Resilience of large investments and critical infrastructures in Europe to climate change.
Date: 2015-12-11
Institutions: Institute for the Protection and Security of the Citizen
Subjects: adaptation to climate change, building industry, building materials, building regulations, building safety, building technique, environmental risk prevention, hazard science, investment policy, natural hazard, research report, urban infrastructure

Resilience of large investments and critical 
infrastructures in Europe to climate change 

Final report for DG CLIMA 

AA 071303/2012/630715//CLIMA.C.3 – JRC 32971-2012 NFP 
"Resilience of large investments in Europe to climate change (CCMFF)” 

Edited by  

Giovanni Forzieri, Alessandra Bianchi, 
Mario A. Marin Herrera, Filipe Batista 
e Silva, Luc Feyen and Carlo Lavalle 

2015 

1 

EUR 27598 EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Resilience of large investments and critical 
infrastructures in Europe to climate change 

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This publication is a Technical report by the Joint Research Centre, the European Commission’s in-house science service. It aims to 
provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not imply a 
policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is 
responsible for the use which might be made of this publication. 

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

European Commission 
Joint Research Centre 
Institute for Environment and Sustainability 

Contact information 
Luc Feyen 
Address: Joint Research Centre, Via Enrico Fermi 2749, TP 122, 21027 Ispra (VA), Italy 
E-mail: luc.feyen@jrc.ec.europa.eu 
Tel.: +39 0332 789258 

JRC98159 

EUR 27598 EN 

ISBN 978-92-79-54003-5 

ISSN 1831-9424 

doi:10.2788/171858 

© European Union, 2015 

Reproduction is authorised provided the source is acknowledged. 

Images: JRC Photo Gallery - All images copyright (c) European Union 2015 

How to cite: Forzieri, G., Bianchi, A., Marin Herrera, M.A., Batista e Silva, F., Feyen, L. and Lavalle, C., 2015. Resilience of large 
investments and critical infrastructures in Europe to climate change. EUR 27598 EN. Luxembourg (Luxembourg): Publications Office of 
the European Union. 

3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Scientific contributions 

Scientific coordination  

Luc Feyen (Institute for Environment and Sustainability (IES), JRC, Italy) 

Carlo Lavalle (IES, JRC, Italy) 

Hazard analysis 

Giovanni Forzieri (IES, JRC, Italy)  

Luc Feyen (IES, JRC, Italy) 

Simone Russo (IPSC, JRC, Italy) 

Michalis Vousdoukas (IES, JRC, Italy) 

Lorenzo Alfieri (IES, JRC, Italy) 

Stephen Outten (Nansen Environmental and Remote Sensing Center, Norway) 

Mirco Migliavacca (Max Planck Institute for Biogeochemistry, Germany) 

Alessandra Bianchi (IES, JRC, Italy) 

Rodrigo Rojas (Land and Water, CSIRO, Australia) 

Alba Cid (Environmental Hydraulics Institute, IH Cantabria, Spain) 

Sensitivity analysis 

Antoine Leblois (Laboratoire d'Économie Forestière, INRA, France) 

Giovanni Forzieri (IES, JRC, Italy)  

Luc Feyen (IES, JRC, Italy) 

Exposure analysis 

Mario A. Marin Herrera (IES, JRC, Italy) 

Alessandra Bianchi (IES, JRC, Italy) 

Filipe Batista e Silva (IES, JRC, Italy) 

Ricardo Barranco (IES, JRC, Italy) 

Carlo Lavalle (IES, JRC, Italy) 

Risk analysis 

Giovanni Forzieri (IES, JRC, Italy)  

Alessandra Bianchi (IES, JRC, Italy) 

Luc Feyen (IES, JRC, Italy) 

Mario A. Marin Herrera (IES, JRC, Italy) 

Filipe Batista e Silva (IES, JRC, Italy) 

Adaptation analysis 

Giovanni Forzieri (IES, JRC, Italy)  

Luc Feyen (IES, JRC, Italy) 
Alessandra Bianchi (IES, JRC, Italy) 

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Executive Summary 

Critical  infrastructures  refer  to  the  array  of  physical  assets,  functions  and  systems 

that  are  vital  for  ensuring  the  EU’s  health,  wealth,  and  security.  Ensuring  climate 

resilience of critical infrastructures and EU regional investments and measures is an 

important  component  of  EU  policies  and  funding  mechanisms.  Information  on  the 

resilience  of  critical  infrastructures  and  large  investments  to  climate  change, 

however, is staggeringly lacking in literature. The CCMFF project aim was to fill this 

gap by providing insight on current and future impacts of climate extremes on the 

present  stock  of  critical  infrastructures  in  Europe  and  on  regional  investments 

under the EU Cohesion Policy for the 2007-2013 programming period.  

The  project  performed  the  first  comprehensive  multi-hazard  multi-sector  risk 

assessment  for  Europe  under  climate  change  to  identify  the  most  vulnerable  and 

impacted  regions  in Europe throughout the  21st  century. The  methodology  applied 

integrates  a  set  of  coherent,  high-resolution  climate  hazard  projections,  a  detailed 

harmonized  representation  of  sectorial  physical  assets,  productive  systems  and 

investments,  and  estimates  of  their  sensitivity  based  on  surveyed  expert  opinion 

and  literature  review.  The  three  components  are  linked  with  recorded  climate 

disaster damages in order to derive quantitative estimates of risk under current and 

future  climate  conditions.  Costs  required  to  make  infrastructures  and  investments 

climate  resilient  are  evaluated  based  on  possible  avoided  damage  scenarios  and 

cost-benefit information derived from literature. 

The key findings of the study can be summarized as follows 

  Europe will see a progressive and very strong increase in overall climate 

hazard with a prominent spatial gradient towards south-western regions. 

Key hotspots emerge particularly along coastlines and in floodplains. 

  Climate hazard impacts to critical infrastructures and EU regional 

investments may strongly rise in Europe: damages could triple by the 

2020s, multiply six-fold by mid-century, and amount to more than 10 times 

5 

 
 
 
 
present damages by the end of the century.  

  Economic losses are highest for the industry, transport and energy sectors. 

The strongest increase (>1,500% by the end of the century) in damage is 

projected for the energy and transport sectors, and for EU investments in 

environment and tourism.  

  Floods currently account for approximately half of climate hazard 

damages, but in the future droughts and heatwaves may become the most 

damaging hazards.  

  Substantial resources may be required to increase the resilience of critical 

infrastructures and EU regional investments against future climate 

 

Impact and adaptation costs do not fall equally across Europe. Southern 

and south-eastern European countries will be most impacted.  

Notwithstanding that the numbers presented may be subject to uncertainty, they do 

highlight  some  important  issues.  The  distribution  of  economic  costs  in  space  and 

amongst  sectors  provides  an  indication  of  the  regions  and  sectors  that  may  face 

substantial efforts  for making present and planned critical  infrastructures  resilient 

to future climate. The  disproportionate distribution of impacts across the EU leads 

to  the  question  of  how  these  costs  could  be  shared.  A  better  understanding  of  the 

regional  and  sector  distribution  of  impacts  could  aid  in  orienting  further  EU 

investments  such  that  Cohesion  policy  also  gains  meaning  as  a  burden  sharing 

instrument for adaptation to climate change. 

We further stress that the myriad of climate change impacts go far beyond those of 

the 7 climate hazards considered in this study; hence, it should be kept in mind that 

the  damages  presented  here  only  reflect  a  fraction  of  the  potential  climate  change 

impacts to society. 

6 

 
 
 
 
  
Table of Contents 

SCIENTIFIC CONTRIBUTIONS 

EXECUTIVE SUMMARY 

1 

INTRODUCTION 

1.1 

1.2 

2 

2.0 

2.1 

2.2 

General aim and settings 

Scientific challenges 

MULTI-HAZARD ASSESSMENT IN EUROPE UNDER CLIMATE CHANGE 

Key messages 

Introduction 

Methods 

2.2.1  Climate hazard indicators 
2.2.2 
2.2.3 
2.2.4  Combining multiple hazards 

Frequency of extreme events in current and future climate 
Expected Annual Fraction Exposed 

2.3 

Results and discussion 

Single hazard projections 

2.3.1 
2.3.2  Changes in overall and concurrent exposures to climate hazards 

34 

Main limitations and knowledge gaps 

References 

VULNERABILITY OF CRITICAL INFRASTRUCTURES TO CLIMATE HAZARDS 

Key messages 

Introduction 

Literature on climate sensitivity of infrastructures 

Expert survey 

References 

2.4 

2.5 

3 

3.0 

3.1 

3.2 

3.3 

3.4 

4 

EXPOSURE: CRITICAL INFRASTRUCTURES AND EU REGIONAL INVESTMENTS 

4.0 

Key messages 

7 

4 

5 

9 

9 

11 

16 

16 

18 

20 
20 
21 
21 
22 

25 
25 
31 

37 

39 

42 

42 

44 

46 

51 

59 

68 

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4.1 

4.2 

4.3 

Introduction 

EU regional investments: Cohesion policy funds 

Critical infrastructures 

4.3.1  Data collection 
4.3.2  Harmonization 

4.4 

4.5 

5 

5.0 

5.1 

5.2 

Main limitations and challenges 

References 

MULTI-HAZARD RISK ASSESSMENT IN EUROPE UNDER CLIMATE CHANGE 

Key messages 

Introduction 

Methods 

5.2.1  Hazards 
5.2.2 
Exposure 
5.2.3 
Sensitivity 
5.2.4  Multi-hazard risk framework 

5.3 

Results 

5.3.1 
5.3.2 

Impacts to critical infrastructures 
Impacts on EU investments of 2007-2013 programming period 

Main limitations and knowledge gaps 

References 

5.4 

5.5 

6 

6.0 

6.1 

6.2 

6.3 

Key messages 

Introduction 

Climate hazard mitigation measures 

Costs and effectiveness of adapting to climate change 

Indicative costs of adapting infrastructures to climate change 

6.3.1 
6.3.2  Climate proofing EU investments 

6.4 

References 

ANNEX I  STRUCTURE OF CPF AND CI RISK MAPS 

ANNEX II 

CPF AND CI RISK MAPS 

8 

70 

72 

78 
79 
81 

87 

90 

92 

92 

96 

98 
98 
99 
100 
100 

104 
104 
119 

124 

128 

133 

135 

136 

138 
139 
146 

154 

157 

162 

CLIMATE RESILIENT CRITICAL INFRASTRUCTURES AND INVESTMENTS 

133 

 
 
1  Introduction 

1.1  General aim and settings 

Critical  infrastructures  represent  a  large  economic  value,  both  as  capital  asset  and 

essential  element 

in  the 

functioning  of  the  economy.  Different  types  of 

infrastructures  and  investments  have  different  levels  of  vulnerability  to  climate 

change.  Moreover,  as  climate  change  impacts  are  manifested  locally,  individual 

assets have different hazard exposures depending on the geographic location.  

The potential negative consequences of climate change and the need to increase the 

resilience of our society to unwelcome impacts has received increased attention in 

the scientific and policy debate in recent years. The EU has made strong efforts for 

augmenting  the  profile  of  climate  change  in  their  budget  and  policies.  This  is 

expressed by the following actions.  

  The European Council has set as political objective to earmark at least 20% of 

the entire EU budget for climate-relevant actions in the period 2014-20201.  

  The current programming period is the first in which climate considerations 

have been included. Major projects funded by ESIF will need to be screened 

against  climate-related  vulnerabilities  and  necessary  adaptation  measures 

need to be reported2.  

  For  European  and  Structural  Investment  Funds  (ESIF)  there  is  now  the 

specific  requirement  that  adaptation  to  climate  change  is  part  of  the 

horizontal principle of sustainable development3.  

  One of the 11 thematic objectives under the new ESIF interventions includes 

specific measures for adaptation (Thematic Objective 5 – Promoting climate 

change adaptation, risk prevention and management)4.  

1 Conclusions of the European Council (7/8 February 2013) as regards the Multiannual Financial Framework. 
2 Article 101 of Regulation (EU) No 1303/2013 of the European Parliament and of the Council of 17 December 2013; Commission 
Implementing Regulation (EU) 2015/207 – Annex II, Section F.8. 
3 Article 8 of Regulation (EU) No 1303/2013 of the European Parliament and of the Council of 17 December 2013. 
4 Article 9 of Regulation (EU) No 1303/2013 of the European Parliament and of the Council of 17 December 2013. 

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  Guidelines  and  tools  that  have  become  available  on  how  to  take  into 

consideration  climate  change  adaptation  actions  in  EU  funded  investments 

and measures5,6. 

Despite  this  increased  attention  and  recent  scientific  advances,  quantitative 

information  on  the  vulnerability  and  risks  of  critical  infrastructures  and  large 

investments  to  climate  change  and  variability  is  barely  available  in  literature.  The 

CCMFF  project  aims  to  fill  this  gap  by  evaluating  current  and  future  impacts  of 

climate  extremes  on  the  current  stock  of  critical  infrastructures  in  Europe  and  on 

regional investments under the Cohesion Policy for the period 2007-2013.  

Critical infrastructure are defined by the EC7 as “an asset, system or part thereof (…) 

which  is  essential  for  the  maintenance  of  vital  societal  functions,  health,  safety, 

security, economic or social well-being of people, and the disruption or destruction 

of which would have a significant impact in a Member State as a result of the failure 

to  maintain  those  functions”.  According  to  this  definition,  critical  infrastructures 

considered  in  this  study  include  existing  transport  systems  (roads,  railways, 

airports,  ports,  and  inland  waterways),  energy  production  (renewable  and  non-

renewable  energy  power  plants)  and  transmission  (electricity  distribution  and 

transmission  infrastructures,  gas  pipelines)  systems,  heavy  industries  (metal, 

chemical,  mineral  +  refineries),  water  and  waste  treatment  facilities,  and  social 

infrastructures (education and health). 

The EU regional investments refer to projects financed by the Cohesion policy (CP), 

also referred to  as  ‘Regional policy’.  They are a  key financial instrument  in the EU,   

redistributing  about  a  third  of  the  entire  EU  budget  (347  billion  €  for  the  period 

2007–2013) among European regions based on economic and population indicators 

such  as  regional  and  national  prosperity,  unemployment  rates  and  population 

5 CLIMA.C.3/SER/2011/0011: Methodologies for climate proofing investments and measures under cohesion and regional policy and 
the common agricultural policy.  
6 ENV/CLIMA.C.3/SER/201l/0037r - Guidelines for project managers: "Making vulnerable investments climate resilient". 
7 COUNCIL DIRECTIVE 2008/114/EC of 8 December 2008 on the identification and designation of European critical infrastructures and 
the assessment of the need to improve their protection. 

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density8, with the aim of achieving social, economic and territorial cohesion across 

the EU. 

1.2  Scientific challenges 

Five  fundamental  scientific  challenges  have  been  identified  which  have  driven  the 

strategic  research  activities  within  the  project.  The  problem  to  be  addressed  is 

highly complex and the development of a comprehensive assessment framework for 

appraising  the  risks  to  critical  infrastructures  of  climate  hazards  in  present  and 

future  climates  requires  the  integration  of  different  scientific  disciplines  across 

physical, social and economic fields.  

Challenge  1:  Understanding  how  multiple  climate  extremes  will  evolve  in 

Europe along the twenty-first century.  

Europe is expected to face major impacts from a changing climate over the coming 

decades.  The  hazard  to  society  and  environment  will  be  largely  connected  to 

changes  in extreme  climate events, due to their disproportionate rise  compared to 

changes in climatological averages. Threats will be more pronounced in areas prone 

to  multiple  climate  hazards.  In  this  context,  a  multi-hazard  assessment  accounting 

for  possible  regional  variations  in  intensity  and  frequency  of  climate  extremes  is 

essential to identify areas potentially more exposed to climate change. The detailed 

multi-hazard assessment at pan-European scale as the one presented herein has not 

been documented in literature.  

Challenge  2:  Developing  a  spatially  coherent  dataset  of  existing  critical 

infrastructures.  

Geographic information on infrastructures is crucial to assess with spatial detail the 

potential  impacts  of  climate  change.  Datasets  of  existing  infrastructures  collected 

8 European  Council  (2006)  Regulation  (EC)  No  1083/2006  laying down general provisions on the European Regional Development 
Fund,  the  European  Social  Fund  and  the  Cohesion  Fund  and repealing Regulation (EC) No 1260/1999. 

11 

 
 
 
 
 
                                                        
and maintained by different organizations in Europe lack homogeneity in spatial and 

thematic  coverage  and  measurement  units.  Filling  incompleteness  in  the  source 

databases  and  designing  a  harmonization  framework  to  allow  for  comparability 

between  infrastructures  are  key  tasks  to  properly  accounting  for  regional 

differences in infrastructures distribution and quantifying the potential exposure.     

Challenge  3:  Determining  the  sensitivity  to  climate  hazards  of  critical 

infrastructures and EU-funded projects.  

Climate sensitivity of a given infrastructure/investment depends on multiple factors 

including  the  nature  of  the  climate-induced  shock  (e.g.,  temperature-based  stress, 

dry  spells,  flood  inundation,  fire  of  windstorm  damage),  the  on-site  structure  and 

processes (e.g., physical properties of the material) and the input sources exploited 

(e.g.,  water,  energy).  Establishing  the  climate  sensitivity  for  a  large  set  of 

infrastructures/investments is essential to comprehensively account for the diverse 

degrees of susceptibility of the current multifaceted socio-economic systems.  

Challenge  4:  Assessing  impacts  at  pan-European  scale  consistently  and 

comparably across multiple sectors and climate-related hazards. 

Projected  increases  in  frequency  of  multiple  climate  hazards  in  many  regions  of 

Europe  emphasize  the  relevance  of  a  multi-hazard  multi-sector  risk  assessment. 

This  requires  the  integration  of  the  climate  hazard,  exposure  and  sensitivity 

components  within  a  complex  framework  that  allows  to  translating  modelled 

impacts  into  cost  figures.  Deriving  a  comprehensive  and  comparable  measure  of 

expected  monetary  damages  is  fundamental  to  provide  useful  guidance  for 

developing adaptation strategies and mitigate impacts of multiple hazards in Europe 

in relation to critical infrastructures. 

Challenge 5: Appraising the cost of adaptation of infrastructures. 

