Towards a European wide vulnerability framework : a flexible approach for vulnerability assessment using composite indicators.
Date: 2023-06-26
Institutions: Joint Research Centre
Subjects: data collection, database, disaster area, environmental cooperation, environmental risk prevention, exchange of information, hazard science, natural disaster, research report, risk management

Towards a European 
wide vulnerability  
framework 

A flexible approach for  
vulnerability assessment  
using composite indicators 

Eklund, G., Sibilia, A., Salvi, A., Antofie, T-E., 
Rodomonti, D., Salari, S., Poljansek, K., 
Marzi, S., Gyenes, Z., Corbane, C. 

2023 

ISSN 1831-9424 

EUR 31515 EN 

 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It 
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Contact information  
Name: Gustav EKLUND 
Email: Gustav.EKLUND@ec.europa.eu 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC118850 

EUR 31515 EN 

PDF 

ISBN 978-92-68-03470-5 

ISSN 1831-9424 

doi:10.2760/353889  KJ-NA-31-515-EN-N 

Luxembourg: Publications Office of the European Union, 2023 

© European Union, 2023 

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How to cite this report: Eklund, G., Sibilia, A., Salvi, A., Antofie, T-E., Rodomonti, D., Salari, S., Poljansek, K., Marzi, S., Gyenes, Z., Corbane, C., 
Towards a European wide vulnerability framework, Publications Office of the European Union, Luxembourg, 2023, doi:10.2760/353889, 
JRC118850.  

 
 
 
 
 
 
 
 
 
 
 
 
 
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents  

Abstract ...................................................................................................................................................................1 

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

1 

Introduction .......................................................................................................................................................3 

2  Theoretical framework ......................................................................................................................................4 

2.1  Measuring vulnerability ............................................................................................................................5 

2.2  Composite indicators ................................................................................................................................6 

2.3  Dimensions of the Vulnerability Index......................................................................................................6 

2.4  RDH Vulnerability Sub-indices ..................................................................................................................7 

3  Operationalization of the Vulnerability Index .................................................................................................10 

3.1  Data collection ........................................................................................................................................10 

3.2  Data processing work flow .....................................................................................................................11 

3.2.1  Missing data ................................................................................................................................12 

3.2.2 

Normalisation ..............................................................................................................................13 

3.2.3  Weighting .....................................................................................................................................14 

3.2.4 

Cross-level Aggregation, Averages and Normalisation ..............................................................15 

3.2.5 

Correlation ...................................................................................................................................16 

3.2.6 

Limitations ...................................................................................................................................19 

4  Results .............................................................................................................................................................20 

5  Conclusions and next steps .............................................................................................................................22 

References ............................................................................................................................................................23 

List of abbreviations and definitions ....................................................................................................................26 

List of boxes ..........................................................................................................................................................27 

List of figures ........................................................................................................................................................28 

List of tables .........................................................................................................................................................29 

Annexes .................................................................................................................................................................30 

Annex 1. Indicators ..........................................................................................................................................30 

Annex 2. Indicator references .........................................................................................................................36 

i 

 
 
 
 
 
 
 
 
 
 
Abstract  

Vulnerability is one of the three risk components considered in disaster risk assessment, together with hazard 
and exposure. Since we cannot reduce the occurrence and severity of natural hazards, reducing vulnerability is 
one of the main opportunities for reducing disaster risk. In this report we present a cross-scale indicator for 
measuring vulnerability at the European level. The indicator is composed of four dimensions (social, economic, 
political  and  environmental)  and  is  meant  to  capture  the  systemic  vulnerability  to  disasters  at  different 
administrative levels (Country, NUTS2 and NUTS3 – see description of these levels in chapter 3). We derive an 
index  with  values  between  0  and  10,  which  encloses  the  four  dimensions  in  a  multi-level  approach.  The 
analyses  presented  build  on  the  data  and  the  vulnerability  framework  implemented  in  the  Disaster  Risk 
Management  Knowledge  Centre  (DRMKC)  Risk  Data  Hub  (RDH)  for  the  assessment  of  disaster  risk  from 
multiple hazards across Europe. 

1 

 
 
 
Acknowledgements 

The authors thank Joy Ommer for the valuable contribution on the developing of the framework concept for 
the Risk Data Hub’s vulnerability index during her stage at the JRC. The authors also thank Maureen Wood and 
Giulio Caperna, of the European Commission’s Joint Research Centre, for the careful review of this report and 
the useful comments. 

Authors 

Gustav Eklund 

Andrea Sibila 

Andrea Salvi 

Tiberiu-Eugen Antofie 

Davide Rodomonti 

Sandro Salari 

Karmen Poljansek 

Sepher Marzi 

Zsuzsanna Gyenes 

Christina Corbane 

2 

1 

Introduction  

The  shift  from  Disaster  Management  to  Disaster  Risk  Management  introduced  a  change  from  reactive  to 
proactive measures to deal with disasters and their potential impacts. This implies the need for understanding 
disaster risk and its core components for a better anticipation of disaster impacts. With the Sendai framework 
this need was defined as the first priority and motivated the development of the DRMKC RDH in 2015 (Antofie 
et  al,  2020;  Antofie  et  al,  2022;  Salvi  et  al,  2022).  The  RDH  covers  several  dimensions  of  Disaster  Risk 
Management: from the  understanding of the impacts of  disaster events to the calibration and validation of 
risk  models  and  up  to  the  evaluation  of  progresses  in  disaster  risk  reduction  measures.  Through  a  better 
collection, curation and sharing of disaster risk and loss data, the mission of the DRMKC RDH is to support the 
collective effort towards a more resilient future at a Europe-wide level. 

The  RDH  includes  a  risk  analysis  module  which  aims  to  provide  a  way  to  perform  risk  assessment  across 
Europe at different levels of spatial aggregation.  The  purpose of the  risk analysis module is to improve the 
access to and sharing of curated European-wide risk data. The module hosts structured data on potential risk 
components: hazard, exposure and vulnerability. This structure is considered across multiple hazards, assets 
and geographical scales.  

The vulnerability and the exposure components are the main drivers of risk (Cardona et. al., 2012) that need 
to be measured and monitored. The RDH provides location and spatial extent of the risk drivers, enabling the 
assessment  of  the  impacts  of  extreme  hazard  events  and  consequently,  the  development  of  adequate 
mitigation strategies. 

Conceptual  frameworks  show  the  importance  of  reducing  the  risk  by  reducing  vulnerability  and  exposure. 
From  the  various  dimensions  of  vulnerability,  the  RDH  considers  the  social,  economic,  political  and 
environmental  dimensions,  in  order  to  measure  the  predisposition  of  regions  to  potential  impacts  from 
hazardous events. 

The purpose of developing the RDH vulnerability index is to obtain a fit-for-purpose vulnerability component 
to be used in the estimation of risk. Furthermore, and in order to function in the RDH structure, it is essential 
that the RDH vulnerability index can be assessed in different geographical units (Country, NUTS2, NUTS3) and 
over various scales (Europe-wide, National, Sub-national). 

Not all assets, systems or communities with the same level of exposure to a specific hazard are equally at 
risk, since vulnerability has a fundamental role in shaping its magnitude. A comprehensive risk analysis cannot 
overlook  this  aspect.  Nonetheless,  vulnerability  has  not  yet  been  conceptualized  in  the  complexity  and  with 
the holistic view which is needed for risk management measures. This report aims to support the estimation 
of disaster risk by developing a new approach to conceptualise and calculate vulnerability.    

Although, research on vulnerability advances at a fast pace, one of the main issues is the lack of a general 
definition.  Definitions  vary  depending  on,  for  instance,  theoretical  or  methodological  approaches  (Manyena, 
2006).  In  accordance  with  the  practice  of  the  DRMKC  and  for  the  purpose  of  the  RDH,  this  paper  adopts  a 
definition of vulnerability where  it is defined in the context with risk assessment, but also coping capacities 
(Box  1.).  Hence,  vulnerability  is  defined  as  'the  characteristics  and  circumstances  [and  processes]  of  [an 
individual],  a  community,  system  or  asset  that  make  it  susceptible  to  the  damaging  effects  of  a  hazard[s]' 
(UNISDR, 2016). 

With  this  in  mind,  the  aim  of  the  research  is  to,  firstly,  develop  the  vulnerability  framework  needed  to 
calculate disaster risk and, secondly, to design a metric that provides: 

• 

• 

an overview of the vulnerability of a spatial entity and its inhabitants, and 

a base for comparison between spatial units.  

3 

 
2  Theoretical framework  

A first idea of vulnerability was conceptualised in the late 70s when it was recognised that some people are 
more  affected  by  hazards  than  others.  Chambers  (1983)  introduced  vulnerability  as  one  component  of  the 
deprivation  trap.  In  this  context,  he  defined  vulnerability  as  the  'lack  of  buffer  against  contingencies' 
(Chambers,  1983:  103).  This  definition  leaves  room  for  multiple  interpretations.    In  the  past  two  decades, 
research  on  vulnerability  reached  a  high  number  of  contributions.  This  growing  interest  towards  this  risk 
component  can  be  linked  to  the  development  of  the  Sendai  Framework  and  its  precursor,  the  Hyogo 
Framework  for  Action  (HFA),  which  set  as  one  of  its  priorities  the  reduction  of  peoples’  vulnerability.  The 
increase in vulnerability research led to the diversification of definitions as Birkmann (2007) concluded in the 
widening  of  the  concept:  starting  from  the  concept  of  an  intrinsic  characteristic  extending  to  the  people-
centred  approach  of  Chambers,  to  the  inclusion  of  coping  capacity  (Bohle,  Downing  and  Watts,  1994; 
Weichselgartner  and  Bertens,  2000)  (see  Box  1).  An  additional  expansion  was  introduced  by  considering 
adaptive capacity of society and systems (Adger, 2004). 

Sensitivity  defines  the  grade  to  which  a  system  is  affected  by  an  event,  either  positively  or  adversely; 
adaptive  capacity  measures  the  ability  to  adsorb  an  event  and  to  adjust  to  potential  damage,  hence  the 
flexibility of the communities to anticipate, respond and recovery from an event (Gupta et al, 2020; Naumann 
et al, 2019; Carter et al, 2018; Asadzadeh et al, 2017; Swart et al, 2012; Gallopín, 2006). 

Box 1. Vulnerability and Capacity 

While vulnerability initially was defined as a condition, others argue for a dualistic concept where the ability to 
cope  with  adverse  effects  plays  the  counterpart  to  vulnerability.  In  particular,  coping  capacity  refers  to  the 
abilities  to  react  to  hazardous  events  and  to  reduce  possible  harm  (Almeida,  2017).  This  considers  the 
availability  of  needed  resources  to  act  but  also  the  ability  to  manage  and  utilise  those  (Birkmann,  2013). 
Delaminating  capacity  from  adaptive  capacity,  coping  capacity  encompasses  abilities  that  are  needed  for 
short-term management, whereas, adaptive capacities are the long-term abilities such as needed for climate 
change adaptation. 

Considering a multifaceted vulnerability, linkages can be drawn to the concept of resilience. The controversial 
relation  and  delimitation  of  vulnerability  and  resilience  is  further  explained  in  Box  2.  Overall,  it  can  be 
highlighted  that  the  definition  of  vulnerability  varies  with  the  research  objective,  the  methodology  or  the 
desired outcome.  

