Smart specialisation benchmarking and assessment : pilot study on wind energy.
Date: 2017-03-10
Institutions: Joint Research Centre
Subjects: Cohesion Fund, energy policy, energy research, energy technology, fund (EU), innovation, regional cooperation, research and development, research report, wind energy

Smart specialisation 
benchmarking and assessment: 
pilot study on wind energy  

Jiménez Navarro, Juan Pablo 

Uihlein, Andreas 

2017 

EUR 28515 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: Jiménez Navarro, Juan Pablo 
Address: P.O. Box 2, 1755 ZG Petten, The Netherlands 
Email: juan-pablo.jimenez-navarro@ec.europa.eu 
Tel.: +31 223 56-5214 

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

JRC106035 

EUR 28515 EN 

PDF 

ISBN 978-92-79-66661-2 

ISSN 1831-9424 

doi:10.2760/816204 

Luxembourg: Publications Office of the European Union, 2017 

© European Union, 2017 

The reuse of the document is authorised, provided the source is acknowledged and the original meaning or 
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How to cite this report: Jiménez-Navarro, J.P., Uihlein, A., Smart specialisation benchmarking and assessment: 
pilot study on wind energy, EUR 28515 EN, doi:10.2760/816204 

All images © European Union 2017 

Smart specialisation benchmarking and assessment: pilot study on wind energy  

Smart specialisation Platform on Energy aims at supporting the implementation of regional smart specialisation 
strategies  and  the  optimal  and  effective  uptake  of  cohesion  funds  in  the  energy  area.  To  that  end,  regional 
cooperation  is  a  key  element  as  a  way  of  accelerating  successful  regional  projects.  This  report  proposes  and 
tests a methodology to identify potential regions to work with, based on structural similarities and recommends 
potential partnership amongst them. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Executive summary ............................................................................................... 2 

1  Introduction ...................................................................................................... 4 

2  Objective .......................................................................................................... 5 

3  Methodology ..................................................................................................... 6 

3.1  Selection of regions ..................................................................................... 6 

3.2  Identification of energy parameters ............................................................... 6 

3.3  Data collection ............................................................................................ 8 

3.3.1  Wind energy deployment ...................................................................... 8 

3.3.2  Academia ............................................................................................ 8 

3.3.3  Sectorial structure ............................................................................... 8 

3.4  Analysis of similarities amongst regions .......................................................... 9 

3.5  Weighting & principal component analysis ..................................................... 10 

3.6  Quantification of potential correlation ........................................................... 10 

4  Results ........................................................................................................... 11 

4.1  Selection of regions ................................................................................... 11 

4.2  Energy parameters .................................................................................... 11 

4.2.1  Socio-economic dimension .................................................................. 11 

4.2.2  Energy price dimension ...................................................................... 13 

4.2.3  Energy use dimension ........................................................................ 15 

4.2.4  Wind energy deployment dimension ..................................................... 15 

4.2.5  Academia dimension .......................................................................... 16 

4.2.6  Sectorial structure dimension .............................................................. 18 

4.2.7  Innovation capacities dimension .......................................................... 19 

4.3  Analysis of similarities ................................................................................ 20 

4.3.1  Dimensional similarity matrixes ........................................................... 20 

4.3.2  Overall similarity matrix ..................................................................... 24 

4.4  Weighting & Principal component analysis ..................................................... 25 

4.4.1  Weighting method.............................................................................. 25 

4.4.2  Principal component analysis ............................................................... 26 

4.5  Analysis of potential correlation ................................................................... 28 

5  Conclusions .................................................................................................... 31 

Conclusions related to the methodology ....................................................... 31 

5.1  31 

5.2  Conclusions related to wind ......................................................................... 32 

References ......................................................................................................... 33 

List of abbreviations and definitions ....................................................................... 35 

i 

 
List of figures ...................................................................................................... 36 

List of tables ....................................................................................................... 37 

Annexes ............................................................................................................. 38 

Annex I  Energy price parameters .................................................................... 39 

Annex II 

Energy use .................................................................................... 44 

Annex III  Wind energy deployment ................................................................ 45 

Annex IV 

Academia indicators ....................................................................... 48 

Annex V 

Innovation capacities ...................................................................... 50 

Annex VI 

Annex VII Results of PCA analysis for non-weighted dataset ................ 51 

ii 

Authors 

Jiménez Navarro, Juan Pablo 

Uihlein, Andreas 

1 

Executive summary 

This  work  was  carried  out  in  the  framework  of  the  Smart  Specialisation  Platform  on 
Energy  (S3PEnergy).  It  aims  at  supporting  the  implementation  of  regional  smart 
specialisation  strategies  and  the  optimal  and  effective  uptake  of  cohesion  funds  in  the 
energy area. 

This  report  aims  to  foster  cooperation  amongst  regions  with  common  interest  in  a 
particular  energy  theme,  which  allows  mutual  learning  amongst  regions.  Consequently, 
the  objective  of  this  work  is  to  provide  and  test  a  methodology  to  identify  similarities 
within  a  group  of  regions  with  a  particular  interest  in  wind  energy.  This  analysis 
facilitates the identification of leading regions and regions with structural similarities and 
recommends potential partnerships amongst them. 

Policy context 

Research  and  innovation  strategies  for  smart  specialisation  (RIS3)  have  become  a 
requirement for those regions that want to take advantage of ESIF funds allocated under 
the  thematic  objective  dedicated  to  strength  research,  technological  development  and 
innovation (TO1). To succeed in the implementation of those strategies, cooperation and 
mutual  learning  are  key  element  to  take  advantage  of  lessons  learnt  in  the  use  of 
structural funds. 

Key conclusions 

Based  on  the  analysis  carried  out,  the  cluster  of  regions  that  demonstrate  interest  in  a 
particular energy technology is a complex exercise. Although from a theoretical analysis 
some  conclusions  may  be  extracted,  i.e.  the  high  level  of  similarities  amongst  regions 
within  the  same  country,  and  some  potential  regional  clusters  could  be  identified, 
clustering  process  should  follow  a  bottom-up  approach.  Therefore,  regions  should 
describe their specific needs and find ideal partners to advance in solving those needs. In 
this  regard,  this  work  provides  a  methodology  to  narrow  down  the  number  of  potential 
partners.  

Main findings 

This  work  reveals  the  complexity  of  representing  a  particular  energy  technology 
deployment by a limited set of parameters. Additionally, some efforts are still required to 
break  national  data into  regional level  (NUTS2  level),  which  actually limits  the  selection 
of parameters to be assessed.  

Despite  the  uncertainty  concerning  which  dataset  better  describes  the  regional  wind 
energy  status,  it  is  demonstrated  that  a  reduced  number  of  variables  is  enough  to 
characterise  the  level  of  similarities  amongst  regions.  Still,  this  conclusion  has  to  be 
validated  for  other  technologies  and  requires  extending  the  number  of  regions  under 
analysis. 

Based on the set of regions under analysis, it is worth to highlight the lack of correlation 
amongst  them  for  the  different  dimensions  assessed.  Thus,  although  available  wind 
energy  resource  was  expected  to  be  the  main  driver  for  the  deployment  of  the  wind 
sector in a region, this availability is not correlated with the size of the private sector or 
the innovation capacities in the regions. However wind innovation capacities are affected 
by the economic development of the regions.  

The analysis also shows how industrial electricity prices are inversely correlated with the 
relative GPD in regions, having an impact in terms of competitiveness for regions. 

2 

 
Related and future JRC work 

In  case  this  methodology  become  of  interest  to  regions,  the  methodology  could  be 
applied to other technologies and regions across Europe. 

3 

1 

Introduction 

Smart  specialisation  aims  at  promoting  a  more  efficient  uptake  of  public  funding  in 
research and innovation. Energy is a topic with high interest amongst regions registered 
at the S3 Platform on smart specialisation [Jiménez Navarro & Uihlein 2016]. Thus, under 
the  umbrella  of  the  Smart  Specialisation  Platform  (S3P),  a  thematic  platform  on  energy 
was  set  up  in  2015.1  The  Smart  Specialisation  Platform  on  Energy  (S3PEnergy)  is  the 
space  where  Member  States,  regions  and  community  members  receive  support  for  the 
optimal  and  effective  uptake  of  Cohesion  Funds  for  sustainable  energy.  The  S3PEnergy 
also  promotes  energy  innovation  activities  at  national,  regional  and  local  level  through 
the identification of technologies and innovative solutions that support, in the most cost-
effective way, the EU energy policy priorities. 

One  of  the  most  important  aspects  of  the  concept  of  smart  specialisation  strategies  is 
territorial  cooperation  [European  Commission  2011].  Lessons  learnt  may  represent  a 
valuable  input  in  order  to  succeed  in  the  implementation  of  national  or  regional 
strategies.  Therefore,  the  identification  of  lighthouse  regions  in  specific  topics  is 
fundamental to achieve a similar level of deployment in other regions of Europe. 

This  identification  needs  a  comparison  framework  that  leads  to  significant  conclusions 
concerning the status of territorial units in specific aspects. Therefore, the definition of a 
set  of  key  performance  indicators  (KPI)  to  determine  a  fair  comparison  framework  is 
required to assess the status of regions in a specific aspect. 

In the framework of the smart specialisation concept [Foray & Goenega 2013], a regional 
benchmarking  methodology  has  been  developed  to  identify  reference  regions  and  to 
measure  how  close/far  regions  are  from  each  other.  Proposed  benchmarking  exercises 
include  issues  related  to  geo-demography,  human  resources,  technology  structure, 
sectorial structure  or institutions and values  [Navarro  et al. 2014]. A benchmarking can 
provide into insights about possibilities for learning and transfer of best practises.Regions 
with similar structural conditions could benefit most from knowledge transfer and mutual 
learning.  

Regarding energy, a regional characterisation through a limited number of indicators is a 
complex  exercise.  Firstly,  because  of  the  energy  sector  itself  shows  a  high  complexity 
itself  which  is  difficult  to  portray  with  a  limited  amount  if  indicators.  Secondly,  data  is 
often not available for several indicators and regions.  

Therefore,  the  definition  of  a  set  of  KPIs  supported  by  available  data  and  capable  to 
characterise  the  status  of  a  region's  energy  aspects  will  contribute  to  developing  a 
comparative analysis between regions and finally promote cooperation amongst regions. 

1 http://s3platform.jrc.ec.europa.eu/s3p-energy. 

4 

  
 
                                           
2 

Objective 

This  report  aims  to  facilitate  the  comparison  of  the  energy  sector  status  for  a  specific 
energy  technology  of  EU  regions  with  the  final  purpose  of  identifying  most  advanced 
regions  in  each  aspect  as  well  as  establishing  a  potential  group  of  regions  which  share 
similar characteristics.  

To  this  end,  the  objective  of  this  work  is  to  define  and  test  a  feasible  comparison 
framework for a particular energy technology and for a limited set of  European regions. 
This work serves as a case study for further analysis involving other energy technologies 
and regions. 

Therefore,  the  proposed  comparison  framework  is  built  to  be  easily  replicated  for  other 
regions  and  technologies,  and  can  be  considered  a  proof  of  concept.  Once  defined  and 
tested,  it  allows  an  integrated  analysis  combining  regions  and  energy  technologies 
leading  to  the  assessment  of  the  level  of  similarity  amongst  technologies  or  amongst 
regions. 

For  the  proposed  case  study,  wind  energy  has  been  selected  as  an  appropriate 
technology. It does not present a high level of interactions and/or overlapping with other 
low  carbon  energy  technologies.  This  aspect  is  expected  to  facilitate  also  the  data 
retrieval process. 

The selection of regions is based on the information provided in their smart specialisation 
strategies.  Therefore,  all  regions  with  interest  in  developing  wind  energy  in  their 
territories are included in the analysis [Jiménez Navarro & Uihlein 2016]. 

