

€20 discount on 
electricity bill per year  

€3 discount on electricity 
bill per year  

No change 

 

Which option would you choose? 

 

 

52 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Suppose  that  to  reduce  the  number  and  duration  of  power  outages,  new  investments  are  needed  for  the 
electricity network. These investments would have to be funded through an increase in the electricity bill.  

I am going to show you 5 hypothetical choice cards showing various options for investment over the next 5 
years  and  associated  cost  to  you.  Each  card  will  have  the  current  situation,  and  two  alternative  improved 
options  with  different  costs  in  the  form  of  an  increase  in  your  annual  electricity  bill.  Before  choosing  your 
favourite option in each card, consider your household’s budget and the impact that your choice would have 
on your household’s budget.  

32.  Choose your preferred option 

Option A 

Option B 

Current 
situation 

4 

5 hours 

10 

2 hours 

3 

4 

10 hours 

10 hours 

2 

10 

2 hours 

10 hours 

Number of planned power outages in 
the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power outages 
in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

Number of planned power outages in 
the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power outages 
in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

€20 increase in annual 
electricity bill 

€5 increase in annual 
electricity bill 

Which option would you choose? 

 

 

33.  Choose your preferred option 

Option A 

Option B 

2 hours 

10 hours 

5 

10 

5 hours 

10 hours 

No change 

 

Current 
situation 

4 

No change 

 

Current 
situation 

4 

3 

2 

4 

5 hours 

10 

5 hours 

3 

5 hours 

2 

€3 increase in annual 
electricity bill 

€10 increase in annual 
electricity bill 

Which option would you choose? 

 

 

34.  Choose your preferred option 

Option A 

Option B 

Number of planned power outages in 
the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power outages 
in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

2 hours 

10 hours 

10 

10 

2 hours 

10 hours 

10 hours 

€1 increase in annual 
electricity bill 

€3 increase in annual 
electricity bill 

No change 

 

Which option would you choose? 

 

 

53 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Which option would you choose? 

 

 

€20 increase in annual 
electricity bill 

€1 increase in annual 
electricity bill 

35.  Choose your preferred option 

Number of planned power outages in 
the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power outages 
in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

36.  Choose your preferred option 

Number of planned power outages in 
the next 5 years 
Duration of planned power outages 
in the next 5 years 
Number of unplanned power outages 
in the next 5 years 
Duration of unplanned power 
outages in the next 5 years 
Electricity bill 

Option A 

Option B 

2 

4 

Current 
situation 

4 

10 hours 

5 hours 

10 hours 

2 

2 hours 

5 

10 

2 hours 

10 hours 

No change 

 

Current 
situation 

4 

Option A 

Option B 

2 

3 

2 hours 

10 hours 

10 hours 

5 

5 

10 

10 hours 

2 hours 

10 hours 

€5 increase in annual 
electricity bill 

€3 increase in annual 
electricity bill 

No change 

 

Which option would you choose? 

 

 

54 

 
 
 
 
 
 
 
 
 
 
 
 
Section G: Long term Security of Energy Supply 

The  European  Union  (EU)  imports  more  than  half  of  all  the  energy  it  consumes.  Its  import  dependency  is 
particularly high for crude oil (more than 90 %) and natural gas (66 %). The total import bill is more than €1 
billion per day. 

Many countries heavily rely on a single supplier, including some that rely  entirely on Russia for their natural 
gas. This dependence leaves them vulnerable to supply disruptions, whether caused by political or commercial 
disputes, or infrastructure failure.  

37.  How important is having reliable and affordable energy supply for the following people? Please tick the box 

that best represents your view 

You  
Your family 
Your country 
European Union 
Future generations 

Very 
important 
1 
 
 
 
 
 

Important 

2 
 
 
 
 
 

Moderately 
Important 
3 
 
 
 
 
 

Slightly 
Important 
4 
 
 
 
 
 

Not Important 

5 
 
 
 
 
 

The  EU  Energy  Security  Strategy  describes  a  roadmap  to  2030  that  EU  member  states  need  to  follow  to 
increase their energy security, that is, to have more reliable and affordable energy and be less dependent on 
imports of energy. In particular, the strategy aims to reduce imports of oil by 3 %, gas by 14 % and coal by 
12 % compared to the business as usual scenario by 2030. These goals will be achieved by: 

 

saving energy,  

  producing more local renewable energy  

  making it easier to transport gas and electricity around Europe,  

 

finding different ways and routes to import energy,  

  building good relationships with suppliers and distributers, and 

  having common goals when negotiating with other countries.  