Box 2. Vulnerability and Resilience 

Throughout  literature  definitions  of  vulnerability  are  closely  related  to  the  definition  of  resilience  where 
resilience is defined as ‘the ability of a system, community or society exposed to hazards to [cope, adapt and 
recover] from the effects of a hazard’ (UNISDR, 2016). In particular, resilience requires an inclusive concept of 
coping capacity (short-term), adaptive capacity (long-term) and the ability to recover, considering society as a 
whole.  In  contrast,  vulnerability  describes  the  condition  of  a  social  unit  or  an  asset.  In  Cutter  (2003), 
vulnerability may govern the ability of various groups to respond, and the condition and characteristics of a 
community,  such  as  social  inequalities  of  people,  partially  influences  this.  Overall,  the  relationship  between 
vulnerability and resilience has been broadly discussed in the literature.  There are some studies considering 
resilience and vulnerability as counterparts belonging to the same continuum on the opposite poles (Manyena, 
2006).  On  the  contrary,    some  other  studies  argue  that  resilience  and  vulnerability  are  strictly  related  but 
cannot be used interchangeably as the opposite terms. Gallopín (2006) addresses resilience as the response 
capacity  component  of  vulnerability  and  explains  fundamental  differences  using  the  behaviour  of  dynamic 
systems being exposed to external perturbations.  

Recalling the people-centred approach of Chambers, vulnerability is primarily defined by social and economic 
characteristics  of  a  community  considering  that  groups  with  less  financial  resources  are  more  vulnerable. 
Using  socio-economic  indicators  for  assessing  vulnerability  gained  in  interest  due  to  its  simplified  view  and 
calculation, and the relatively easy access to baseline data for measuring it.  

Multi-dimensional aspects of vulnerability 

Until  the  beginning  of  the  21th  century,  initial  work  in  this  field  focused  primarily  on  either  people-centred 
vulnerability or on the  vulnerability of the environment or buildings and infrastructure.   Cutter et al.  (2003) 
drew  attention  to  the  influence  of  those  dimensions  on  each  other.  They  investigated  the  components  of 
social  vulnerability.  Hence,  vulnerability  is  determined  by  social  but  also  economic  inequalities  between 

4 

individuals.  Moreover,  they  aimed  to  understand  place-based  vulnerabilities.  At  this  point,  connections 
between  social  vulnerability  and  the  biophysical  vulnerability  were  investigated.  Nonetheless,  the  focus 
remained on social vulnerability. In order to gain a holistic view, vulnerability needs to be approached in multi-
dimensional way by considering social, economic, political and environmental dimensions. Political structures 
provide an insight into the empowerment of individuals, a community or a system.  

Dynamic nature of vulnerability 

According to the UNISDR (2016) vulnerability is defined as 'the characteristics and circumstances’ which can 
be explained as the ‘root causes’ (Wisner et al. 2004) and can be imagined as snapshot of a community at 
this specific location before a hazard strikes. In particular, a condition that makes a community susceptible to 
potential impacts. Definitions agree on this point. According to this perspective, vulnerability is considered as 
static. However, knowing that social conditions and economic resources may change abruptly, it is reasonable 
to  update  vulnerability  in  space  and  time,  and  also  to  consider  its  dynamic  character  through  projections 
(Frigerio et al., 2018). 

Multi-scale aspects of vulnerability 

Economic  resources  can  be  further  differentiated:  resources  can  be  of  an  individual,  a  household,  a 
community  or  even  a  country.  Adger  (1999)  divided  levels  of  vulnerability  into  individual/household  and 
collective.  Whereas,  collective  resources  encompass  community,  regional  and  national  level.  Schneiderbauer 
and Ehrlich (2004) separated the mentioned levels and further included regional and cultural levels. The need 
for differentiation between levels can be best explained by considering the vulnerabilities and capacities of an 
individual/household, a local administrative or a country’s government.   

2.1  Measuring vulnerability 

The multitude of definitions can be reflected in the amount of approaches to measure vulnerability. There is a 
vast  amount  of  literature  on  assessing  and  measuring  vulnerability.  It  ranged  from  qualitative,  to  semi-
quantitative to quantitative approaches. There has been a growing interest in quantitative approaches in the 
recent years. Two  ground-breaking quantitative methodologies are  vulnerability curves and indicator-based-
methodology (IBM). 

Vulnerability curves have been widely used to assess physical vulnerability in the context of hazard intensity 
and  the  probability  of  loss  and  damage.  This  method  is  utilised  to  present  the  loss  and  damage  as  a 
dependent variable of the hazard. Future research is orientated towards multi-parameter vulnerability curves 
(Papathoma-Köhle,  M.,  2016;  Almeida,  2017).  However,  this  approach,  using  vulnerability  curves,  is 
predominantly designed for assessing physical vulnerability. Furthermore, this method is neglecting changes 
of influencing factors.  

In contrast, an indicator-based  methodology is designed  to identify the  underlying  variables of vulnerability 
which  can  be  of  different  dimensions  such  as  social  or  economic.  Furthermore,  this  method  strives  to 
understand the root causes and drivers for change in space and time (Almeida, 2017; Frigerio et al., 2018). 
Wisner et al. 2003 reflected on the change towards quantitative methodologies and highlighted that one of 
the  major  drawbacks  to  adopting  quantitative  methods is the  neglection  of  qualitative  values, for instance, 
the perceptions of the population. 

Qualitative approaches can be based on interviews or group discussions. The disadvantage of these methods 
appears when aggregating the outcome to larger scales.  

In  the  context  of  the  RDH  the  aim  is  to  measure  vulnerability  on  various  levels  for  understanding  the 
vulnerable  elements  for  further  prioritisation  and  decision-making  in  a  comparable  manner  across 
administrative units and countries. An IBM is the most suitable method for capturing vulnerability in different 
dimensions,  on  different  levels  and  for  different  hazards.  Vulnerability  curves  may  be  suitable  and  more 
detailed  for  different  hazards.  However,  they  are  limited  in  providing  an  understanding  of  the  underlying 
factors  and  influences.  Furthermore,  they  are  limited  to  physical  vulnerability.  An  index  for  vulnerability  at 
national  and  sub-national  scales  can  support  decision  makers  by  providing  the  big  picture  (UNISDR,  2005). 
This  can be established by  developing composite indicators, which  will be explained further in the following 
section. 

5 

2.2  Composite indicators 

It is a challenge to communicate science to policymakers and the public; the outcome of the research can be 
complex and difficult to comprehend. The concept of composite indicators strives to anticipate this issue by 
simplifying complex and multi-dimensional data variety. The idea is to compress this wide spectrum in order 
to  transmit  the  useful  content  by  keeping  the  content  clear  and  to  the  point  (Nardo  et  al.,  2005;  Almeida, 
2017). 

Recalling the multi-dimensionality of vulnerability, as defined above, and, in addition, the interlinkage between 
those  dimensions,  composite  indicators  are  the  most  feasible  method  for  compiling,  combining  and 
communicating  this  complexity.  In  this  context,  composite  indicators  entail  several  advantages  in  regard  to 
the objective of the RDH and the target group. A good example in this sense is the Index for Risk Management 
INFORM - composite indicator developed by JRC (De Groeve et.al., 2014b) as a tool for understanding the risk 
of humanitarian crisis and disasters. 

The compression to the focal point can equip policymakers and the public with useful and clear information 
which  can  support  future  decisions.  It  aims to  display  a  'big  picture'  (Nardo  et  al.,  2005)  on  spatial  (and  in 
future timely) differences in vulnerability. On the one hand, this picture can be utilised for prioritisation of DRR 
strategies or actions and, on the other  hand, it  has the ability to monitor and assess  the  progress of these 
strategies and measures (Almeida, 2017). 

Considerable  attention  must  be  taken  to  the  creation  of  an  index  and  the  communication;  thus,  issues  of 
misleading information or misinterpretation can be prevented. The issue of misinterpretation can be alleviated 
by  a  transparent  methodology  and  indicator  selection  (Angeon  and  Bates,  2015).  This  step  is  of  most 
importance  for  the  interpretation  and  use  but  furthermore,  for  anticipating  the  risk  of  subjective  influence 
(Nardo et al., 2005). 

In  accordance  with  the  purpose  of  the  RDH,  composite  indicators  can  facilitate  the  communication  of  most 
vulnerable factors. Wisner et al. (2003) highlighted the advantage of spatial comparison of vulnerability. The 
selected  indicators  are  comparable  spatially  and  over  time,  which  can  be  used  for  monitoring  policy 
implementation.  These  factors  highlight  the  importance  of  designing  the  composite  indicators  and  the 
methodology according to the use for policies. 

2.3  Dimensions of the Vulnerability Index 

Vulnerability  is  influenced  by  different  components  as  well  as  their  interlinkages.  In  our  framework  the 
vulnerability is comprised by four dimensions:  

• 

• 

• 

The social dimension explains condition and processes of individuals and the entire population. Here, 
the  conditions  refer  to  health  aspects,  social  interactions,  population  distribution  and  demography 
and, to an extent, dwellings (Angeon and Bates, 2015).  

The  second  dimension  describes  the  economic  situation  of  individuals,  the  population  and  the 
government. Post-disaster property loss and the effects of business disruption have been stated as 
the  constituents  of  the  economic  component,  revealing  the  operational  role  of  businesses  and 
organizational  and  institutional  entities  (Marzi  et  al,  2019;  Angeon  and  Bates,  2015;  Eriksen  and 
Kelly, 2006).  

The political dimension deals with quality of government and their actions. High institutional quality 
and  governance  can  ensure  effective  implementation  of  emergency  planning,  as  well  as  climate 
change adaptation and vulnerability policies. Accountability and trust in institutions and officials is an 
important  element  of  organizational  resilience  and  business  continuity  (Marzi  et  al,  2019).  The 
vulnerability  of  ecological  systems  can  be  associated  with  various  factors  related  to  biodiversity, 
redundancies, response diversity, governance and management policies.  

•  Environmental  and  ecosystem  aspects  of  vulnerability  have  been  embedded 

the 
ecological/ecosystem dimension in previous studies (Gupta et al, 2020; Andersson et al, 2015; Folke, 
2006).  Expansion  and  conservation  of  protected  areas  and  ecological  corridors  leads  to  preserving 
ecosystem  services  and  reduced  ecological  vulnerability,  which  are  the  core  elements  of  green 
infrastructure planning in Europe (Marzi et al, 2019; Eriksen and Kelly, 2006). 

in 

The social dimension may be linked with the economic dimension, for instance, the lack of financial resources 
and a low disposable income can influence social participation or the level of children’s education. Therefore, 

6 

vulnerability  can  be  seen  as  an  outcome  of  all  dimensions  and  their  linkage.  Regarding  prior  studies  that 
primarily approached dimensions separately, here, our aim lies on the equivalent treatment of the dimensions 
on every level. 

2.4  RDH Vulnerability Sub-indices  

While some approaches such as Adger (1999) as well as Schneiderbauer and Ehrlich (2004) argued for the 
differentiation  of  vulnerability  by  geographical  scale,  Eriksen  and  Kelly  (2006)  point  out  that  due  to  the 
differentiation of vulnerability at all levels, the scale of each indicator is critical for their representativeness. 
Since the fundamental scale of  vulnerability is local, given that  communities are different from each other, 
the  aggregation  to  a  single  geographic  level  (for  example  at  national  scale  only)  can  lead  to  the  loss  of 
important  details  and  consequently  to  distort  conclusions.  Against  this  background,  vulnerability  of  an 
individual  can  include  personal  and  physical  conditions.  Whereas,  setting  the  focus  at  national  level,  the 
possibility  of  personal  loss  does not  play  the  primary  role.  At  this level,  vulnerability  can  be  described  as a 
collective issue (Adger, 1999), and it can be distinguished between scales and levels. Levels, in this context, 
refer to  functional  levels, or  geographical  units  (Country,  NUTS2, NUTS3), while  scale  refers  to  comparative 
level (Europe-wide, National, sub-National). Therefore, vulnerability was tailored into levels for the integration 
into  the  RDH  and  will  be  examined  at  three  different  levels  (Figure  1):  individual/household  level,  local 
administrative level, and national level. 