The  analysis  is  carried  out  based  on  the  definition  of  dimensions  containing  different 
parameters  related  to  wind  technology  to  certain  extend.  As  mentioned  in  the  previous 
section,  the  main  challenges  faced  in  this  analysis  are  the  selection  of  parameters  that 
could  better  define  the  technology  status  in  regions  and  data  availability.  These  two 
issues are directly linked since data access limits  the initial identification of parameters. 
Therefore,  as  a  proof  of  concept,  this  analysis  explores  data  availability.  The 
methodology  also  tests  the  application  of  dimensional  reduction  techniques  such  as  the 
principal component analysis in order to simplify the study and detect critical parameters. 

Considering how difficult it is to set a comprehensive list of parameters that best defines 
the status of a technology, a realistic list according to available information is presented. 
Ultimately,  the  proposed  analysis  allows  the  integration  of  new  parameters  in  case  new 
data becomes available. 

5 

3 

Methodology 

The proposed methodology is based on the following steps: 

  selection  of  regions  with  proven  wind  energy  interests  in  their  smart  specialisation 

strategies; 
 
identification of energy parameters to compare; 
  collection of information from different sources; 
  analysis of energy similarities between regions; 
  quantification of potential correlations. 

3.1 

Selection of regions 

The  selected  group  of  regions  is  composed  of  those  that  have  declared  interest  in  their 
specific  smart  specialisation  strategies  according  to  [Eye@RIS3  2015]  and  included  in 
[Jiménez  Navarro  &  Uihlein  2016].  This  initial  list  has  been  revised  in  a  second  stage 
after  the  collection  of  information  has  been  performed  to  remove  regions  with  poor 
information available (Section 4). 

3.2 

Identification of energy parameters 

Ideally,  and  from  a  broad  perspective,  the  energy  sector  analysis  should  include  four 
main  aspects:  social,  economic,  environmental  and  institutional  aspects  [IEA  2001]. 
Some  works  propose  parameters  to  be  considered  to  analyse  sustainable  development 
[IEA  2001].  In  the  case  of  specific  energy  technology  status  at  regional  level,  some  of 
them  may  be  considered.  However,  there  is  no  consensus  about  the  parameters  to  be 
incorporated to characterise regional energy technology status.  

Next to a lack of consensus on which parameters to include, data availability determines 
the choice of energy parameters that can be considered. In addition to this, despite the 
fact that at Member State (MS) level available information is enough to establish robust 
analysis,  NUTS2  level  means  a  challenge  in  terms  of  data  acquisition.  Most  databases 
analysed  include  very  detailed  information  on  MS  level  but  not  at  regional  level. 
Therefore,  the  challenge  is  to  break  down  national  information  to  regional  level,  a  task 
that until now remains unsolved for some indicators.  

Taking into account all the above, a list of indicators was developed and is presented in 
Table  1  together  with  the  data  sources  used  and  the  geographical  coverage.  The 
proposed list of indicators is organised in seven dimensions that will be used in following 
steps to carry out analysis to establish correlation amongst them. 

6 

 
 
 
 
 
 
 
 
 
Table 1  List of indicators used for the wind regional benchmarking 

Dimension  Elements  Parameter 
Population 
Socio-
economic 
Urban  
Region 
wealth 

Coverage 
Regional 
Regional 
Regional 
Regional 
National 

Year 
2013 
2013 
2013 
2013 
2014 

Source 
[Eurostat 2015a] 
[Navarro et al. 2014] 
[Eurostat 2015b] 
[Eurostat 2015b] 
[Eurostat 2015c] 

National 

2014 

[Eurostat 2015d] 

National 

2014 

[Eurostat 2015e] 

National 

2014 

[Eurostat 2015f] 

National 

2014 

[Eurostat 2015c] 

National 

2014 

[Eurostat 2015d] 

National 

2014 

[Eurostat 2015e] 

National 

2014 

[Eurostat 2015f] 

Regional 

2009 

[Eurostat 2013] 

Total population 
Urban population 
Absolute GDP 
Relative GDP 
Gas price for 
domestic 
Gas price for 
industrial 
Electricity price for 
domestic 
Electricity price for 
industrial 
Gas price for 
domestic 
Gas price for 
industrial 
Electricity price for 
domestic 
Electricity price for 
industrial 
Heating degree  
days 
Installed capacity 

Energy price End use 

energy price 
before taxes 

End use 
energy price 
after taxes 

Energy use  Energy 

Regional 

Regional 

Regional 

Regional 

Regional 

Capacity factor 

Wind energy 
deployment 

Number of wind 
farms 

Regional in- 
house 
Knowledge 

demand 
Wind 
capacity 
Wind 
production 
Wind 
deployment 
Academia  Universities  Number of 
universities 
Number of 
publications 
Share of wind 
publications 
Share of wind 
publications in the 
energy area  
Number of 
companies with 
innovative activities 
Representativeness 
of the region 
All sectors 
Regional 
Regional 
Business sector 
Government sector  Regional 
Regional 
Education sector 
Regional 
Non-profit sector 
Regional 
Number of 
researchers 

R&D 
expenditure  

Innovation 
capacities 

Energy 
sector size 

Sectorial 
structure 

Regional 

Regional 

Regional 

Regional 

R&D 
personnel 
and 
researchers  

7 

2014 

2014 

2014 

2016 

GlobalData / 
Renewable UK 
[Gonzalez Aparicio et 
al. 2016] 
GlobalData / 
Renewable UK 
[Scopus 2016] 

2016 

[Scopus 2016] 

2016 

[Scopus 2016] 

2016 

[Scopus 2016] 

2015 

[BNEF 2015] 

2015 

[BNEF 2015] 

2013 
2013 
2013 
2013 
2013 

[Eurostat 2016a] 
[Eurostat 2016a] 
[Eurostat 2016a] 
[Eurostat 2016a] 
[Eurostat 2016a] 
[Eurostat 2016a] 

 
 
 
 
3.3  Data collection 

Most of the data could be obtained directly from different data sources, and only some of 
them  (related  to  the  dimensions  'wind  energy  deployment',  'academia'  and  'sectorial 
structure')  required  specific  data  processing.  The  following  sections  briefly  explain  how 
data was retrieved in these cases. 

3.3.1  Wind energy deployment 

Concerning wind energy deployment, information on the number of wind farms and their 
installed  capacity  has  been  retrieved  from  a  commercial  database  [The  Wind  Power 
2015] and completed with data produced by the JRC related to geographical information. 
It  should  be  noted  that  3.5 %  of  records  did  not  provide  enough  information  neither  in 
terms  of  installed  capacity  nor  in  terms  of  geographical  information  that  may  lead  to  a 
NUTS2 classification. This figure is sufficient to carry out the proposed analysis. 

Data  on  electricity  production  from  wind  energy  was  not  available  in  most  cases  at 
regional  level.  However  regional  capacity  factors  have  been  derived  based  on  EMHIRES 
[Gonzalez  Aparicio  et  al.  2016].  With  this  information,  wind  energy  production  can  be 
directly obtained. 

3.3.2  Academia 

For the analysis of the dimension 'academia', the authorities file of European universities 
[Daraio  2015]  has  been  used  (Annex  IV).  Specific  wind  information  in  terms  of 
publication  has  been  retrieved  during  February  2016  from  Scopus  [Scopus  2016]. 
Queries  have  been  developed  filtering  in  the  following  order:  universities,  energy  area 
and wind:  

( AF-ID ( "University of Exeter"   60026479 ) )  AND  ( wind )  AND  ( LIMIT-
TO ( SUBJAREA ,  "ENER" ) ) 

3.3.3  Sectorial structure 

The  analysis  of  the  sectorial  structure  is  based  on  a  comprehensive  list  of  European 
active  companies  working  in  the  field  of  wind  energy  including  more  than  4 000 
companies  [BNEF  2015].  However,  despite  the  fact  that  at  country  level  information  is 
available, information related to city or zip code is not complete (Figure 1). Therefore, to 
allocate  companies  in  regions,  coordinates  of  records  based  on  geographical  data 
(headquarter, place, location) have been calculated and classified into NUTS2 areas.  

According to Figure 1, the lack of information available for a specific country affects also 
regions  within  that  country.  To  overcome  this  issue,  it  has  been  assumed  that  regions 
perform as its related country does. In other words, if one of the countries has 80 % of 
geographical data available, it is considered that regions in this country will also have the 
same share. So, for a particular region with 100 wind energy companies with information 
about geographical location available in a country where the location is know for 80 % of 
the  companies,  the  final  sectorial  size  considered  for  this  region  would  be  125 
companies. 

8 

 
Figure 1  Share of wind energy companies with no geographical information available 

3.4  Analysis of similarities amongst regions  

Once data was collected and processed, the benchmarking analysis has been performed 
using a dissimilarity or distance matrix (Equation 1). 

D= [

𝟎
⋮
𝒔𝒚𝒎

𝒅(𝟏, 𝟐)    ⋯ 𝒅(𝟏, 𝒏)

𝟎       
⋯

] 

⋮
𝟎

Equation 1 

n = number of parameters considered 

d(i,j) = distance between region i and  j 

sym = symmetric elements 

Every element of the matrix was calculated by applying the Minkowski distance (Equation 
2). 

𝑛
𝑑(𝑖, 𝑗) = (∑ |𝑝𝑖,𝑘 − 𝑝𝑗,𝑘|
𝑘=1

𝑟

1/𝑟
)

 , 𝑟 ≥ 1  

Equation 2 

n ≡ number of parameters considered  

pi,k ≡ value of the parameter k for the region i 

d(i,j) ≡ distance between region i and  j 

r ≡ order of Minkowski distance 

9 

 
 
 
For  this  specific  analysis,  a  Euclidean  distance  (r=2)  has  been  assumed.  Euclidean 
distance  represents  the  natural  perception  of  distance  between  two  points  and  can  be 
also applicable in multidimensional problems.  However the Euclidean distance may offer 
poor  results  when  parameters  present  different  ranges,  being  those  with  high  ranges 
those  that  tend  to  dominate  [Cornish  2007].  To  overcome  this  issue,  parameters  have 
been normalised (Equation 3). 

′ =
𝑝𝑖,𝑘

𝑝𝑖,𝑘 − 𝑝𝑘̅̅̅
𝜎𝑘

Equation 3 

′  ≡ normalised value of the parameter k for the region i 
pi,k

pi,k ≡ value of the parameter k for the region i 

pk̅̅̅ ≡ average value of the parameter k 

σk ≡ standard deviation of the parameter k 

At  a  first  stage,  no  specific  weights  were  introduced  meaning  all  55  parameters  (30 
introduced explicitly in Table 1 and energy prices breakdown in Annex I) were weighted 
equally  (1/55).  For  each  of  the  dimensions  presented  a  particular  distance  matrix  was 
created.  

3.5  Weighting & principal component analysis 

After the similarity analysis, weighting and principal component analyses are applied. 

Weighting  analysis  is  applied  to  have  a  clear  understanding  on  the  role  of  different 
dimensions.  By  applying  weights  at  dimension  level,  we  ensure  every  one  of  the  seven 
dimensions has an equal impact in the description of the regions no matter the amount of 
variables within the dimension. 

The principal component analysis is applied to assess whether it is possible to reduce the 
number  of  parameters  or  not.  This  analysis  determines  which  parameters  better  define 
the  differences  between  regions  and  then  discard  those  that  do  not  provide  additional 
information. 

3.6  Quantification of potential correlation 

It is important to note that  the matrixes presented in section  3.4 just provide distances 
amongst  regions  but  do  not  explain  which  region  is  performing  better  or  worse.  To 
identify  those  regions  with  high  level  of  wind  deployment,  a  correlation  analysis  was 
carried  out  in  the  final  step  of  the  proposed  methodology.  This  analysis  also  allowed 
establishing  relations  between  dimensions,  leading  to  determine  key  drivers  for  the 
deployment of wind energy in regions. 