To  make  optimal  use  of  limited  resources  for  investments  in  climate  impact 

mitigation, information about the cost and the appropriateness and effectiveness of 

12 

 
 
 
 
adaptation is needed. This involves an evaluation of different adaptation options and 

their  costs  and  benefits.  This  task  is  complex  as  comprehensive  frameworks  for 

addressing  costs  and  benefits  of  adaptation  options  are  largely  absent  and 

quantitative information on these aspects are very limited and fragmented.  

These  five  challenges  raised  a  series  of  methodological  issues  and  scientific 

questions  that  have  been  addressed  in  a  coherent  workflow  of  which  the  different 

steps  are  detailed  separately  in  Chapters  2-6.  We  focus  on  seven  climate  hazards 

including  heat  and  cold  waves,  wildfires,  droughts,  river  and  coastal  floods  and 

windstorms. Future climate hazards in Europe have been generated for an ensemble 

of regional climate simulations under a “business-as-usual” (SRES A1B) greenhouse 

gas  (GHG)  emissions  trajectory  over  the  1990-2100  period.  Climate-induced 

expected  annual  damages  to  infrastructures  and  EU-funded  investments  related  to 

the industry, energy, transport, social, environment and tourism and ICT sectors are 

quantified. Although these sectors do not cover the full range of possible societally 

relevant  climate-change  impacts,  they  include  crucial  aspects  of  livelihoods  for 

Europe.  Cost  estimates  reported  in  this  study  refer  to  damages  to  property  due  to 

direct  physical  contact  with  the  hazard  and  damages  related  to  the  reduction  in 

primary  sources  and  productivity.  The  methodology,  graphically  represented  in 

Figure  1.1,  integrates  a  set  of  coherent,  high-resolution  climate  hazard  projections 

(Chapter  2),  a  detailed  harmonized  representation  of  sectorial  physical  assets  and 

productive systems and investments (Chapter 4), and semi-quantitative estimates of 

their sensitivity based on surveyed expert opinion and literature review (Chapter 3). 

The  three  above-mentioned  components  have  been  combined  in  a  coherent 

vulnerability  framework  based  on  recorded  climate  disasters  in  order  to  derive 

quantitative estimates of risk (Chapter 5). Additional costs required to climate proof 

infrastructures  and  investments  are  then  evaluated  based  on  possible  avoided 

damage scenarios and cost-benefit analysis (Chapter 6). 

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Figure 1.1 Flow diagram of the proposed multi-hazard multi-sector risk assessment. 

Results are shown  throughout the  document in  spatial maps as  well as  aggregated 

for  five  European  regions  to  simplify  interpretation:  Southern  (SEU),  Western 

(WEU), Central (CEU), Eastern (EEU) and Northern (NEU) Europe (Figure 1.2). 

14 

 
 
 
Figure  1.2  European  regions.  Grouping  of  countries  in  macro-areas  shown  in  different  colors. 

Southern Europe (Albania, Bulgaria, Bosnia and Herzegovina, Croatia, Cyprus, Greece, Italy, Kosovo, 

Malta, Montenegro, Portugal, Republic of Macedonia, Serbia, Slovenia, Spain, Turkey), Central Europe 

(Austria,  Czech  Republic,  Germany,  Liechtenstein,  Poland,  Slovakia,  Switzerland),  Western  Europe 

(Belgium,  France,  Ireland,  Netherlands,  Luxembourg,  United  Kingdom),  Eastern  Europe  (Belarus, 

Estonia, Hungary, Latvia, Lithuania, Republic of Moldova, Romania, Russian Federation, Ukraine), and 

Northern  Europe  (Denmark,  Iceland,  Finland,  Norway,  Sweden).  Hatched  areas  are  only  covered 

(sometimes partly) in the hazard assessment and not in the risk assessment.  

This  work  provides  the  first  comprehensive  multi-hazard  multi-sector  risk 

assessment for Europe under climate change and identifies the most vulnerable and 

impacted  regions  in  Europe  throughout  the  21st  century.  We  believe  that  it 

significantly contributes to a better understanding and awareness that is crucial for 

the management of future climate risks.  

15 

 
 
 
 
2  Multi-hazard assessment in Europe under climate change 

2.0  Key messages 

  Projected  changes  in  the  occurrence  of  the  seven  climate  extremes  depict 

important  variations  in  hazard  scenarios  with  large  spatial  patterns  modulated 

by local climate conditions.  

o  Heat  waves  show  a  progressive  and  highly  significant  increase  in 

frequency all over Europe. By the end of this century, a current 100-year 

heatwave  may  occur  almost  every  year  in  Southern  Europe,  whereas  in 

other regions of Europe such events may happen every 3 to 5 years.   

o  Cold waves show an opposite significant trend with current cold extremes 

tending to mostly disappear in Europe in more distant futures.   

o  Streamflow droughts are projected to become more severe and persistent 

in  Southern  and  Western  Europe,  with  current  100-yr  events  that  could 

occur  approximately  every  2  to  5  years  by  2080,  respectively.  In  other 

regions  of  Europe  an  opposite  trend  can  be  expected,  with  a  strong 

reduction in drought frequency in most areas.  

o  In  most  regions  of  Europe  wildfires  may  happen  more  frequently  in  the 

future, especially in Southern, Eastern and Central Europe, albeit that the 

signal is not always very strong and only significant in limited areas.   

o  Western  Europe  shows  a  consistent  rise  in  future  flood  occurrence 

(current  100-yr  events  could  manifest  every  ~30  years  in  2080s).  In 

other regions projections of river floods show higher spatial and temporal 

variability,  with  lower  and  less  significant  changes.  In  Southern  and 

Eastern  Europe  more  areas  (30%)  show  a  significant  decrease  (vs  10% 

with  increase)  in  flood  hazard,  in  Northern  Europe  areas  with  a 

significant increase in flood hazard (24%) balance those with a significant 

decrease  (23%),  and  in  Central  Europe  more  areas  show  a  significant 

increase (26%) than decrease (15%).  

16 

 
o  Coastal floods show a progressive and pronounced increase in recurrence 

frequency  along  Europe’s  coastlines  mainly  due  to  sea  level  rise,  with  a 

current  100-yr  event  that  may  occur  every  2  to  8  years,  or  even  sub-

annually in Eastern Europe.  

o  Evidence  for  changes  in  windstorms  remains  largely  elusive.  Areas  with 

increases  in  windstorm  hazard  are  mainly  located  in  Western,  Eastern 

and Northern Europe, while Southern regions present slight reductions in 

windstorm frequency.  

  Europe will see a progressive and strong increase in overall climate hazard with 

a prominent spatial gradient towards south-western regions.  

o  By  the  end  of  this  century,  76%  of  the  area  in  Southern  Europe  is 

expected  to  be  exposed  annually  to  at  least  one  climate  hazard  with  a 

current  100-year  intensity,  or  more  than  15-fold  the  baseline  value.  For 

the other regions in Europe changes are somewhat less pronounced, but 

still  considerable:  for  Western,  Central,  Eastern  and  Northern  Europe, 

about 50% (+1,021%), 36% (+732), 31% (+614) and 29% (+597%) of the 

territory, respectively, will by the end of this century be exposed annually 

to at least one hazard with a current 100-year intensity. 

o  Due  to  the  increase  in frequency of  multiple hazards in  many regions  of 

Europe, the joint annual exposure expectancy to multiple hazards shows 

rises much sharper than  for single hazards.  In  Southern Europe, 25% of 

the  area  could  be  annually  exposed  to  at  least  two  hazards  with  a  100-

year intensity by the end of this century, or nearly 250-fold the baseline 

value.  When  considering  three  hazards,  the  increase  is  700-fold.  For  the 

other regions, rises in joint annual exposure expectancy for two and three 

hazards,  respectively,  amount  to  24,500%  and  9,500%  for  Western 

Europe, 6,250% and 2,100% for Central Europe, 4,335% and 1,400% for 

Eastern Europe, and 1,324% and 1,000% for Northern Europe.  

  Key hotspots emerge particularly along coastlines and in floodplains in Southern 

and Western Europe, which are often highly populated and economically pivotal.    

17 

 
 
 
2.1  Introduction 

Europe is expected to face major impacts from a changing climate over the coming 

decades (Collins et al., 2013). The hazard to society and environment will be largely 

connected  to  changes  in  extreme  climate  events  due  to  their  disproportionate  rise 

compared  to  the  corresponding  change  in  climatological  averages  (Rummukainen, 

2012). Threats will be more pronounced in areas prone to multiple climate hazards. 

In  this  context,  a  multi-hazard  assessment  accounting  for  possible  regional 

variations  in  intensity  and  frequency  of  climate  extremes  is  essential  to  identify 

areas potentially more exposed to climate change. 

A  number  of  climate  change  impact  studies  at  the  European  level  have  been 

achieved, usually for a single specific climate or weather hazard, such as river floods 

(Lehner et al., 2006; Rojas et al., 2012), coastal floods (Hinkel et al., 2010; Nicholls 

and  Klein,  2005),  heat  waves  (Christidis  et  al.,  2015;  Fischer  and  Schär,  2010), 

streamflow droughts (Forzieri et al., 2014; Lehner et al., 2006), windstorms (Nikulin 

et  al.,  2011;  Outten  and  Esau,  2013)  and  wildfires  (Bedia  et  al.,  2013;  Mirco 

Migliavacca et al., 2013). The study of multiple hazards poses two major challenges: 

(a)  hazards  are  not  directly  comparable  as  their  processes  and  describing  metrics 

differ; and (b) hazards can interact triggering cascade effects and coupled dynamics. 

In  the  existing  literature,  the  first  issue  has  been  mainly  addressed  through 

standardization  approaches,  such  as  classification  of  hazard 

intensity  and 

development  of  continuous  indices  (Dilley,  2005;  Kappes  et  al.,  2012;  Lung  et  al., 

2013).  While  these  approaches  represent  a  starting  point,  they  describe  only  a 

limited  set  of  climate  hazards  and  the  techniques  used  to  make  different  hazards 

comparable  are  largely  subjective  and  inconsistent.  The  second  issue  has  been 

addressed  mainly  qualitatively  through  descriptive  matrices  where  coupled 

mechanisms are conceptualized based on multi-hazard  dynamics observed at local 

scale and largely influenced by landscape figures (Gill and Malamud, 2014; Kappes 

et  al.,  2012).  Deeper  data-driven  investigations  are  needed  before  interactions 

between  hazards  can  be  reliably  incorporated  into  large-scale  predictive  systems. 

Thus, in this study we mainly focus on the first above-mentioned challenge.  

18 

 
 
Through  a  unique  collaborative  effort  of  different  European  modelling  groups,  a 

consistent  set  of  climate  hazard  modelling  data  has  been  produced  for  this  study 

including  heat  and  cold  waves,  river  and  coastal  floods,  droughts,  wildfires  and 

windstorms. Future climate hazards in Europe have been generated for an ensemble 

of regional climate simulations under a “business-as-usual” (SRES A1B) greenhouse 

gas  (GHG)  emissions  trajectory  (Solomon,  2007)  and  synthesized  in  a  coherent 

multi-hazard  framework.  The  method  is  based  on  the  analysis  of  the  changes  in 

frequency  of  climate-induced  extreme  events  and  the  corresponding  variations  in 

expected  annual  exposure  to  these  events.  The  latter  is  hereafter  defined  as 

Expected  Annual  Fraction  Exposed  (EAFE),  where  the  fraction  can  relate  to  any 

variable of interest (e.g., population, infrastructure, cropland). For a range of hazard 

severities, single-hazard EAFEs and changes therein are combined into multi-hazard 

indices  to  synthesize  the  potential  exposure  to  multiple  climate  hazards  (2.2 

Methods).  

This  work  provides  the  first  comprehensive  multi-hazard  assessment  for  Europe 

under climate change and focuses in particular on the comparability amongst single 

hazards  and  on  the  degree  of  overlap  between  areas  exposed  to  multiple  hazards 

throughout  this  century.  The  overall  goal  is  to  identify  geographic  areas  with  the 

highest  potential  exposure  to  multiple  climate  hazards  in  order  to  better  steer 

adaptation  efforts  and  land  planning  across  Europe.  It  is  worth  to  stress  that  this 

chapter  should  not  be  confused  with  a  risk  assessment  study.  A  risk  assessment 

implies the combination of hazard, vulnerability and spatial distribution of exposed 

assets, which is presented in Chapter 5.  

19 

 
 
 
 
 
 
2.2  Methods 

2.2.1  Climate hazard indicators 

The  analysis  focuses  on  seven  critical  climate  hazards  for  Europe:  heat  and  cold 

waves, river and coastal floods, droughts, wildfires and windstorms. Climate hazard 

indicators  were  derived  for  the  baseline  (1981-2010),  2020s  (2011-2040),  2050s 

(2041-2070)  and  2080s  (2071-2100)  for  an  ensemble  of  bias-corrected  climate 

projections  obtained  from  different  regional  climate  model  simulations  under  the 

A1B emissions scenario (Solomon, 2007) (Table 2.1). 

Table 2.1 Climate simulations used to derive climate hazard indicators in the period 1981–2100. 

Heatwaves were defined by the Heat Wave Magnitude Index (HWMI) that is based 

on  the  3-day  maximum  temperature  anomalies  (Russo  et  al.,  2014).  Cold  waves 

were  similarly  calculated  by  referring  to  minimum  temperatures.  Return  levels  of 

heat and cold waves were retrieved by empirical cumulative distribution functions. 

Wildfires were derived from projections of the monthly percentage of burned area 

(Mirco  Migliavacca  et  al.,  2013).  Beta  functions  were  selected  to  fit  the  annual 

fractions  of  burned  area  and  to  derive  extreme  events.  Extreme  windstorms  were 

calculated  using  the  Generalized  Pareto  distributions  that  have  been  derived 

through a peak-over-threshold analysis for daily maximum wind speeds (Outten and 

20 

Driving GCM RCM Acronyms Heat waves Cold waves Droughts Wildfires River floods Windstorms HadCM3Q16 ARPEGE ARPEGE BCM ECHAM5-r3 HadCM3Q0 ECHAM5-r3 HadCM3Q0 ECHAM5-r3 BCM ECHAM5-r3 HadCM3Q3 RCA3.0 ALADIN-RM5.1 HIRHAM5 HIRHAM5 HIRHAM5 CLM RACMO2 HadRM3Q0 REMO RCA3.0 RCA3.0 RCA3.0 C4I-RCA-HadCM3 CNRM-ALADIN-ARPEGE DMI-HIRHAM5-ARPEGE DMI-HIRHAM5-BCM DMI-HIRHAM5-ECHAM5 ETHZ-CLM-HadCM3 KNMI-RACMO2-ECHAM5 METO-HadRM3-HadCM3 MPI-REMO-ECHAM5 SMHI-RCA-BCM SMHI-RCA-ECHAM5 SMHI-RCA-HadCM3 X  X  X  X X  X  X  X  X X  X X X X X X X X X X X X X  X  X  X X  X X X X X X X X X X X X    X X     X X    
 
 
Esau,  2013).  Relative  Sea  Level  Rise  projections  were  combined  with  current 

extreme  value  distributions  of  total  water  levels  obtained  using  a  peak-over-

threshold  approach  (Cid  et  al.,  2014;  Pardaens  et  al.,  2011).  Following,  a  static 

inundation approach was applied to generate inundation maps along the coastline. 

For  inland  flooding  the  annual  maximum  discharges  and  flood  inundation  maps 

were  derived  from  earlier  works  (Rojas  et  al.,  2013,  2012).  For  drought  the 

minimum  discharges  and  return  levels  were  obtained  from  a  previous  study 

(Forzieri et al., 2014). 

2.2.2  Frequency of extreme events in current and future climate 

Baseline  return  levels  (RL,b)  of  the  climate  hazard  indicators  with  return  periods 

(TR,b)  from  2  to  100  years  were  obtained  at  each  grid  cell.  Future  return  periods 

(TR,f) of RL,b were calculated by inversion of the fitted probability functions (G).  

[2.1] 

Climate model variability was quantified by the coefficient of variance of the future 

return periods retrieved for the different climate realizations. The significance of the 

changes  in  climate  hazard  was  evaluated  by  the  Kolmogorov-Smirnov  test  applied 

on  the  annual  values  of  future  time  windows  versus  baseline,  separately  for  each 

climate model. 

2.2.3  Expected Annual Fraction Exposed 

By  analogy  with  risks  of  extreme  events  that  are  often  communicated  in  terms  of 

expected annual impact, the fraction expected to be annually exposed to a hazard – 

the  Expected  Annual  Fraction  Exposed  (EAFE)  –  was  calculated  by  integrating  the 

exposure  to  hazard  events  over  the  probability  of  occurrence  distribution  of  the 

hazard. The EAFE to hazard events with return period ≥  TR was  obtained as in the 

following: 

21 

bL,bL,fR,RG=RT11 
 
 
 
 
 
 
 
 
 
 
 
[2.2] 

where f is the exposure-probability function. In the case of river and coastal floods f 

is  a  dummy  function  with  value  1  when  the  pixel  is  flooded,  0  otherwise.  For  the 

remaining  climate  hazard  indicators  f  is  a  constant  function  equal  to  1,  under  the 

assumption that exposure to the hazard is spread homogeneously within the pixel. 

Future  return  periods  retrieved  from  equation  [2.1]  are  used  to  truncate  the 

integration  for  future  EAFE.  For  pixels  with  non-significant  changes  we  keep 

baseline values for future EAFE. EAFE ranges between 0 (no fraction exposed to the 

hazard) and 1 (whole  fraction expected to be annually exposed to climate hazard). 

The use of EAFE allows comparing quantitatively multiple hazards characterized by 

different processes and time scales based on a common intensity scale derived from 

the probability of occurrence of extreme events in the current climatology.  

2.2.4  Combining multiple hazards 

To  quantify  the  total  expected  annual  exposure  to  multiple  hazards  we  define  the 

Overall  Exposure  Index  (OEI).  Under  the  assumption  that  the  considered  hazards 

are mutually non-exclusive, from the inclusion-exclusion principle of combinatorics 

the OEI can be expressed as follows for a given 

:   

[2.3] 

where  i refers to  the  hazard-specific  EAFE, n  is the  number of hazards considered, 

the  last  sum  runs  over  all  subsets  I  of  the  indices  {1,…,n}  containing  exactly  k 

elements, and  

[2.4] 

22 

LR0RRT1dpf=TEAFERTnkkInIRIkRniiRTEAFETEAFETOEI1,...,1111RIiiRITEAFETEAFE: 
 
  
 
 
 
 
 
 
 
 
 
  
 
 
 
 
  
 
 
 
 
 
 
expresses  the  intersection  of  all  those  EAFEi  with  index  in  I.  Equation  [2.3] 

quantifies the expected annual exposure to at least one climate hazard. To account 

for the overall exposure to m overlapping hazards, equation [2.3] can be generalized 

using the intersections of m EAFEs in place of single-hazard components. Here, we 

use m values up to three to quantify different degrees of overlap amongst hazards. 