The implementation of the strategy will require an increase in energy prices now for EU Member States to be 
able to undertake all the investments needed for a more reliable and affordable energy in the future. If the 
strategy is not implemented, sudden prolonged energy disruptions could occur in the future, as well as huge 
fluctuations in electricity prices. 

38.  How important do you think it is to increase the reliability and the affordability of energy supply implementing 

the EU Energy Security Strategy? 

Very important 

Important 

Moderately 
Important 

Slightly 
Important 

Not Important 

1 

2 

3 

4 

5 

Suppose  that  an  increase  in  the  electricity  bill  for  the  next  5  years  was  used  to  fund  the  EU  Energy 
Security Strategy. You will see higher and a lower increase in the electricity bill and will be asked whether you 
would  be  willing  to  pay  it  to  guarantee  the  reliability  and  the  affordability  of  energy  until  2030.  Before 
answering, please think carefully about the consequences of paying the increase in the electricity bill as your 
disposable  income  for other  expenditure  would  decrease.  If  you  decide  that  you  are  not  willing  to  pay,  you 
should  consider  that  if  EU  Energy  Security  Strategy  would  not  be  implemented,  sudden  prolonged  energy 
disruptions, as well as huge fluctuations in the price of electricity may occur.  

You should present the question 45 treating 25 % of the sample putting the monetary value of 10, 25 % with 
the value 20, 25 % with 50 and the last 25 % with 100.  

55 

 
 
 
 
 
 
 
 
In question 46 (not 36 as specified in brackets!) we repeat the request of a willingness-to-pay asking a higher 
value  

If 45 is a YES with a value of 10 euros in 46 you should ask 20 euros 

If 45 is a YES with a value of 20 euros in 46 you should ask 50 euros 

If 45 is a YES with a value of 50 euros in 46 you should ask 100 euros 

If 45 is a YES with a value of 100 euros in 46 you should ask 200 euros 

With answer NO to 45 the respondent should always go to 47. Here we ask lower values 

If 45 is a NO or Don’t know with a value of 10 euros in 47 you should ask 5 euros 

If 45 is a NO or Don’t know with a value of 20 euros in 47 you should ask 10 euros 

If 45 is a NO or Don’t know with a value of 50 euros in 47 you should ask 20 euros 

If 45 is a NO or Don’t know with a value of 100 euros in 47 you should ask 50 euros 

39.  Would you be willing to support the implementation of the EU Energy Security Strategy to guarantee the 

reliability and the affordability of energy until 2030 if your annual electricity bill was €[10, 20, 50, 100] more 
expensive for the next five years? 

 YES [go to question 46 ] 

 NO [go to question 47]  

 Don’t know [go to question 47] 

40.  [bidhigh] Would you be willing to support the implementation of the EU Energy Security Strategy to guarantee 
the reliability and the affordability of energy until 2030 if your annual electricity bill was € [20, 50, 100, 200] 
more expensive for the next five years? 

 YES [go to question 48 ] 

 NO [go to question 48]  

 Don’t know [go to question 48] 

41.  [bidlow] Would you be willing to support the implementation of the EU Energy Security Strategy to guarantee 
the reliability and the affordability of energy until 2030 if your annual electricity bill was €[5, 10, 20, 50] 
more expensive for the next five years? 

 YES  
 NO  
 Don’t know 

What is the highest increase in your annual electricity bill for the next five years that you would be willing 
to pay to implement the EU Energy Security Strategy to guarantee the reliability and the affordability of 
energy at least until 2030? 
€_______ 
Which reasons best describe your choices to pay or not to pay for the implementation of the EU Energy 
Security Strategy? [Tick all that apply] 

 The reliability and the affordability of energy are important 
 I cannot afford to pay more for my electricity bill 
 I am not interested in the reliability and the affordability of energy supply 
 I don’t believe that the Energy Security Strategy can be implemented 
 I don’t believe the hypothetical scenario of an increase in electricity bill 
 Others, such as the government and industry, should pay for the Energy Security Strategy. 