Figure 1. Sub-indexes of vulnerability. The sub-indices at each level aims to describe the vulnerable condition which we 
have associated with it, e.g. vulnerability of the individual/households at NUTS3 level, and so on.  

Source: JRC, 2021. 

Sub-index: Individual/Household (NUTS3-level) 

The sub-index estimated at the level of NUTS3, represents individuals’ and households’ social and economic 
condition. The condition determines the susceptibility of the citizens; thus, here, the citizens are in the centre 
of interest. This layer aims to address local administrations but also the citizens  themselves who can act for 
decreasing their own susceptibility. Social factors focus primarily on the one hand, on the density and number 
of  people  living  in  an  area  which  at  the  same  time  can  indicate  the  degree  of  urbanisation  (Fekete,  2009; 
Armaş  et  al.,  2017).  Family  structures  are  closely  linked  to  individual/household  economic  vulnerability  and 
the possible need for assistance in everyday life (Shirley, Cutter and Boruff, 2003; Flanagan et al., 2011; Koks 
et al., 2015). Economic factors are described by the amount of people being employed and, therefore, having 
income and resources for recovering from disasters (Chen et al., 2013). In contrast, employment can increase 
the vulnerability of people when employed in an affected sector, i.e. agriculture. Furthermore, people’s values 
can  be  susceptible,  especially  taking  properties  into  account.  Damages  on  their  properties  can  have  a 
substantial financial impact on citizens. 

Sub-index: Local Administrative (NUTS2-level) 

The sub-index estimated at NUTS2-level has several foci. The social dimension deals with social interactions. 
It seeks to cover social participation and cohesion within an administrative unit (Iglesias, Moneo and Quiroga, 
2009;  Naumann  et  al.,  2014).  Social  participation  can  be  of  importance  for  sharing  local  knowledge  or  for 
assisting  more  vulnerable  neighbours.  The  economic  dimension  draws  connection 
the 
individual/household  level  to  the  administrative  unit  by  capturing  household  income  and  the  share  of  the 
population  suffering  material  deprivation.  At  this  level,  economic  sectors  gain  an  important  role  in  context 
with hazards. Depending on the hazard, sectors can suffer more than others during and after a disaster. The 
diversity  in  susceptibility  claims  for  a  differentiating  vulnerability  by  sectors.  Economic  change  can  indicate 

from 

7 

 
the economic situation of the NUTS2 and the trend on future growth or decrease. Moreover, economic change 
can reflect on the job opportunities in a region which in turn can have an influence on political mind-setting 
(Dijkstra, Poelman and Rodríguez-Pose, 2018). Whereas the political  dimension assesses the quality of local 
government. 

Sub-index: National Structures (Country-level) 

The approach to the sub-index at country level aims to constrain the choice of indicators to country structures 
and management: the social dimension integrates projections of population, demographic change, migration 
as  well  as  on  social  cohesion.  These  projections  aim  to  identify  the  conditions  and  characteristics  of  the 
population  until  2030.  Projections  foster  policy-makers  knowledge  on  trends  for  decision-making.  It  further 
presents  the  dynamic  of  the  social  dimension  as  well  as  future  demands  and,  this  way,  links  current  and 
future susceptibility. Additionally, health conditions are explained on this level. Here, health is aggregated by 
people’s perception and their economic ability to afford health care. The country’s economic situation plays a 
major role in disaster management. Moreover, the aggregation of the national GDP implies the susceptibility 
of  national  accounts  according  to  the  share  of  a  sector.  On  the  other  hand,  the  savings  of  the  population 
determines the populations financial situation. To gain an overall view on the population’s financial resources, 
the GINI coefficient supports the understanding of financial inequalities of a country and at the same time, it 
provides  an  overview  on  the  share  of  the  economically  vulnerable  population.  The  political  dimension 
encompasses the governmental capability which includes the population’s perception on the efficiency as well 
as political stability and the proactivity of governments in terms of climate change  adaptation and disaster 
risk reduction strategies and plans. Including people’s perception on the efficiency of the government aims to 
integrate qualitative information since this represents additionally the degree of trust and confidence of the 
population in their government. 

Bottom-up / Top-down 

Drawing  on  the  previous  sections’  rationale,  the  aim  is  to  develop  a  method  for  the  index  that  takes  into 
account  that  indicators  at  any  given  level  have  an  impact  on  the  vulnerability  at  the  other  levels.  The 
vulnerabilities of individuals/households, aggregated over municipalities and provinces, will have an influence 
on the vulnerability of the region and/or the Country. Likewise, it is argued that the vulnerabilities at Country 
level, such as political stability, will influence the vulnerability at the individual and country level. Considering 
this we  have developed a  bottom-up / top-down approach for indicator aggregation, creating separate sub-
indices at each level which then are combined in order to compute the final vulnerability index scores. 

Indicators  for  individual/household  level  are  clearly  mirroring  present  conditions.  Whereas  indicators  for 
community level widen in timely manners; thus, the focus remains on the present,  but some indicators  can 
show linkages to the past and/or future. In contrast, at national level there is a need for temporal inclusive 
indicators, and their future projections are incorporated. 

Vulnerability is calculated by aggregating all dimensions at each level, NUTS3, NUTS2 and Country, creating 
sub-indices  for  all  areas and  levels.  Subsequently  the  sub-indices  are  aggregated  based  on  which  level  the 
vulnerability is calculated for. The Country level vulnerability consist of the country sub-index, the aggregation 
of all NUTS2 sub-indices and the aggregation of all NUTS3 sub-indices. NUTS3 level vulnerability is calculated 
by  aggregating  the  dimensions  at  NUTS3  level,  and  then  adding  systemic  sub-indices  from  NUTS2  and 
Country level. More detailed information regarding the cross-level aggregation is addressed in section 3.2.4. 

8 

Figure 2. Bottom-up / Top-down approach for computing the multi-scale vulnerability index. 

Source: JRC, 2022 

9 

 
3  Operationalization of the Vulnerability Index 

The indicators selected for each of the four dimensions (section 2.3) were carefully chosen based on different 
considerations: the selection was performed in alignment with policies at various levels but primarily referring 
to  the  Sustainable  Development  Goals  (SDGs)  and  the  Sendai  Framework.  Secondly,  indicators  were  picked 
based  on  validated  research  on  resilience  and  vulnerability  (deductive  approach).  In  particular,  the  choice 
should cover two key aspects related to the vulnerability of communities, which are sensitivity and adaptive 
capacity  (see  introduction  in  chapter  2).  Finally,  the  selection  of  indicators  is  furthermore  based  on  them  
being  openly  available,  representative,  complete  and  effective.  The  final  selection  of  indicators  and 
methodology has been widely shared with experts within the field.  

3.1  Data collection 

Overall,  the  composite  vulnerability  index  encompasses  43  unique  indicators  split  across  the  three  above-
mentioned  levels  (19  indicators  are  at  country  level,  15  cover  the  NUTS2  and  9  are  related  to  NUTS3). 
Considering that they are unique, each indicator is linked to one level only. 

Table 1 provides an overview of all the indicators, including additional information such as the related level, 
the  dimension  they  describe,  and  the  way  they  affect  vulnerability.  The  “+”  reveals  a  direct  proportionality 
between the indicator and the vulnerability, meaning that higher values of the indicator tend to increase the 
vulnerability;  vice  versa,  the  “-”  stands  for  an  inverse  proportionality  between  the  indicator  and  the  index. 
Hence, higher values of the indicator lead to a decrease to the vulnerability.  

The main data source is Eurostat, which covers 34 indicators, while the remaining ones are sourced from the 
World  Bank  (WBG),  UNESCO,  Worldwide  Governance  Indicators  (WGI),  European  Environment  Agency  (EEA, 
ClimateADAPT), University of Gothenburg (GU), World Resources Institute (WRI) and Copernicus (CORINE). The 
data  is  collected  for  31  countries.  In  accordance  to  the  purpose  of  the  RDH,  the  data  covers  291  NUTS2 
regions and 1390 NUTS3 regions. 

The data collection is based on the principle of open data which implies that all the mentioned data sources 
are  free.  In  this  case,  one  downside  can  be  represented  by  the  data  availability.  This  explains  the  lower 
number of indicators at  NUTS3 level. It is indeed  complex to  retrieve extensive EU  wide  datasets at such a 
detailed scale.  

Annex 1 and Annex 2 contain comprehensive tables detailing each indicator, rationale for the selection and 
references in literature.  

Level 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 
Country 

NUTS2 

Table 1: The 43 indicators used to build the vulnerability index of the RDH. 

Dimension 
Social 
Social 
Social 
Social 
Social 
Social 
Social 
Economic 
Economic 
Economic 
Economic 
Political 
Political 
Political 
Environment 
Environment 
Environment 
Environment 

Social 

Hazard-independent Indicator 
Projected population change (increase) 
Children at-risk-of-poverty 
Disabled people with need for assistance 
Long-term care (health) expenditure 
Change in Age-dependency 
Self-reported unmet need for medical care 
Perceived Good Health 
Gross National Saving 
GDP per capita 
Income Inequality 
Cultural heritage 
Governmental efficiency 
Political Stability 
National Adaptation Strategies 
Environmental protection expenditure 
Climate related economic losses 
Common farmland bird index 
Natura 2000 protected areas 

Vulnerability 
(+) 
(+) 
(+) 
(-) 
(+) 
(+) 
(-) 
(-) 
(-) 
(+) 
(+) 
(-) 
(-) 
(-) 
(-) 
(+) 
(-) 
(-) 

Data Provider 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
WBG 
Eurostat 
Eurostat 
UNESCO 
WGI 
WGI 
Climate-Adapt 
Eurostat 
Eurostat 
Eurostat 
Eurostat 

Life expectancy 

(-) 

Eurostat 

10 

Level 

NUTS2 
NUTS2 

NUTS2 

NUTS2 
NUTS2 
NUTS2 
NUTS2 
NUTS2 
NUTS2 
NUTS2 
NUTS2 
NUTS2 
NUTS2 

NUTS3 
NUTS3 
NUTS3 
NUTS3 
NUTS3 
NUTS3 
NUTS3 
NUTS3 
NUTS3 

Dimension 

Hazard-independent Indicator 

Vulnerability 

Data Provider 

Social 
Social 

Social 

Social 
Social 
Economic 
Economic 
Economic 
Economic 
Economic 
Political 
Environment 
Environment 

Social 
Social 
Social 
Social 
Economic 
Economic 
Economic 
Economic 
Environment 

Hospital beds per 100'000 population 
Participation in Social Networks 
Information (Frequency of internet access: 
once a week (including every day)) 
People at risk of poverty or social exclusion 
People with tertiary education 
Severe material deprivation rate 
Household income 
Motorways 
Railways 
Employment rate 
Regional Quality of Government index 
Urban area classified as green space 
Urban land cover 

Population density 
Net migration 
Young dependency 
Old dependency 
NUTS3 GDP per capita vs country average 
Gross Value Added (at basic prices) 
Power plants per 100'000 inhabitants 
Patent applications to the EPO 
Soil erosion 

(-) 
(-) 

(-) 

(+) 
(-) 
(+) 
(-) 
(-) 
(-) 
(-) 
(-) 
(-) 
(+) 

(+) 
(+) 
(+) 
(+) 
(-) 
(-) 
(-) 
(-) 
(+) 

Eurostat 
Eurostat 

Eurostat 

Eurostat 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
GU 
CORINE 
CORINE 

Eurostat 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
Eurostat 
WRI 
Eurostat 
Eurostat 

3.2  Data processing work flow 

The  estimate  of  the  composite  index  is  performed  through  an  automated  code-based  workflow.  The 
development  of  this  kind  of  process  offers  great  advantages  in  terms  of  the  time  spent  to  retrieve  the 
required data; to clean it and turn it into a suitable format for the vulnerability estimation; to perform future 
update of the index (repeatability of the process). From a high-level perspective, the process consists of the 
following main steps: 

• 

• 

Sub-index calculation – hence the computation of the three geographic sub-indices, namely: country, 
NUTS2 and NUTS3; 

Sub-index aggregation  – once the  geographic  sub-indices are defined, they are  combined to return 
the composite index for each administrative level. 