To  obtain  a  final  mark  per  region  and  dimension,  the  average  value  of  the  normalised 
parameters  under  a  particular  dimension  and  region  has  been  calculated.  As  a  result  a 
matrix of dimension 'number of dimensions' x 'number of dimension' is obtained. 

10 

 
 
 
4 

Results 

4.1 

Selection of regions 

According to the methodology presented, regions that have included wind energy in their 
smart specialisation strategy as an interest are presented in Table 2. Taking into account 
the  limited  size  of  the  sector  for  the  case  of  FR94  in  terms  of  the  dimensions  'sectorial 
structure',  'academia',  and  'innovation  capacities',  it  has  been  removed.  The  particular 
characteristics of FR94, pacific island geographically disconnected from Europe, require a 
separate  analysis  and  comparison  will  not  provide  clear  conclusion.  Therefore,  the  final 
analysis covers 12 regions from 7 different European countries. 

Table 2  Region with wind energy interests 

NUTS ID 
DE94 
ES11 
ES12 
ES13 
FR21 
FR23 
FR25 
FR51 
FR94 
PL63 
PT11 
SE33 
UKK3 
Source: [Eye@RIS3 2015] 

Country code 
DE 
ES 
ES 
ES 
FR 
FR 
FR 
FR 
FR 
PL 
PT 
SE 
UK 

NUTS level 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 
3 

Region/Country name 
Weser-Ems 
Galicia 
Principado de Asturias 
Cantabria 
Champagne-Ardenne 
Haute-Normandie 
Basse-Normandie 
Pays de la Loire 
Réunion 
Pomorskie 
Norte 
Övre Norrland 
Cornwall and Isles of Scilly 

This  list  provides  an  interesting  combination  of  regions  as  it  mixes  both  regions  from 
different countries as well as regions within the same country. Having the combination of 
regions within the same country and from different countries allows assessing the impact 
of  national  framework  effect  in  the  penetration  of  the  technology.  It  should  be  also 
mentioned  that  a  common  geographical  aspect  exists:  apart  from  FR94  and  SE33,  all 
regions  belong  to  the  Atlantic  Arc  being  therefore  coastal  regions.  So,  it  may  be 
envisaged that one of the main driver for this group of regions is the resource availability 
in coastal areas linked to the deployment of off-shore wind. 

4.2 

Energy parameters 

4.2.1  Socio-economic dimension 

The  four  parameters  considered  for  the  socio-economic  dimension  are  show  in  Table  3. 
They  give  a  clear  picture  of  the  economic  development  of  regions.  Figure  2  shows  the 
absolute and relative gross domestic product (GDP) of regions. Spanish regions (Galicia, 
Asturias, and Cantabria) have a similar GDP per inhabitant as well as French regions. The 
relative  GDP  in  the  Swedish  region  is  much  higher  compared  to  the  other  regions 
exceeding  EUR  40 000  per  inhabitant.  On  the  contrary,  relative  GDP  of  Pomorskie  in 
Poland is only about EUR 10 000. 

11 

 
Table 3  Parameters under the 'socio-economic' dimension 

Element 
Population 
Urban 
Region wealth 

Parameter 
Total population 
Population in urban areas 
Absolute gross domestic product 
Relative gross domestic product 

Unit 
No of inhabitants 
No of inhabitants 
EUR 
GDP/inhabitant 

Regarding  population  values  and  level  of  urban  population,  there  is  no  correlation 
between  regions  in  the  same  country  (Figure  3).  Therefore  the  effect  of  the  national 
economic in the country has a clear impact in the level of average incomes but not in the 
way the territory is organised. Level of urban or rural population depends on the specific 
characteristics of regions. 

Figure 2  GDP (left) and relative GDP (right) according to region 

Figure 3  Population (left) and rate of urban population (right) according to region 

12 

 
 
 
4.2.2  Energy price dimension 

For the energy price dimension, data considered includes; 

  Energy source: electricity or gas 
  User: final or industrial users  
  Taxes: price before or after taxes 
  Consumption band, depending on the energy source 

In  data  presented  in  Table  4,  just  energy  sources,  users  and  taxes  are  presented  to 
simplify the understanding of the comprehensive list of parameters. However, according 
to energy pricing schemes, levels of consumptions also determine different energy prices. 
Depending  on  the  energy  sources  different  bandwidths  are  defined.  Annex  I  includes 
detailed information about the energy prices.  

It is also important to note that same prices have been considered per MS. Therefore this 
dimension does not lead to any difference amongst regions in the same country. 

Table 4  Parameters under the 'energy price' dimension 

Element 
End use energy price 
before taxes 

End use energy price 
before taxes 

Parameter 
Gas Price for Domestic 
Gas Price for Industrial 
Electricity Price for Domestic 
Electricity Price for Industrial 
Gas Price for Domestic 
Gas Price for Industrial 
Electricity Price for Domestic 
Electricity Price for Industrial 

Unit 
EUR/kWh 
EUR/kWh 
EUR/kWh 
EUR/kWh 
EUR/kWh 
EUR/kWh 
EUR/kWh 
EUR/kWh 

To understand energy prices amongst countries, average final energy price per user and 
energy source are presented (Figure 4). To represent the average price of electricity for 
domestics, prices shown in Annex I (Table 24) have been considered. 

Figure 4  Average final energy prices according to Member State 

13 

 
 
At  this  stage,  a  first  correlation  was  established  with  the  socio-economic  dimension 
presented  in  section  4.2.1.  We  expected  that  regions  with  higher  GDP/inhabitant  have 
higher energy prices. However, in case of electricity, SE33 - representing Sweden - does 
not show the highest  domestic energy cost but does  for domestic gas prices (Figure 5). 
On the contrary, PL63 – representing Poland- has the lowest price for electricity but not 
for gas. The case of Portugal is remarkable. It shows a high value in the electricity cost 
for  domestic  (0.25  EUR/kWh)  even  though  its  relative  low  value  in  terms  of  GDP  per 
inhabitant.  

Figure 5 

Final domestic energy prices and relative GDP according to region 

For  electricity,  the  industrial  price  trend  is  inversely  dependent  with  the  relative  GDP 
increase  when  the  opposite  would  be  expected.  For  the  case  of  gas,  there  is  almost  no 
dependency  with  GDP  (Figure  6).  Therefore,  another  important  conclusion  is  that  as 
economic  driver,  countries  with  low  GDP  have  to  transform  their  energy  market  to 
achieve at least same prices as those with higher GDP. 

Figure 6 

Final industrial energy prices and relative GDP according to region 

14 

 
 
The correlation between GDP and energy price will be presented and discussed in section 
4.4 in more detail. 

4.2.3  Energy use dimension 

The list of proposed parameters is presented in (Table 5). The only parameter for which 
complete  data  is  available  is  the  Heating  Degree  Days  (HDD)2  parameter.  More 
information  about  the  data  availability  for  the  'energy  use'  dimension  is  available  in 
Annex II. 

Table 5  Parameters under the 'energy use' dimension 

Element 
Energy demand 

Parameter 
Heating Degree Days 

Unit 
HDD 

As expected, the Swedish region shows highest HDD, followed by the Polish and German 
regions (Figure 7). The regions in Spain and Portugal show the lowest number of HDD. 

Figure 7  Heating Degree Days 2009 

4.2.4  Wind energy deployment dimension 

The parameters used for the assessment of wind energy deployment are shown in Table 
6.  As  mentioned  in  section  3.3.1,  wind  energy  deployment  is  characterized  by  three 
parameters  (number  of  wind  turbines,  installed  wind  energy  capacity  and  regional 
capacity  factor).  They  are  shown  in  Table  7.  From  these  three  parameters,  both  the 
average size of wind power plants in the regions as well as the energy produced can be 
derived. 

Table 6  Parameters under the 'wind energy deployment' dimension  

Element 
Wind capacity 

Parameter 
Installed capacity 

Unit 
MW wind energy in operation 

2 HDD indicate regional heating demand  

15 

 
 
                                           
Wind energy production 
Wind deployment 

Energy produced 
Number of wind farms 

GWh produced 
No 

Table 7  Results for wind energy deployment  

NUTS ID 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

Wind farms 
(no) 
532 

Installed capacity 
(MW) 
3502 

Capacity factor  
(%) 
0.25 

180 

22 

4 

122 

25 

32 

61 

35 

127 

30 

26 

3405 

514 

38 

1647 

245 

258 

561 

592 

1539 

533 

112 

0.23 

0.15 

0.39 

0.16 

0.32 

0.36 

0.32 

0.29 

0.23 

0.09 

0.56 

Figure  8  shows  the  results  of  the  assessment.  In  most  of  the  cases,  the  electricity 
produced  correlates  with  the  installed  capacity.  In  the  case  of  the  UKK3,  a  very  high 
capacity factor leads to a relatively high electricity production compared to other regions. 

Figure 8 

Installed wind energy capacity and electricity production from wind 

Annex  III  provides  detailed  information  and  maps  about  wind  energy  deployment  in 
every assessed region. 

4.2.5  Academia dimension 

Table 8 summarises the parameters used for the 'academia' dimension. In addition to the 
total number of publications, the share of wind publications compared to all publications 
in  the  area  of  energy  and  the  share  of  wind  publications  compared  to  all  publications 
have been calculated. 

16 

 
 
Table 8  Parameters under the 'academia' dimension  

Element 
Universities 
Regional in-house knowledge  Number of wind publications 

Parameter 
Number of universities 

Share of wind publications 
Share of wind publications in the energy 
area 

Unit 
No 
No 
% 

% 

Results from academia dimension show, in terms of number of publications in the field of 
wind  energy,  high  figures  for  DE94  with  a  relatively  small  number  of  academic  entities 
(3) in comparison with other regions (Figure 9). Also PT11 represents a high number of 
publications  from  its  9 institutions.  On  the  contrary,  for  PL63  the  relative  importance  of 
wind is very limited with 20 publications from 9 entities. 

Figure 9  Number of wind publication vs. number of universities 

In  Figure  10  (left),  the  share  of  wind  publications  compared  to  the  total  number  of 
scientific  publications  (2016)  is  shown.  DE94,  ES13  and  PT11  show  a  relatively  high 
share (0.57 %, 0.34 %, and 0.28 %, respectively). Other regions, such as the regions in 
France, Poland and Sweden show very low shares (between 0.05 % and 0.15 %).  

When we look at the share of wind publications compared to all scientific publications in 
the  area  of  energy  (Figure  10  ,  right),  we  see  a  similar  picture.  However,  the  Spanish 
regions  and  UKK3  score  better  with  relatively  high  shares  (11.9 %  to  17.9 %).  DE94 
leads with 27.4 %. 

The complete list of universities considered in the study can be consulted in Annex IV. 

17 

 
 
 
Figure 10:  Share of wind publications compared to all (left) and all energy-related (right) 

publications  

4.2.6  Sectorial structure dimension 

Table 9 shows the parameters used to assess the sectorial structure of the regions.  

Table 9  Parameters under the 'sectorial structure' dimension 

Element 
Energy sector size 

Parameter 
Number of wind companies  
Share of wind companies in the regions compared 
to total number of wind companies in the 
corresponding MS 

Unit 
No 

% 

Based  on  the  estimations  introduced  in  section  3.3.3,  the  total  number  of  wind  energy 
companies  assumed  per  regions  is  presented  in  Figure  11.  Numbers  range  from  4  for 
ES13  to  82  for  DE94.  It  has  to  be  kept  in  mind  that  the  number  of  companies  is 
estimated  since  for  many  companies  identified,  the  geographical  location  was  not 
available (section 3.3.3). 

Together  with  the  absolute  number  of  wind  companies  in  a  region,  the  importance  of  a 
region  for  the  wind  sector  of  their  countries  is  shown  in  Figure  11.  High  shares  can  be 
seen  for  PT11  which  hosts  about  25 %  of  all  Portuguese  wind  companies,  DE94  and 
PL63, where 10.7 % and 12.6 % of total national companies are located. 