56 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section H: Options for energy security and personal preferences 

We ask you to say if you agree or disagree with the following statements: 

42.  I want to reduce my energy consumption 

1 

2 

3 

4 

5 

l

y
l
e
t
e
p
m
o
C

e
e
r
g
a
s
i
d

e
e
r
g
a

l

y
l
e
t
e
p
m
o
C

6 

7 

43.  If I don’t support the implementation of the EU 
Energy Security Strategy, and then I will be 
restricted with the use of energy, I will later wish 
that I had 

44.  If I don’t support the implementation of the EU 

Energy Security Strategy, and then my family will be 
restricted with the use of energy, I will later feel 
bad for my family 

1 

2 

3 

4 

5 

6 

7 

1 

2 

3 

4 

5 

6 

7 

45.  There are different ways in which the security of the supply of energy could be improved. How do 
you evaluate the following options? (1 = very unacceptable to 7 = very acceptable & 1 = very negative to 
7 = very positive) 

Increase centralized energy production (as nuclear, coal and gas fired power plants) 

Very unacceptable 

Very negative 

1  2 

1  2 

3 

3 

4 

4 

5 

5 

6 

6 

7  Very acceptable 

7  Very positive 

Increase decentralized, more local, energy production (e.g. private solar panels, wind turbines) 

Very unacceptable 

Very negative 

1  2 

1  2 

3 

3 

4 

4 

5 

5 

6 

6 

7  Very acceptable 

7  Very positive 

Use less energy during peak times by adjusting your energy behaviour: You yourself decide which 
devices you will use during non-peak times making decisions yourself about which devices you will 
wait to use until there is a large supply of energy (for example the washing machine). You have 
control over which devices will be turned on at non-peak times. 

Very unacceptable 

Very negative 

1  2 

1  2 

3 

3 

4 

4 

5 

5 

6 

6 

7 

7 

Very acceptable 

Very positive 

57 

 
 
 
 
 
 
 
 
 
     
 
 
Use less energy during peak times by letting technologies schedule your energy use. Convenience 
technology turns on devices during non-peak times. For example, a grid in the house turns devices 
(for example your washing machine) on only when there is a large supply of energy, you don’t have 
to do anything yourself. You do not have control over which devices will be turned on during non-
peak times. 

Very unacceptable 

Very negative 

1  2 

1  2 

3 

3 

4 

4 

5 

5 

6 

6 

7  Very acceptable 

7  Very positive 

46.  Among the following inconveniences created by interruptions in the supply of electricity, please 

select those more relevant to you in the following list 

 Non-operating security and alarm systems   Suddenly remaining in the dark 

 Interrupted telecommunications 

 Inability to use the TV 

 Inability to use the PC 

 No heating  

 Inability to recharge mobile phones 

 Inability to recharge electric vehicles 

 Inability to cook 

 Spoiled food in the fridge 

 Spoiled food in the freezer 

 Remaining stuck in a closed room 

 Remaining stuck in an elevator 

 Damage to appliances 

 Loss of data from your computer 

 other ………………………………………. 

47.  What was your total household income before taxes during the past 12 months? 

<question is empty allowed> 

 

 

 

 

 

 

Less than 10,000 Euros 

10,000 to 29,999 Euros 

30,000 to 59,999 Euros 

60,000 to 99,999 Euros 

100,000 to 149,999 Euros 

150,000 or more Euros 

The interview is now completed.  

If  you  are  interested,  the  aggregated  survey  results  and  analysis  from  this  survey  will  be  available  at 
http://publications.jrc.ec.europa.eu/repository/ in the coming months (search for 'survey on VOLL').  

We thank you for your kind participation. 

58 

 
 
 
 
 
Annex B: Technical Description of the phases of the fieldwork from SSI 

Kick-off call 

After the project had been awarded and the questionnaire draft was ready, SSI had a kick-off call to review 
the project and to clarify the questionnaire, quotas and timeline details. 

Questionnaire delivery and review 

 

Sander Ooms and Rafael de Kock (dedicated project management team) had a kick-off call with Sergio 
Giaccaria and Tilemahos Efthimiadis and conducted a thorough questionnaire review to ensure: 

  All skip patterns are logical and correctly point to questions in the survey 

  Question numbers and punch values are consistent, don’t overlap, match with routing 

instructions 

  Wording, spelling, grammar is consistent for the Dutch market 

  Numeric questions have data ranges as specified 

Questionnaire programming, testing and revisions 

SSI  expected  changes  and  revisions  to  happen  along  the  way  and  have  developed  a  system  to  incorporate 
them without risking errors.  

Before and during the programming of the questionnaire SSI: 

 

Requested and received the localized questionnaire in Dutch, Portuguese and Estonian in Word format 
from the European Commission.  

 

Requested changes via e-mail in a table and in an Excel file with question number for each change. 

 

SSI compiled questions/clarifications in batches for review. 

 

SSI controlled each version of the questionnaire and submitted a highlighted version for the European 
Commission’s approval after each round or revision. 

 

SSI alerted the European Commission when changes were incorporated and tested. 