The  core  of  the  sub-index  calculation  resides  in  the  processing  of  the  indicators.  Overall, the  indicators  are 
treated in the same way, while differences may arise in the data cleaning process due to the different format 
of the data sources. However, all of them follow those essential steps: 

• 

Treatment  of  missing  data  by  linear  regression  for  indicators  in  which  a  clear  pattern  can  be 
detected; 

•  Check of skewness and kurtosis: those two parameters are commonly used to assess the normality 
of a  distribution. Thus, the  check is performed  for each indicator to verify that its distribution is at 
least close to a normal one, and in order to limit the presence of outliers that in turn may limit the 
informative  power  of  single  indicators.  In  this  case  if  absolute  skewness  is  higher  than  2  or  the 
kurtosis  is  above  3.5,  the  script  performs  a  Winsorization  of  the  distribution  using  the  2%  at  the 
bottom  and  the  98%  at  the  top  as  threshold  for  the  process.  It  means  that  any  observed  value 
greater than the 98th percentile is lowered to equal the 98th percentile and any observed value lower 
than the 2nd percentile is raised to equal the 2nd percentile. The check is performed again and if one 
of the two parameters is still above its limit, then a Log transformation is applied; 

•  Data normalisation (0-10): once the dataset is fully cleaned, values are normalised in a scale 0-10 to 

make them comparable across different indicators. 

11 

 
Figure 3. The data processing workflow applied to the indicators. All the processed indicators are aggregated to define the 
sub-index (NUTS3, NUTS2 or country).  

Source: JRC, 2023 

Those steps are required in order to make the indicator usable for the sub-index definition. As shown in Figure 
3,  all  the  processed  indicators  are  aggregated  in  a  single  table  and  then  through  few  calculations  sub-
dimensions, dimension and the final sub-indices are set. 

3.2.1  Missing data 

Indicators  are  chosen  also  by  considering  data  availability  and  their  completeness.  Unfortunately, there  are 
cases  where  the  indicators  contain  empty  records  for  some  administrative  units  of  the  sub-regional  levels. 
When it happens, the process automatically checks for the data within the upper geographic levels with the 
aim to fill the records as much as possible. Therefore, starting form a NUTS3 administrative unit with missing 
data, the system checks the NUTS2 to which it belongs first, then the NUTS1 and eventually the country. Once 
the information from the upper level is retrieved, there are two possibilities: 

1)  The  information  cannot  be  proportionally  split  among  the  empty  areas,  so  the  empty  records  will 
contain  the  same  value  of  the  upper  level.  An  example  is  the  Information  indicator  (Frequency  of 
internet access: once a week (including every day)), which is measured as a percentage. In this case, 
it  is  not  possible  to  allocate  portions  of  the  value  at  NUTS1  level  to  the  underlying  NUTS2,  hence 
those NUTS2 will have the same value of the NUTS1; 

2)  The  information  can  be  split  among  the  empty  areas  with  the  help  of  other  data  such  as  the 
population  or  the  area  of  the  administrative  units.  The  Hospital  beds  per  100,000  population 
indicator presents some NUTS2 areas with no data; therefore, the NUTS1 information is fetched and 
proportionally split across the underlying  NUTS2  based on the number of  hospitals located in each 
area. In this case, if the empty NUTS2 do not cover the whole NUTS1 (it means that there are some 
NUTS2 within that  NUTS1 where the information is available), then the  known values are  removed 
from the total of the NUTS1 and only the “unknown hospital beds” are proportionally allocated to the 
empty NUTS2. 

12 

 
However, there are cases where the data is not available at all and the records remain empty. Even  though 
this  is  not  ideal,  such  situation  is  relatively  rare,  in  particular  for  countries  within  the  EU,  and  it  does  not 
prevent the computation of the sub-index. Table 2 shows the frequency of missing data points by indicators 
at each administrative level for all countries, EU countries and non-EU countries.  

Table 2: Frequency of missing data by administrative level and dimensions 

All countries 

EU countries 

Non-EU countries  
(CH, IS, NO, UK) 

Country level indicators 

3.90% 

2.14% 

15.79% 

Social dimension 

Economic dimension 

Political dimension 

0.46% 

0.00% 

0.00% 

Environmental dimension 

14.19% 

0.00% 

0.00% 

0.00% 

8.15% 

NUTS2 level indicators 

3.90% 

1.6 % 

Social dimension 

Economic dimension 

0.14% 

6.96% 

Political dimension 

19.59% 

Environmental dimension 

1.69% 

0.18% 

4.08% 

0.83% 

2.08% 

3.57% 

0.00% 

0.00% 

55.00% 

12.9 % 

0.00% 

19.29% 

100.00% 

0.00% 

NUTS3 level indicators 

6.17% 

2.76% 

23.90% 

Social dimension 

Economic dimension 

0.00% 

12.79% 

Environmental dimension 

4.38% 

0.00% 

5.88% 

1.28% 

Source: JRC, 2023 

0.00% 

48.67% 

20.44% 

Eventually, there is an additional case given by missing value in the time series of the indicator. It means that 
the indicator has the values for all the administrative units, but it misses some years for few of them. In this 
case a linear regression is used for interpolation and the missing years are filled with the estimated value. 

3.2.2  Normalisation 

Data normalisation process is required to bring all indicators on a common scale for further comparison and 
aggregation (Balaganesh et al, 2020; Grigorescu et al, 2021). As briefly mentioned, it consists of a min/max 
rescaling where actual values are transformed to a 0-10 scale. The procedure involves two different formulas 
based  on  the  existing  relationship  between  the  indicator  and  the  vulnerability.  If  higher  values  of  the 
indicators correspond to a higher vulnerability, then the rescaling is performed in the following way: 

𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 𝑠𝑐𝑜𝑟𝑒 =

𝑥 − 𝑀𝑖𝑛 𝑥
𝑀𝑎𝑥 𝑥 − 𝑀𝑖𝑛 𝑥

∗ 10 

Else, if higher values of the indicator correspond to a lower vulnerability, then the rescaling is: 

𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟 𝑠𝑐𝑜𝑟𝑒 = 10 −

𝑥 − 𝑀𝑖𝑛 𝑥
𝑀𝑎𝑥 𝑥 − 𝑀𝑖𝑛 𝑥

∗ 10 

In both the cases, 𝑥 is the actual value that is going to be rescaled; 𝑀𝑖𝑛 𝑥 is the minimum value recorded in 
the interval considered; 𝑀𝑎𝑥 𝑥 is the maximum value recorded in the interval considered. 

13 

 
3.2.3  Weighting 

The complexity of aggregating different dimensions at various scales can be tackled as in the below diagram. 
In this case all dimensions (social, environmental, economic and physical) have the same weight at each level, 
and  all  indicators  within  a  dimension  have  equal  weights.  In  the  version  currently  deployed  on  the  RDH 
however,  all  four  dimensions  are  not  available  at  each  level.  The  effect  of  this  is  a  non-equal  weight 
distribution  once  put  together  into  an  index  over  all  levels  since  each  sub-index  has  the  same  weight.  The 
general review under-way will to an extent remedy this, even though it can still be discussed whether social 
and economic  shall  have a higher weight, thus they have more influence (depending  on the purpose of the 
vulnerability layer). 

Figure 4: Weights distribution across the RDH dimensions of vulnerability 

Source: JRC, 2022 

14 

 
 
 
 
3.2.4  Cross-level Aggregation, Averages and Normalisation 

As  explained  in  paragraph  3.2,  each  sub-index  is  defined  through  a  three-step  process  which  involves  the 
average of the sub-dimension first and then the average of the dimensions (Figure 5). 

Figure 5. Calculation of the sub-indices (or components) at the different geographical levels; the process comes right after 
the processing of the indicators. 

Source: JRC, 2022 

Once  the  sub-indices  are  calculated,  few  more  calculations  are  performed  in  order  to  return  the  composite 
index for each level. In this case, there are differences based on the administrative unit considered. For the 
NUTS3, the process is straightforward, since there is a simple arithmetic average of the three raw sub-indices. 

𝑽𝒖𝒍𝒏𝒆𝒓𝒂𝒃𝒊𝒍𝒊𝒕𝒚 𝒊𝒏𝒅𝒆𝒙 (𝑵𝑼𝑻𝑺𝟑𝒊,𝒋,𝒌)

=

𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑖) + 𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝑁𝑈𝑇𝑆2𝑖,𝑗) + 𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝑁𝑈𝑇𝑆3𝑖,𝑗,𝑘)
3

At NUTS2 level, first the average of NUTS3 within each NUTS2 is calculated (AVG NUTS3) and then its result is 
used in the average of the raw sub-indices. 

𝑽𝒖𝒍𝒏𝒆𝒓𝒂𝒃𝒊𝒍𝒊𝒕𝒚 𝒊𝒏𝒅𝒆𝒙 (𝑵𝑼𝑻𝑺𝟐𝒊,𝒋)

=

𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑖) + 𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝑁𝑈𝑇𝑆2𝑖,𝑗) +

3

∑

𝑛
𝑘=1

𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝑁𝑈𝑇𝑆3𝑖,𝑗.𝑘)

𝑛

Where n is the number of NUTS3 geographical areas within the NUTS2 area assessed.  

Finally,  for  countries,  the  first  step  is  represented  by  the  average  of  NUTS2  and  NUTS3  sub-indices  within 
each  country.  The  two  averages  are  used  in  the  final  arithmetic  average  of  the  raw  sub-indices  in  order to 
calculate the vulnerability index at country level. 

𝑽𝒖𝒍𝒏𝒆𝒓𝒂𝒃𝒊𝒍𝒊𝒕𝒚 𝒊𝒏𝒅𝒆𝒙 (𝑪𝒐𝒖𝒏𝒕𝒓𝒚𝒊)

𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑖) +

=

∑

𝑚
𝑗=1

𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝑁𝑈𝑇𝑆2𝑖,𝑗)

𝑚

3

∑

𝑛
𝑘=1

𝑆𝑢𝑏 𝑖𝑛𝑑𝑒𝑥 (𝑁𝑈𝑇𝑆3𝑖,𝑗.𝑘)

𝑛

+

Where  m  and  n  respectively  are  the  number  of  NUTS2  and  NUTS3  geographical  areas  within  the  country 
assessed.  

15 

 
 
 
 
  
3.2.5  Correlation 

The estimate of the independent vulnerability is qualified by using the Pearson correlation method in order to 
perform  correlation  analysis  of  the  variables  considered  for  each  sub-index.  The  aim  is  to  both  detect  a 
possible  multicollinearity  of  the  indicators  and  check  for  their  validity  from  a  mathematical  perspective. 
Multicollinearity happens when variables are highly correlated to each other. When this condition takes place, 
it  complicates  the  understanding  of  how  variables  affect  the  phenomenon  because  both  variables  describe 
the  same  effect,  and  in  this  case,    it  is  better  to  remove  one  of  them.  On  the  other  hand,  a  very  low 
correlation can highlight possible independent variables, which means that they are not related to each other. 
As a  consequence, their aggregation may  result less informative than intended, since  poorly and negatively 
correlated components of an aggregation tend to cancel each other out in the aggregated object. 