The  share  of  the  wind  sectors  of  all  other  regions  compared  to  the  respective  national 
wind  sector  does  not  exceed  10 %.  However,  it  is  important  to  note  that  none  of  the 
regions  considered  includes  the  capital  city  of  the  Member  State  where  often,  many 
companies usually establish their headquarters. Just to give an estimation of this effect, 
the regions of Madrid (ES30) and Île de France (FR10) include  about 38 % and 46 % of 
national  companies  in Spain  and  France  respectively.  This  effect  of  big  cities  might  also 
explain the results of PT11 as it includes Porto, the second largest city in Portugal.  

18 

 
 
 
 
Figure 11:  Number of wind companies and regional share compared to national level  

4.2.7  Innovation capacities dimension 

In  Table  10,  the  parameters  used  to  assess  the  dimension  'innovation  capacities'  are 
shown. 

Table 10 Parameters under the 'innovation capacities' dimension 

Element 
R&D expenditure 

Parameter 
All sectors 
Business sector 
Government sector 

Unit 
EUR/inhabitant 
EUR/inhabitant 
EUR/inhabitant 

Concerning innovation capacities, SE33 shows the largest  R&D investments, followed by 
three  French  regions  (FR23,  FR25,  FR51)  and  DE94.  R&D  investments  are  smallest  in 
UKK3,  and  PL63.  When  looking  at  relative  numbers  (R&D  investments  per  capita),  the 
gap between SE33 and the other regions is even more pronounced (Annex V).  

When  we  look  at  the  private  sector,  SE33  shows  lower  levels  than  FR23,  meaning  that 
the  contribution  of  government,  higher  education  and  private  non-profit  sectors  is 
considerably  high.  The  case  of  UKK  is  also  interesting  because  here,  95  %  of  the 
investment comes from private sector. On the contrary,  the share  of public investments 
is highest in ES13 or PL63 with about 19 % and 16 %, respectively. 

19 

 
 
Figure 12  Regional R&D expenditure including private and public sector 

4.3  Analysis of similarities 

Similarity  matrixes  are  demonstrated  useful  for  multi  dimension  comparison  and 
clustering  [Mooi  &  Sarstedt  2011].  However,  as  similarity  matrixes  are  based  on  the 
relative  distance  between  elements  to  be  compared,  they  do  not  allow  for  a  ranking  or 
classification  amongst  elements.  In  other  words,  they  provide  a  relative  comparison  to 
enable  clustering  processes.  The  following  dimension  matrixes  give  an  idea  about  how 
similar  regions  are.  A  higher  value  of  the  element  'i,j'  in  the  matrix,  means  a  greater 
distance between regions 'i' and 'j'. 

4.3.1  Dimensional similarity matrixes 

Socio-economic dimension 

The similarity matrix for the socio-economic dimension is shown in  

Table  11.  For  this  dimension,  SE33  is  the  region  farthest  away  from  the  other  regions 
showing an average distance of 3.9, followed by FR51 with an average distance of 3.47.  

The two regions that have less in common are PL63 and SE33 with a distance of 4.87. It 
is  interesting  to  recall  again  the  results  of  the  individual  parameters  of  this  dimension 
population  (section  4.2.1)  where  PL63  and  SE33  show  very  similar  GDP  but  a  high 
difference  in  relative  GDP.  Also,  population  and  urban  population  are  very  different  for 
those two regions. A number of regions show high similarity, including FR21, FR23, ES13, 
and UKK3.  

An  interesting  observation  can  be  made  within  regions  in  the  same  country.  Although, 
from a socio-economic point  of view, regions within the same country were  expected to 
show similar values for this dimension, the similarity amongst them is low. For example, 
the  distance  between  ES11  and  FR21  is  lower  (1.71)  than  the  distance  between  ES11 
and ES13 (2.54).  

20 

 
Table 11 Similarity matrix for the 'socio-economic' dimension  

Region 

DE94  ES11  ES12  ES13  FR21  FR23  FR25  FR51  PL63  PT11  SE33  UKK3 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

0.00  1.61  2.59  3.34  1.97  1.07  2.93  1.94  3.01  3.02  4.37  3.29 

1.61  0.00  2.20  2.54  1.71  1.34  2.20  2.28  1.68  1.54  4.28  2.54 

2.59  2.20  0.00  1.80  1.74  1.66  2.91  4.21  1.75  3.36  4.41  1.57 

3.34  2.54  1.80  0.00  1.50  2.31  1.72  4.43  2.13  3.15  3.15  0.28 

1.97  1.71  1.74  1.50  0.00  1.02  1.44  3.19  2.34  2.83  2.91  1.50 

1.07  1.34  1.66  2.31  1.02  0.00  2.26  2.69  2.32  2.81  3.77  2.24 

2.93  2.20  2.91  1.72  1.44  2.26  0.00  3.34  2.79  2.54  2.34  1.93 

1.94  2.28  4.21  4.43  3.19  2.69  3.34  0.00  3.93  2.81  4.85  4.49 

3.01  1.68  1.75  2.13  2.34  2.32  2.79  3.93  0.00  2.03  4.87  2.06 

3.02  1.54  3.36  3.15  2.83  2.81  2.54  2.81  2.03  0.00  4.73  3.25 

4.37  4.28  4.41  3.15  2.91  3.77  2.34  4.85  4.87  4.73  0.00  3.33 

UKK3 

3.29  2.54  1.57  0.28  1.50  2.24  1.93  4.49  2.06  3.25  3.33  0.00 

Average  

2.65  2.17  2.56  2.40  2.01  2.14  2.40  3.47  2.63  2.92  3.91  2.41 

Energy price dimension 

In the case of the energy price dimension, based on the assumptions of a homogeneous 
national  energy  market  price,  regions  in  the  same  country  such  as  the  Spanish  and 
French regions show a distance of 0 (Table 12). 

In  total,  38  different  parameters  (energy  prices)  are  compared  (Annex  I)  including 
industrial  and  domestic  prices,  gas  and  electricity  prices  and  prices  with  and  without 
taxes.  So,  the  distance  matrix  reflects  the  effects  of  consumer  profile,  energy  sources 
and  taxation  schemes.  For  the  energy  price,  Sweden  &  United  Kingdom  and  Sweden  & 
Germany are the most separated countries; meanwhile Poland and France are the closest 
when we compare regions from two different countries.  

Table 12 Distance matrix for the 'energy price' dimension group indicators 

Region 

DE94  ES11  ES12  ES13  FR21  FR23  FR25  FR51  PL63  PT11  SE33  UKK3 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

0.00  7.86  7.86  7.86  9.51  9.51  9.51  9.51  10.20  10.79  13.06  8.65 

7.86  0.00  0.00  0.00  8.71  8.71  8.71  8.71  10.32  7.58  11.09  7.21 

7.86  0.00  0.00  0.00  8.71  8.71  8.71  8.71  10.32  7.58  11.09  7.21 

7.86  0.00  0.00  0.00  8.71  8.71  8.71  8.71  10.32  7.58  11.09  7.21 

9.51  8.71  8.71  8.71  0.00  0.00  0.00  0.00  6.44  9.36  8.47  9.49 

9.51  8.71  8.71  8.71  0.00  0.00  0.00  0.00  6.44  9.36  8.47  9.49 

9.51  8.71  8.71  8.71  0.00  0.00  0.00  0.00  6.44  9.36  8.47  9.49 

9.51  8.71  8.71  8.71  0.00  0.00  0.00  0.00  6.44  9.36  8.47  9.49 

10.20  10.32  10.32  10.32  6.44  6.44  6.44  6.44  0.00  12.04  11.20  9.99 

10.79  7.58  7.58  7.58  9.36  9.36  9.36  9.36  12.04  0.00  9.86  11.08 

13.06  11.09  11.09  11.09  8.47  8.47  8.47  8.47  11.20  9.86  0.00  13.80 

UKK3 

8.65  7.21  7.21  7.21  9.49  9.49  9.49  9.49  9.99  11.08  13.80  0.00 

Average  

9.48  7.17  7.17  7.17  6.31  6.31  6.31  6.31  9.10  9.45  10.46  9.37 

21 

 
Energy use dimension 

In  this  case  as  only  one  parameter  is  considered,  the  distance  is  the  same  as  what  the 
Heating Degree Days parameter indicates. In the overall similarity matrix, which includes 
all  the  parameters  (section  4.3.2),  normalised  values  for  this  parameter  have  been 
incorporated. 

Wind energy deployment dimension 

In  the  case  of  deployment,  there  is  not  identified  a  certain  level  of  proximity  amongst 
regions in the same country for Spain, but it is in the case of French regions. The reason 
for this behaviour is probably the existence of resources in each region beyond legislation 
or national support. However as it can be checked for regions in Spain, ES11 is far from 
the other two, even when they are geographically close and it could be assumed similar 
levels of available resources. 

Table 13 Distance matrix for the 'wind deployment' dimension group indicators 

Region 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

Average  

DE94  ES11  ES12  ES13  FR21  FR23  FR25  FR51  PL63  PT11  SE33  UKK3 

0.00  2.40  4.31  4.72  3.24  4.40  4.41  4.05  4.52  4.14  3.42  4.89 

2.40  0.00  2.68  3.25  1.59  2.87  2.94  2.55  2.99  2.50  1.94  3.67 

4.31  2.68  0.00  1.95  1.15  1.34  1.67  1.35  1.17  0.64  1.20  3.29 

4.72  3.25  1.95  0.00  2.38  0.63  0.37  0.82  0.81  1.38  2.82  1.44 

3.24  1.59  1.15  2.38  0.00  1.80  2.02  1.57  1.80  1.17  0.59  3.37 

4.40  2.87  1.34  0.63  1.80  0.00  0.33  0.36  0.27  0.75  2.20  1.98 

4.41  2.94  1.67  0.37  2.02  0.33  0.00  0.46  0.58  1.06  2.46  1.65 

4.05  2.55  1.35  0.82  1.57  0.36  0.46  0.00  0.56  0.72  2.03  1.97 

4.52  2.99  1.17  0.81  1.80  0.27  0.58  0.56  0.00  0.67  2.14  2.21 

4.14  2.50  0.64  1.38  1.17  0.75  1.06  0.72  0.67  0.00  1.49  2.66 

3.42  1.94  1.20  2.82  0.59  2.20  2.46  2.03  2.14  1.49  0.00  3.91 

4.89  3.67  3.29  1.44  3.37  1.98  1.65  1.97  2.21  2.66  3.91  0.00 

4.05  2.67  1.89  1.87  1.88  1.54  1.63  1.49  1.61  1.56  2.20  2.82 

Academia structure dimension 

In terms of the role of wind in the academia, a national pattern can be observed both for 
Spain  and  France  with  the  exception  of  FR51  (Table  14).  The  regions  that  are  most 
similar to other regions are SE33, ES12, and FR25. The regions that differ most from the 
others  are  DE94  and  PT11.  This  can  be  explained  by  the  very  high  number  of  scientific 
publications and universities in PT11 and the very high number of scientific publications 
in DE94 (section 4.2.5). 