The scheme of calculation in a simplified case with three hazards is shown in Figure 

2.1. 

Figure  2.1  Schema  of  calculation  of  Overall  Exposure  Index.  Example  of  calculation  of  Overall 

Exposure Index in a simplified case with three hazards, here visualized by coloured circles, named A, 

B  and  C,  respectively.  Grey  areas  represent  the  Overall  Exposure  Index  with  different  degree  of 

overlapping. 

To  identify  areas  subject  to  large  increases  in  exposure  to  multiple  hazards,  we 

define the Change Exposure Index (CEI). CEI expresses the number of hazards - of a 

given  baseline  return  level  -  with  a  future  relative  increase  in  EAFE over  a  certain 

threshold  (20%,  100%  and  1000%).  The  use  of  three  different  thresholds  allows 

capturing moderate, strong and extreme  changes  in hazard exposure. Note  that an 

increase in EAFE of a current 100-year event by 20%, 100% or 1000% means that 

the  future  event  will  happen  every  80,  50,  or  9  years.  The  number  of  hazards  for 

which  there  is  an  increase  in  exposure  to  the  hazard  over  the  given  threshold  is 

calculated  in  each  grid  cell  and  then  aggregated  at  NUTS3  level  as  the  0.99th 

percentile  of  this  distribution  over  all  cells.  The  99th  percentile  of  the  exposure 

23 

 
 
 
 
change  distribution  within  the  NUTS3  region  excludes  local  fitting  extrapolation 

errors  and  is  considered  representative  of  the  maximum  degree  of  change  in 

exposure. CEI allows identifying key hotspots subject to predefined levels of change 

in exposure.  

Both  OEI  and  CEI  are  calculated  for  each  return  period  and  time  slice  using  the 

ensemble median of all climate model combinations for each hazard as inputs. Note 

that multi-hazard indices in regions close to the geographical limits of our domain, 

such  as  Island,  Russian  Federation  and  Turkey,  may  be  underestimated  due  to  the 

incomplete spatial coverage of some hazards (see Figure 2.2). 

Figure  2.2  Spatial  domains  of  climate  hazard  models.  Note  that  the  model  domain  for  floods  and 

droughts does not include Cyprus and Malta, the coastal model domain excludes only Cyprus.  

24 

 
 
 
 
 
 
 
 
 
2.3  Results and discussion 

2.3.1  Single hazard projections 

Figure 2.3 shows the projected changes in frequency of climatic extreme events with 

respect  to  current  climate,  where  increasing  (decreasing)  hazard  occurrences  are 

denoted by lines under (over) the bisector, the coefficient of variance (CV) describes 

the  inter-model  spread  (climate  uncertainty)  and  S  values  the  percentage  of  area 

subject  to  significant  changes  (5%  level).  The  frequency  analysis  is  complemented 

with  the  corresponding  variations  in  Expected  Annual  Fraction  Exposed  (EAFE) 

shown in Figure 2.4 both in terms of its magnitude and relative change with respect 

to the baseline. Spatial patterns of EAFE are shown in Figure 2.5. Note that climate-

related  uncertainty  in  the  frequency  of  extreme  events  translates  into  analogous 

estimates of uncertainties in EAFE, which are not shown here to avoid redundancy.  

Heat waves show a progressive and highly significant increase in frequency all over 

Europe (S>74% in near future climate, approaching 100% in all regions by the end 

of  this  century),  with  larger  climate  variability  in  long-term  scenarios  (40≤CV≤60) 

and  a  more  pronounced  intensification  in Southern  Europe  (where  current  100-yr 

events could occur almost every year in the 2080s) (Figure 2.3). Consistently, EAFE 

values show a progressive increase as time proceeds, especially in Southern Europe 

where,  by  the  end  of  the  century,  up  to  60%  of  the  territory  could  be  annually 

exposed to a current 100-year heat wave (Figure 2.4).  

Cold  waves  show  an  opposite  trend  with  current  cold  extremes  tending  to  mostly 

disappear in Europe in more distant futures (current 2-yr event may occur less than 

every  100  years  by  the  end  of  the  century,  significant  almost  everywhere,  Figure 

2.3).  Accordingly,  cold  waves  could  experience  a  rapid  decrease  in  EAFE  and  a 

change up to -100% by the end of the century (Figure 2.4).  

Streamflow  droughts  may  become  more  severe  and  persistent  in  Southern  and 

Western  Europe  (current  100-yr  events  could  occur  approximately  every  2  to  5 

years  by  2080,  respectively,  S≥85)  resulting  from  the  reduced  precipitation  and 

increased evaporative demands with higher temperatures (Figure 2.3). This leads to 

25 

 
 
 
 
a  consistent  increase  in  EAFE  and  by  the  end  of  the  century  over  25%  of  the 

territories  could  be  affected  every  year  by  baseline  100-yr  droughts  (Figure  2.4). 

Northern,  Eastern  and  Central  Europe  show  an  opposite  tendency  with  a  strong 

reduction  in  drought  frequency  caused  by  higher  precipitation  that  outweigh  the 

effects of increased evapotranspiration (Forzieri et al., 2014). Such effects translate 

into consistent  decreases  in EAFE by up  to 100%. Significance increases  with time 

while climate variability shows variable  tendencies  depending on the return  levels 

(S>75% and CV over 60% by the end of the century). 

Most  of  Europe,  especially  Western,  Eastern  and  Central  regions,  could  experience 

an increase in the frequency of extreme wildfires (current 100-yr events will occur 

every  5  to  50  years)  with  a  progressive  rise  in  significance  and  model  agreement 

(S>10% and CV≤60% by the end of the century) (Figure 2.3). Interestingly, Southern 

Europe  shows  a  decrease  in  the  frequency  of  very  extreme  events,  which  is  likely 

due  to  the  expected  reduction  in  net  primary  productivity  of  terrestrial  ecosystem 

that may limit the fuel availability and, ultimately, the propagation of large wildfires 

(Mirco  Migliavacca  et  al.,  2013).  Progressive  increases  in  EAFE  are  visible  for 

wildfires over the whole domain (one to three-folds the baseline value, Figure 2.5).  

River  floods  show  in  general  more  spatial  variability  and  fluctuations  with  time  in 

the  frequency  of  extreme  events  as  well  as  a  larger  climate-induced  spread 

compared  to  the  other  hazards  (higher  CV  values,  Figure  2.3).  This  relates  to  the 

high  variability  in  projected  geographical  patterns  of  heavy  precipitation  intensity 

due to structural and parametric model uncertainty and internal climate variability 

(Fischer et al., 2013). Western Europe shows a consistent rise in future flood hazard 

(current  100-yr  events  could  manifest  every  ~30  years  in  2080s,  S  up  to  70%), 

mainly as a result of a pronounced increase in average and extreme rainfall (Rojas et 

al., 2012).  Such effects result in a  50-100% increase  in future  EAFE (Figure  2.4).  A 

modest  but  significant  decrease  in  river  flood  frequency  is  projected  in  Southern, 

Central  and  Eastern  regions,  in  the  latter  because  of  the  strong  reduction  in 

snowmelt  induced  river  floods,  which  offsets  the  increase  in  average  and  extreme 

precipitation.  

26 

 
 
 
 
Figure  2.3  Changes  in  frequency  of  climatic  hazards.  Baseline  (x-axis)  versus  future  (y-axis)  hazard  return  periods  for  2020s  (green),  2050s  (blue)  and  2080s  (red)  for  specific 
hazards. Return period values shown are the zonal median for different European regions of the grid-cell ensemble median return period of the experiments driven by the different 
climate  realizations.  Circle  sizes  represent  the  coefficient  of  variance  (CV)  amongst  climate  models  and  S  values  explicit  the  percentage  of  cells  within  a  region  with  significant 
decrease/increase (-/+). Note that future scenarios with outstanding decrease/increase in frequency are out of plot margins (cold waves, droughts and coastal floods), projections of 
windstorm are only available for 2080s, significance analysis and climate variability have not been retrieved for coastal floods. 

27 

 
 
Coastal  floods  show  a  progressive  and  pronounced  increase  in  recurrence  along 

Europe’s  coastlines  chiefly  caused  by  sea  level  rise  (current  100-yr  event  may 

manifest every 2 to  8 years, or even sub-annually in Eastern  Europe, in the 2080s, 

Figure  2.3)  and  leading  to  strong  increase  in EAFE  (Figure  2.4).  Noteworthy  is  the 

pronounced  increase  in  EAFE  in  Eastern  Europe  as  a  consequence  of  the  rapid 

intensification  of  inundations  over  the  Danube  delta.  Due  to  sea  level  rise  the 

increase  in  frequency  of  occurrence  of  extreme  coastal  events  is  so pronounced  in 

this  region  that  current  100-year  (and  more  frequent)  events  will  happen  sub-

annually by the 2050s and 2080s, which explains why the curves for the 2050s and 

2080s drop below the x-axis and are not visible.  

Evidence  for  changes  in  windstorms  remains  largely  elusive  (S<16%)  and  with 

considerable inter-model spread for larger return levels (up to CV>60% for current 

100-yr  events,  Figure  2.3).  Areas  with  increases  in  windstorm  hazard  are  mainly 

located  in  Western,  Eastern  and  Northern  Europe,  while  Southern  regions  present 

slight reductions in frequency as observed in previous studies (Nikulin et al., 2011; 

Outten and Esau, 2013). EAFE of windstorms show modest changes with respect to 

the baseline (up to ±10%, Figure 2.4). 

Interestingly, larger increases in EAFE can be observed at higher return levels and 

for  long-term  scenarios  due  to  the  progressive  intensification  of  very  extreme 

events.  This  occurs  also  in  regions  prevalently  experiencing  a  reduction  (or  slight 

change) in future frequency of climate hazards, such as Central and Eastern Europe 

for  droughts,  Southern  Europe  for  wildfires  and  Southern,  Central  and  Northern 

Europe for floods. The  apparent contradiction manifests where few localized areas 

experience a very large increase in frequency that outweighs the opposite tendency 

occurring  in  most  of  the  region.  The  projected  changes  in  single-hazard  exposed 

fractions suggest that future hazard scenarios will considerably deviate from those 

observed  in  current  climate,  especially  for  climate  hazards  strongly  linked  to 

temperature rises (e.g., heat and cold waves, droughts and coastal floods). 

28 

 
 
 
 
 
Figure 2.4 Changes in single-hazard Expected Annual Fraction Exposed. Baseline hazard return levels (on the x-axis) versus corresponding future EAFE (on the y-
axis) for specific hazards. EAFE values shown are zonal averages for different European regions of the grid-cell ensemble median EAFE of the experiments driven 
by the different climate  realizations. Bars  refer to a future scenario period as labelled in the top-left panel. Colours reflect the relative change in  region-average 
EAFE with respect to the baseline. Note that EAFE lower than 0.001 (e.g., for cold waves) are out of plot margins. 

29 

 
 
Figure 2.5 Spatio-temporal patterns of 100-yr Expected Annual Fraction Exposed.  Spatial and temporal variations of Expected Annual Fraction Exposed to 100-yr 

climate  hazards.  Note  that  values  for  river  and  coastal  floods  are  aggregated  at  NUTS3  level  (see  Figure  1)  to  better  visualize  their  effects.  Values  refer  to  the 

ensemble medians of experiments driven by the available climate models. 

30 

 
 
 
2.3.2  Changes in overall and concurrent exposures to climate hazards 

Figure  2.6a  shows  the  overall  exposure  to  combinations  of  all  hazards  aggregated 

for each European region, expressed by the Overall Exposure Index that accounts for 

the  overlapping  of  hazards.  Spatio-temporal  patterns  of  the  100-yr  EAFE  for  OEI1, 

OEI2 and OEI3 are presented in Figure 2.6b. For all regions in Europe, the fraction 

exposed  to  at  least  one  hazard,  expressed  by  EAFE  for  OEI1,  will  progressively 

increase  throughout  this  century,  with  the  increase  being  more  pronounced  for 

higher return periods.  In Southern Europe, about 76% of the area  will be annually 

exposed to at least one climate hazard with a current 100-year intensity by the end 

of  this  century,  more  than  a  15-fold  increase  compared  to  the  present  situation 

(currently  nearly  5%  of  the  area  is  expected  to  annually  exposed  to  at  least  one 

hazard  of  this  intensity).  The  distribution  in  space  of  the  100-yr  EAFE  for  OEI1 

(Figure  2.6b,  left  column)  shows  that  by  the  end  of  this  century  in  many  areas  in 

Southern Europe the whole territory will be annually exposed to at least one hazard 

of  this  intensity.  This  is  mainly  caused  by  the  large  increase  in  heat  and  drought 

hazard projected for the most southern regions of Europe. For the other regions in 

Europe,  the  fractions  annually  exposed  and  the  increase  therein  with  time  are 

somewhat less pronounced, but still considerable: for Western, Central, Eastern and 

Northern Europe, about 50%, 36%, 31% and 29% of the territory, respectively, will 

by the end of this century be exposed annually to at least one hazard with a current 

100-year intensity, corresponding to increases of 1021%, 732%, 614%, and 597%, 

respectively.  

The EAFE for OEI2, expressing the fraction that is expected to be annually exposed 

to  at  least  two  hazards,  shows  even  steeper  increases  with  time  in  all  regions  of 

Europe. Again, the highest raise is projected for Southern Europe, with about 25% of 

the  area  that  could  be  annually  exposed  to  at  least  two  hazards  with  a  100-year 

intensity  by  the  end  of  this  century,  or  nearly  250-fold  the  baseline  value  (which 

corresponds  to  approximately  0.1%  in  the  different  regions  of  Europe).  The 

distribution  in  space  of  the  100-yr  EAFE  for  OEI2  (Figure  2.6b,  middle  column) 

shows the  highest  values  in most  of Southern Europe and also further north along 

coasts and in river flood plains. For the other regions, increases in EAFE(100-yr) for 

31 

 
 
 
OEI2  are  somewhat  less  pronounced  but  still  very  high,  with  rises  of  9,500%  for 

Western  Europe,  2,100%  for  Central  Europe,  1,400%  for  Eastern  Europe  and 

1,000% for Northern Europe.  

The  results  above  for OEI1 and OEI2 show that  the  joint probability of areas  to be 

annually exposed to multiple hazards is much smaller than for single hazards  (due 

to  the  combination  of  single  hazard  probabilities),  but  that  the  increases  in  joint 

probability  rise  much  sharper  than  for  single  hazards.  This  is  because  in  many 

regions  of  Europe  multiple  climate  hazards  will  occur  more  frequent  under  future 

climate. This is exemplified by the EAFE for OEI3. By the end of the century 0.7% of 

the  area  in  Southern  Europe  is  expected  to  be  annually  exposed  to  at  least  three 

hazards of a 100-year intensity. Albeit that this may seem small, this corresponds to 

a  staggering  increase  of  more  than  70,000%  (or  700-fold  the  current  value).  The 

projected increases in OEI3 for the other regions by the end of the century amount 

to  24,500%  for  Western  Europe,  6,250%  for  Central  Europe,  4,335%  for  Eastern 

Europe and 1,324% for Northern Europe.  

These results suggest that the entire Europe will likely face a progressive increase in 

overall  exposure  to  climate  extremes,  with  a  prominent  spatial  gradient  towards 

south-western  regions  (Figure  2.6b)  and  along  coastlines  and  in  flood  plains.  Heat 

waves,  droughts  and  coastal  flooding,  which  particularly  strong  increases  in  such 

regions,  provide  the  most  relevant  contribution  in  the  estimation  of  future  OEI 

(Figure 2.5). 

Spatial patterns of the Change Exposure Index (CEI) are presented in Figure 2.7b for 

the current 100-year event hazard intensity. The CEI maps represent the number of 

hazards  that  show  moderate,  strong  and  extreme  (+20%,  +100%  and  +1000%, 

respectively)  increases  in  EAFE.  For  areas  in  dark  blue  (CEI  =  0)  none  of  the  7 

hazards considered shows the threshold level increase in EAFE, whereas for areas in 

red (CEI = 4) four hazards reach the defined level of increase in EAFE. Regions with 

high  CEI  values  reveal  potential  key  hotspots  that  will  be  prone  to  an  increase  in 

exposure  to  multiple  hazards.  These  are  mainly  located  along  coastlines  and  in 

floodplains in Southern and Western Europe where inland and coastal flooding will 

32 

 
 
 
 
 
be  likely  relevant  in  combination  with  temperature-related  hazards  (see  also 

hazard-specific  contributions  shown  in  Figure  2.8).  More  exposed  regions  include 

the British Isles, the North Sea area, north-western parts of the Iberian Peninsula, as 

well  as  parts  of  France,  the  Alps,  Northern  Italy  and  Balkan  countries  along  the 

Danube  River.  These  areas,  even  if  they  may  present  lower  overall  exposure  to 

climate hazards compared to other regions in Europe (Figure 2.6), will be prone to 

the  largest  changes  in  exposure  to  multi-hazard,  which  potentially  increases  the 

presence of concurrent hazards and therefore results in larger risks. 

Figure  2.7a  shows  for  each  European  region  the  aggregated  spatial  extent 

experiencing pronounced  increases  in EAFE for at least  four hazards, as  expressed 

by  CEI=4  (hence  the  areas  coloured  in  red  in  Figure  2.7b),  calculated  for  the 

different threshold levels of increase in EAFE and return periods between 2 and 100 

years. It confirms that increases in hazards are more pronounced for higher return 

periods and for the long-term scenarios. Regions most prone to increases in multiple 

hazards are Southern and Western Europe. 

33 

 
 
 
Figure 2.6 Projections in  Overall Exposure Index. Left  (a) Baseline  return levels (on the  x-axis) versus corresponding  future Expected Annual Fraction Exposed 

(EAFE)  (on  the  y-axis)  calculated  as  zonal  average  for  different  European  regions  resulting  from  the  combination  of  all  climate  hazards  accounting  for  the 

overlapping of one, two or three hazards (OEI1, OEI2 and OEI3, respectively). Bars and colours visualized as in Figure 2.5; Right (b) spatio-temporal pattern of 100-

yr EAFE for OEI1, OEI2 and OEI3.  