 

SSI checked the Dutch language questionnaire, as Sander Ooms is a native Dutch speaker. Sander Ooms 
shared his thoughts and gave feedback on wording and grammar. This was implemented upon approval 
by the European Commission.  

  Multiple Dutch, Portuguese and Estonian language experts from the European Commission, checked the 

translated test links for quality and wording.  

 

SSI sent test links to the European Commission for final review and approval after SSI had done a 
thorough quality review. 

Soft launch - Pilot 

The soft launch gatherd 5%-10% of the required completes. 

59 

 
 
 
 
 
 
Data quality check 

After soft launch completion, SSI provided the interim data and a  report of incidence,  length and drop rate. 
There were no deviations from the bid specifications.  

Full launch 

Once the soft launch – pilot data was approved, SSI moved to the full launch. Here, quota management was 
important.  The  SSI  team  carefully  monitored  quota  and  adjusted  sample  as  needed.  SSI  kept  the  European 
Commission team regularly updated. 

Data delivery 

SSI  conducted  data  checks  to  remove  excessive  speeders  and  completes  showing  evidence  of  fraud  or 
repeated  inattention.  SSI  used  multiple  checks  before  flagging  the  data  based  on  speeding  and  bad  open 
answers. 

SSI delivered final cleaned SPSS and Excel data files to you within hours of fieldwork completion.  

60 

 
 
 
 
  
 
Measuring Success Rates 

Please  note  that  SSI  is unable  to  share  response  rates with  the  European  Commission.  Measuring  response 
rates in a multi-source, routed environment is practically impossible  – in fact it is very difficult to calculate 
them  in  any  online  environment.  As  mentioned,  SSI  employs  a  routing  environment  to  efficiently  allocate 
willing  participants  to  surveys  they  are  best  suited  for  and  are  more  likely  to  be  able  to  complete.  This 
reduces  the  self-selection  bias  associated  with  invitation-based  methods  and  increases  participant 
satisfaction  with  the  market  research  process.  There  is  therefore  no  concept  of  a  response  rate  except  the 
conversion from being asked to do a specific survey once in the router and starting that survey. 

AAPOR (the American Association for Public Opinion Research) believes that the best that can be provided for 
a  non-probability  access  panel  is  a  “participation  rate”  since  numbers  of  contacts  at  the  first  stage 
(recruitment)  are  unknown.  Note:  since  we  do  not  send  survey-specific  invitations,  the  SSI  definition  of 
participation rate is the number of starts which did not drop out of the survey. They also recognize that panel 
management  processes  (particularly  how  often  inactive  panelists  are  removed  from  the  database)  has  an 
effect  on  participation  rates.  Thus any  measurement  of  ‘response’  (participation)  will  not  be  an  indicator of 
panel quality per se nor necessarily comparable to the same panel over time, nor comparable to other panels. 

Interview Length 

Please note that interview length per country can differ due to varying internet speeds.  

Country 

Median survey length 

Netherlands 

16:31 minutes 

Portugal 

19:26 minutes 

Estonia 

24:00 minutes 

61 

 
Quota control 

SSI  delivered  a  report  link  to  the  European  Commission  so  that  fieldwork  progress and  quota  management 
could be monitored. 

SSI had a quota on version per country. Please see below the completed per version (A, B, C, D and E) and per 
country (Estonia, Netherlands and Portugal):  

62 

 
 
All soft launch completes (between 109-132 for each  country) were considered as version A and WTA first. 
That’s why those numbers are a bit higher. After the soft launch / pilot SSI added the other 4 versions and the 
WTA First and WTP First logic. 

Quota - Total Number of completes 

63 

 
 
 
In accordance with the European Commission, the target for the oldest age group for Estonia was relaxed and 
completes were compensated in the 55-64 age group. 

64 

 
 
 
 
GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the 
address of the centre nearest you at: http://europea.eu/contact 

On the phone or by email 

Europe Direct is a service that answers your questions about the European Union. You can contact this 
service: 

- by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

- at the following standard number: +32 22999696, or 

- by electronic mail via: http://europa.eu/contact 

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa 
website at: http://europa.eu 

EU publications 
You can download or order free and priced EU publications from EU Bookshop at: 
http://bookshop.europa.eu. Multiple copies of free publications may be obtained by contacting Europe 
Direct or your local information centre (see http://europa.eu/contact). 

 
 
 
 
K
J
-
N
B
-
2
9
5
1
2
-
E
N
-
N

doi:10.2760/05174

ISBN 978-92-79-98283-5