Table 14 Distance matrix for the 'academia' dimension group indicators  

Regions 

DE94  ES11  ES12  ES13  FR21  FR23  FR25  FR51  PL63  PT11  SE33  UKK3 

DE94 

ES11 

ES12 

ES13 

FR21 

0.00  3.67  3.27  2.99  5.20  5.38  4.95  4.42  5.35  3.48  4.04  3.25 

3.67  0.00  1.24  1.49  1.78  2.01  1.55  1.69  2.55  3.57  1.21  0.60 

3.27  1.24  0.00  0.68  2.04  2.20  1.80  2.27  3.23  3.67  1.03  1.12 

2.99  1.49  0.68  0.00  2.42  2.59  2.18  2.53  3.49  3.94  1.65  1.33 

5.20  1.78  2.04  2.42  0.00  0.25  0.25  1.96  2.38  4.65  1.40  2.23 

22 

  
FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

5.38  2.01  2.20  2.59  0.25  0.00  0.47  2.05  2.40  4.75  1.53  2.46 

4.95  1.55  1.80  2.18  0.25  0.47  0.00  1.85  2.37  4.48  1.20  2.00 

4.42  1.69  2.27  2.53  1.96  2.05  1.85  0.00  1.00  3.19  1.78  2.20 

5.35  2.55  3.23  3.49  2.38  2.40  2.37  1.00  0.00  3.71  2.61  3.10 

3.48  3.57  3.67  3.94  4.65  4.75  4.48  3.19  3.71  0.00  3.52  3.53 

4.04  1.21  1.03  1.65  1.40  1.53  1.20  1.78  2.61  3.52  0.00  1.41 

UKK3 

3.25  0.60  1.12  1.33  2.23  2.46  2.00  2.20  3.10  3.53  1.41  0.00 

Average 

4.18  1.94  2.05  2.30  2.23  2.37  2.10  2.27  2.92  3.86  1.94  2.11 

Sectorial structure dimension 

For  this  dimension,  we  have  looked  at  the  structure  of  the  wind  energy  sector  in  the 
region (section 4.2.6). Clearly, PT11, PL63 and DE94 stand out. In the case of DE94, this 
is  because  of  a  high  number  of  companies  while  in  the  case  of  PT11  and  PL63,  it  is 
because  the  regions'  share  compared  to  Portugal  is  very  high.  The  Spanish  and  French 
regions show greatest similarities. 

Table 15 Distance matrix for the 'sectorial structure' dimension group indicators 

Regions 

DE94  ES11  ES12  ES13  FR21  FR23  FR25  FR51  PL63  PT11  SE33  UKK3 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

0.00  1.44  2.87  3.71  3.56  3.56  3.45  3.33  2.91  3.26  2.81  3.38 

1.44  0.00  1.45  2.31  2.14  2.14  2.02  1.90  1.60  2.63  1.37  1.98 

2.87  1.45  0.00  0.85  0.70  0.70  0.58  0.48  1.31  3.08  0.33  0.52 

3.71  2.31  0.85  0.00  0.21  0.21  0.33  0.46  1.79  3.60  1.02  0.33 

3.56  2.14  0.70  0.21  0.00  0.00  0.13  0.26  1.58  3.39  0.83  0.23 

3.56  2.14  0.70  0.21  0.00  0.00  0.13  0.26  1.58  3.39  0.83  0.23 

3.45  2.02  0.58  0.33  0.13  0.13  0.00  0.13  1.47  3.28  0.70  0.20 

3.33  1.90  0.48  0.46  0.26  0.26  0.13  0.00  1.36  3.17  0.57  0.25 

2.91  1.60  1.31  1.79  1.58  1.58  1.47  1.36  0.00  1.81  0.99  1.60 

3.26  2.63  3.08  3.60  3.39  3.39  3.28  3.17  1.81  0.00  2.76  3.41 

2.81  1.37  0.33  1.02  0.83  0.83  0.70  0.57  0.99  2.76  0.00  0.73 

UKK3 

3.38  1.98  0.52  0.33  0.23  0.23  0.20  0.25  1.60  3.41  0.73  0.00 

Average 

3.12  1.91  1.17  1.35  1.18  1.18  1.13  1.11  1.64  3.07  1.17  1.17 

Innovation capacities dimension 

Under  this  dimension,  the  Spanish  regions  show  very  high  similarities  with  a  maximum 
distance of 0.51 (Table 16). In terms of individual regions, SE33 is very distinct from the 
other regions since its R&D spending is more than twice compared to the following region 
(FR23)  as  shown  in  section  4.2.7.  UKK3  with  least  R&D  investment  is  also  dissimilar 
compared  to  other  regions.  DE94  presents  an  average  in  terms  of  R&D  investment  and 
R&D personnel and thus show highest similarity on average. 

Table 16 Distance matrix for the 'innovation capacities' dimension group indicators 

Regions 

DE94  ES11  ES12  ES13  FR21  FR23  FR25  FR51  PL63  PT11  SE33  UKK3 

23 

 
 
 
DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

0.00  0.81  0.69  1.12  0.88  2.32  0.86  1.03  1.16  0.85  4.28  1.66 

0.81  0.00  0.14  0.48  1.34  3.12  1.66  1.77  0.60  0.92  4.56  1.37 

0.69  0.14  0.00  0.51  1.30  3.01  1.55  1.64  0.71  0.93  4.48  1.45 

1.12  0.48  0.51  0.00  1.80  3.42  1.93  1.93  0.99  1.40  4.33  1.79 

0.88  1.34  1.30  1.80  0.00  2.18  1.09  1.54  1.28  0.56  4.94  1.28 

2.32  3.12  3.01  3.42  2.18  0.00  1.49  1.62  3.34  2.70  4.49  3.45 

0.86  1.66  1.55  1.93  1.09  1.49  0.00  0.49  1.97  1.44  4.06  2.29 

1.03  1.77  1.64  1.93  1.54  1.62  0.49  0.00  2.19  1.79  3.66  2.64 

1.16  0.60  0.71  0.99  1.28  3.34  1.97  2.19  0.00  0.74  5.12  0.80 

0.85  0.92  0.93  1.40  0.56  2.70  1.44  1.79  0.74  0.00  4.98  0.88 

4.28  4.56  4.48  4.33  4.94  4.49  4.06  3.66  5.12  4.98  0.00  5.77 

UKK3 

1.66  1.37  1.45  1.79  1.28  3.45  2.29  2.64  0.80  0.88  5.77  0.00 

Average  

1.42  1.52  1.49  1.79  1.65  2.83  1.71  1.85  1.72  1.56  4.60  2.12 

4.3.2  Overall similarity matrix 

All  parameters  assessed  can  be  combined  into  one  single  distance  matrix  (Table  17). 
From  this  matrix,  it  is  clear  that  regions  in  the  same  country  have  highest  similarities: 
ES11,  ES12  and  ES13  on  the  one  hand  and  FR21,  FR23,  FR25  and  FR51  on  the  other 
hand. On the contrary the most separated regions are SE33 and UKK3 followed by SE33 
and DE94. To understand the reasons behind these results, an analysis of correlation was 
performed  and  is  presented  in  section  4.5.  Clearly,  the  similarities  are  driven  by 
parameters that could not be retrieved on regional level but where national data had to 
be used. This was the case for energy prices, a dimension which included a high number 
of  parameters  (38).  Subsequently,  we  will  explore  various  effects  of  weighting  and  PCA 
on the results (section 4.4).  

Table 17 Comprehensive overall similarity matrix 

Regions  DE94  ES11  ES12  ES13  FR21  FR23  FR25  FR51  PL63  PT11  SE33  UKK3 

9.5  10.5  11.1  12.3  12.8  12.8  12.2  13.3  13.0  16.1  11.6 

0.0 

9.5 

10.5 

11.1 

12.3 

0.0 

4.0 

5.0 

9.6 

4.0 

0.0 

2.9 

9.4 

5.0 

2.9 

0.0 

9.7 

12.8  10.3 

9.8  10.1 

12.8  10.0 

9.7 

9.4 

12.2 

9.9  10.2  10.4 

13.3  11.4  11.2  11.4 

9.6  10.3  10.0 

9.9  11.4 

9.3  13.5 

9.4 

9.8 

9.7  10.2  11.2 

9.7  13.4 

9.7  10.1 

9.4  10.4  11.4  10.0  13.3 

9.0 

8.5 

7.9 

0.0 

3.0 

2.7 

4.4 

7.8 

3.0 

0.0 

2.8 

3.8 

8.2 

2.7 

2.8 

0.0 

3.9 

7.9 

4.4 

3.8 

3.9 

0.0 

8.1 

7.8  11.5  10.8  10.8 

8.2  11.8  11.1  11.1 

7.9  11.4  10.6  10.6 

8.1  11.0  11.3  11.5 

0.0  13.1  13.9  11.3 

13.0 

9.3 

9.7  10.0  11.5  11.8  11.4  11.0  13.1 

0.0  13.5  12.9 

16.1  13.5  13.4  13.3  10.8  11.1  10.6  11.3  13.9  13.5 

0.0  16.4 

UKK3 
Average 

11.6 

12.3 

9.0 

9.2 

8.5 

9.0 

7.9  10.8  11.1  10.6  11.5  11.3  12.9  16.4 

0.0 

9.2 

8.4 

8.6 

8.3 

8.8  10.7  11.6  13.1  11.1 

The  overall  similarity  matrix  can  also  be  represented  in  2D  dimensions  which  helps 
understanding  [Buja  et  al.  2008].  Figure  13  reinforces  the  conclusion  of  similarities 

24 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

 
 
amongst  regions  in  the  same  country.  PL63,  DE94,  PT11  and  SE33  are  show  least 
similarities.  

Figure 13  2D distance representation of the overall similarity matrix. Non-weighted analysis 

4.4  Weighting & Principal component analysis 

As  presented  in  the  previous  section,  the  number  of  parameters  under  a  particular 
dimension  may  affect  the  multidimensional  analysis.  In  particular,  the  energy  price 
dimension,  which  includes  a  high  number  of  parameters  (48)  at  MS  level,  produces  a 
concentration of regions within the same country (see Figure 13). To minimise the effect 
of  aggregation  we  have  assessed  two  methods:  weighting  and  principal  component 
analysis. 

4.4.1  Weighting method 

In the weighting method, we have applied a coefficient for all the parameters within the 
same dimension equal to the inverse of the sum of parameters within a given dimension. 
By  doing  this,  we  ensure  all  the  dimensions  have  the  same  effect  in  the  complete 
analysis.   

Results obtained are shown in Table 18 and Figure 14 

Table 18 Comprehensive overall similarity matrix. Weighted version 

Regions 

DE94  ES11  ES12  ES13  FR21  FR23  FR25  FR51  PL63  PT11  SE33  UKK3 

DE94 

0.00  1.70  2.42  2.79  2.54  2.81  2.73  2.55  2.70  2.63  3.93  2.70 

25 

 
 
 
 
ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

1.70  0.00  1.32  1.76  1.58  1.98  1.74  1.62  2.05  1.88  4.17  1.85 

2.42  1.32  0.00  0.93  1.15  1.47  1.28  1.45  1.81  2.03  4.03  1.41 

2.79  1.76  0.93  0.00  1.36  1.54  1.06  1.50  1.91  2.32  4.01  0.96 

2.54  1.58  1.15  1.36  0.00  0.98  0.87  1.26  1.55  2.37  3.49  1.41 

2.81  1.98  1.47  1.54  0.98  0.00  0.78  1.07  1.79  2.50  3.61  1.59 

2.73  1.74  1.28  1.06  0.87  0.78  0.00  1.01  1.62  2.27  3.56  1.20 

2.55  1.62  1.45  1.50  1.26  1.07  1.01  0.00  1.81  2.07  3.84  1.71 

2.70  2.05  1.81  1.91  1.55  1.79  1.62  1.81  0.00  2.10  3.26  1.70 

2.63  1.88  2.03  2.32  2.37  2.50  2.27  2.07  2.10  0.00  4.59  2.40 

3.93  4.17  4.03  4.01  3.49  3.61  3.56  3.84  3.26  4.59  0.00  3.98 

UKK3 

2.70  1.85  1.41  0.96  1.41  1.59  1.20  1.71  1.70  2.40  3.98  0.00 

Average 

2.68  1.97  1.76  1.83  1.69  1.83  1.65  1.81  2.03  2.47  3.86  1.90 

Figure 14  2D distance representation of the overall similarity matrix. Weighted analysis 

If  Figure  13  and  Figure  14  are  compared,  it  is  clear  that  when  the  dimensions  are 
weighted  the  effect  of  parameters  at  national  level  is  reduced.  Therefore,  a  weighting 
approach is required to avoid national effects. 