34 

 
 
 
Figure 2.7 Projections in Change Exposure Index. Left (a) Baseline return levels (on the x-axis) and corresponding area exposed to at least four hazards (CEI=4) 

with  relative  increases  over  20%,  100%  and  1000%  with  respect  to  the  baseline  (CEI20,  CEI100  and  CEI1000,  respectively)  for  different  European  regions;  bars 

grouped as in Figure 2.5; Right (b) spatio-temporal patterns of CEI20, CEI100 and CEI1000 calculated for 100-yr events. 

35 

 
 
 
 
Figure 2.8 Hazard-specific contributions to the Change Exposure Index calculation. NUTS3 regions whose relative increase in 100-yr EAFE is larger than 20% (in 

red). 

36 

 
 
 
 
 
2.4  Main limitations and knowledge gaps 

Despite  the  depth  of  this  study,  results  should  be  viewed  in  light  of  the  potential 

uncertainty  sources  and  caveats  of  the  proposed  methodology.  The  multi-hazard 

maps  are  dependent  on  the  chosen  set  of  climate  hazard  indicators:  the  use  of 

diverse  input  hazards  (e.g.,  hail,  landslides)  might  lead  to  different  findings.  We 

argue that the set of hazards selected includes the most relevant hazards for Europe 

in terms of average  annual losses  and deaths  (“NatCatSERVICE | Munich Re,” n.d.). 

Metrics used to represent the selected climate hazards are crucial for the resulting 

impact  scenarios:  changes  in  return  periods  depend  on  the  time  scale  selected  to 

characterize  an  event  type,  e.g.  1-day  temperature  extremes,  weekly  heatwaves  or 

seasonal heat anomalies experience different changes in return periods (Perkins and 

Alexander,  2012;  Trenberth  et  al.,  2014).  In  our  approach  we  focus  on  hazard-

specific metrics of impact relevance that have been documented in recent literature. 

Details  on  the  sensitivity  analysis  and  calibration/validation  exercises  for  each 

single hazard are reported in the references (Cid et al., 2014; Forzieri et al., 2014; M. 

Migliavacca  et  al.,  2013;  Outten  and  Esau,  2013;  Rojas  et  al.,  2012;  Russo  et  al., 

2014). We recognize that extreme value fitting may introduce additional uncertainty 

in  the  projections  of  climate  hazards  especially  at  high  return  periods.  Recent 

studies,  though,  documented  its  secondary  role  with  respect  to  the  inter-model 

spread (Forzieri et al., 2014; Rojas et al., 2012).  

We  apply  a  conservative  approach  without  accounting  explicitly  for  hazard 

interrelations that could lead to greater impacts. Regions exposed to the overlap of 

multiple  hazards  and  subject  to  concurrent  increases  in  single-hazard  EAFEs, 

however, are indicative of a more likely exacerbation of the overall impacts due to 

inter-hazard  triggering  relationships.  Estimation  of  probabilities  of  coincidental  or 

cascading events would require finer time resolution of hazard metrics (here annual 

or  monthly)  and  a  better  knowledge  of  the  inter-hazard  physical  interactions  and 

coupled processes.  

The  socioeconomic  scenarios  driving  GHG  emissions,  the  sensitivity  of  the  climate 

models to GHG concentrations and the specific hazard modelling utilized are subject 

37 

 
 
 
 
to uncertainty, and all are relevant in influencing the final multi-hazard assessment. 

The use of different climate model ensembles for each hazard may have introduced 

additional  artefacts  (Table  2.1).    However,  recent  studies  suggest  that  the  reduced 

subsets  utilized  in  this  study  for  some  hazards  largely  preserve  main  statistical 

properties  of  the  initial  12-member  ensemble  (Russo  et  al.,  2013).  The  use  of 

identical - and possibly larger - ensembles could allow to better capturing climate-

related uncertainties (Kharin et al., 2013; Sillmann et al., 2013). We used a different 

baseline and only one future time window for windstorms. New dedicated runs for 

windstorms for the remaining temporal periods were not feasible within this study. 

We  understand  that  such  diversity  may  limit  the  comparability  with  the  other 

hazards;  however,  changes  in  extreme  winds  seem  to  be  lower  compared  to  the 

other  climate  hazards,  hence  the  potential  bias  is  expected  to  play  a  minor  role. 

Analyses of the multi-hazard indices are performed using the ensemble median of all 

climate model combinations for each hazard as input because only one single GCM-

RCM  configuration  is  common  amongst  the  hazards.  While  the  median  can  be 

considered  a  robust  estimate  of  single-hazard  ensembles,  this  inevitably  hampers 

the  analysis  of  how  single-hazard  uncertainties  (Figure  2.4)  propagate  to  the 

combined metrics. 

38 

 
 
 
 
 
 
 
 
 
 
 
2.5  References 

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Cid, A., Castanedo, S., Abascal, A.J., Menéndez, M., Medina, R., 2014. A high resolution 
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Wehner,  2013:  Long-term  Climate  Change:  Projections,  Commitments  and 
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Dilley,  M.,  2005.  Natural  Disaster  Hotspots:  A  Global  Risk  Analysis.  World  Bank 
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Fischer, E.M., Beyerle, U., Knutti, R., 2013. Robust spatially aggregated projections of 
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Fischer,  E.M.,  Schär,  C.,  2010.  Consistent  geographical  patterns  of  changes  in  high-
impact European heatwaves. Nat. Geosci. 3, 398–403. doi:10.1038/ngeo866. 

Forzieri, G., Feyen, L., Rojas, R., Flörke, M., Wimmer, F., Bianchi, A., 2014. Ensemble 
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Kappes,  M.S.,  Keiler,  M.,  Elverfeldt,  K.  von,  Glade,  T.,  2012.  Challenges  of  analyzing 
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Hallegatte, S., Logar, I., Meyer, V., Schwarze, R., Thieken, A.H., 2014. Costing natural 
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Khabarov, N., Krasovskii, A.A., Marcolla, B., San Miguel-Ayanz, J., Ward, D.S., Cescatti, 
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European Coasts, Environmental Science. Springer Berlin Heidelberg, pp. 199–226. 

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41 

 
 
 
3  Vulnerability of critical infrastructures to climate hazards 

3.0  Key messages 

  There  is  limited  understanding  of  vulnerability  of  infrastructures  to  different 

hazards and quantitative information on the sensitivity of critical infrastructures 

to climate hazards is largely absent.  

  Critical infrastructures are vulnerable to the various hazards in a myriad of ways. 

Some key vulnerabilities for each sector are exemplified in the Table 3.0 below. 

Table 3.0 Examples of main vulnerabilities of the sectors to the climate hazards 

Energy 

Transport 

Industry 

Social 

Heat 

Cold 

Drought 

reduced power plant 
efficiency due to 
higher water 
temperature 
required for cooling 

material degradation 
and buckling of 
roads, rails and 
bridges due to 
thermal expansion 

structural damage to 
distribution lines 
due to ice and snow 
loads 

buckling of roads, 
rails and bridges due 
to thermal 
contraction 

reduction in 
hydropower 
potential and biofuel 
production 

reduced navigability 
of rivers and 
channels 

Wildfire 

reduction in biofuel 
sources 

deterioration of 
roads, railways and 
power lines 

increased cost for 
cooling and 
refrigeration 

increased cost for 
cooling 

water pipes 
vulnerable to 
frost/icing 

increased cost of 
heating during cold 
episodes 

water quality 
degradation, 
reduction in usable 
water and increase 
in treatment costs 

structural damages 
due to drought-
induced subsidence 
and permafrost 
thawing 

structural damages 
to industrial sites 

destruction of social 
infrastructures 

Flood 

structural damages 
to energy production 
sites and transport 
networks 

reduction of 
structural integrity 
of surface and 
subgrade material 

structural damages 
to industrial sites, 
increasing cost for 
water treatment 

structural damage to 
social infrastructures 
and reduction in 
operational services 

Windstorm 

disruption of 
transmission and 
distribution 
networks 

structural damages 
to transport facilities 

structural damages 
to industrial systems 
equipment 

structural damages 
to social structures 
and facilities 

  To 

ensure 

comparability 

in 

the  multi-hazard 

and  multi-

infrastructure/investment context considered in CCMFF, qualitative sensitivities 

have  been  derived  for  the  thematic  priorities  of  the  EU  Cohesion  Policy  Funds 

(CPF) and for types of key infrastructures to the considered climate hazards by 

42 

 
 
 
 
integrating  information  from  an  extended  literature  review  with  a  survey  that 

was conducted among a pool of experts in the considered sectors.  

 

In  the  survey,  70%  of  the  possible  combinations  of  hazard  (7)  and 

infrastructures/investments  (50)  were  considered  sensitive  by  more  than  80% 

of the respondents, while 40% of the themes are sensitive for more than 90% of 

the respondents. In general, the sensitivity is highest for inland floods, sea level 

rise and forest fires, whereas the sensitivity of infrastructures to drought seems 

less important.  

  The expert survey generally corroborates the findings reported in the literature 

and  the  more  robust  findings  of  the  survey  correspond  to  higher  consensus  in 

the scientific community.  

  Albeit  that  the  derived  sensitivity  classes  are  subject  to  exposure,  information 

and individual bias, and that infrastructure-specific vulnerability may show large 

variation  depending  on  the  institutional,  economic,  and  technological  context, 

they  provide  an  indication  of  general  sensitivities  of  different  types  of 

infrastructures  and  key  economic  assets  to  climate  hazards  and  may  help  in 

orienting policy interventions for climate adaptation and resilience.  

43 

 
 
 
 
 
 
 
 
 
 
 
 
3.1  Introduction 

An evaluation of the risk of a critical infrastructure or investment from a hazardous 

climate event requires a consideration of the element’s vulnerability, further herein 

also  referred  to  as  sensitivity.  We  define  vulnerability  in  accordance  to  the  SREX 

report  (IPCC,  2012)  as  the  propensity  or  predisposition  of  the  infrastructure  to  be 

adversely  affected  when  exposed  to  a  climate  hazard.  The  potential  degree  to 

experience harm constitutes an internal characteristic of the affected infrastructure 

and is specific to the climate hazard.  

Evaluating the effects of climate hazards on key economic assets is a complex issue 

because  of  incomplete  scientific  methodologies  and  limited  understanding  of 

vulnerability of infrastructures to different hazards. The most important approaches 

for  the  analysis  of  the  physical  vulnerability  of  infrastructures  are  the  use  of 

vulnerability curves, damage matrices or vulnerability indicators.  

Vulnerability curves (frequently also referred to as damage, fragility, or risk curves) 

relate event intensity and resulting damages to a certain building type. While these 

functions offer continuous vulnerability information in relation to the degree of the 

hazard (for example, damage to an infrastructure for 1 m of flooding), they require 

extensive  information  on  damaged  buildings  and  are  therefore  typically  only 

available  for  extensive  and  wide-spread  processes  like  flooding  (Kappes  et  al., 

2012a).  Also,  albeit  that  there  is  a  wide  variety  of  flood  damage  functions  in  use 

internationally,  they  differ  substantially 

in  their  approaches  and  economic 

estimates, and current methodologies for estimating infrastructural damage are not 

as  well  developed  as  methodologies  for  the  estimation  of  damage  to  (residential) 

buildings (Jongman et al., 2012). Moreover, for other hazards like drought, impacts 

are much more difficult to quantify and link to specific events and their magnitude, 

hence damage functions more difficult to construct.  

Vulnerability  matrices  provide  discrete  damage  information  for  classified  hazard 

intensities  and  are  either  based  on  observed  damages  or  on  rough  appraisals. 

Despite  the  advantage  of  providing  (semi-)  quantitative  information,  damage 

44 

 
 
 
 
 
functions  and  matrices  often  relate  damage  with  only  one  characteristic  of  the 

building, mainly the building type, hereby neglecting the properties of the element 

at  risks  (such  as  building  age  or  number  of  floors).  The  method  of  vulnerability 

indicators, on the other hand, allows to integrating different building characteristics 

in  the  (qualitative)  vulnerability  description.  However,  whereas  socio-economic 

indicators are more widely used to describe the multiple characteristics of humans, 

institutions  and/or  societies  that  contribute  to  their  overall  vulnerability, 

significantly  less  experience  with  vulnerability  indicators  has  been  acquired  in  the 

physical vulnerability context. Moreover, since vulnerability is primarily regarded as 

a  characteristic  of  the  element  at  risk,  only  in  very  few  cases  hazard-specific 

vulnerabilities are assessed in this way (Kappes et al., 2012b).  

In  the  CCMFF  project,  rather  than  looking  in  very  detail  at  a  specific  critical 

infrastructure in a particular setting, for which it is already very difficult to appraise 

its  vulnerability,  the  aim  is  to  evaluate  potential  impacts  to  types/classes  of 

infrastructures  and  investments  across  a  great  territorial  diversity  with  a  wide 

variety of socio-economic settings and physical boundary conditions in Europe. This 

includes, for example, different building standards, positioning of the infrastructure 

in the  landscape, its relation to the  economy and depending sectors, as  well as  the 

existence  of  special  protection  measures  (hard  infrastructures  like  dikes  or  soft 

measures like early warning systems).   

To  ensure  comparability  in  the  multi-hazard  and  multi-infrastructure/investment 

context  considered  in  CCMFF,  a  common  method  has  to  be  adopted  for  the 

vulnerability  assessment.  We  therefore  derived  general  qualitative  sensitivities  for 

the  thematic priorities of the  EU Cohesion Policy Funds (CPF)  and  for  types of  key 

infrastructures  to  the  considered  climate  hazards  by  integrating  information  from 

an  extended  literature  review  with  a  survey  that  was  conducted  among  a  pool  of 

experts in the considered sectors. The performed analysis is an attempt to fill a gap 

in the scientific knowledge but also to provide a tractable database for assessing and 

comparing  sensitiveness  within  future  multi-hazard/multi-sector  climate  change 

impacts and adaptation studies.  

45 

 
 
 
 
3.2  Literature on climate sensitivity of infrastructures 

It  is  not  the  aim  of  this  report  to  provide  an  extensive  overview  of  the  literature 

related  to  potential  impacts  of  climate  extremes  on  infrastructures  in  different 

sectors.  For  a  comprehensive  overview  we  refer  the  reader  to  AR5  of  the  IPCC, 

2014). Rather, we performed this review to understand what has been reported in 

the  scientific literature  about  climate hazard  sensitivities  of critical  infrastructures 

in order to supplement the expert survey (see section 4.3) in deriving vulnerability 

classes for types of infrastructures for the different climate hazards considered.  

A  summary  of  the  literature  for  the  different  sectors  and  hazards  is  presented  in 

Table 3.1, 3.2, 3.3 and 3.4. Studies looking at the sensitivity of multi-sectorial critical 

infrastructures to climate hazards are practically not existing. This observation may 

be  explained  by  the  need  for  transdisciplinary  knowledge  and  collaboration  for  a 

review building on the existing literature in several sectors that is huge. The amount 

and  detail  of  coverage  in  literature  of  these  aspects  varies  strongly  on  the  sector 

considered and the hazard. 

For the energy sector, notwithstanding the variety of threats by climate extreme and 

their  potential  impacts  on  electricity  generation  and  transmission  systems,  fuel 

infrastructure and transport systems, the range of the range of impacts modelled in 

the  literature  is  still  rather  limited  (IPCC,  2014).  Most  studies  related  to  energy  in 

relation to climate (hazards) have looked at climate impacts on changes in demand 

(typically  related  to  changes  in  average  temperature),  whereas  on  the  production 

side  the  majority  of  studies  focus  on  renewable  energy  (hydropower,  biomass) 

sources  in  relation  to  average  climate  changes,  especially  when  compared  to  its 

share in the current installed capital/energy mix.  

Studies on the impact of climate hazards on the transport sector to date have been 

mostly  qualitative,  and  only  few  quantitative  assessments  are  available.  Recently 

three  FP7  projects,  EWENT  (Extreme  Weather  impacts  on  European  Networks  of 

Transport  –  http://ewent.vtt.fi),  ECCONET  (Effects    of    climate    change    on    the  

inland  waterway  networks  -  www.ecconet.eu)  and  WEATHER  (Weather  Extremes: 

46 

 
 
 
 
 
Assessment  of  Impacts  on  Transport  and  Hazards  for  European  Regions  - 

www.weather-project.eu), have studied impacts of weather phenomena to different 

aspects  of  the  European  transport  systems.  The  main  results  of  these  projects  are 

summarized  in  Michaelides  et  al.,  2014)  and  several  more  detailed  documents  can 

be found on the project websites. Notwithstanding these and other recent advances, 

systematic and detailed knowledge  on  climate  change  vulnerability  of and impacts 

on  critical  transport  infrastructures  remains  limited  in  the  literature  (IPCC,  2014), 

especially with respect to climate extremes.  

Research on the potential effects of climate hazards on industrial facilities and social 

(health and education) infrastructures is very limited, apart from some studies that 

focus on buildings in general.  

Table 3.1, 3.2, 3.3 and 3.4 together with the paragraphs above show that many of the 

threats of extreme weather to different sectors are acknowledged and qualitatively 

described  in  the  literature,  but  rather  few  quantitative  assessment  are  available. 

Information  on  the  sensitivity  of  infrastructures  and  economic  assets  in  different 

sectors to  climate hazards is  not only scattered around many scientific disciplines, 

but also varies with local to regional boundary conditions that apply to the study. As 

such, there is no aggregated or global view allowing a comparison and application of 

a  homogeneous  methodological  approach 

for  assessing  the  sensitivity  of 

infrastructures to climate hazards across Europe.  

We  therefore  opted  to  construct  a  sensitivity  matrix  on  the  basis  of  a  survey 

amongst experts (described in the next section), where the literature review serves 

to explain the channels through which the impacts are transmitted and to verify the 

robustness of the sensitivity matrix.  

47 

 
 
 
 
 
 
 
Table 3.1 Overview of vulnerability (and impacts) of energy assets to climate hazards reported in literature 

Energy 

Heat waves 
Structural damages due 
to expansion of different 
materials (Pryor et al. 
2010). 

Reduction of structural 
integrity due to melting 
permafrost (Ebinger, 
2011, Cruz et al., 2013).  

Increased resistance on 
the power lines. 

Decrease in power plant 
efficiency due to higher 
water temperature 
required for cooling 
systems (van Vliet et al., 
2012, van Vliet et al., 
2013, Rubbelke et al., 
2011, Chandramowli et 
al., 2014, EEA, 2012 ; 
Linnerud et al., 2011; 
Rübbelke, and Vögele, 
2011). 