Figure  6,  Figure  7  and  Figure  8  display  the  correlograms  computed  for  each  sub-index.  Overall, dimensions 
correlate as expected, with the exception of the environmental dimension, which correlates poorly at country 
level  and  at  NUTS2  level.  This  could  be  explained  by  the  fact  that  the  physical  environment  is  not  directly 
linked to socio-economic and political factors. In other words, changes within the society or political/economic 
decisions  do  not  have  an  immediate  effect  on  the  environment,  hence  the  interlinkages  between  those 
dimensions are much more complex. 

In any case, the environmental dimension itself shows a good correlation of its own indicators, which indicates 
that there is coherence between the data and the conceptual framework. 

16 

Figure 6. Correlation analysis for the Country Sub-index. Red squares highlight the dimensions (social, economic, political 
and environmental), while black squares defined the sub-dimensions. 

Source: JRC, 2022 

17 

 
Figure 7. Correlation analysis for the NUTS2 sub-index. Likewise, Figure 6 red squares define the dimensions and the black 
squares the sub-dimensions considered. 

Source: JRC, 2022 

A  rather  different  situation  is  observed  at  NUTS3  level,  where  the  environmental  dimension  is  highly 
correlated  with  the  sub-index,  meanwhile  the  social  and  the  economic  ones  exhibit  a  different  behaviour. 
Probably,  the  variability  at  local  level  is  affected  by  multiple  factors  which  outline  a  fairly  complex 
background.  Therefore,  the  composite  index  could  struggle  to  capture  all  those  hidden  aspects.  In  these 
circumstances it is useful to have the possibility to disaggregate the index, so that it is possible to visualise 
the individual components, i.e. dimensions and sub-dimensions separately.  

Nevertheless, all the dimensions show a good correlation of their own indicators. Thus, also in this case they 
are properly described by the set of the defined indicators. 

18 

 
Figure 8. Correlation analysis for the NUTS3 sub-index. Again, red squares define the dimensions and the black squares 
the sub-dimensions considered. 

Source: JRC, 2022 

3.2.6  Limitations 

It can be challenging to find indicators/information with sufficient geospatial coverage (even resolution) and 
frequency  of  update  to  cover  all  aspects  defined  in  the  theoretical  framework.  Indices  developed  at  higher 
administrative or statistical levels (e.g. national) do not consider the inherent  variability at lower levels (e.g. 
provincial), even though we are trying to remedy that in our approach. Using multiple-scale assessments from 
the  local/municipal  level  up  to  a  regional/national  level  can  improve  the  consistency  of  indices  across 
geographical scales (Marzi et al., 2018; O’Brien et al., 2004). The spatial scope in this framework is currently 
limited  to  NUTS3.  Data  availability,  and  in  particular  data  availability  at  different  administrative  levels,  is 
unfortunately somewhat limited within certain dimensions. Finally, this framework does not take into account 
organizational and technological aspects that may affect vulnerability (Salvi, 2022).   

19 

 
4  Results 

The application of the methodology  described in the  previous  sections allows generating  maps showing the 
spatial distribution of the  vulnerability index in relation to the  multi-level approach taken into consideration 
within  the  analysis.  The  first  map  (Figure  9),  displays  the  value  for  the  index  at  country  level  and  clearly 
shows a gradient that follows the SE to NW direction. Countries on the Mediterranean Sea and of East Europe 
appear to be more vulnerable than countries in the North. The sub-indices of the index can help to understand 
this  pattern  by  highlighting  how  countries  on  the  Mediterranean  coasts  and  East  Europe  tend  to  have  high 
values for most of the sub-indices considered.  

Disaggregating further, to the local administrative areas (NUTS2 and NUTS3), the multi-level approach allows 
us  to  provide  different  ways  of  visualising  the  index.  Considering  first  NUTS2  (Figure  10),  the  index  is 
visualised across Europe. Hence, the index of each sub-national administrative area is visualised based on a 
comparison  made  on  the  whole  continent.  As  a  result,  the  map  highlights  the  same  pattern  observed  at 
country level; East Europe and the Mediterranean areas still seem to be the most vulnerable areas. 

This  is  different  from  when  NUTS2  and/or  NUTS3  are  visualised  within  their  own  country  (Figure  11).  The 
visualisation allows us to observe differences in terms of vulnerability within the national scale. In this case, 
the pattern changes a lot, and makes it easier to observe areas that are more vulnerable within each country. 
In addition, the scale considered for visualisation of the vulnerability within the selected area is not related to 
the  European  scale  anymore,  but  to  the  countries  themselves.  It  means  that  there  is  no  relation  to  any 
possible gradient observed for entire Europe. 

Figure 9. Vulnerability index at country level. 

Source: JRC, 2023 

20 

 
 
 
Figure 10. Vulnerability index at NUTS2 level. 

Source: JRC, 2023 

Figure 11. Vulnerability index at NUTS2 level visualised at country scale , compared to the vulnerability index at NUTS3 
level visualised at country scale. 

Source: JRC, 2023 

21 

 
 
5  Conclusions and next steps 

The DRMKC RDH developed a Vulnerability Composite Index in order to depict the susceptibilities to disaster of 
various assets to social-economic and environmental systems.  The index attempts to measure the ability of 
communities,  individuals  and  institutions  to  withstand  shocks  presented  by  hazards  while  maintaining  an 
acceptable level of functioning for the systems considered.  

The  RDH  vulnerability  index  is based  on  four  dimensions:  social, economic, environmental  and political.  It  is 
based on literature review and knowledge coming from the collaboration with various scientific organisations. 
It addresses the contextual elements both at national and subnational level, presenting the spatial distribution 
at the level of administrative units (Country, NUTS2, NUTS3), Europe wide. Furthermore, the application of the 
index  to  multiple  geographical  levels  allows  to  provide  a  more  complete  overview  of  the  vulnerability  by 
considering both the local and the national variations which contribute to the total vulnerability.  

The availability of data proved to be the key issue in the development of national and sub-national indicators 
used to measure the vulnerability of social, economic, political and environmental systems. The index will be 
regularly  revised,  with  the  aim  to  identify  gaps  and  identify  new  indicators  to  cover  more  exhaustively  the 
four  dimensions.  In  general,  the  vulnerability  index  is  dynamic  in  the  sense  that  the  process  of  review  and 
revision is foreseen to be continuous. It is essential that the assessment of adequate choices for the measure 
of vulnerability takes into consideration the aggregation levels and data availability.  

Overall, the composite indicator approach proves to be flexible and efficient in describing vulnerability within 
the RDH. Essentially, the index only assesses the overall vulnerability of the communities that is not related to 
a  specific  hazard  or  asset.  For  this  reason,  further  developments  are  planned  in  order  to  add  also  hazard-
specific indicators (physical dimension of vulnerability) and to provide a more complete information about the 
vulnerability to disasters. 

22 

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25 

 
 
 
 
List of abbreviations and definitions  

DRM  

Disaster Risk Management 

DRMKC  Disaster Risk Management Knowledge Centre 

EEA 

EU 

GDP 

GU 

HFA 

IBM  

IPCC 

JRC 

European Environment Agency 

European Union 

Gross Domestic Product 

University of Gothenburg 

Hyogo Framework for Action 

Indicator based methodology 

Intergovernmental Panel on Climate Change 

Joint Research Center 

NUTS 

Nomenclature des Unités territoriales statistiques 

NW 

RDH 

SDG 

SE 

Noth West 

Risk Data Hub 

Sustainable Development Goals 

South East 

UNISDR  United Nations International Strategy for Disaster Reduction Secretariat 

WCDR  World Conference on Disaster Reduction 

WBG 

World Bank 

WGI 

WRI 

Worldwide Governance Indicators 

World Resources Institute 

26 

 
List of boxes  

Box 1. Vulnerability and Capacity ............................................................................................................................................................................4 

Box 2. Vulnerability and Resilience .........................................................................................................................................................................4 

27 

 
List of figures  

Figure 1. Sub-indexes of vulnerability. The sub-indices at each level aims to describe the vulnerable condition 
which we have associated with it, e.g. vulnerability of the individual/households at NUTS3 level, and so on. ......7 

Figure 2. Bottom-up / Top-down approach for computing the multi-scale vulnerability index. ........................................9 

Figure 3. The data processing workflow applied to the indicators. All the processed indicators are aggregated 
to define the sub-index (NUTS3, NUTS2 or country). ............................................................................................................................... 12 

Figure 4: Weights distribution across the RDH dimensions of vulnerability .............................................................................. 14 

Figure 5. Calculation of the sub-indices (or components) at the different geographical levels; the process 
comes right after the processing of the indicators. .................................................................................................................................. 15 

Figure 6. Correlation analysis for the Country Sub-index. Red squares highlight the dimensions (social, 
economic, political and environmental), while black squares defined the sub-dimensions. ........................................... 17 

Figure 7. Correlation analysis for the NUTS2 sub-index. Likewise, Figure 6 red squares define the dimensions 
and the black squares the sub-dimensions considered. ......................................................................................................................... 18 

Figure 8. Correlation analysis for the NUTS3 sub-index. Again, red squares define the dimensions and the 
black squares the sub-dimensions considered. ............................................................................................................................................ 19 

Figure 9. Vulnerability index at country level. ............................................................................................................................................... 20 

Figure 10. Vulnerability index at NUTS2 level............................................................................................................................................... 21 

Figure 11. Vulnerability index at NUTS2 level visualised at country scale , compared to the vulnerability index 
at NUTS3 level visualised at country scale..................................................................................................................................................... 21 

28 

 
List of tables 

Table 1: The 43 indicators used to build the vulnerability index of the RDH. ........................................................................... 10 

Table 2: Frequency of missing data by administrative level and dimensions .......................................................................... 13 

29 

 
Annexes  

Annex 1. Indicators 

Description  of  the  indicators  included  in  the  framework.  The  column  Reference  IDs  refers  to  the  IDs  of  the 
bibliographic  sources  contained  in  Annex  2.  Each  time  an  indicator is  mentioned  or its  conceptualisation  is 
defined in an article then it is linked to that specific source. 

Scale 

Hazard-
independent 
Indicator 

Country 

Projected 
population 
change 

Country 

Children at-risk-
of-poverty 

Country 

Disabled people 
with need for 
assistance 

Country 

Long-term care 
(health) 
expenditure 

Country 

Change in Age-
dependency 

Country 

Self-reported 
unmet need for 
medical care 

Country 

Country 

Perceived Good 
Health 

Gross National 
Saving 

Country 

GDP per capita 

Description 

Type 

Sub-type° 

Reference IDs 

Population change in the future will increase 
the vulnerability if the population growths. 
This indicator presents the dynamic of 
vulnerability. Future projection is used since 
the current situation is covered by 
population density and future change has a 
great impact on vulnerability. 
Children at risk of poverty also indicate a 
future trend: for children already being at 
risk of poverty, the likelihood that they will 
be more vulnerable in terms of financial 
resources and/or social exclusion should be 
considered. This can further have an 
influence on political structures. 
People with need for assistance are more 
vulnerable because of their dependency. This 
indicator takes disabled people and people 
who reported the need for assistance into 
account since young and old dependency is 
covered by age-dependency it only covers 
the working age population (15-64 years). 
A higher expenditure for long-term care 
identifies the capability of a country to take 
care of people who need medical assistance. 
Thus, it is related to the economic welfare of 
a community: higher expenses are related to 
a lower vulnerability. The indicator is related 
to the expenses dedicated to health 
functions, excluding capital investment. 
Demographic change in the future will 
increase the vulnerability: a positive 
demographic change increases the 
population with age-dependency. This 
indicator presents the dynamic of 
vulnerability. Future projection is used since 
the current situation is covered by age-
dependency and future change has a great 
impact on vulnerability. 
This indicator explains the vulnerability of 
people that are unable to afford medical 
needs. In case of a disaster and the need for 
medical service they will be more 
economically vulnerable and it can have an 
effect on their health. 
People with perceived good health are less 
vulnerable due to their ability to manage 
themselves and help others. 
The national savings represents the 
countries’ economic vulnerability. 