4.4.2  Principal component analysis 

Once the weighted analysis was carried out, the next step was to assess if we can reduce 
the number of parameters to the minimum that provides  an appropriate variance in the 
dataset. To do that, we perform the principal component analysis [Shlens 2003]. 

Firstly,  the  PCA  is  applied  to  the  parameters  without  applying  weights  as  presented  in 
section 4.3.2 and then to the weighted according (section 4.4.1). 

26 

 
 
Some  considerations  that  have  to  be  taken  into  account  when  performing  the  principal 
component analysis are: 

  The  number  of  retained  components  has  to  be  able  to  explain  at  least  80%  of 

variance. 

  Only  those  parameters  with  a  factor  values  higher  than  0.20  in  absolute  value 

have been retained. 

When the PCA is applied to non-weighted data, 4 components are required and a total of 
23  variables  are  kept,  which  represent  a  reduction  of  58 %  compared  to  the  original 
dataset. (see Annex VI). If data are previously weighted,  3 components are required to 
explain  at  least  80%  of  variance  (Figure  21).  Then,  only  12  parameters  (78%  of 
reduction) are sufficient to explain the dataset (Table 19) 

100.0%

90.0%

80.0%

70.0%

60.0%

50.0%

40.0%

30.0%

20.0%

10.0%

0.0%

46.6% 

23.2% 

11.4% 

1

2

3

indiviudal explained variance

cumulative explanied variance

Figure 15 Cumulative explained variance ratio. PCA weighted dataset 

Table 19 Parameters included in the reduced dataset. PCA weighted 

Dimension 

Socio-economic 

Wind energy deployment 

Energy demand 

Academia 

Parameters 

Relative GDP 

Installed capacity (MW) 

Number of wind farms 

Heating Degree Days 

Number of universities 

Number of wind publication 

Sectorial structure 

Number of companies with innovative activities 

Share of wind publication in the energy area 

Share of wind publications  

Innovation capacities 

Representativeness of the regional sector at the 
national level. Energy sector size 
R&D expenditures. All sectors 

R&D expenditures. Government sector 

27 

 
 
 
 
The  reduced  dataset  includes  6  out  of  7  dimensions.  The  energy  price  dimension  is  the 
only dimension not present, which was the only dimension with information at MS level. 

Comparison of results 

In  order  to  assess  whether  the  proposed  dataset  is  providing  the  required  information, 
the 2D regional distance is compared before and after applying the PCA (Figure 16). 

Figure 16  2D distance representation of the overall similarity matrix. Weighted analysis and 

dimensional reduction by PCA 

As  it  can  be  assessed  in  Figure  16,  dimensional  reduction  has  produced  similar  results 
compared to the analysis with the complete dataset. From a clustering perspective, both 
analyses  provide  a  well-defined  cluster  including  ES13,  ES12,  UKK3  and  the  4  French 
regions (FR21, FR25, FR23 and FR51). On the other hand, in both cases DE94 and SE33 
remain as very separate entities. 

4.5  Analysis of potential correlation 

Further  to  the  information  introduced  in  the  previous  section,  the  distance  matrix  is 
presented through a graphical correlation matrix providing information about dependency 
between dimensions (Figure 17). This matrix is symmetric showing a perfect correlation 
in the principal diagonal because each region is compared with itself. For the rest of the 
elements some interesting conclusions can be extracted. 

First of all, there are no significant correlations between any of the dimensions analysed. 
The  correlation  coefficients  range  from  0.002  to  0.686  (Table  20).  The  dimensions  that 
show  a  somehow  higher  correlation  are  the  academia  and  the  sectorial  structure, 
innovation  capacities  and  energy  use,  and  wind  energy  deployment  and  sectorial 
structure. 

28 

 
 
 
 
Figure 17  Dimension matrix   

Table 20 Correlation coefficient r between dimensions after applying PCA 

Socio 
economic 

Energy 
use 

Wind 
energy 
deployment 

Academia 

Sectorial 
Structure 

Innovation 
capacities 

Socio 
economic 

1.000 

29 

 
 
 
 
 
 
 
 
 
 
 
 
Energy use 
Wind 
energy 
deployment 
Academia 

Sectorial 
Structure 

Innovation 
capacities 

0.119 

1.000 

0.407 

0.013 

0.059 

0.210 

1.000 

0.034 

1.000 

0.010 

0.043 

0.180 

0.255 

1.000 

0.546 

0.014 

0.655 

0.011 

0.211 

1.000 

According  to  the  data  presented,  wind  energy  deployment  is  correlated  with  the 
innovation  capacities  in  the  region  but  not  with  the  sectorial  structure  as  it  would  be 
expected.  Additionally,  the  socio-economic  dimension  is  considerably  correlated  with 
innovation capacities and wind energy deployment. Then we can conclude that the three 
of them are linked. 

In  relation  to  the  sectorial  structure,  this  dimension  is  not  highly  correlated  with  any 
other  dimension  although  it  would  be  expected  with  innovation  capacities.  Academia  is 
also  not  correlated  with  any  other  dimension  when  it  was  likely  to  be  linked  to  the 
sectorial structure. 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
5 

Conclusions 

Methodology objective 

This  pilot  study  developed  a  methodology  to  assess  the  status  or  regions  related  to 
energy  and  applied  it  to  wind  energy  as  a  case  study.  The  ultimate  objective  of  such 
analysis  is  to  identify  regions  that  are  possibly  more  advanced  and  regions  that  may 
benefit  most  from  learning.  The  methodology  helps  to  understand  barriers  and  gaps 
towards  regional  deployment  of  energy  technologies  as  well  as  the  identification  of  the 
set  of  parameters  that  better  defines  similarities  amongst  regions.  Ultimately,  the 
methodology  pursues  the  creation  of  regional  clusters  including  regions  with  similar 
problems or level of development. 

5.1 

Conclusions related to the methodology 

Set of parameters and data availability 

The main challenge we faced in this analysis was the selection of parameters that could 
better define the technology status in regions and data availability. These two issues are 
directly linked since data access limits initial identification of parameters. Therefore, as a 
proof of concept, the analysis explores data availability.  

Available  information  varies  amongst  regions,  even  in  the  case  of  same  data  sources. 
This  situation  may  prevent  from  extending  the  analysis  to  a  larger  number  of  regions. 
The  case  of  parameters  related  to  energy  use  (such  as  final  energy  consumption)  is 
especially  critical,  (Table  29  and  Table  30).  An  extended  analysis  including  different 
sources  may  be  crucial  to  map  energy  supply  and  demand  in  regions.  In  any  case,  the 
data for the final list of parameters used in this work is available for most of the regions 
in  Europe.  Therefore,  the  analysis  could  be  extended  to  regions  but  not  increased  in 
terms of new parameters. 

Lack of regional information 

To overcome the lack of information at regional level and in order to carry out extensive 
statistical  analyses,  proxy  values  derived  from  the  national  level  could  be  developed. 
However  singularities  of  regions  amongst  regions  in  the  same  countries  will  be  lost  be 
interpolating  from  MS  level.  As  presented  in  the  previous  sections,  energy  price 
dimension comprises a set of parameters defined at MS level. Therefore, this dimension 
increases  the  level  of  similarities  amongst  regions  in  the  same  country  as  it  was 
discussed  in  section  4.2.2.  So,  data  at  MS  level  has  to  be  processed  and  weighted  to 
minimise this national effect. 

Data structure 

To  simplify  the  understanding  of  the  data  analysis,  data  has  been  structured  in 
dimensions  that  encompass  one  or  several  parameters.  These  dimensions  allow 
comparison between regions and establishing correlations. Through these dimensions we 
come  to  conclusions  such  as  the  existence  of  a  negative  correlation  between  the  socio-
economic  status  and  energy  prices  or  a  positive  between  innovation  capacities  and  the 
energy use in regions (Figure 17). 

Statistical analysis to reduce number of parameters 

Due  to  the  lack  of  a  pre-defined  comprehensive  list  of  parameters  because  of  the 
complexity of the regional energy systems, a dimensional reduction was assessed. It was 
expected that the dimension energy price may not be needed when defining differences 

31 

 
 
 
amongst regions. Even more, some other parameters may not be necessary to determine 
regional similarities.  

For  this  reason,  PCA  was  applied.  As  presented  in  section  4.4.2,  the  dataset  could  be 
reduced  to  only  12  parameters  instead  of  55.  As  presented  in  Table  19,  all  dimensions 
remain  in  the  reduced  dataset  except  for  the  energy  price  dimension  as  it  was  initially 
expected. 

Replicability & Robustness  

As mentioned, the initial goal of this study was the definition of a methodology that could 
be easily replicated for other energy technology and other regions.  

Although  most  of  the  proposed  data  is  available  for  every  European  region,  the  major 
drawback  of  the  methodology  is  that  for  some  further  technologies  specific  parameters 
are not easy to define, especially for those technologies that show interactions with many 
other technologies. This is for example the case of heating and cooling technologies that 
may  appear  in  combination  with  renewables  and  some  other  energy  solutions.  In  any 
case, the methodology could be easily extended to many other technologies like ocean or 
solar (including CSP, STE and PV). 

In terms of robustness  from an statistical point of  view, it is important to highlight that 
the more regions we have the more robust conclusions are. In our case, 12 regions have 
been assessed. However if more region from different countries are included, results will 
be more reliable. 

Concerning the PCA and due  to the limited number  of regions, it is required to run  the 
PCA  exercise  for  a  different  set  of  regions  under  different  technologies  and  compare 
whether the retained parameters woulc be the same as in the wind pilot case. 

5.2 

Conclusions related to wind 

Based on the analysis carried out, the following conclusions can be extracted: 

Regions  within  the  same  country  present  a  high  level  of  similarities,  even  when  energy 
prices are out of the analysis as in the case of the PCA application (Figure 15). 

Regional overview 

SE33 remains as outlier in most of the dimension except for Academia and Energy Use.  

The Spanish and French regions and UKK3 rank similarly. This is the reason why they are 
good candidates to define a working group based on the variables considered. 

Although  PL63  and  PT11  rank  low  in  the  socio-economic  dimension  they  show  average 
values in the other dimensions. In particular, PT11 ranks high for academia and sectorial 
structure. 

Correlation overview 

There is no strong correlation between dimensions. Only innovation capacities seem to be 
correlated  with  socio-economic  and  wind  energy  deployment  dimensions.  Therefore,  we 
can  conclude  that  innovation  support  the  development  of  wind  energy  at  the  regional 
scale. 

Final mark 

Finally, to ensure the consistency of the outcomes from the analysis, more regions will be 
required.  In  the  future,  when  more  regions  show  their  interest  in  wind,  they  will  be 
incorporated in this analysis. 