Reduction in biofuels 
sources (Moiseyev et al., 
2011; Verkerk et al., 
2011). 

Reduced performance of 
solar photovoltaic 
modules in hot weather 
as electrons movement 
is slower in hot 
materials (EEA, 2012). 

Cold 
Structural damages 
due to ice and snow 
loads overhead 
distribution lines 
(Bompard et al., 
2013, McColl et al., 
2012) and ice-
induced changes in 
pipeline pressures 
(Humphrey et al, 
2008). 

Increased corrosion 
on energy systems 
(Ebinger 2011; 
Cruz et al., 2013). 

Reduction in 
hydropower 
potential due to 
water freezing 
(Lehner et al. 2005; 
Mideska et al. 
2010). 

Reduction in 
biofuels sources 
(Mideska et al., 
2010). 

Droughts 
Reduction of structural integrity due 
to melting of permafrost and drought-
induced subsidence (Ebinger, 2011, 
Cruz et al., 2013).  

Deterioration of power systems 
caused by overexploitation of 
irrigation and water pumping 
(Rubbelke et al., 2011; Chandramowli 
et al., 2014; Klein et al., 2013). 

Decrease in power plant efficiency 
due to higher water temperature and 
lower water volumes required for 
cooling systems (Patt et al., 2013; 
Rubbelke et al., 2011; Paskal, 2010; 
Sieber, 2013; Arent et al., 2014; 
Mideksa, et al., 2010; Van Vliet, et 
al., 2013; Förster et al., 2010; 
Ebinger, 2011; EEA, 2012 ; Linnerud 
et al., 2011; Rübbelke, and Vögele, 
2011).  

Deterioration of cooling systems due 
to excessive biological growth and 
clog water intages (Cruz et al., 2013, 
Sieber, 2013). 

Reduction in biofuels sources 
(Moiseyev et al., 2011; Verkerk et al., 
2011). 

Reduction in hydropower potential 
due to reduced water volumes (van 
Vliet et al., 2012, van Vliet et al. 
2013, Schaeffer et al. 2012, Lehner et 
al. 2005). 

Wildfires 
Damages to 
power systems 
equipment 
(Bompard et al., 
2013) 

Reduction in 
biofuels sources 
(Boisvenue, 
2005; Bompard 
et al., 2013). 

Damage to 
pipelines and 
electricity 
transmission 
lines from 
bushfires 
(IPCC, 2014).  

River and coastal floods 
Structural damages to energy 
production sites and transport 
networks due to direct impacts of 
overflows, reduced soil stability and 
induced mass movements (soil 
erosion, landslide, siltation) (Ebinger, 
2011; Paskal et al., 2010; Brown et 
al., 2014; Bompard et al., 2013; 
Ebinger et al., 2011; Klein et al., 
2013; Kovats et al., 2014; Brown, 
2014; Chandramowli et al., 2014).  

Damages to power systems 
equipment due to debris and 
pollution in cooling water flows 
required (Sieber et al., 2013; Cruz et 
al.; 2013).  

Short-circuiting and power failure on 
electrical systems (Brown et al., 
2014). Disable corrosion protection 
equipment and produce pitting 
(Humphrey et al., 2008; Cruz et al., 
2013; Bompard et al., 2013). 

Reduction in biofuels sources 
(Mideska et al., 2010; Ebinger et al., 
2011) 

Reduction in hydropower production 
due to increased silting of sediment 
into reservoirs due to increased 
erosion and sediment displacement. 

Windstorms 
Structural damages to 
power systems equipment 
and storage tanks due to 
wind pressure or debris 
impact (Pryor et al. 2010; 
Ebinger, 2011; Wilbanks et 
al., 2012; Bompard et al., 
2013), Arent et al., 2014). 

Overloads of tidal and 
wave energy plants 
(Paskal, 2010). 

Disruption of electricity 
lines (transmission and 
distribution networks) and 
damages to cables due to 
falling trees (Bompard et 
al., 2013; Arent et al. 2014, 
McColl et al. 2012).  

Short-circuiting triggering 
possible fires especially 
with storage of liquid 
flammable hydrocarbons 
(Bompard et al., 2013; 
Sieber 2013; Ebinger 
2011). 

Reduction in biofuels 
sources (Boisvenue, 2005; 
Bompard et al., 2013). 

Extreme storm gusts may 
damage wind turbines 
(EEA, 2012). 

48 

 
 
 
 
 
 
 
Table 3.2 Overview of vulnerability (and impacts) of transport assets to climate hazards reported in literature 

Heat waves 
Transport  Buckling of roads, rails and 

bridges due to thermal 
expansion; structural material 
degradation; melting of 
asphalt and increased rutting 
and softening of pavement, 
signaling problems (Arent et 
al., 2014, Peterson et al. 2008, 
Jaroszweski et al. 2010; 
Mehrotra et al., 2011; 
Chinowski et al., 
2012; Chinowsky et al., 2013; 
Mehrotra et al., 2011 ; Suarez, 
2005 ; Peterson et al., 2008 ; 
Dobney et al., 2009 ; 
Eichhorst, 2009; Thornes et 
al., 2012 ; Cramer et al., 
2014 ; Mills and Andrey , 
2002 ; Molarius et al., 2011 ; 
Oslakovic et al., 2013, EEA, 
2011; Nolte et al., 2011). 

Failures of power lines 
(Nemry et al., 2012). 

Overheating impacts on 
infrastructure equipment, 
lifetime reduction, reliability 
of the electronic and electric 
components (e.g., rail rolling 
stock equipment) (EEA, 
2012); 

Slope instabilities due to the 
thawing of permafrost in 
alpine regions (EEA, 2012) 

Deterioration 
of roads, 
railways and 
power lines 
(Peterson et 
al., 2008).   

Droughts  Wildfires 
Reduced 
clearance 
under 
waterway 
bridges, 
reduced 
navigability 
of rivers 
and 
channels 
due to low 
level 
streamflows 
(Jonkeren 
et al., 2007; 
Jonkeren et 
al., 2013) 

Earthworks 
desiccation, 
increased 
abrasion of 
mechanical 
components 
(EEA, 
2012) 

Cold 
Buckling of roads, 
rails and bridges 
due to thermal 
contraction (Enei 
et al., 2011; 
Koetse et al., 
2009; Oslakovic et 
al., 2013). 

Icing of aircraft 
wings and 
disruptions of 
airport functioning 
(Pejovic et al., 
2009; Doll et al., 
2013; Przuluski, 
2012). 

Ice accumulation 
on vessels, decks, 
riggings, and 
docks (Molarius et 
al., 2013; 
Humphrey et al., 
2008 ; Mehrotra et 
al., 2011). 

Boat traffic 
disrupted due to 
thick river ice 
cover 
(Schweighofer et 
al., 2014). 

River and coastal floods 
Reduction of structural integrity of surface and 
subgrade material due to wave action and induced-
mass movements (erosion, landslide, subsidence) 
(Wright et al., 2012; Peterson et al. 2008; Eichhorst, 
2009; Jaroszweski et al., 2010; Arent et al., 2014; 
Nemry et al., 2012; Cramer et al., 2014). 

Scour on bridges and embankments, track and rail 
lineside equipment failure (Suarez, 2005; Humphrey 
et al., 2008; NRC, 2008 ; Peterson et al., 2008 ; 
Nemry et al., 2012 ; Eichhorst, 2009 ; Palin et al., 
2013 ; Cramer et al., 2014 ; Mills and Andrey, ; 
2002 ; Thornes et al., 2012 ; Molarius et al., 2011 ; 
Oslakovic et al., 2013). 

Structural damages to transport mode facilities 
(Hallegatte et al., 2011, Brown, 2014). 

Increased deterioration of infrastructures that lack a 
fouling-resistant design against salt water, Peterson et 
al., 2008). 

Disruption of transport vehicles (Koetse et al., 2009; 
Molarius et al., 2013).  

Reduced clearance under waterway bridges, reduced 
navigability of rivers and channels due to increased 
sedimentation (Jonkeren et al., 2007; Jonkeren et al., 
2013; Humphrey et al., 2008; Molarius et al., 2013; 
Koetse et al., 2009; Arent et al., 2014; Eichhorst et 
al., 2009; Schweighofer et al.,2014). 

Drainage systems, tunnels, increased scour of bridges. 
Risk of weather-related delays in all modes of 
services (EEA, 2012) 

Damage to rail installations, catenary, all modes 
potential traffic disruption (EEA, 2012) 

Windstorms 
Structural damages to transport 
facilities (bridges, flyovers, 
electric tracks with overhead 
cables, train platforms, street 
lighting and signs) due to wind 
pressure or debris impact 
(Eichhorst, 2009; Enei et al., 
2011; Molarius et al., 2011). 

Damage to rail installations, 
catenary, all modes potential 
traffic disruption (EEA, 2012) 

Short-circuiting along electrical 
cables (Burlando et al., 2010; Enei 
et al., 2011; Molarius et al., 2011) 

Obstruction of roads and rails due 
to fallen vegetation (Burlando et 
al., 2010; Molarius et al., 2011; 
Oslakovic et al., 2013). 

Air and boat traffic disrupted due 
to high turbolences (PWC, 2010; 
Kulesa et al., 2003). 

Piled shipping containers may tip 
over (PWC, 2010; TRB, 2008) 

Reduced clearance under 
waterway bridges, reduced 
navigability of rivers and channels 
due to floating debris (Jonkeren et 
al., 2013) 

Reduction in all transport modes 
(EEA, 2012) 

49 

 
 
 
 
Table 3.3 Overview of vulnerability (and impacts) of industrial assets to climate hazards reported in literature 

Heat waves 
Industry  Enhanced degradation of 

materials and structures (Arent 
et al., 2014 ; Delpla et al., 2009; 
van Vliet et al., 2012; Tang et 
al., 2013) 

Structural damages to landfill 
due to increased methane 
production and potential 
leachate escape (Bebb et al., 
2003). 

Increased cost for cooling and 
refrigeration.  

Higher treatment costs due to 
increase in distribution of 
vermin and pests (Bebb et al., 
2003).  

Reduction in decomposition rate 
leading to lower operability and 
productivity (Bebb et al., 2003). 

Cold 
Water 
pipes 
vulnerable 
to 
frost/icing 
(Bebb et 
al., 2003; 
Whitehead 
et al., 2009; 
PWC, 
2010) 

Wildfires 
Forest fires 
affect the 
viability of 
mining 
operations 
and 
potentially 
increases 
operating, 
transportatio
n and 
decommissio
ning costs 
(IPCC, 
2014) 

Droughts 
Structural damages due to 
drought-induced 
subsidence and permafrost 
thawing (Tang et al., 
2013; PWC, 2010) 

Water quality degradation, 
reduction in drinkable 
water and increase in 
treatment costs (Tang et 
al., 2013; Delpla et al., 
2009; Whitehead et al., 
2009; Cisneros et al., 
2014; van Vliet et al., 
2012)  

Reduction in 
decomposition rate 
leading to lower 
operability and 
productivity (Bebb et al., 
2003). 

River and coastal floods 
Structural damages to industrial sites due to direct 
impacts of overflows, reduced soil stability and induced 
mass movements (soil erosion, landslide, siltation) 
(Burton et al., 2005; Ahmed, 2012; Cisneros et al., 2014; 
Cozzani et al., 2010; Krausmann et al., 2011). 

Increasing cost for water treatment due to release of 
pollutants into the drainage and sewer system when 
brownfields are exposed to overflows (Major et al., 
2011). 

Overburden of drainage and sewer systems (Neu man et 
al., 2014; Delpla et al., 2009). Damages to transportation 
and transfer structures leading to disruption in water 
supply (Langeveld, 2013). 

Increased intrusion of salty water, damages for 
withdrawals, water quality degradation, wells pumping 
cut off, increasing cost for water treatment and reduction 
of water available for industrial purposes (Delpla et al., 
2009; Ahmed, 2012; Major et al., 2011; Cozzani et al., 
2010; Krausmann et al., 2011). 

Windstorms 
Structural damages to 
industrial systems 
equipment due to wind 
pressure or debris 
impact (Krausmann et 
al., 2011; Tannahill et 
al., 2011). 

Pipelines vulnerable to 
wind gust, strong 
winds may contribute 
to initiating 
unfavorable 
geodynamic processes 
and falling trees over 
the distribution system 
(Whitehead et al., 
2009; Depla, 2009; 
Wilbanks et al., 2012). 

Table 3.4 Overview of vulnerability (and impacts) of social (education and health) assets to climate hazards reported in literature 

Heat waves 

Social  Enhanced degradation of materials and 

structures (Hertin et al., 2003; Stewart et al., 
2011). 

Cold 

Droughts 
Structural damages due to drought-induced 
subsidence and permafrost thawing (Corti et al., 
2009, 2011) 

Higher dependence of people to water 
availability and quality (Pita et al., 2013) 

Higher dependence of people to water availability 
and quality (Pita et al., 2013) 

Overheating buildings, such as houses, hospitals, 
schools, (Crump et al., 2009; DCLG, 2012) 

Drought-induced soil subsidence and associated 
damage to dwellings (Corti et al., 2009) 

Wildfires  River and coastal floods  Windstorms 

Structural damage to 
social infrastructures and 
reduction in operational 
services (Carmichael et 
al., 2012; Hallegatte et 
al., 2011a) 

Structural damages to 
education and health 
structures and facilities 
due to wind pressure or 
debris impact (Hallegatte 
et al., 2011b; Pita et al., 
2013; Stewart et al., 2011). 

50 

 
 
 
 
 
 
 
3.3  Expert survey 

To  derive  a  comparable  vulnerability  measure  in  the  multi-hazard  and  multi-

infrastructure/investment  context  considered  in  CCMFF  a  web-based  survey  was 

run  amongst  specialists,  using  the  secured  European  Commission  tool:  EUSurvey 

(http://ec.europa.eu/eusurvey).  In  the  survey  the  key  economic  assets  and 

investments were grouped into the following sectors: (1) transport,, 2) energy, (3) 

information, 

industrial  and 

social 

infrastructures  and 

investments, 

(4) 

environmental/waste management and/or water treatment infrastructures, and (5) 

conservation of cultural and/or natural heritage. Based on the literature review, in a 

first screening of the allocations under the Cohesion Policy 2007-2013 programming 

period,  36  out  of  the  86  priority  themes  were  considered  not  sensitive  to  climate 

hazards, so they were not retained in the survey (indicated without colour in Table 

3.5).  For  each  sector  about  500  experts  were  inquired  to  complete  the  survey, 

including facility operators, authors and editorial boards of peer-reviewed journals 

in  the  field  of  climate  change  and  sector-specific  structural  engineering.  In  the 

survey, sensitivity was defined as: “the  degree  an asset  or system is affected when 

exposed to a climate hazard”. Experts assigned anonymously a degree of sensitivity 

(high, moderate, low, no) of sector-specific  infrastructures  to  each  of the  7  climate 

hazards  considered.  About  10%  of  the  invited  experts  responded,  resulting  in  a 

sample  size  of  approximately  50  per  investment/infrastructure  type  and  hazard. 

The  modes  of  the  Likert  distributions  were  considered  representative  of  the 

sensitivities,  and  in  case  of  low  consensus  amongst  the  experts  and/or  strong 

disagreement  with  reported  impacts  or  sensitivities  some  adjustments  were  made 

based on the literature review.  

The resulting sensitivity matrix for the Cohesion policy thematic priorities is shown 

in  Table  3.5,  for  the  critical  infrastructures  in  Table  3.6.  Green  =  no,  yellow  =  low, 

orange = medium, and red = high sensitivity.   

51 

 
 
 
 
 
Table  3.5  Sensitivities  of  CPF  priority  themes  based  on  expert  survey  and  literature  Red  =  high, 

orange = medium, yellow = low, and green = no sensitivity. No color indicates the CPF priority themes 

that  a  priori  were  assumed  to  be  not  sensitive  to  climate  hazards,  hence  was  not  included  in  the 

survey.  DR =  drought, FL =  inland flooding; FF =  forest  fires; CL = cold; HE = heatwaves; SLR = sea 

level  rise;  and  W  =  windstorms.  (*)  CPF  does  not  foresee  investments  in  power  plants  so  the 

sensitivity values for transmission/distribution are used instead).  

Code  Thematic priority name 

DR  FL 

FF 

CL  HE 

SLR  WI 

4 

2 

3 

1  R&TD activities in research centres 
R&TD infrastructure and centres of 
competence 
Technology transfer and improvement 
of cooperation networks 
Assistance to R&TD, particularly in 
SMEs 
Advanced support services for firms 
and groups of firms 
Assistance to SMEs for the promotion 
of environmentally-friendly products 
Investment in firms directly linked to 
7 
research and innovation 
8  Other investment in firms 

5 

6 

9 

10 

11 

Other measures to stimulate research 
and innovation 
Telephone infrastructures (including 
broadband networks) 
Information and communication 
technologies 
Information and communication 
technologies (TEN-ICT) 

12 
13  Services and applications for citizens 
14  Services and applications for SMEs  
15  Other measures  
16  Railways 
17  Railways (TEN-T) 
18  Mobile rail assets 
19  Mobile rail assets (TEN-T) 
20  Motorways 
21  Motorways (TEN-T) 
22  National roads 
23  Regional/local roads 
24  Cycle tracks 
25  Urban transport 
26  Multimodal transport 
27  Multimodal transport (TEN-T) 
28 
Intelligent transport systems 
29  Airports 

52 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Inland waterways (regional and local) 
Inland waterways (TEN-T) 

30  Ports 
31 
32 
33  Electricity* 
34  Electricity (TEN-E)* 
35  Natural gas* 
36  Natural gas (TEN-E)* 
37  Petroleum products* 
38  Petroleum products (TEN-E)* 
39  Renewable energy: wind 
40  Renewable energy: solar 
41  Renewable energy: biomass 

42 

43 

44 

Renewable energy: hydroelectric, 
geothermal and other 
Energy efficiency, co-generation, 
energy management 
Management of household and 
industrial waste 
Management and distribution of water 
(drink water) 

45 
46  Water treatment (waste water) 
47  Air quality 

48 

49 

50 

Integrated prevention and pollution 
control 
Mitigation and adaption to climate 
change 
Rehabilitation of industrial sites and 
contaminated land 
Promotion of biodiversity and nature 
protection 

51 
52  Promotion of clean urban transport 
53  Risk prevention 

Other measures to preserve the 
environment and prevent risks 

54 
55  Promotion of natural assets 

56 

57 

Protection and development of 
natural heritage 
Other assistance to improve tourist 
services 
Protection and preservation of the 
cultural heritage 

58 
59  Development of cultural infrastructure 

Other assistance to improve cultural 
service 
Integrated projects for urban and rural 
regeneration 
Development of life-long learning 
systems and strategies 
Design of innovative and more 
productive ways of organising work 

60 

61 

62 

63 

53 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
64 

65 

66 

67 

68 

69 

70 

71 

72 

73 

Development of special services for 
employment and training 
Modernisation and strengthening 
labour market institutions 
Implementing active and preventive 
measures on the labour market 
Measures encouraging active ageing 
and prolonging working live 
Support for self-employment and 
business start-up 
Measures to improve access to 
employment 
Specific action to increase migrants' 
participation in employment 
Integration and re-entry into 
employment for disadvantaged people 
Design, introduction and 
implementing of reforms in education 
Measures to increase participation in 
education and training 
Developing human potential in 
research & innovation 

74 
75  Education infrastructure 
76  Health infrastructure 
77  Childcare infrastructure 
78  Housing infrastructure 
79  Other social infrastructure 
79  Other social infrastructure 

Promoting partnerships, pacts and 
initiatives 
Mechanisms for improving good policy 
and programme design 
Compensation of any additional costs 
due to accessibility 
Compensation of additional costs due 
to market forces 
Compensation of additional costs due 
to climate conditions 
Preparation, implementation, 
monitoring and inspection 
Evaluation and studies; information 
and communication 

80 

81 

82 

83 

84 

85 

86 

54 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.6  Sensitivities  of  critical  infrastructures  based  on  expert  survey  and  literature  Red  =  high, 

orange = medium, yellow = low, and green = no sensitivity.  