The GDP per capita is a measure of the 
wealth of the population. The higher is the 
indicator the lesser is the vulnerability. 

30 

Sensitivity 

Cohesion 

5, 10, 11, 15, 
21, 36, 55, 65 

Sensitivity 

Housing / 
Cohesion 

5, 10, 42, 45, 
65 

Sensitivity 

Housing / 
Cohesion 

10, 11, 12, 15, 
41, 45, 52, 54, 
65 

Adapt. 
Capacity 

Cohesion 

5, 24, 32, 41, 
45, 46, 55, 65 

Sensitivity 

Housing / 
Cohesion 

6, 9, 10, 11, 
12, 15, 16, 20, 
33, 41, 45, 65 

Sensitivity 

Cohesion 

10, 15, 43, 45 

Sensitivity 

Cohesion 

5, 15, 43, 45 

Adapt. 
Capacity 

Economic 
resource 

Adapt. 
Capacity 

Economic 
resource 

5, 7, 10, 22, 
32, 65 
5, 7, 8, 10, 11, 
13, 16, 19, 22, 
24, 27, 30, 32, 
33, 35, 36, 40, 
45, 46, 47, 65 

Scale 

Hazard-
independent 
Indicator 

Country 

Income 
Inequality 

Country 

Cultural heritage 

Country 

Governmental 
efficiency 

Country 

Political Stability 

Country 

National 
Adaptation 
Strategies 

Country 

Environmental 
protection 
expenditure 

Country 

Climate related 
economic losses 

Country 

Production, 
value added and 
exports in the 
environmental 
goods and 
services sector 

Country 

Common 
farmland bird 
index 

Description 

Type 

Sub-type° 

Reference IDs 

Sensitivity 

Economic 
resource 

7, 10, 11, 20, 
24, 27, 30, 33, 
41, 42, 43, 45, 
46, 47, 65 

Sensitivity 

Institutions / 
Environment 

37, 38 

Adapt. 
Capacity 

Institutions 

Sensitivity 

Institutions 

6, 8, 11, 15, 
24, 32, 36, 45, 
46, 55 

6, 11, 36, 45, 
46, 55 

Adapt. 
Capacity 

Institutions / 
Environment 

5, 10, 11, 17, 
24, 32, 42 

Adapt. 
Capacity 

Institutions / 
Environment 

5, 6, 7, 10, 22, 
32, 42, 45, 46, 
55 

Adapt. 
Capacity 

Institutions / 
Environment 

5, 6, 7, 10, 17, 
22, 24, 32, 42, 
45, 46, 55 

Adapt. 
Capacity 

Institutions / 
Environment 

5, 6, 7, 10, 17, 
22, 24, 32, 42, 
45, 46, 55 

Sensitivity 

Environment 

10, 11, 17, 22, 
39, 40, 42, 46, 
55 

The Gini-coefficient describes the inequality 
of incomes in a country. The greater the gap 
between low and high salaries is, the more 
people are vulnerable. 
The indicator is a mix of structural, economic 
and social aspects. Buildings included in the 
list of Unesco Heritage Sites are usually 
more vulnerable to extreme events due to 
their age and the way they are bult. 
Consequently, if a site is heavily damaged, it 
will have a high cost of re-construction. In 
addition, heritage sites are landmarks and 
their lost has a social impact on the 
communities. 
Governmental efficiency is an important 
indicator for calculating the efficiency on 
national level before, during and after a 
hazard strikes.  
Political Stability can imply the efficiency of 
a government, their international 
cooperation and their focus on needs of the 
country. 
National Adaptation strategies can represent 
the effort and mind-setting of the national 
government for DRR actions.  
The environmental protection expenditure 
quantifies the millions of Euros invested in 
equipment and plant for pollution control 
and in equipment and plant linked to cleaner 
technology ('integrated technology') by the 
activities under the economic activities 
identified by the NACE Rev. 2 classification. 
Higher investments lead to a lower 
environmental impact from those activities, 
therefore to a lower pressure. As a result, the 
ecosystems can increase their resilience 
against other stressful events.   
The economic losses that come from 
weather and climate-related disasters are a 
good indicator of the coping capacity of a 
country. The vulnerability of a country will be 
higher if it has to dedicate a lot of economic 
resources to that kind of events. 
The indicator provides the quantity of 
resources that come from products for 
environmental protection prevent, reduce 
and eliminate pollution or any other 
degradation of the environment. They 
include measures undertaken to restore 
degraded habitats and ecosystems. 
Examples are electric vehicles, catalysts and 
filters to decrease pollutant emissions, 
wastewater and waste treatment services, 
or noise insulation works. A country capable 
to produce more services of this kind can 
create a less vulnerable environment. 
Birds are high in the food chain; hence they 
can be considered as a good indicator for 
the overall state of health of ecosystems 
and biodiversity. The farmland bird indicator 
acts as a proxy to assess the biodiversity 
status of agricultural environments across 
Europe. A high level of biodiversity is related 

31 

Scale 

Hazard-
independent 
Indicator 

Description 

Type 

Sub-type° 

Reference IDs 

to a more resilient environment, since the 
diversification of the ecological niches 
guarantees the subsistence of the main 
environmental processes. 

The Natura 2000 network is composed by 
special protection areas (SPA) and proposed 
sites of Community importance (pSCI) that 
are subsequently designated under the 
Habitats Directive as special areas of 
conservation (SAC). Protected areas identify 
ecosystems with a high ecological value 
which should include a high level of 
biodiversity as well. Since a well-diversified 
environment is less vulnerable, countries 
that have a high % share of Natura 200 
areas have a less vulnerable environment. 
Life expectancy in this context takes only the 
overall health situation of the population 
into account. Thus, the higher the expectancy 
the better is the health situation. 
The number of hospital beds per 100,000 
inhabitants is measure of the adaptive 
capacity of a community. A higher value is 
related to the capability of an area to aid 
people affected by events, therefore it is 
related to a lower vulnerability overall. 
This indicator transmits a picture on social 
interaction. Nowadays, interactions in social 
networks can present one part of social 
interactions. To bear in mind: it does not 
replace social interactions in persons. 

Country 

Natura 2000 
protected areas 

NUTS2 

Life expectancy 

NUTS2 

Hospital beds 
per 100'000 
population 

NUTS2 

Participation in 
Social Networks 

Sensitivity 

Environment 

10, 11, 17, 20, 
22, 24, 27, 28, 
39, 40, 41, 42, 
45, 46, 55 

Sensitivity 

Cohesion 

6, 24, 33, 45, 
46, 55 

Adapt. 
Capacity 

Access 

4, 5, 6, 7, 10, 
12, 13, 15, 21, 
30, 32, 36, 37, 
42, 54, 55, 65 

Adapt. 
Capacity 

Cohesion 

2, 15, 27, 32, 
36, 41, 52, 55 

The more people are connected to 
information services the better early warning 
can be disseminated.  

Adapt. 
Capacity 

Cohesion 

2, 15, 27, 32, 
36, 41, 52, 55 

Information 
(Frequency of 
internet access: 
once a week 
(including every 
day)) 

People at risk of 
poverty or social 
exclusion 

NUTS2 

NUTS2 

NUTS2 

 This indicator is based on economic 
indicators (People living in households with 
very low work intensity; severe material 
deprivation rate; at risk of poverty rate) 
which can lead to social exclusion. 

Primary and 
lower secondary 
education (levels 
1 and 2) 

A higher rate of people with lower level of 
education tend to increase the vulnerability 
due to a low level of awareness. It's 
inversely proportional to the tertiary 
education indicator. 

Sensitivity 

Cohesion 

Sensitivity 

Education 

NUTS2 

People with 
tertiary 
education (levels 
5-8) 

People with higher obtained education are 
believed to be less vulnerable due to their 
degree of knowledge and capable to react 
faster in case of an event.  

Adapt. 
Capacity 

Education 

NUTS2 

Severe material 
deprivation rate 

The material deprivation rate stands for 
existing poverty. Poverty is traditionally 
defined on the availability of financial 
means.  

Sensitivity 

Economic 
resource 

32 

5, 7, 8, 9, 15, 
27, 36, 65 

3, 4, 6, 7, 8, 9, 
12, 15, 16, 19, 
20, 21, 23, 26, 
30, 33, 36, 40, 
43, 45, 46, 47, 
52, 54, 55 
3, 4, 6, 7, 8, 9, 
12, 15, 16, 19, 
20, 21, 23, 26, 
28, 30, 32, 33, 
36, 40, 43, 45, 
46, 47, 52, 54 
,55 

5, 7, 9, 10, 12, 
26, 27, 42, 45, 
55, 65 

Description 

Type 

Sub-type° 

Reference IDs 

Scale 

Hazard-
independent 
Indicator 

NUTS2 

Household 
income 

NUTS2 

Motorways 

NUTS2 

Railways 

The household income is calculated as the 
balance of primary household income 
calculated in PPS. This anticipates the effect 
of commuting on regional differences and is 
calculated in context with the country. 
Household income describes a household’s 
possible financial resources which make 
them less vulnerable. 

The access to major road network describes 
the connectivity and remoteness of places. 
The shorter the distance, the lower the 
vulnerability. 

The access to major railways describes the 
connectivity and remoteness of places. The 
shorter the distance, the lower the 
vulnerability. 

Adapt. 
Capacity 

Economic 
resource 

Adapt. 
Capacity 

Access 

Adapt. 
Capacity 

Access 

NUTS2 

Employment 
rate 

Higher employment rates present a 
population with more individuals having 
financial resources stability for recovery. 

Adapt. 
Capacity 

Economic 
resource 

2, 5, 7, 10, 15, 
16, 20, 26, 27, 
32, 41, 45, 49, 
65 

2, 7, 8, 9, 12, 
15, 16, 21, 22, 
24, 27, 30, 32, 
35, 36, 41, 42, 
45, 49, 52, 54, 
55, 65 
2, 7, 21, 22, 
27,32, 36, 42, 
45, 49, 52, 55, 
65 
3, 9, 10, 11, 
12, 15, 20, 27, 
32, 35, 36, 41, 
42, 43, 45, 46, 
47, 52, 54, 65 

2, 6, 8, 11, 15, 
24, 27, 33, 36, 
45, 55 

NUTS2 

Regional Quality 
of Government 
index 

NUTS2 

Urban area 
classified as 
green space 

NUTS2 

Urban land cover 

NUTS3 

Population 
density 

NUTS3 

Net migration 

The EQI is a result of novel survey data on 
regional level governance within the EU. The 
data focusses on both perceptions and 
experiences with public sector corruption, 
services and their allocation and quality. This 
indicator represents citizens opinions and 
therefore implies their trust in the 
government. 
Green spaces, such as parks, trees and 
gardens, can help city’s resilience to the 
effects of climate change and extreme 
weather events. They have a cooling effect 
during period of hot temperatures and 
decrease the effect of the superficial run-off 
in case of extreme precipitations in urban 
areas. In addition, green areas are also 
related to the more developed areas of a 
city, hence they can highlight the more 
prosperous districts which usually are the 
most resilience. 
Built up urban area based on CORINE data. 
There is robust evidence that the amount of 
artificial areas, such as buildings and other 
structures, intensifies heat and can 
exacerbate the urban heat island (UHI) 
effect (EEA 2012). This will make an area 
more sensitive to the effects of high 
temperatures and heatwave. 