32 

 
 
 
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[Foray  &  Goenega  2013]  Foray  D  and  Goenega  X:  The  Goals  of  Smart  Specialisation 

(18). DOI:10.2791/20158 

[Gonzalez Aparicio et al. 2016] Gonzalez Aparicio I, Andreas Z, Francesco C, Fabio M and 

Thomas H: EMHIRES dataset Part I : Wind power generation 

[IEA 2001] International Energy Agency: Indicators for Sustainable Energy Development 

(20) 

[Jiménez  Navarro  &  Uihlein  2016]  Jiménez  Navarro  JP  and  Uihlein  A:  Mapping  regional 

energy interests for S3PEnergy 

[Mooi  &  Sarstedt  2011]  Mooi  E  and  Sarstedt  M:  Cluster  Analysis.    Springer  Berlin 
Heidelberg,  Berlin,  Heidelberg.  Available  at:  http://dx.doi.org/10.1007/978-3-642-
12541-6_9 

[Navarro  et  al.  2014]  Navarro  M,  Gibaja  JJ,  Franco  S,  Murciego  A,  Gianelle  C,  Hegy  FB 
and  Kleibrink  A:  Regional  benchmarking  in  the  smart  specialisation  process : 
Identification of reference regions based on structural similarity (1–35) 

33 

[Scopus 2016] Scopus: Scopus. URL: http://www.scopus.com/ 

[Shlens 2003] Shlens J: A tutorial on principal component analysis 

[The Wind Power 2015] The Wind Power: The wind power wind farm dataset 

34 

 
List of abbreviations and definitions 

GDP 

Gross Domestic Product 

HDD 

Heating Degree Days  

KPI 

Key Performance Indicators 

KTOE  Kilo tonne of Oil Equivalent 

MS 

Member State 

NUTS   Nomenclature of territorial units for statistics 

PCA 

Principal Component Analysis 

RIS3 

Regional Research and Innovation Strategies for Smart Specialisation 

35 

 
 
 
 
List of figures 

Figure 1  Share of wind energy companies with no geographical information available . 9 

Figure 2  GDP (left) and relative GDP (right) according to region .............................. 12 

Figure 3 

Population (left) and rate of urban population (right) according to region ..... 12 

Figure 4  Average final energy prices according to Member State ............................. 13 

Figure 5 

Final domestic energy prices and relative GDP according to region .............. 14 

Figure 6 

Final industrial energy prices and relative GDP according to region ............. 14 

Figure 7  Heating Degree Days 2009 .................................................................... 15 

Figure 8 

Installed wind energy capacity and electricity production from wind ............ 16 

Figure 9  Number of wind publication vs. number of universities .............................. 17 

Figure 10:  Share of wind publications compared to all (left) and all energy-related (right) 
publications.......................................................................................... 18 

Figure 11:  Number of wind companies and regional share compared to national level ... 19 

Figure 12  Regional R&D expenditure including private and public sector..................... 20 

Figure 13  2D  distance  representation  of  the  overall  similarity  matrix.  Non-weighted 
analysis ............................................................................................... 25 

Figure 14  2D distance representation of the overall similarity matrix. Weighted analysis26 

Figure 15 Cumulative explained variance ratio. PCA weighted dataset ......................... 27 

Figure 16  2D distance representation of the overall similarity matrix. Weighted analysis 
and dimensional reduction by PCA .......................................................... 28 

Figure 17  Dimension matrix ................................................................................. 29 

Figure 18  Installed wind farms in DE94, ES11, ES12 and ES13. ................................ 45 

Figure 19  Installed wind farms in FR21, FR23, FR25 and FR51. ................................. 46 

Figure 20  Installed wind farms in PL63, PT11, SE33 and UKK3. ................................. 47 

Figure 21  Cumulative explained variance ratio. PCA non-weighted dataset ................. 51 

36 

 
 
 
List of tables 

Table 1 

List of indicators used for the wind regional benchmarking .......................... 7 

Table 2 

Region with wind energy interests........................................................... 11 

Table 3 

Parameters under the 'socio-economic' dimension .................................... 12 

Table 4 

Parameters under the 'energy price' dimension ......................................... 13 

Table 5 

Parameters under the 'energy use' dimension .......................................... 15 

Table 6 

Parameters under the 'wind energy deployment' dimension ....................... 15 

Table 7 

Results for wind energy deployment ........................................................ 16 

Table 8 

Parameters under the 'academia' dimension ............................................. 17 

Table 9 

Parameters under the 'sectorial structure' dimension ................................. 18 

Table 10  Parameters under the 'innovation capacities' dimension ............................. 19 

Table 11  Similarity matrix for the 'socio-economic' dimension ................................. 21 

Table 12  Distance matrix for the 'energy price' dimension group indicators ............... 21 

Table 13  Distance matrix for the 'wind deployment' dimension group indicators ......... 22 

Table 14  Distance matrix for the 'academia' dimension group indicators ................... 22 

Table 15  Distance matrix for the 'sectorial structure' dimension group indicators ....... 23 

Table 16  Distance matrix for the 'innovation capacities' dimension group indicators ... 23 

Table 17  Comprehensive overall similarity matrix .................................................. 24 

Table 18 Comprehensive overall similarity matrix. Weighted version ........................... 25 

Table 19  Parameters included in the reduced dataset. PCA weighted ........................ 27 

Table 20  Correlation coefficient r between dimensions after applying PCA ................. 29 

Table 29: Total final energy consumption (ktoe) from 2009 to 2013 ........................... 44 

Table 30: Total final energy consumption (ktoe) by households from 2009 to 2013 ...... 44 

Table 31  List of universities in Wind interest regions .............................................. 48 

Table 32  Total  intramural  R&D  expenditure  (GERD)  by  sectors  of  performance  and 
NUTS 2 regions 2013 (EUR/inhabitant) .................................................... 50 

Table 33  Total R&D personnel by 2013 ................................................................. 50 

Table 34  Parameters included in the reduced dataset. PCA non-weighted.................. 51 

37 

 
 
Annexes 

38 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex I 

Energy price parameters 

Gas prices for domestic users 

Table 21  Gas price before taxes and levies for domestic users according to the range of energy 

consumption in 2014 

NUTS2 
Code 

Before taxes and levies 

Band D1 : Consumption 
< 20 GJ 

Band D2 : 20 GJ < 
Consumption < 200 GJ 

Band D3 : Consumption > 
200 GJ 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

Source: [Eurostat 2015c] 

0.0810 

0.0986 

0.0986 

0.0986 

0.1249 

0.1249 

0.1249 

0.1249 

0.0526 

0.0966 

0.1106 

0.0924 

0.0513 

0.0769 

0.0769 

0.0769 

0.0626 

0.0626 

0.0626 

0.0626 

0.0407 

0.0802 

0.0626 

0.0615 

0.0474 

0.0474 

0.0474 

0.0562 

0.0562 

0.0562 

0.0562 

0.0510 

0.0390 

0.0709 

0.0511 

0.0535 

Table 22  Gas final price for domestic users according to the range of energy consumption in 2014 

NUTS2 
Code 

Final price 

Band D1 : Consumption < 20 
GJ 

Band D2 : 20 GJ < 
Consumption < 200 GJ 

Band D3 : Consumption > 
200 GJ 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

0.1063 

0.1221 

0.1221 

0.1221 

0.1512 

0.1512 

0.1512 

0.1512 

0.0647 

0.1256 

0.1739 

0.0970 

Source: [Eurostat 2015c] 

0.0634 

0.0708 

0.0708 

0.0708 

0.0621 

0.0621 

0.0621 

0.0621 

0.0479 

0.0923 

0.0995 

0.0561 

0.0681 

0.0959 

0.0959 

0.0959 

0.0762 

0.0762 

0.0762 

0.0762 

0.0500 

0.1039 

0.1138 

0.0646 

39 

 
 
 
 
Electricity prices for domestic 

Table 23  Electricity price before taxes and levies for domestic sector in wind interest regions 

according to the range of energy consumption 

NUTS2 
Code 

Before taxes and levies 

Band DA : 
Consumption < 
1 000 kWh 

Band DB : 1 
000 kWh < 
Consumption < 
2 500 kWh 

Band DC : 2 
500 kWh < 
Consumption < 
5 000 kWh 

Band DD : 5 
000 kWh < 
Consumption < 
15 000 kWh 

Band DE : 
Consumption > 
15 000 kWh 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

0.2576 

0.3667 

0.3667 

0.3667 

0.2087 

0.2087 

0.2087 

0.2087 

0.1411 

0.2192 

0.2547 

0.2383 

0.1647 

0.2166 

0.2166 

0.2166 

0.1268 

0.1268 

0.1268 

0.1268 

0.1161 

0.1392 

0.1430 

0.2124 

0.1440 

0.1861 

0.1861 

0.1861 

0.1094 

0.1094 

0.1094 

0.1094 

0.1097 

0.1301 

0.1194 

0.1917 

0.1317 

0.1659 

0.1659 

0.1659 

0.0974 

0.0974 

0.0974 

0.0974 

0.1062 

0.1254 

0.0986 

0.1731 

0.1275 

0.1405 

0.1405 

0.1405 

0.0924 

0.0924 

0.0924 

0.0924 

0.1065 

0.1210 

0.0806 

0.1598 

Source: [Eurostat 2015e] 

Table 24  Electricity final price for domestic sector in wind interest regions according to the range 

of energy consumption 

NUTS2 
Code 

Final price 

Band DA : 
Consumption < 
1 000 kWh 

Band DB : 1 
000 kWh < 
Consumption < 
2 500 kWh 

Band DC : 2 
500 kWh < 
Consumption < 
5 000 kWh 

Band DD : 5 
000 kWh < 
Consumption < 
15 000 kWh 

Band DE : 
Consumption > 
15 000 kWh 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

0.4336 

0.4664 

0.4664 

0.4664 

0.2676 

0.2676 

0.2676 

0.2676 

0.1794 

0.3940 

0.3559 

0.2503 

Source: [Eurostat 2015e] 

0.3230 

0.2755 

0.2755 

0.2755 

0.1806 

0.1806 

0.1806 

0.1806 

0.1486 

0.2420 

0.2162 

0.2230 

0.2974 

0.2367 

0.2367 

0.2367 

0.1620 

0.1620 

0.1620 

0.1620 

0.1408 

0.2231 

0.1867 

0.2013 

40 

0.2827 

0.2110 

0.2110 

0.2110 

0.1491 

0.1491 

0.1491 

0.1491 

0.1364 

0.2132 

0.1607 

0.1817 

0.2714 

0.1787 

0.1787 

0.1787 

0.1440 

0.1440 

0.1440 

0.1440 

0.1368 

0.2008 

0.1383 

0.1679 

 
 
 
 
 
 
Gas prices for industrial 

Table 25  Gas price before taxes and levies for industrial sector in wind interest regions according 

to the range of energy consumption 

NUTS2 
Code 

Final price 

Band I1 : 
Consumptio
n < 1 000 
GJ 

Band I2 : 1 
000 GJ < 
Consumptio
n < 10 000 
GJ 

Band I3 : 10 
000 GJ < 
Consumptio
n < 100 000 
GJ 

Band I4 : 
100 000 GJ 
< 
Consumptio
n < 1 000 
000 GJ 

Band I5 : 1 
000 000 GJ 
< 
Consumptio
n < 4 000 
000 GJ 

Band I6 : 
Consumptio
n > 4 000 
000 GJ 

0.0235 

0.0309 

0.0309 

0.0309 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

0.0443 

0.0548 

0.0548 

0.0548 

0.0579 

0.0579 

0.0579 

0.0579 

0.0421 

0.0726 

0.0572 

0.0580 

0.0420 

0.0450 

0.0450 

0.0450 

0.0454 

0.0454 

0.0454 

0.0454 

0.0416 

0.0590 

0.0456 

0.0383 

0.0361 

0.0369 

0.0369 

0.0369 

0.0360 

0.0360 

0.0360 

0.0360 

0.0359 

0.0437 

0.0356 

0.0332 

0.0278 

0.0342 

0.0342 

0.0342 

0.0295 

0.0295 

0.0295 

0.0295 

0.0317 

0.0377 

0.0319 

0.0277 

0.0255 

0.0320 

0.0320 

0.0320 

0.0261 

0.0261 

0.0261 

0.0261 

0.0302 

0.0358 

0.0323 

0.0245 

Source: [Eurostat 2015d] 

Table 26  Gas final price for industrial sector in wind interest regions according to the range of 

energy consumption 

NUTS2 
Code 

Final price 

Band I1 : 
Consumptio
n < 1 000 
GJ 

Band I2 : 1 
000 GJ < 
Consumptio
n < 10 000 
GJ 

Band I3 : 10 
000 GJ < 
Consumptio
n < 100 000 
GJ 

Band I4 : 
100 000 GJ 
< 
Consumptio
n < 1 000 
000 GJ 

Band I5 : 1 
000 000 GJ 
< 
Consumptio
n < 4 000 
000 GJ 

Band I6 : 
Consumptio
n > 4 000 
000 GJ 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