ENERGY 
Nuclear power plants 
Coal fired power plants 
Gas fired power plants 
Oil fired power plants 
Electricity 
transmission/distribution 
Gas pipelines 
Wind 
Solar 
Biomass 
Hydro 

TRANSPORT 
Rails 
Roads 
Airports 
Ports 
Inland Waterways 

INDUSTRY 
Metals 
Chemical 
Refineries 
Minerals 
Water/waste 
management 

SOCIAL 
Education 
Health 

drought 

flood 

SLR 

fire 

cold 

heat 

wind 

drought 

flood 

SLR 

fire 

cold 

heat 

wind 

drought 

flood 

SLR 

fire 

cold 

heat 

wind 

drought 

flood 

SLR 

fire 

cold 

heat 

wind 

In 

the  survey,  70%  of 

the  possible  combinations  of  hazard  (7)  and 

infrastructures/investments  (50)  were  considered  sensitive  by  more  than  80%  of 

the  respondents  while  40%  of  them  are  sensitive  for  more  than  90%  of  the 

respondents. In general, the sensitivity is highest for inland floods, sea level rise and 

forest  fires,  whereas  the  sensitivity  of  infrastructures  to  drought  seems  less 

important.  

55 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
The expert survey generally corroborates the findings reported in the literature and 

the  more  robust  findings  correspond  to  higher  consensus  in  the  scientific 

community. The consensus about the survey sensitiveness is linked to the number of 

referenced impacts found for  droughts, flood  and forest fires, while it  is  much less 

pronounced  for  heat  waves,  frost/snow/cold  and  wind.  There  is  also  clearly  a 

relation  between  the  number  of  impact  channels  inventoried  in  the  literature  and 

the average sensitivity assessed by experts, albeit that the number of channels does 

not give any information about the strength of a given channel nor the scale of the 

potential impacts.  

We acknowledge different potential sources of bias in the survey: 

  Exposure  bias:  respondents  seem  to  sometimes  confound  (potentially 

unintentionally) exposure as part of the vulnerability when they indicate the 

level  of  sensitivity.  The  first  evidence  of  the  existence  of  such  bias  is  the 

difference  in  sensitivity  indicated  by  experts  between  sea-level  rise/storm 

surges  and  floods  for  some  investments  (for  instance  telephone  and 

broadband networks, high impact while for sea level rise/storm surges  it is 

low, which can only be justified by a difference in exposure). Other evidence 

of  such  bias  is  the  heterogeneity  of  answers  from  the  survey  for  related 

infrastructures; for example, regional roads are estimated as more sensitive 

to frost/snow/cold and floods than motorways and national roads. Exposure 

bias  was  removed  to  the  extent  possible  based  on  literature  impacts  and 

sensitivities. 

 

Information bias, both in the literature review and the survey: a general bias 

towards some more well-known and largely studied infrastructures vs. other 

ones to which the academic community has shed less light on (less studies on 

old technologies in energy production, apart from nuclear energy, especially 

compared  to  renewable  energy).  Some  discrepancies  between  both 

assessment methods may also be the result of information bias. Impacts that 

are often  discussed in  the  academic and  grey  literature  (because associated 

with  higher  probability  of  occurrence  or  higher  exposure)  seem  to  be  less 

acknowledged in the survey than in the review (meaning stronger bias in the 

56 

 
 
 
survey  than  in  the  literature  review,  potentially  showing  that  this  heuristic 

bias may be stronger when people try to gather expertise quickly to answer a 

survey  online  rather  than  when  they  write  an  article  or  choose  a  topic  to 

study). For instance, the impact of droughts and heat waves on asphalt (both 

in the case of road transportation, which is very largely acknowledged by the 

literature,  and  of  airports,  the  latter  being  associated  with  much  less 

references) and on bridges is not well assessed by experts. 

 

Individual bias: The individual/personal representation of the overall impact 

of climate hazards and change was verified and shown to be very limited. We 

checked for individual bias by dropping the global representation of climate 

hazard  impact  within  sectors,  i.e.  removing  the  average  of  all  answers  for 

each respondent, and this check lead to the same results. Some heterogeneity 

between  sector-specific  samples  (distinct  panels  of  experts),  however,  may 

further bias the comparisons of results between sectors. 

We  further  acknowledge  that  our  approach  has  several  limitations,  including  the 

following: 

  Sensitivities for a given investment/infrastructure to a given hazard depend 

on the institutional and economic environment, especially on the upward and 

downward  side  of  the  production  chain  and  thus  on  the  dependency 

networks  of  critical  infrastructure  which  are  complex  systems.  Different 

degrees  of  interconnectivity  of  infrastructures  indeed  lead  to  different 

criticalness in case of a hazard  (Kröger,  2008). Currently, there is, however, 

no  satisfactory  set  of  metrics  or  models  that  articulate  the  risk  of  failures, 

either  naturally  caused  or  human  induced,  for  highly  interdependent 

infrastructures.   

  Technological  heterogeneity  among  infrastructures  may  also  have  a  great 

role  in  the  sensitivity  estimation.  For  example,  power  plants  with  closed 

circuit  cooling  systems  are  more  efficient  and  less  vulnerable  to  rising  air 

temperatures.  

57 

 
 
 
  The  life  span  of  existing  or  planned  infrastructure  can  influence  its  current 

and  future  vulnerability.  For  example,  wind  power  systems  have  lower  life 

spans making them more adaptable in the long run. 

We implicitly consider in our survey exercise that experts are aware of the existing 

context  (for  instance  the  share  of  installed  capital  for  a  given  technology  of  an 

infrastructure) and that they take this knowledge into account when answering the 

survey. Keeping these caveats in mind, our approach provides indications of general 

sensitivities of different types of infrastructures and key economic assets to climate 

hazards  and  may  help  in  orienting  policy  interventions  for  climate  adaptation  and 

resilience. Further herein, when the vulnerability and exposure  are combined with 

current  climate  hazards  it  is  detailed  how  the  qualitative  sensitivity  matrix  is 

translated into quantitative estimates of impacts per sector.  

58 

 
 
 
 
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4  Exposure: critical infrastructures and EU regional investments 

4.0  Key messages 

  Exposure refers to the inventory of the assets that may be affected by hazardous 

events.  The  assessment  of  exposure  may  address  many  classes  of  assets,  their 

characteristics and geographical distribution. The value of an asset is function of 

its usefulness to an individual, group of individuals or the society as a whole. 

  Two types of assets have been considered in this study: EU regional investments 

and current critical infrastructures in Europe. 

  EU regional investments refer specifically to the investments under the Cohesion 

policy  during  the  programming  period  2007-2013  in  the  EU27.  The  funds 

allocated  to  European  regions  by  the  Cohesion  policy  are  classified  in  86 

categories  of  expenditure.  In  this  study,  a  total  of  50  categories  of  expenditure 

were  considered  potentially  vulnerable  to  natural  hazards,  which  accounts  for 

53.1%  of  the  2007-2013  investment  program,  or  roughly  185  billion  Euros. 

Investments  in  transport  and  environment  and  tourism  account  for  more  than 

75% of all vulnerable investments. Other vulnerable domains of investment are 

social, technology and communication and energy infrastructure. Regions in the 

South and Eastern parts of Europe are the main beneficiaries. 

  A critical infrastructure is an asset which is essential for the maintenance of vital 

societal  functions,  health,  safety,  security,  economic,  or  social  well-being  of 

people, as defined in the Council Directive 2008/114/EC. In this study, a total of 

24  infrastructure  types  were  considered  including  transport,  energy,  industry 

and social infrastructures. 

  Data  from  various  open  and  proprietary  sources  were  collected  to  build  a  geo-

database  storing  both  the 

location  and  key  attributes  of  each  critical 

infrastructure in vector format. The vector layers were then converted to raster 

layers  and 

‘harmonized’  using  a  procedure  implemented  in  Geographical 

Information  Systems  to  minimize  potential  data  completeness  issues  and  to 

allow  the  comparability  between  infrastructures  of  the  same  sector.  The 

68 

 
 
harmonized  infrastructure  layers  represent  both  the  location  of  infrastructures 

and  their  ‘intensity’.  The  intensity  of  a  particular  infrastructure  type  in  a  given 

location is a function of both the infrastructure’s characteristics and its location, 

which defines its potential usefulness and value to society. 

  The  geo-database  of  critical  infrastructures  can  be  used  to  map  the  location  of 

each 

infrastructure  type 

in  Europe.  The  spatial  distribution  of  critical 

infrastructures  varies  considerably  across  Europe.  For  many  types  of 

infrastructures, there is a strong correlation between population density and the 

presence  of  infrastructures.  For  example,  highly  populated  areas  are  generally 

well  served  by  transport  and  social  infrastructure.  Some  specific  infrastructure 

types, however, are absent in many countries. For example, nuclear power plants 

are  present  in  only  a  limited  set  of  countries,  and  navigable  inland  waterways 

are mostly present in Central Europe. 

  The  presence  of  an  infrastructure  at  a  given  location  (exposure)  does  not 

necessarily imply that such infrastructure is at risk. To evaluate risk, the location 

of infrastructures needs to be combined with the probability of occurrence and 

intensity of a hazard and its vulnerability to the hazard (Chapter 5).  

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4.1  Introduction 

Exposure refers to the people, property or any other interest that would be subject 

to  a  given  hazardous  event,  and  loss  is  a  measure  of  its  direct  or  indirect  socio-

economic  consequences  (Mileti  1999,  UNDP  2004,  Lerner-Lam  2007).  Exposure  is 

therefore a fundamental component of disaster risk (IPCC 2012). Disaster risk exists 

only  when  all  its  three  components  occur  simultaneously  in  a  given  geographical 

area:  climate/weather-driven  hazardous  events  (hazard)  and  exposed  assets  that 

are vulnerable to those events (exposure and vulnerability). To illustrate, if there is 

a  hazard  occurring  in  a  geographical  area  without  any  assets,  no  losses  can  be 

expected, and no stakes are at risk. Similarly, there is no risk if the assets exposed to 

a  particular  hazard  are  not  vulnerable  to  it.  Exposure  and  vulnerability  are, 

therefore,  tied  closely.  However,  due  to  practical  and  methodological  convenience, 

exposure and vulnerability are treated separately in this study. 

The previous chapter focused on vulnerability, i.e. the propensity or predisposition 

of assets to be adversely affected (IPCC 2012). Vulnerability depends on the intrinsic 

characteristics of the assets that make them more or less sensitive to different types 

of hazardous events, in other words, the factors that contribute to loss, including the 

physical fragilities of buildings and infrastructure that may amplify losses (Lerner-

Lam  2007).  This  chapter  focus  on  exposure  only,  and  refers  specifically  to  the 

inventory of assets and their geographical distribution. 

An  ‘asset’  is  anything  that  is  useful  to  somebody  or  something9.  Consequently,  an 

asset always holds some sort of value, which often can be measured or expressed in 

monetary  terms.  Assets  can  be  classified  as  material/physical  (e.g.  goods, 

infrastructure),  financial  (e.g.  cash,  investments),  or  intangible  (e.g.  know-how, 

culture).  In  agreement  with  DG  CLIMA,  two  specific  types  of  assets  have  been 

considered in this study: 

  EU regional investments 

  Critical infrastructures 

9 According to Cambridge, Oxford and Collins dictionaries (consulted online in June 2015). 

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The EU regional investments refer to projects financed by the Cohesion policy (CP), 

also referred to as ‘Regional policy’. The Cohesion policy is part of the multi-annual 

financial  framework  (MFF),  which  regulates  the  annual  budgets  of  the  European 

Union typically over periods of seven years. In the programming period 2007-2013, 

the Cohesion policy accounted for about 1/3 of the total MFF budget, and a similar 

share has been agreed for the on-going programming period (2014-2020). 

The  Cohesion  policy  is  put  into  practice  through  ‘operational  programmes’  (OPs), 

which  are  policy  instruments  that  define  the  investment  priorities  for  regions, 

countries,  or  trans-boundary  regions.  The  Cohesion  policy  aims  at  kick-starting 

growth, employment, competitiveness, and development on a sustainable basis, and 

thus  aiming  to  reduce  economic,  social  and  territorial  disparities  between  regions. 

The  Cohesion  policy’s  funds  are  used  in  a  range  of  domains  of  intervention,  like 

infrastructure,  environment,  research  and  development,  aid  to  the  private  sector, 

human resources, and technical assistance. Not all the domains of intervention and 

specific projects supported by the Cohesion policy are vulnerable to natural hazards. 

The  objective is  therefore  to  focus  on  those  that  are  vulnerable  to natural  hazards 

and quantify the potential risks given future climate conditions. 

Data  regarding  EU  funded  projects  was  made  available  by  the  Directorate-General 

for  Regional  and  Urban  Policy  of  the  European  Commission  (DG  REGIO),  which 

manages  the  EU  Cohesion  policy.  In  this  project,  we  used  data  regarding  the 

Cohesion  policy  2007-2013  only.  Specific  investment  allocation  data  for  the  on-

going  programming  period  (2014-2020)  were  not  available  during  the  writing  of 

this  report.  In  section  4.2,  we  provide  more  details  on  the  configuration  of  the 

Cohesion policy, and on the data used to quantify EU funded projects per region and 

per type of investment. 

‘Critical  infrastructure’  has  been  defined  as  “an  asset,  system  or  part  thereof  (…) 

which  is  essential  for  the  maintenance  of  vital  societal  functions,  health,  safety, 

security, economic or social well-being of people, and the disruption or destruction 

of which would have a significant impact in a Member State as a result of the failure 

to  maintain  those  functions”  (Council  Directive  2008/114/EC).  In  this  study  we 

71 

 
 
 
 
 
selected a large set of infrastructure types that may classify as ‘critical’ according to 

the  definition  above.  These  broadly 

include  transport,  energy, 

industrial, 

environmental  and  social  infrastructures.  The  objective  was  therefore  to  collect 

geospatial  data  on  specific  infrastructure  types  at  the  highest  spatial  resolution 

possible, for all the EU+EFTA countries. To our knowledge, this study has been the 

first one that attempted to collect and catalogue in the most complete, detailed and 

harmonized way data regarding critical infrastructures in the context of natural risk 

assessment  at  EU  level.  Section  4.3  describes  in  more  detail  the  collected  critical 

infrastructure data. 

4.2  EU regional investments: Cohesion policy funds 

The basis for the current model of the Cohesion policy was laid in the 1988 reform. 

Since  then,  the  structural  funds  were  integrated  into  an  overarching  policy,  with 

strategic guidelines defined by the Commission and strong involvement of Member 

States  and  regions  that  drafted  operational  and  regional  development  plans  on  a 

multi-annual basis. Different strategic objectives were set for regions depending on 

their economic situation, but with particular emphasis on the lagging regions of the 

community  (Manzella  and  Mendez  2009).  Since  then,  a  total  of  four  multi-annual 

programs  have  been  implemented:  1989-1993,  1994-1999,  2000-2006  and  2007-

2013. The ongoing multi-annual program covers the period 2014-2020. 

In  the  2007-2013  programming  period,  the  Cohesion  policy  was  operationally 

structured in three different funds, each with specific objectives: The Cohesion Fund 

(CF),  the  European  Social  Fund  (ESF),  and  the  European  Regional  Development 

Fund (ERDF). The latter aims at strengthening economic and social cohesion in the 

European  Union  and  specific  measures  consist  of  direct  aid  to  small  and  medium 

enterprises (SMEs), co-financing of infrastructure linked to research and innovation, 

information  and  telecommunication,  environment,  energy  and  transport,  and 

technical assistance measures. Actors in all regions of the EU can be funded by the 

ERDF. The European Social Fund supports actions that contribute to improve human 

capital  and  social  integration  in  order  to  increase  access  to  the  labour  market  and 

72 

 
 
 
 
 
create  opportunities  for  employment.  Only  the  most  developed  EU  regions 

(GDP/capita  >90%  of  the  EU  average)  are  not  eligible  for  ESF  funding.  Finally,  the 

Cohesion Fund invests in key trans-European transport and energy networks, while 

improving  the  environment  by  increasing  energy  efficiency,  renewable  energy 

production, inter-modality and mass public transportation. Only the  regions under 

the ‘Convergence’ objective (GDP/capita <75% of the EU average) are eligible for the 

CF. 

A total of 347 billion Euros were distributed among the three funds, with 201 billion 

Euros allocated to the ERDF, 76 billion Euros to the ESF and 70 billion Euros to the 

CF. In terms of regional distribution, the regions under the ‘Convergence’ objective 

benefited  the  most,  with  a  total  of  283  billion  euros  allocated.  While  the  total 

amount of the Cohesion policy represented a very small portion of the EU’s GDP (~ 

0.3%), in less developed regions, the yearly allocated investments can represent as 

high as 5% of their annual product. 