Population density can be seen as an 
indicator for, firstly, the total population that 
may be exposed and, secondly, the denser 
places are the higher is the vulnerability. 

Net migration from the previous year 
indicates the tendency of (inter-)national 
immigration and therefore, the amount of 
people living in the NUTS 3 less than 1 year. 
People living in a place less than one year 
can have a higher vulnerability which can be 
reasoned by less local knowledge for 

33 

Sensitivity 

Institutions 

Sensitivity 

Environment 

4, 5, 6, 7, 10, 
11, 15, 19, 22, 
35, 36, 41, 43, 
49 

Sensitivity 

Environment 

Sensitivity 

Housing / 
Cohesion 

5, 6, 7, 10, 11, 
15, 16, 19, 22, 
35, 36, 41, 43, 
49, 54 

4, 6, 9, 10, 
11,12, 13, 21, 
24, 26, 33, 35, 
36, 37, 43, 49, 
55, 65 

Sensitivity 

Housing / 
Cohesion 

10, 11, 15, 36, 
65 

Scale 

Hazard-
independent 
Indicator 

Description 

Type 

Sub-type° 

Reference IDs 

disaster risk, preparedness and response or 
due to language issues. 

NUTS3 

Young 
dependency 

Young people can be more sensitive and less 
responsive to extreme weather events. 

Sensitivity 

Housing / 
Cohesion 

NUTS3 

Old dependency 

Old people are more susceptible to harm 
during extreme events. 

Sensitivity 

Housing / 
Cohesion 

2, 3, 4, 5, 6, 7, 
9, 10, 11, 12, 
15, 16, 20, 21, 
23, 24, 30, 33, 
35, 41, 43, 45, 
47, 48, 52, 54, 
55, 65 
2, 3, 4, 5, 6, 7, 
9, 10, 11, 12, 
15, 16, 20, 21, 
23, 24, 28, 30, 
33, 35, 41, 43, 
45, 47, 48, 52, 
54, 55, 65 
5, 7, 8, 10, 11, 
13, 16, 19, 22, 
32, 33, 35, 36, 
40, 42, 45, 65 

Adapt. 
Capacity 

Economic 
resource 

Adapt. 
Capacity 

Economic 
resource 

5, 7, 10, 11, 
32, 36, 40, 42, 
45, 65 

Adapt. 
Capacity 

Economic 
resource 

17, 23, 32, 36, 
37, 55, 65 

NUTS3 

NUTS3 GDP per 
capita vs 
country average 

NUTS3 

Gross Value 
Added (at basic 
prices) 

NUTS3 

Power plants per 
100'000 
inhabitants 

The indicator compares the local GDP per 
capita to the country one. It is a measure of 
the local variability of the vulnerability in 
terms of economic resources. 
Gross Value Added (GVA) is defined as 
output value at basic prices less 
intermediate consumption valued at 
purchasers' prices. GVA is calculated before 
consumption of fixed capital. The resources 
that a city has can be a good indicator of a 
city’s sensitivity in terms of extreme weather 
events and climate change. If a city has a 
lower than average GVA, then it may have 
been more susceptible to damage from all 
types of extreme weather events. A city with 
low resources may not be able to 
adequately address climate change 
adaptation due to other pressures. 
This indicator shows the power plants per 
head of population in the NUTS3 unit. The 
more power plants that there are, the 
greater the chance that there may be 
redundancy. Redundancy is an important 
concept in resilience. Redundancy 
demonstrates that there is excess capacity 
in given system means that during crises, 
the system may still be able to retain 
functionality. If there are more power plants 
in a NUTS3 area than the EU average, this 
may mean that alternative ways of 
providing energy to a given population may 
be found. 

34 

Scale 

Hazard-
independent 
Indicator 

NUTS3 

Patent 
applications to 
the EPO 

NUTS3 

Soil erosion 

Description 

Type 

Sub-type° 

Reference IDs 

This indicator shows the number of patent 
applications to the European Patent Office 
(EPO) per 1000 population. Technology and 
innovation are important in helping a city to 
adapt to climate change e.g. investment in 
new flood technologies or building 
technologies that can help to mitigate heat. 
The ability of a country or urban area to 
invest in technological solutions, is thought 
to be an indicator of its adaptive capacity. 
Therefore, number of patents per year is 
used as a proxy indicator reflecting this 
issue. 
Given that the soil erosion by water is one of 
the most widespread forms of soil 
degradation in Europe, the indicator is a local 
measure of the environmental vulnerability 
of an area. 

Adapt. 
Capacity 

Economic 
resource 

15, 17, 24, 27, 
30, 32, 36, 37, 
40, 47 

Sensitivity 

Environment 

10, 11, 22, 23, 
26, 40 

35 

 
 
 
 
 
Annex 2. Indicator references 

Bibliographic sources used for the identification of the indicators used within the vulnerability framework. The 
IDs mentioned in the first column are linked to the respective indicator in Annex 2. 

Reference 
ID 

Title 

1 

2 

3 

4 

5 

6 

7 

8 

How to construct and validate an Integrated 
Socio-Economic Vulnerability Index: 
Implementation at regional scale in urban 
areas prone to flash flooding 

Measuring Household Resilience in Hazard-
Prone Mountain Areas: A Capacity-Based 
Approach 

Integrated flood vulnerability assessment of 
villages in the Waimanu River Catchment in 
the South Pacific: the case of Viti Levu, Fiji. 

Socio-economic and environmental 
vulnerability to heat-related phenomena in 
Bucharest metropolitan area 

Connecting people and place: a new 
framework for reducing urban vulnerability 
to extreme heat 

Urban Vulnerability Indicators. A joint report 
of ETC-CCA and ETC-SIA 

Map book urban vulnerability to climate 
change: Factsheets 

Dynamics of Socioeconomic Exposure, 
Vulnerability and Impacts of Recent 
Droughts in Argentina 

Citation & Link 

Aroca-Jiménez, E., Bodoque, J. and García, J., 2020. How 
to construct and validate an Integrated Socio-Economic 
Vulnerability Index: Implementation at regional scale in 
urban areas prone to flash flooding. Science of The 
Total Environment, 746, p.140905. 

https://doi.org/10.1016/j.scitotenv.2020.140905 
Tan, J., Peng, L. and Guo, S., 2020. Measuring Household 
Resilience in Hazard-Prone Mountain Areas: A Capacity-
Based Approach. Social Indicators Research, 152(3), 
pp.1153-1176. 

https://doi.org/10.1007/s11205-020-02479-5 
Begg, S., De Ramon N’Yeurt, A. and Iese, V., 2021. 
Integrated flood vulnerability assessment of villages in 
the Waimanu River Catchment in the South Pacific: the 
case of Viti Levu, Fiji. Regional Environmental Change, 
21(3). 

https://doi.org/10.1007/s10113-021-01824-9 
Grigorescu, I., Mocanu, I., Mitrică, B., Dumitraşcu, M., 
Dumitrică, C. and Dragotă, C., 2021. Socio-economic and 
environmental vulnerability to heat-related phenomena 
in Bucharest metropolitan area. Environmental 
Research, 192, p.110268. 

https://doi.org/10.1016/j.envres.2020.110268 
Wilhelmi, O. and Hayden, M., 2010. Connecting people 
and place: a new framework for reducing urban 
vulnerability to extreme heat. Environmental Research 
Letters, 5(1), p.014021. 

http://dx.doi.org/10.1088/1748-9326/5/1/014021 
Swart, Rob & Fons, Jaume & Geertsema, W. & Hove, 
Bert & Jacobs, C.M.J., 2012. Urban Vulnerability 
Indicators. A joint report of ETC-CCA and ETC-SIA.  

https://www.researchgate.net/publication/283419662_U
rban_Vulnerability_Indicators_A_joint_report_of_ETC-
CCA_and_ETC-SIA 

Timmerman, JG, Bacciu, V, Coninx, I, Fons, J, Gregor, M, 
Havranek, M, Jacobs, CMJ, Loehnertz, M, Peltonen, L, 
Sainz, M & Swart, RJ 2015. Map book urban 
vulnerability to climate change: Factsheets. European 
Environment Agency, Copenhagen. 

https://research.wur.nl/en/publications/map-book-urban-
vulnerability-to-climate-change-factsheets 
Naumann, G., Vargas, W., Barbosa, P., Blauhut, V., 
Spinoni, J., Vogt, J., 2019. Dynamics of Socioeconomic 
Exposure, Vulnerability and Impacts of Recent Droughts 
in Argentina. Geosciences 9, 39. 

https://doi.org/10.3390/geosciences9010039 

36 

 
 
 
 
 
 
 
 
9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

Development of a Matrix Based Statistical 
Framework to Compute Weight for 
Composite Hazards, Vulnerability and Risk 
Assessments 

Vulnerability assessments of coastal river 
deltas - categorization and review 

Putting vulnerability to climate change on 
the map: a review of approaches, benefits, 
and risks 

Spatial vulnerability assessment of floods in 
the coastal regions of Bangladesh 

Temporal and spatial variation 
characteristics of disaster resilience in 
Southwest China’s mountainous regions 
against the background of urbanization 

Operationalizing a concept: The systematic 
review of composite indicator building for 
measuring community disaster resilience 

Top-down assessment of disaster resilience: 
A conceptual framework using coping and 
adaptive capacities 

Is resilience capacity index of Chinese region 
performing well? Evidence from 26 
provinces 

Designing, planning, and managing resilient 
cities: A conceptual framework 

Resilience: The emergence of a perspective 
for social–ecological systems analyses 

Kabir, Akter, Karim, Haque, Rahman and Sakib, 2019. 
Development of a Matrix Based Statistical Framework 
to Compute Weight for Composite Hazards, Vulnerability 
and Risk Assessments. Climate, 7(4), p.56. 

https://doi.org/10.3390/cli7040056 
Wolters, M. and Kuenzer, C., 2015. Vulnerability 
assessments of coastal river deltas - categorization and 
review. Journal of Coastal Conservation, 19(3), pp.345-
368. 

https://doi.org/10.1007/s11852-015-0396-6 
Preston, B., Yuen, E. and Westaway, R., 2011. Putting 
vulnerability to climate change on the map: a review of 
approaches, benefits, and risks. Sustainability Science, 
6(2), pp.177-202. 

https://doi.org/10.1007/s11625-011-0129-1 
Roy, D. and Blaschke, T., 2013. Spatial vulnerability 
assessment of floods in the coastal regions of 
Bangladesh. Geomatics, Natural Hazards and Risk, 6(1), 
pp.21-44. 

https://doi.org/10.1080/19475705.2013.816785 
Huang, P., Pan, H., Peng, L., Chen, T. and Xu, J., 2020. 
Temporal and spatial variation characteristics of 
disaster resilience in Southwest China’s mountainous 
regions against the background of urbanization. Natural 
Hazards, 103(3), pp.3783-3802. 

https://doi.org/10.1007/s11069-020-04155-w 
Asadzadeh, A., Kötter, T., Salehi, P. and Birkmann, J., 
2017. Operationalizing a concept: The systematic review 
of composite indicator building for measuring 
community disaster resilience. International Journal of 
Disaster Risk Reduction, 25, pp.147-162. 