0.0576 

0.0670 

0.0670 

0.0670 

0.0726 

0.0726 

0.0726 

0.0726 

0.0530 

0.0961 

0.1072 

0.0742 

0.0548 

0.0550 

0.0550 

0.0550 

0.0563 

0.0563 

0.0563 

0.0563 

0.0521 

0.0746 

0.0926 

0.0458 

Source: [Eurostat 2015d] 

0.0478 

0.0452 

0.0452 

0.0452 

0.0445 

0.0445 

0.0445 

0.0445 

0.0448 

0.0546 

0.0801 

0.0417 

41 

0.0327 

0.0381 

0.0381 

0.0381 

0.0379 

0.0420 

0.0420 

0.0420 

0.0352 

0.0352 

0.0352 

0.0352 

0.0393 

0.0469 

0.0755 

0.0343 

0.0351 

0.0394 

0.0394 

0.0394 

0.0306 

0.0306 

0.0306 

0.0306 

0.0371 

0.0441 

0.0760 

0.0301 

 
 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
Electricity prices for industrial 

Table 27  Electricity price before taxes and levies for industrial sector in wind interest regions 

according to the range of energy consumption 

NUTS2 
Code 

Final price 

Band IA 
: 
Consum
ption < 
20 MWh 

Band IB : 
20 MWh < 
Consumpti
on < 500 
MWh 

Band IC : 
500 MWh 
< 
Consumpti
on < 2 
000 MWh 

Band ID : 
2 000 
MWh < 
Consumpti
on < 20 
000 MWh 

Band IE : 
20 000 
MWh < 
Consumpti
on < 70 
000 MWh 

Band IF : 
70 000 
MWh < 
Consumpti
on < 150 
000 MWh 

Band IG : 
Consumpti
on > 150 
000 MWh 

DE94 

0.1403 

0.1029 

0.0808 

0.0714 

0.0627 

0.0608 

: 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

0.2782 

0.1482 

0.1110 

0.0979 

0.0792 

0.0736 

0.0646 

0.2782 

0.1482 

0.1110 

0.0979 

0.0792 

0.0736 

0.0646 

0.2782 

0.1482 

0.1110 

0.0979 

0.0792 

0.0736 

0.0646 

0.1168 

0.0884 

0.0687 

0.0604 

0.0572 

0.0521 

0.1168 

0.0884 

0.0687 

0.0604 

0.0572 

0.0521 

0.1168 

0.0884 

0.0687 

0.0604 

0.0572 

0.0521 

0.1168 

0.0884 

0.0687 

0.0604 

0.0572 

0.0521 

: 

: 

: 

: 

0.1464 

0.1071 

0.0786 

0.0665 

0.0611 

0.0557 

0.0517 

0.1585 

0.1272 

0.1052 

0.0891 

0.0767 

0.0697 

0.1325 

0.0769 

0.0661 

0.0593 

0.0528 

0.0456 

: 

: 

0.1726 

0.1447 

0.1290 

0.1179 

0.1176 

0.1149 

0.1126 

Source: [Eurostat 2015f] 

Table 28  Electricity final price for industrial sector in wind interest regions according to the range 

of energy consumption 

NUTS2 
Code 

Final price 

Band IA 
: 
Consum
ption < 
20 MWh 

Band IB : 
20 MWh < 
Consumpti
on < 500 
MWh 

Band IC : 
500 MWh 
< 
Consumpti
on < 2 
000 MWh 

Band ID : 
2 000 
MWh < 
Consumpti
on < 20 
000 MWh 

Band IE : 
20 000 
MWh < 
Consumpti
on < 70 
000 MWh 

Band IF : 
70 000 
MWh < 
Consumpti
on < 150 
000 MWh 

Band IG : 
Consumpti
on > 150 
000 MWh 

DE94 

0.2909 

0.2302 

0.1992 

0.1761 

0.1512 

0.1412 

: 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

0.3539 

0.1885 

0.1412 

0.1245 

0.1007 

0.0936 

0.0822 

0.3539 

0.1885 

0.1412 

0.1245 

0.1007 

0.0936 

0.0822 

0.3539 

0.1885 

0.1412 

0.1245 

0.1007 

0.0936 

0.0822 

0.1702 

0.1350 

0.1090 

0.0944 

0.0841 

0.0647 

0.1702 

0.1350 

0.1090 

0.0944 

0.0841 

0.0647 

0.1702 

0.1350 

0.1090 

0.0944 

0.0841 

0.0647 

0.1702 

0.1350 

0.1090 

0.0944 

0.0841 

0.0647 

: 

: 

: 

: 

0.1860 

0.1375 

0.1025 

0.0876 

0.0810 

0.0744 

0.0694 

42 

 
 
 
 
 
PT11 

SE33 

UKK3 

0.2328 

0.1812 

0.1460 

0.1241 

0.1082 

0.1030 

0.1663 

0.0968 

0.0833 

0.0747 

0.0667 

0.0577 

: 

: 

0.2125 

0.1798 

0.1606 

0.1458 

0.1442 

0.1406 

0.1372 

Source: [Eurostat 2015f] 

43 

Annex II 

Energy use 

Total energy consumption 

Table 29: Total final energy consumption (ktoe) from 2009 to 2013  

NUTS2 Code 

2009 

2010 

2011 

2012 

2013 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

2,643.564 

2,734.018 

2,725.405 

2,566.841 

1,312.412 

1,352.243 

1,343.235 

1,311.513 

793.069 

813.102 

799.814 

770.47 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

963.67 

Source: [Eurostat 2015g] 

Final energy consumption by households 

Table 30: Total final energy consumption (ktoe) by households from 2009 to 2013  

2009 

2010 

2011 

2012 

2013 

Weser-Ems 

Galicia 

Principado de Asturias 

Cantabria 

Champagne-Ardenne 

Haute-Normandie 

Basse-Normandie 

Pays de la Loire 

Pomorskie 

Norte 

Övre Norrland 

1,033.025 

1,095.095 

1,089.144  637.592 

377.08 

156.81 

400.683 

398.111 

217.768 

167.959 

166.326 

126.548 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

: 

Cornwall and Isles of Scilly 

354.14 

Source: [Eurostat 2015g] 

44 

 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex III 

Wind energy deployment 

In this section location of wind farms for different regions under study are presented. In 
the  maps,  the  size  of  the  bubbles  represents  the  size  of  the  farms  in  terms  of  power 
capacity installed. 

Figure 18  Installed wind farms in DE94, ES11, ES12 and ES13.  

45 

 
 
Figure 19  Installed wind farms in FR21, FR23, FR25 and FR51. 

46 

 
 
 
 
 
Figure 20  Installed wind farms in PL63, PT11, SE33 and UKK3. 

47 

 
 
 
 
 
 
Annex IV 

Academia indicators 

Identification of regional universities have been based on the Authorities file of European 
Universities [Daraio 2015]. 

Table 31 List of universities in Wind interest regions 

NUTS2 
Code 

Universities 

DE94 

Carl von Ossietzky Universität Oldenburg 

ES12 

ES13 

FR21 

Universität Osnabrück  

Universität Vechta  

Universidad de Oviedo  

Universidad de Cantabria  

Université de technologie de Troyes  

Université de Reims Champagne-Ardenne  

FR23 

Institut national des sciences appliquées de Rouen  

Université de Rouen 

FR25 

Université de Caen Basse-Normandie  

École nationale supérieure d'ingénieurs de Caen  

FR51 

Ecole centrale de Nantes  

Université de Nantes  

École nationale d'ingénieurs des techniques des industries agricoles et 
alimentaires  

École nationale supérieure des mines de Nantes  

Université catholique de l'Ouest  

Université d'Angers  

Université du Maine  

PL63 

Akademia Marynarki Wojennej w Gdyni  

Gdański Uniwersytet Medyczny  

Akademia Morska w Gdyni  

Akademia Muzyczna im. Stanisława Moniuszki w Gdańsku  

Akademia Pomorska w Słupsku  

Akademia Sztuk Pięknych w Gdańsku  

Akademia Wychowania Fizycznego i Sportu im. Jędrzeja Śniadeckiego w Gdańsku  

Politechnika Gdańska  

Uniwersytet Gdański  

PT11 

Universidade do Minho  

Universidade do Porto  

Universidade de Trás-os-Montes e Alto Douro  

Instituto Politécnico de Bragança  

Instituto Politécnico do Porto  

Universidade Fernando Pessoa  

Universidade Lusíada (Porto)  

Universidade Lusófona do Porto 

48 

 
Universidade Portucalense Infante D. Henrique  

SE33 

Umeå universitet  

Luleå tekniska universitet  

Source: [Daraio 2015] 

49 

 
 
Annex V 

Innovation capacities 

Table 32 Total intramural R&D expenditure (GERD) by sectors of performance and NUTS 2 regions 

2013 (EUR/inhabitant) 

All sectors 

Business 
enterprise 
sector 

Government 
sector 

Higher 
education 
sector 

Private 
non-
profit 
sector 

DE94 

235 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

169.7 

172.1 

186.5 

210.3 

400.2 

327.9 

351.3 

98.4 

187.1 

SE33 

1082.8 

UKK3 

43.9 

Source: [Eurostat 2016a] 

144.6 

77.8 

90.5 

65.6 

143.9 

332.6 

213.3 

227.5 

51.4 

97.8 

242.6 

41.7 

20.4 

25.2 

26 

34.7 

2.3 

2.3 

15.8 

25.3 

15.8 

6.9 

75.9 

0 

Table 33 Total R&D personnel by 2013 

NUTS2 code 

Total R&D personnel (2013) 

70 

: 

55.4 

: 

64.2 

65.3 

98.8 

98.4 

31.2 

81.8 

764.4 

2.2 

: 

: 

0.1 

: 

: 

: 

: 

: 

0.1 

0.7 

: 

0 

DE94 

ES11 

ES12 

ES13 

FR21 

FR23 

FR25 

FR51 

PL63 

PT11 

SE33 

UKK3 

9734 

17710 

6688 

3585 

: 

: 

: 

: 

8575 

31315 

7770 

699 

Source: [Eurostat 2016b] 

50 

 
 
 
 
 
Annex VI 

Annex VII Results of PCA analysis for non-weighted dataset 

100.0%

90.0%

80.0%

70.0%

60.0%

50.0%

40.0%

30.0%

20.0%

10.0%

0.0%

36.7% 

21.3% 

13.4% 

11.2% 

1

2

3

4

indiviudal explained variance

cumulative explanied variance

Figure 21  Cumulative explained variance ratio. PCA non-weighted dataset 

Table 34 Parameters included in the reduced dataset. PCA non-weighted 

Parameters 

Population 2013 
Absolute GDP 
Relative GDP 
Band DB : 1 000 kWh < Consumption < 2 500 kWh 
Band DC : 2 500 kWh < Consumption < 5 000 kWh 
Band DD : 5 000 kWh < Consumption < 15 000 kWh 
Band DE : Consumption > 15 000 kWh 
Band DE : Consumption > 15 000 kWh.1 
Band I2 : 1 000 GJ < Consumption < 10 000 GJ 
Band I3 : 10 000 GJ < Consumption < 100 000 GJ 
Band I5 : 1 000 000 GJ < Consumption < 4 000 000 GJ 
Band I1 : Consumption < 1 000 GJ.1 
Band I2 : 1 000 GJ < Consumption < 10 000 GJ.1 
Band I3 : 10 000 GJ < Consumption < 100 000 GJ.1 
Band I4 : 100 000 GJ < Consumption < 1 000 000 GJ.1 
Band I5 : 1 000 000 GJ < Consumption < 4 000 000 GJ.1 
Band IB : 20 MWh < Consumption < 500 MWh.1 
Installed capacity  
Number of wind farms 
Number of universities 
Number of companies 
Representativeness of the regional sector at the national level. Energy sector size 
R&D expenditures. Government sector 

51 

 
 
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