As  the  manager  of  the  Cohesion  policy,  the  mission  of  the  DG  REGIO  is,  first,  to 

ensure that the available financial instruments contribute to a sustainable economic, 

social  and  territorial  cohesion  by  reducing  disparities  between  the  levels  of 

development  of  regions  and  countries  of  the  European  Union,  and  furthermore  to 

ensure  that  these  objectives  are  not  met  at  high  environmental  cost,  and  that 

potential  negative  impacts  are  foreseen  and  minimized.  A  study  on  direct  and 

indirect  land  use  impacts  of  the  Cohesion  policy  has  been  done  by  the  JRC  at  the 

request  of  the  DG  REGIO  (Batista  e  Silva  et  al.  2013b).  However,  impacts  of  future 

weather  and  climate-driven  hazardous  events  on  EU  investments  have  not  been 

thoroughly  assessed  until  this  JRC  study  commissioned  by  the  Directorate-General 

for Climate Action (DG CLIMA). 

In  this  project,  we  used  data  regarding  the  Cohesion  policy  2007-2013,  which 

consisted of allocated investments per NUTS2 region (271 regions) and per category 

of  expenditure  (86  categories).  The  categories  can  be  as  specific  as  ‘R&TD 

infrastructure’,  ‘Support  for  self-employment  and  business  start-up’,  ‘Motorways’, 

‘Multimodal  transport’,  ‘Ports’,  ‘Renewable  energy:  solar’,  ‘Integrated  projects  for 

73 

 
 
 
 
 
urban  and  rural  regeneration’, 

‘Promotion  of  natural  assets’, 

‘Education 

infrastructure’,  to  mention  just  a  few.  The  data  was  provided  by  DG  REGIO  in  a 

matrix of investment allocations, with regions in rows and expenditure categories in 

columns. 

Specific  investment  allocation  data  by  the  Cohesion  policy  for  the  2014-2020 

programming  period  were  not  available  within  the  writing  of  this  report,  as  many 

operational  programs  were  still  being  finalized.  Operational  programs  can  be 

regional, thematic (national scope) and trans-boundary (multi-regional scope), and 

define  the  strategies  for  investment  during  the  seven  year  programming  period 

given: 1) the overarching EU’s objectives of competitiveness and cohesion and 2) the 

specific  regional  and/or  thematic  goals.  The  OPs  define  in  detail  the  share  of  each 

fund  (ERDF  +  CF  +  ESF)  that  is  used  to  support  each  objective  and  specific 

intervention.  All  summed  together,  the  hundreds  of  OPs  allow  for  a  complete 

portrait of how the EU funds are expected to be used across regions and expenditure 

categories. 

Not  all  the  categories  of  expenditure  are  equally  sensitive  to  natural  hazards.  The 

different  sensitivity  levels  of  investments  to  natural  hazards  have  already  been 

addressed in Chapter 3 of this report. However, a first screening was conducted to 

differentiate  investment  typologies  that  are  more  vulnerable  to  natural  hazards 

from  those  that  are  not.  In  general,  investments  in  infrastructure  or  any  kind  of 

physical  assets  were  considered  potentially  vulnerable  to  natural  hazards,  while 

investments in immaterial actions like financial support to firms and to research and 

development, improvement of the human capital and social inclusion, strengthening 

of institutional capacity, technical assistance or direct compensations to outermost 

regions were considered not vulnerable. Investments targeted to make regions more 

resilient to environmental risks and climate change were not considered vulnerable 

by definition. According to this first screening, 50 expenditure categories out of 86 

were  considered  potentially  vulnerable  to  natural  hazards,  which  accounts  for 

53.1% of the total Cohesion policy investments. 

74 

 
 
 
 
 
The potentially vulnerable expenditure categories were then grouped in 5 relevant 

sectors of investments, namely: transport, energy, environment and tourism, social 

infrastructure  and  information,  communication  and  technology.  The  share  of 

investments  per  each  of  these  sectors  is  depicted  in  Figure  4.1.  Investments  in 

transport  and  environment  and  tourism  represent  more  than  75%  of  the  total 

vulnerable  investments.  Figure  4.2  shows  the  regional  distribution  of  these 

investments,  with  a  quite  remarkable  pattern  of  Southern  and  Eastern  regions 

absorbing most of the investments. Because Croatia joined the EU in 2013, it did not 

benefit  from  the  Cohesion  policy  for  the  period  considered  at  its  fullest.  As  such, 

Croatia was not included in the analysis on EU Regional Investments. 

Figure 4.1 Share of main vulnerable categories of EU regional investments under the Cohesion policy 

2007-2013. 

75 

 
 
 
 
 
 
 
 
 
 
 
 
It  is  finally  worth  noting  that  data  on  specific  projects  were  not  available.  The  DG 

REGIO database has information for only a sample of large projects. Information on 

all specific projects, their nature, characteristics and precise locations would permit 

a  much  more  accurate  quantification  of  future  expected  damages  due  to  natural 

hazards and changing climate conditions. However, such data are still very scattered 

and  have  not  yet  been  systematized.  Therefore,  to  what  concerns  exposed  assets, 

only  allocated  investments  per  region  and  per  category  of  expenditure  have  been 

considered. Moreover, these were assumed to be homogeneously distributed across 

each region’s geographical extent. It was further assumed that allocated investments 

at the begging of the program were implemented during the programs duration. 

76 

 
 
 
 
 
 
Figure 4.2 Regional distribution of Cohesion policy funds, per major type of investment 

77 

        
 
 
4.3  Critical infrastructures 

As already mentioned in section 4.1, we took the definition of critical infrastructure 

from  the  Council  Directive  2008/114/EC,  which  defines  critical  infrastructures  as 

assets  that  are  essential  for  the  maintenance  of  vital  societal  functions,  health, 

safety,  security,  economic  or  social  well-being  of  people.  Such  a  broad  definition 

encompasses necessarily many types of infrastructures. 

The selection of infrastructure types to consider in this study was initially guided by 

the  categories  of  investment  of  the  Cohesion  policy.  As  explained  in  section  4.2,  a 

first  qualitative  screening  was  made  to  select  the  investment  categories  that  were 

potentially vulnerable to natural hazards, which normally corresponded to physical 

infrastructure. From these infrastructure types we retained those that broadly fitted 

the definition of criticality. Although heavy industries are not directly targeted by EU 

investments, this type of infrastructure was added to the list due to their economic 

criticality.  The  results  of  the  vulnerability  survey  were  finally  used  to  validate  and 

adjust  the  selection.  A  total  of  24  specific  infrastructure  types  were  finally 

considered  in  this  study.  These  were  then  classified  in  four  broad  and  meaningful 

sectors:  transport  infrastructure,  energy  infrastructure,  industrial  infrastructure, 

and social infrastructure (see Table 4.1). 

A geographical database with the location of the different infrastructure types was 

constructed  by  compiling  and  preparing  information  gathered  from  multiple  data 

sources  (Marin  Herrera  et  al.,  2015).  To  allow  consistency  and  comparability 

amongst  the  various  infrastructure  layers  within  sectors  the  data  were  further 

‘harmonized’.  The  ‘harmonization’  process  is  described  in  more  detail  in  section 

4.3.2. The final, harmonized infrastructure layers were used in the subsequent risk 

analysis, which is explained in detail in Chapter 5 of this report. A simplified scheme 

of this workflow is depicted in Figure 4.3. 

78 

 
 
 
 
 
Figure 4.3 General workflow followed to collect and harmonize infrastructure geospatial data. 

4.3.1  Data collection  

A large part of this study was devoted to collecting detailed geospatial information 

of  current  critical  infrastructures,  as  a  one-stop-shop  was  not  available.  It  actually 

became  quite  clear  from  the  early  stages  of  the  project  that  information  on 

infrastructures in Europe was rather scattered, with different sources available for 

different infrastructure types, or with different data sources providing data for the 

same infrastructure type. 

The  first  step  was  therefore  to  seek  and  list  all  possible  data  sources  for  each 

infrastructure  type,  explore  and  learn  about  the  main  relevant  characteristics  of 

each data source and then select the optimal way to accurately represent the data. 

The following criteria were used to guide the selection of data sources: 

  European  geographical  coverage:  European  data  sources  were  preferred  over 

national  and  worldwide  sources  to  avoid,  respectively,  inconsistent  data  and  low 

resolution levels; 

  Data  completeness:  the  highest  stated  or  perceived  data  completeness  was 

preferred; 

  Data consistency: data sources with transparent and consistent mapping/reporting 

methodologies; 

  Highest spatial resolution; 

79 

 
 
 
 
 
  Most recent data update; 

  Large  within-sector  thematic  coverage:  Data  sources  which  included  data  on  the 

most infrastructure types within a sector. 

The  resulting  geo-database  consists  of  a  set  of  layers  in  vector  format,  each 

representing one infrastructure type, covering the EU+EFTA countries. In the case of 

the  energy  infrastructure  layers,  additional  information  provided  by  the  PLATTS 

database (proprietary database acquired by the European Commission) was kept, in 

particular the  installed capacity of each power plant, the  diameter of gas  pipelines 

and the voltage of the electricity grid. 

Table 4.1 summarizes the result of the process of infrastructure type selection, and 

their  classification  per  major  sector.  The  data  sources,  the  original  data  structure, 

and the reference dates are specified for each infrastructure type as well. 

80 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4.1 List of infrastructures used in this study, sources used and reference dates. 

Sector 

Sub-sector 

Infrastructure type 

Data structure  Main sources 

Reference 
date 

Roads 

Transport 

Local roads 
Roads of national 
importance 
Motorways 
Railways 
Inland waterways 
Ports 
Airports 
Coal power plants 
Gas power plants 
Oil power plants 
Nuclear power plants 
Biomass and geothermal 
power plants 
Hydro power plants 
Solar power plants 
Wind power plants 
Electricity distribution / 
transmission 
Gas pipelines 
Metal industry 
Mineral industry 
Chemical industry 

Refineries 

Other modes 

Non-renewable 
energy 
production 

Renewable 
energy 
production 

Energy 
transport 

Heavy 
industries 

Water/waste 
treatment 
Education 
Health 

Water and waste 
treatment 
Education infrastructure 
Health infrastructure 

Vector (lines) 

Open Street Map 

2014 

Vector (lines) 

Vector (points) 

GISCO + UNECE 

CORINE Land 
Cover + GISCO 

2006 

Vector (points) 

Vector (points) 

PLATTS 

2013 

Vector (lines) 

Vector (points) 

EPRTR v7 

2013 

Global Energy 
Observatory 

Vector (points) 

EPRTR v7 

2010 

2013 

Vector (points) 

Open Street Map 

2014 

Energy 

Industry 

Social 

4.3.2  Harmonization 

Despite  the  careful  selection  of  data  sources  and  the  various  checks  carried  out  to 

evaluate  the  quality  of  the  data,  an  exhaustive,  quantitative  validation  of  the  data 

was  outside  the  scope  of  this  study.  Nevertheless,  there  were  some  critical  issues 

that had to be taken into account before overlaying the infrastructure data with the 

hazard information. 

81 

 
 
 
 
 
 
Data  completeness  was  the  first  of  those  issues.  First,  some  data  sources  have  a 

limited  scope.  That  is  the  case  of  the  European  Pollutant  Release  and  Transfer 

Register  (EPRTR)  that  was  used  to  obtain  the  location  of  most  of  the  industrial 

infrastructures. The scope of EPRTR is to monitor pollution levels from the largest 

emitters,  thus  ignoring  smaller  ones.  Second,  it  cannot  be  guaranteed  that  data 

sources  such  as  the  Open  Street Map  (OSM)  contain  each  and  every  infrastructure 

location  as  found  in  reality  at  a  given  time.  Moreover,  as  a  voluntary  geographical 

information  project,  the  degree  of  completeness  of  the  OSM  can  vary  considerably 

from country to country and even  from region  to region. Finally, even proprietary, 

more  structured  and  consistent  data  sources  such  as  PLATTS  do  not  report  the 

degree of completeness of their databases. 

Another issue relates to the comparability between different types of infrastructure 

within  the  same  sector.  For  example,  in  terms  of  impacts  in  the  transport  sector, 

how  can  a  port  (which  is  represented  as  a  point  feature  in  a  Geographical 

Information System (GIS)), be  compared to 1  Km of road (which is  represented as 

line feature in a GIS)? Or how can 1 Km of motorway be compared to 1 Km of local 

road? The same applies to the other sectors: how can a metal industry be compared 

to a refinery, or a hospital to a school? A more general question is: how can impacts 

of a given hazardous event on two different infrastructure types be compared using 

a sufficiently straightforward and generic methodology, applicable to all Europe? 

To allow a consistent impact framework, infrastructure types belonging to the same 

sector  thus  required  a  data  transformation  process  to  bring  them  to  a  level  of 

comparability,  in  which  their  relative  importance  could  be  evaluated.  To  illustrate, 

rather  than  describing  transport  infrastructures  by  their  typologies  (categorical, 

discrete  data),  they  would  have  to  be  described  in  terms  of  their  actual  usage  or 

value.  This  data  transformation  process  is  hereinafter  referred  as  ‘harmonization’. 

The harmonization required, first and foremost, a conversion from vector to raster 

data  structure,  with  a  cell  size  of  1  Km  x  1  Km.  Subsequently,  an  ‘intensity’ 

measurement was assigned to each raster cell of each individual infrastructure type, 

with the intensity measurement varying per sector, as reported in Table 4.2. 

82 

 
 
 
 
Figure 4.4 illustrates the procedure applied to the road infrastructures, showing the 

transformation from discrete and categorical vector data to a raster and continuous 

data, with intensity values assigned to individual raster cells (locations). Locations 

with  higher  intensity  values  are  assumed  to  suffer  higher  impacts  in  case  of  a 

hazardous event. 

Table 4.2 Intensity variables used. 

Sector 
Transport infrastructure 
Energy infrastructure 
Industry infrastructure 
Social infrastructure 

Variable 
Annual freight transported 
Electricity produced / transported 
Annual turnover 
Annual expenditure 

Unit 
k tonnes 
k tonnes oil equivalent (ktoe) 
Million EUR (MEUR) 
Million EUR (MEUR) 

Figure  4.4  Illustration  of  the  ‘harmonization’  process:  from  categorical  information  to  a 
continuous indicator of intensity. 

In  all cases, the  intensity data  was  collected from Eurostat10 at national level,  for a 

series of the five most recent years available (typically 2009-2013). The entire series 

was averaged to avoid potential data artifacts, and the resulting values were finally 

assigned to infrastructures. The implicit and desirable outcome of this procedure is 

that  impacts  of  a  hazardous  event  on  infrastructures  differ  depending  on  the 

10 Data collection conducted in early 2015, from Eurostat’s online database. 

83 

 
 
 
 
 
 
 
 
 
                                                        
different  ‘intensity’  values  of  the  infrastructures  (apart  from  differences  in  impact 

due to varying degrees of vulnerability for different infrastructure types).  

Two assignment methods were used: 

  Direct assignment 

  Downscaling  

The  direct  assignment  is  the  most  accurate  way  to  assign  an  intensity  value  to 

infrastructures.  However,  this  method  was  only  applied  to  ports  and  airports,  for 

which Eurostat data were available in detail. This allowed a reliable measurement of 

the usage in terms of annual freight transported for each port and airport. 

The downscaling procedure consisted of disaggregating the national intensity values 

of  each  infrastructure  type  (e.g.  annual  turnover  of  the  metal  industry,  or  total 

electricity  production  from  nuclear  power  plants)  to  the  locations  of  those 

infrastructures  within  the  respective  country.  It  is  worthwhile  noting  that  the 

downscaling  procedure  minimized  the  impact  of  geospatial  data  incompleteness 

issues,  as  the  total  intensity  of  a  given  infrastructure  type  in  a  given  country  was 

preserved within that country. This means that even if an important power plant of 

a  given  country  is  missing  from  the  geo-database,  the  total  produced  electricity  in 

that  country  is  kept,  as  it  is  redistributed  among  all  the  other  power  plants.  This 

does not solve the problem of missing infrastructure in a particular location within a 

country,  but  allows  aggregated  impacts  between  countries  to  be  reasonably 

compared. 

The generic downscaling approach can be described by equation 4.1: 

[4.1] 

where 

Ij,i = intensity of infrastructure j in pixel i 

84 

 
 
 
 
 
 
  
Vj,c = total intensity of infrastructure j (or volume) in country c, as reported by 

Eurostat at national level. 

w = weight = f(j) 

for j = roads, wi,j = f(lengthi,j, capacityi,j, nr. of peoplei,j), with capacity of 

motorways = 5, capacity of national road = 3, capacity or local roads = 2, and 

nr. of people in a predefined radius around i11. 

for j = rails or inland water ways, wi,j = f(lengthi,j, average flow from 

exogenous modeli,j12)  

for j = energy production, wi,j = f(installed capacityi,j13) 

for j = electricity grid, wi,j = f(lengthi,j, voltagei,j4) 

for j = gas pipelines, wi,j = f(lengthi,j, pipeline diameteri,j4) 

for j = industry, wi,j = f(nr. of facilitiesi,j) 

for j = social infrastructure, wi,j = f(nr. of potential usersi,j) = nr of people in 

predefined radius / nr infrastructures in radius 

One major advantage of the harmonization approach is that it allows the summation 

of intensities from different infrastructure types j of the same sector s: 

[4.2] 

In summary, the described harmonization procedure was applied to each individual 

infrastructure type, thus allowing to 

  minimize incompleteness of the geospatial data sources; 

  compare different types of infrastructures in the same unit of measure, and, 

consequently, 

11 Population distribution taken from the JRC population grid map at 100 x 100m resolution 2006. See 
Batista e Silva et al. (2013). 
12 TRANSTOOLS model (JRC-IPTS). Documentation available online: 
http://energy.jrc.ec.europa.eu/transtools/TT_model.html. 
13 As reported by the PLATTS energy geo-database. 

85 

 
 
 
  
 
                                                        
  merge different infrastructure types into four more significant sectors: 

transport, energy, industry, social infrastructure. 

Figure  4.5  shows  some  examples  of  harmonized  infrastructure  layers.  After  the 

harmonization procedure, the layers can be combined with hazard data to derive an 

impact measured in the same units as of the harmonized layers. The translation to 

actual monetary losses can be finally achieved by applying cost coefficients (or cost 

curves) which link the estimated impacts (harmonized layers * hazard) with actual 

observed losses due to natural hazards. This is explained thoroughly in Chapter 5. 