https://doi.org/10.1016/j.ijdrr.2017.09.015 
Parsons, M., Glavac, S., Hastings, P., Marshall, G., 
McGregor, J., McNeill, J., Morley, P., Reeve, I. and Stayner, 
R., 2016. Top-down assessment of disaster resilience: A 
conceptual framework using coping and adaptive 
capacities. International Journal of Disaster Risk 
Reduction, 19, pp.1-11. 

https://doi.org/10.1016/j.ijdrr.2016.07.005 
Wu, Y., Que, W., Liu, Y., Cao, L., Liu, S. and Zhang, J., 
2020. Is resilience capacity index of Chinese region 
performing well? Evidence from 26 provinces. Ecological 
Indicators, 112, p.106088. 

https://doi.org/10.1016/j.ecolind.2020.106088 
Desouza, K. and Flanery, T., 2013. Designing, planning, 
and managing resilient cities: A conceptual 
framework. Cities, 35, pp.89-99. 

https://doi.org/10.1016/j.cities.2013.06.003 
Folke, C., 2006. Resilience: The emergence of a 
perspective for social–ecological systems 
analyses. Global Environmental Change, 16(3), pp.253-
267. 

https://doi.org/10.1016/j.gloenvcha.2006.04.002 

37 

 
 
 
 
 
 
 
 
 
 
19 

20 

21 

22 

23 

24 

25 

26 

27 

Development of composite vulnerability 
index and district level mapping of climate 
change induced drought in Tamil Nadu, India 

Constructing a comprehensive disaster 
resilience index: The case of Italy 

Framework for mapping the drivers of 
coastal vulnerability and spatial decision 
making for climate-change adaptation: A 
case study from Maharashtra, India 

Targeting attention on local vulnerabilities 
using an integrated index approach: the 
example of the climate vulnerability index 

Mapping socio-environmental vulnerability 
to climate change in different altitude zones 
in the Indian Himalayas 

The determinants of vulnerability and 
adaptive capacity at the national level and 
the implications for adaptation 

The Vulnerability of Science and the Science 
of Vulnerability 

Vulnerability of the South African farming 
sector to climate change and variability: An 
indicator approach 

Comparing adaptive capacity index across 
scales: The case of Italy 

Balaganesh, G., Malhotra, R., Sendhil, R., Sirohi, S., Maiti, 
S., Ponnusamy, K. and Sharma, A., 2020. Development 
of composite vulnerability index and district level 
mapping of climate change induced drought in Tamil 
Nadu, India. Ecological Indicators, 113, p.106197. 

https://doi.org/10.1016/j.ecolind.2020.106197 
Marzi, S., Mysiak, J., Essenfelder, A., Amadio, M., Giove, S. 
and Fekete, A., 2019. Constructing a comprehensive 
disaster resilience index: The case of Italy. PLOS ONE, 
14(9), p.e0221585. 

https://doi.org/10.1371/journal.pone.0221585 
Krishnan, P., Ananthan, P., Purvaja, R., Joyson Joe 
Jeevamani, J., Amali Infantina, J., Srinivasa Rao, C., 
Anand, A., Mahendra, R., Sekar, I., Kareemulla, K., Biswas, 
A., Kalpana Sastry, R. and Ramesh, R., 2018. Framework 
for mapping the drivers of coastal vulnerability and 
spatial decision making for climate-change adaptation: 
A case study from Maharashtra, India. Ambio, 48(2), 
pp.192-212. 

https://doi.org/10.1007/s13280-018-1061-8 

Sullivan, C. and Meigh, J., 2005. Targeting attention on 
local vulnerabilities using an integrated index approach: 
the example of the climate vulnerability index. Water 
Science and Technology, 51(5), pp.69-78. 

https://doi.org/10.2166/wst.2005.0111 

Gupta, A., Negi, M., Nandy, S., Kumar, M., Singh, V., 
Valente, D., Petrosillo, I. and Pandey, R., 2020. Mapping 
socio-environmental vulnerability to climate change in 
different altitude zones in the Indian 
Himalayas. Ecological Indicators, 109, p.105787. 

https://doi.org/10.1016/j.ecolind.2019.105787 
Brooks, N., Neil Adger, W. and Mick Kelly, P., 2005. The 
determinants of vulnerability and adaptive capacity at 
the national level and the implications for 
adaptation. Global Environmental Change, 15(2), 
pp.151-163. 

https://doi.org/10.1016/j.gloenvcha.2004.12.006 

Cutter, S., 2003. The Vulnerability of Science and the 
Science of Vulnerability. Annals of the Association of 
American Geographers, 93(1), pp.1-12. 

https://doi.org/10.1111/1467-8306.93101 

Gbetibouo, G., Ringler, C. and Hassan, R., 2010. 
Vulnerability of the South African farming sector to 
climate change and variability: An indicator 
approach. Natural Resources Forum, 34(3), pp.175-187. 

https://doi.org/10.1111/j.1477-8947.2010.01302.x 
Marzi, S., Mysiak, J. and Santato, S., 2018. Comparing 
adaptive capacity index across scales: The case of 
Italy. Journal of Environmental Management, 223, 
pp.1023-1036. 

https://doi.org/10.1016/j.jenvman.2018.06.060 

38 

 
 
 
 
 
 
 
 
 
28 

29 

30 

31 

A measurement of community disaster 
resilience in Korea 

A multidisciplinary multi-scale framework 
for assessing vulnerabilities to global 
change 

Assessing urban adaptive capacity to 
climate change 

Exploring households’ resilience to climate 
change-induced shocks using Climate 
Resilience Index in Dinki watershed, central 
highlands of Ethiopia 

32 

Adaptive capacity and its assessment 

33 

34 

35 

36 

37 

Developing Credible Vulnerability Indicators 
for Climate Adaptation Policy Assessment 

Linkages between vulnerability, resilience, 
and adaptive capacity 

(Non-) robustness of vulnerability 
assessments to climate change: An 
application to New Zealand 

European Climate Risk Typology – Final 
Report 

Identifying spatial clusters of flood exposure 
to support decision making in risk 
management 

Yoon, D., Kang, J. and Brody, S., 2015. A measurement 
of community disaster resilience in Korea. Journal of 
Environmental Planning and Management, 59(3), 
pp.436-460. 

https://doi.org/10.1080/09640568.2015.1016142 
Metzger, M., Leemans, R. and Schröter, D., 2005. A 
multidisciplinary multi-scale framework for assessing 
vulnerabilities to global change. International Journal of 
Applied Earth Observation and Geoinformation, 7(4), 
pp.253-267. 

https://doi.org/10.1016/j.jag.2005.06.011 
Araya-Muñoz, D., Metzger, M., Stuart, N., Wilson, A. and 
Alvarez, L., 2016. Assessing urban adaptive capacity to 
climate change. Journal of Environmental Management, 
183, pp.314-324. 

https://doi.org/10.1016/j.jenvman.2016.08.060 
Asmamaw, M., Mereta, S. and Ambelu, A., 2019. 
Exploring households’ resilience to climate change-
induced shocks using Climate Resilience Index in Dinki 
watershed, central highlands of Ethiopia. PLOS ONE, 
14(7), p.e0219393. 

https://doi.org/10.1371/journal.pone.0219393 
Engle, N., 2011. Adaptive capacity and its 
assessment. Global Environmental Change, 21(2), 
pp.647-656. 

https://doi.org/10.1016/j.gloenvcha.2011.01.019 
Eriksen, S. and Kelly, P., 2006. Developing Credible 
Vulnerability Indicators for Climate Adaptation Policy 
Assessment. Mitigation and Adaptation Strategies for 
Global Change, 12(4), pp.495-524. 

https://doi.org/10.1007/s11027-006-3460-6 
Gallopín, G., 2006. Linkages between vulnerability, 
resilience, and adaptive capacity. Global Environmental 
Change, 16(3), pp.293-303. 

https://doi.org/10.1016/j.gloenvcha.2006.02.004 
Fernandez, M., Bucaram, S. and Renteria, W., 2017. 
(Non-) robustness of vulnerability assessments to 
climate change: An application to New Zealand. Journal 
of Environmental Management, 203, pp.400-412. 

https://doi.org/10.1016/j.jenvman.2017.07.054 
Carter, J., Hincks, S., Connelly, A., Vlastaras, V. and 
Handley, J., 2018. European Climate Risk Typology– 
Final Report. RESIN Support Decision - Making for 
Resilient Cities. [online] Available at: <https://european-
crt.org/files/typology-final-report.pdf> [Accessed 12 
April 2022]. 

https://european-crt.org/files/typology-final-report.pdf 
Röthlisberger, V., Zischg, A. and Keiler, M., 2017. 
Identifying spatial clusters of flood exposure to support 
decision making in risk management. Science of The 
Total Environment, 598, pp.593-603. 

http://dx.doi.org/10.1016/j.scitotenv.2017.03.216 

39 

 
 
 
 
 
 
 
 
 
 
38 

39 

40 

41 

42 

43 

Towards an indicator-based assessment of 
cultural heritage as a cultural ecosystem 
service – A case study of Scottish 
landscapes 

A social–ecological analysis of ecosystem 
services in two different farming systems 

Towards an assessment of adaptive 
capacity of the European agricultural sector 
to droughts 

Piloting a social-ecological index for 
measuring flood resilience: A composite 
index approach 

A localized disaster-resilience index to 
assess coastal communities based on an 
analytic hierarchy process (AHP) 

The Resilience to Emergencies and Disasters 
Index: Applying big data to benchmark and 
validate neighborhood resilience capacity 

44 

Social Capital and Community Resilience 

45 

46 

47 

Reviewing Composite Vulnerability and 
Resilience Indexes: A Sustainable Approach 
and Application 

The pentagon of vulnerability and resilience: 
A methodological proposal in development 
economics by using graph theory 

A spatially explicit scenario-driven model of 
adaptive capacity to global change in Europe 

Stanik, N., Aalders, I. and Miller, D., 2018. Towards an 
indicator-based assessment of cultural heritage as a 
cultural ecosystem service – A case study of Scottish 
landscapes. Ecological Indicators, 95, pp.288-297. 

https://doi.org/10.1016/j.ecolind.2018.07.042 
Andersson, E., Nykvist, B., Malinga, R., Jaramillo, F. and 
Lindborg, R., 2015. A social–ecological analysis of 
ecosystem services in two different farming 
systems. AMBIO, 44(S1), pp.102-112. 

https://doi.org/10.1007/s13280-014-0603-y 
Williges, K., Mechler, R., Bowyer, P. and Balkovic, J., 
2017. Towards an assessment of adaptive capacity of 
the European agricultural sector to droughts. Climate 
Services, 7, pp.47-63. 

https://doi.org/10.1016/j.cliser.2016.10.003 
Kotzee, I. and Reyers, B., 2016. Piloting a social-
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composite index approach. Ecological Indicators, 60, 
pp.45-53. 

http://dx.doi.org/10.1016/j.ecolind.2015.06.018 
Orencio, P. and Fujii, M., 2013. A localized disaster-
resilience index to assess coastal communities based on 
an analytic hierarchy process (AHP). International 
Journal of Disaster Risk Reduction, 3, pp.62-75. 

https://doi.org/10.1016/j.ijdrr.2012.11.006 
Kontokosta, C. and Malik, A., 2018. The Resilience to 
Emergencies and Disasters Index: Applying big data to 
benchmark and validate neighborhood resilience 
capacity. Sustainable Cities and Society, 36, pp.272-
285. 

https://doi.org/10.1016/j.scs.2017.10.025 
Aldrich, D. and Meyer, M., 2014. Social Capital and 
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