86 

 
 
 
Figure 4.5 Examples of harmonized infrastructure layers.

87 

Roads (annual freight transported in k tons)  Rail (annual freight transported in k tons)  Energy production (electricity produced in ktoe)  Energy transport (electricity transported in ktoe)  Education infrastructure (total expenditure in MEUR)  Health infrastructure (total expenditure in MEUR)    
 
 
4.4  Main limitations and challenges 

One  setback  in  this  study  was  the  delay  in  the  preparation  of  the  operational 

programs  for  the  programming  period  2014-2020,  which  prevented  access  to 

detailed regional and sector allocations of the European Structural and Investment 

Funds  (ESIF).  Therefore,  and  contrary  to  what  had  been  initially  planned,  only  the 

2007-2013  regional  investments  were  considered  in  this  study.  Impacts  of  natural 

hazards  on  the  ESIF  for  the  period  2014-2020  depend  intrinsically  on  its 

geographical and sector distribution, which, at the time of this project, were largely 

unknown. 

An  important  limitation  of  the  analysis  carried  out  to  assess  risk  of  EU  regional 

investments  is  related  to  the  assumption  that  investments  were  homogeneously 

distributed  within  NUTS2  regions.  In  reality,  investments  in  regions  may  have 

marked spatial patterns, or target very specific locations within regions, depending 

of course on the type of investment. However, micro-data on past financed projects 

are  extremely  scattered  and  have  not  yet  been  systematized  and  published  by  the 

European Commission, which prevented a more spatially precise analysis. 

The  current  stock  of  critical  infrastructures  has  been  assessed  by  using  the  most 

complete  and  up-to-date  data  sources.  However,  the  exposure  of 

future 

infrastructure  was  not  addressed  in  this  study,  and  remains  still  one  of  the  main 

challenges  in  the  evaluation  of  future  risk.  The  location  of  future  stock  of 

infrastructures  would  require  a  more  sophisticated  approach  based  on  trend 

analysis, or modelling/simulation. Lung et al. (2013), for example, have used results 

from a land use model (Lavalle et al. 2011) which projected future locations of forest 

and industrial/commercial areas.  

Another limitation concerns the lack of information on the completeness level of the 

geographical data sources used in this study. Potential incompleteness was partially 

addressed by the harmonization procedure (see section 4.3.2), which, however, did 

not  prevent  underestimation  of  exposure  at  site-specific  level  when  infrastructure 

88 

 
 
 
 
 
data  were  missing.  As  obvious  at  it  may  seem,  the  quality  of  input  data  affects 

significantly the reliability of the final risk estimates, yet, an actual assessment of the 

input  data  quality  is  not  straightforward,  and  its  influence  on  the  accuracy  of  the 

final impact results remains unknown. 

Various  authors  have  emphasized  the  importance  of  population  data  to  assess 

exposure and risk (e.g. National Research Council 2007a, Freire 2010). Although the 

focus  of  this  study  was  essentially  on  investments  and  critical  infrastructure,  the 

information on the current and future location of people is essential to estimate the 

number  of  users  of  infrastructure,  and  the  potential  indirect  losses  resulting  from 

damaged  infrastructure.  In  an  attempt  to  assess  global  exposure  and  vulnerability 

towards natural hazards, Peduzzi et al. (2009) have used population distribution as 

a proxy of exposure and various socio-economic factors were used as a measure of 

vulnerability. However, the location of people per age and social strata, and across 

different time-frames is still nowadays a key challenge in most countries. 

89 

 
 
 
 
4.5  References 

Batista e Silva F., Gallego J. and Lavalle C., 2013a. A high-resolution population grid 
map for Europe. Journal of Maps, 9, 16-28. 

Batista e Silva F., Lavalle C., Jacobs-Crisioni C., et al., 2013b. Direct and indirect land 
use  impacts  of  the  EU  cohesion  policy  -  Assessment  with  the  Land  Use  Modelling 
Platform. Luxembourg: Publications office of the European Union. 

Freire S., 2010. Modeling of Spatiotemporal Distribution of Urban Population at High 
Resolution  –  Value  for  Risk  Assessment  and  Emergency  Management.  In:  M. 
Konecny  et  al.  (Eds.)  Geographic  Information  and  Cartography  for  Risk  and  Crisis 
Management, Lecture Notes in Geoinformation and Cartography. Springer, Berlin. 

IPCC, 2012. Managing the Risks of Extreme Events and Disasters to Advance Climate 
Change  Adaptation.  A  Special  Report  of  Working  Groups  I  and  II  of  the 
Intergovernmental  Panel  on  Climate  Change.  Cambridge  University  Press, 
Cambridge, UK, and New York, NY, USA, 582 pp. 

Lavalle C., Baranzelli C., Batista e Silva F., Mubareka S., Rocha Gomes C., Koomen E., 
Hilferink  M.,  2011.  A  high  resolution  land  use/cover  modeling  frame  work  for 
Europe:  introducing  the  EU-ClueScanner100  model.  In:  Murgante  B.,  Gervasi  O., 
Iglesias  A.,  Taniar  D.,  Apduhan  B.O.  (Eds.)  Computational  Science  and  its 
Applications  –  ICCSA  2011  International  Conference  Santander,  Spain,  June  2011, 
Proceedings Part 1. Springer, Berlin, Heidelberg, pp. 60–75. 

Lerner-Lam  A.,  2007.  Assessing  global  exposure  to  natural  hazards:  Progress  and 
future trends. Environmental Hazards, 7, 10-19. 

Lung T., Lavalle C., Hiederer R., Dosio A., Bouwer L.M., 2013. A multi-hazard regional 
level  impact  assessment  for  Europe  combining  indicators  of  climatic  and  non-
climatic change. Global Environmental Change, 23, 522-536. 

Manzella  G.P.  and  Mendez  C.,  2009.  The  turning  points  of  the  EU  Cohesion  policy. 
Working paper. 

Marin  Herrera  M.,  Batista  e  Silva  F.,  Bianchi  A.,  Barranco  R.,  Lavalle  C.,  2015.  A 
geographical database of infrastructures in Europe. JRC Technical Report.  

Mileti  D.S.,  1999.  Disaster  by  Design:  a  Reassessment  of  Natural  Hazards  in  the 
United States. Joseph Henry Press, Washington, DC, 351pp. 

National Research Council, 2007a.  Tools and  Methods for Estimating Population at 
Risk from Natural Disasters and Complex Humanitarian Crises. Washington, DC: The 
National Academies Press. 

National  Research  Council,  2007b.  Successful  Response  Starts  with  a  Map: 
Improving  Geospatial  Support  for  Disaster  Management.  Washington,  DC:  The 
National Academies Press. 

90 

 
 
Official  Journal  of  the  European  Union.  Council  Directive  2008/114/EC  of  8 
December  2008  on  the  identification  and  designation  of  European  critical 
infrastructures and the assessment of the need to improve their protection. 

Peduzzi  P.,  Dao  H.,  Herold  C.,  Mouton  F.,  2009.  Assessing  global  exposure  and 
vulnerability towards natural hazards: the Disaster Risk Index. Natural Hazards and 
Earth System Sciences, 9, 1149-1159. 

United  Nations  Development  Program,  2004.  Reducing  Disaster  Risk:  A  Challenge 
for Development. UNDP Bureau for Crisis Prevention and Recovery, New York, 146 
pp. 

91 

 
 
 
5  Multi-hazard risk assessment in Europe under climate change 

5.0  Key messages 

Critical infrastructures 

  Multi-hazard multi-sector impacts 

o  Europe  will  face  a  significant  increase  in  multi-hazard  multi-sector 

damages in the coming decades. Current damages of 3.4 billion €/year in 

the  EU+ (EU28 + Switzerland, Norway and Iceland) are projected to triple 

by  the  2020s,  multiply  six-fold  by  mid-century,  and  rise  up  to  38  billion 

€/year  by  the  2080s  (undiscounted  and  no  socio-economic  changes 

assumed). These numbers reflect only the (combined) damages from the 

7  climate  hazards  to  the  sectors  considered  in  this  study,  hence  they  do 

not reflect the total damages of these hazards to society.  

o  Indicative  estimates  of  total  damages  from  the  7  climate  hazards  to 

society  could  rise  from  currently  12  billion  €/year  to  nearly  80  billion 

€/year by the end of this century.  

  Sector impacts 

o  The  strongest  increase  in  multi-hazard  damages  is  projected  for  the 

energy  sector,  for  which  the  baseline  expected  annual  damage  (EAD)  of 

0.5  billion  €/year  could  rise  to  2.0,  4.4,  and  8.2  billion  €/year  (or 

increases  in  EAD  of  394%,  860%  and  1,612%)  by  the  2020s,  2050s  and 

2080s, respectively.  

o  A  comparable  trend  can  be  observed  for  the  transport  sector,  for  which 

the  baseline  EAD  of  0.8  billion  €/year  is  expected  to  reach  nearly  12 

billion €/year (increase of 1,496%) by the end of this century.  

o  For  industry,  which  shows  the  largest  damages  amongst  the  sectors 

considered, current EAD of 1.5 billion €/year is estimated to surpass 16 

billion €/year by the 2080s, corresponding to a 10-fold increase.  

92 

 
 
 
o  For the social sector, the rising trend in damages is less pronounced, but 

current EAD of 0.6 billion €/year could still more than double by the end 

of this century due to climate change.    

  Hazard contributions 

o  Aggregated over the four sectors, current climate hazard damages relate 

mostly  to  river  floods  (44%)  and  windstorms  (27%).  Their  relative 

contribution to the total damage diminishes rapidly in time. The shares of 

drought and heat strongly rise, covering more than 70% of climate hazard 

damages by the end of the century (vs 12% in present times).  

o  The contribution of wildfires and coastal floods is low, albeit that a strong 

increase in coastal flood damages is projected in the coming century.  

o  The  contribution  of  cold  waves  is  also  low  and  cold-related  damages  in 

Europe could completely disappear with global warming.  

o  Hazard-contributions  vary  amongst  the  sectors.  For  the  social  sector, 

damages  from  windstorms  and  inland  flooding  will  remain  important. 

The industry and energy sector will be most impacted by droughts, with 

56% and 67%, respectively, of sector damages relating to droughts by the 

end  of  this  century.  For  the  transport  sector,  on  the  other  hand, 

heatwaves  will  largely  dominate  future  damages  (92%  of  total  damages 

by 2080s). 

  Regional impacts 

o  For  EU+  as  a  whole,  the  share  of  GDP  at  risk  rises  progressively  from 

0.03% now  to  0.28% by the  end of century. Southern and south-eastern 

countries will be most impacted. For Bulgaria (0.40%), Romania (0.45%) 

and  Italy  (0.49%)  damages  remain  below  0.5%  of  GDP,  whereas  for 

Portugal  (0.77%)  and  Spain  (0.87%)  damages  could  reach  nearly  1%  of 

GDP.  Most  alarming  are  projections  for  Slovenia  and  Croatia,  where 

2.15% and 3.33% of GDP could be annually at risk by the 2080s.     

93 

 
 
 
 
EU Cohesion Policy investments 

  Multi-hazard multi-sector impacts 

o  Annual  damages  to  EU  regional  investments  will  rise  rapidly  from  146 

million €/year, or 0.04% of the total 2007-2013 regional investments, to 556 

million  €/year  (382%  baseline  EAD)  or  0.16%  of  total  investments  by  the 

2020s.  By  the  2050s  damages  further  climb  to  1,109  million  €/year  (761% 

baseline EAD) or 0.32% of total investments, and by the end of this century 

the  annual  risk  amounts  to  1,703  million  €/year  (1,168%  baseline  EAD)  or 

0.49% of total investments.  

  Hazard contributions 

o  Floods  is  currently  the  most  damaging  hazard  to  EU  regional  investments, 

accounting for about half (51%) of total impacts, followed by drought (26%) 

and heat waves (10%).  

o  Drought damages increase significantly, from 38 million €/year now to 888 

million  €/year  by  the  2080s  (2,315%  baseline  EAD),  and  form  the  largest 

share of future damages (52% by the end of the century).  

o  The  strongest  relative  increase  in  damages,  however,  is  projected  for 

heatwaves and coastal flooding (increases both around +4,500%). As a result 

heatwaves  will  become  the  second  damaging  hazard  to  EU  structural 

investments (40% of total damages by 2080s).  

o  Damages  due  to  cold  waves  will  evanesce  in  the  coming  decades,  whereas 

damages due to  wind, floods and fires  show more  moderate increases, with 

absolute damages rising this century by 10%, 30% and 50%, respectively.  

  Sector impacts 

o  Currently 48% of hazard impacts relate to transport investments, and 37% to 

the environment and tourism sector. Annual damages in the transport sector 

rise by 722% from 70 million €/year in the baseline to 573 million €/year by 

the  2080s.  For  the  environment  and  tourism  sector  damages  increase  at  a 

94 

 
 
 
 
rate  double  as  high  (+1,619%)  from  55  million  €/year  now  to  940  million 

€/year by the end of the century. Hence, this sector will under future climate 

account for more than half of the total hazard damages (55% vs 34% for the 

transport sector) to EU investments.  

o  Damages in the energy sector increase by more than tenfold (+1,077%), from 

13 million €/year in the baseline to 157 million €/year by the 2080s.  

o  Impacts  in  the  ICT  and  social  sector  are  smaller  and  show  less  pronounced 

increases. Where damages to ICT investments are expected to rise by 160% 

from 1.9 to 5 million €/year by the 2080s, an increase of 363% from 6 to 28 

million €/year is projected for the social sector.  

  Regional impacts 

o  Where by the 2080s multi-hazard multi-sector expected annual losses for the 

whole EU27 may correspond to 0.49% of total investments, in regions of the 

Iberian and Balkan Peninsulas impacts can amount to 3% of investments.  

o  When  looking  at  sector  level,  relative  impacts  can  even  be  substantially 

higher  in  some  parts  of  Europe.  For  the  transport  sector,  by  the  end  of  the 

century  expected  annual  losses  to  investments  rise  to  about  5%  of  the 

transport investments for Latvia, Romania, Bulgaria and regions of Spain. For 

the energy and the environment and tourism sectors, expected yearly losses 

by  the  2080s  may  rise  up  to,  or  locally  exceed,  1/10  of  the  total  sector 

investment in south-west and south-east regions of the EU. For the social and 

ICT sectors expected annual losses at regional scale remain mostly below 1% 

and 0.5% of sector investments, respectively.  

95 

 
 
 
 
5.1  Introduction 

The projected increases in exposure to multiple climate hazards in many regions of 

Europe, as shown in the hazard assessment of this work (see Chapter 2), emphasize 

the  relevance  of  a  multi-hazard  risk  assessment  to  comprehensively  quantify 

potential  impacts  of  climate  change  and  develop  suitable  adaptation  strategies.  In 

this context, quantifying the future impacts of climatic extremes on critical economic 

sectors,  both  in  terms  of  critical  infrastructures  and  EU-funded  investments,  is 

crucial  due  to  their  key  role  for  human  wellbeing  and  their  effects  on  the  overall 

economy.  

As detailed in Chapter 4 of this report, critical infrastructures describe the existing 

assets and systems that are essential for the maintenance of vital societal functions, 

health, safety, security, economic or social well-being of people, and the disruption 

or destruction of which would have a significant impact as a result of the failure to 

maintain  those  functions.  EU  regional  investments  refer  to  the  investments  under 

the Cohesion policy during the programming period 2007-2013 in the EU27, aimed 

to  specific  regional  development  projects  or  actions.  Of  particular  relevance  in 

relation  to  potential  climate  change  impacts  in  Europe  are  the  transport,  energy, 

industry, social, environment and tourism, and ICT sectors.   

Evaluating  the  effects  of  multiple  climate  hazards  on 

infrastructures  and 

investments  is  a  complex  task  because  of  incomplete  scientific  methodologies  and 

limited  understanding  of  vulnerability  of  infrastructures  (see  Chapter  3).  Existing 

direct cost assessment methods generally focus on specific hazards or sectors by the 

use of susceptibility curves derived analytically under specific conditions (Ciscar et 

al., 2011; Meyer et al., 2013). Difﬁculties in establishing comparisons across hazards 

and  sectors  remain  particularly  relevant  (Kappes  et  al.,  2012).  The  recently 

developed  InterSectoral  Impact  Model  Intercomparison  Project  (ISI-MIP)  offers  a 

framework  for  comparing  multiple  climate  impact  models  within  and  across 

different  sectors  based  on  a  common  set  of  climate  and  socioeconomic  scenarios, 

providing  a  quantitative  estimate  of  impacts  and  uncertainties  (Warszawski  et  al., 

96 

 
 
 
 
2014).  While  this  open  archive  represents  an  unprecedented  opportunity  and  a 

significant step forward to a better understanding of the potential impacts of climate 

change, systematic and detailed knowledge of the risk imposed by multiple climate 

extremes is still lacking.  

This  work  provides  the  first  comprehensive  multi-hazard  multi-sector  risk 

assessment  for  Europe  under  climate  change  and  identifies  the  most  vulnerable 

regions  throughout  the  21st  century.  We  focus  on  seven  climate  hazards  (see 

Chapter  2)  including  heat  and  cold  waves,  wildfires,  droughts,  river  and  coastal 

floods  and  windstorms.  Expected  impacts  to  industry,  energy,  transport,  social, 

environment and tourism, and ICT infrastructures and EU regional investments are 

investigated under current and future climate conditions. Although these sectors do 

not  cover  the  full  range  of  possible  societal  and  environmental  climate-hazard 

impacts,  they  include  crucial  aspects  of  livelihoods  for  Europe.  Cost  estimates 

reported  here  refer  mostly  to  direct  damages  to  infrastructures  due  to  physical 

contact  with  the  hazard  and  to  a  lesser  extent  also  include  damages  related  to  the 

reduction  in  primary  sources  and  productivity  (see  sections  5.2.4  and  5.3.3).  The 

methodology integrates a set of high-resolution climate hazard projections (Chapter 

2), a detailed representation of sectorial physical assets and productive systems and 

investments  (Chapter  4),  and  a  qualitative  appraisal  of  their  sensitivity  based  on 

expert  view  and  literature  review  (Chapter  3).  The  three  above-mentioned 

components  have  been  combined  in  a  vulnerability  framework  with  recorded 

damages from climate disasters in order to derive quantitative estimates of risk. We 

then derive a comprehensive and comparable set of climate hazard cost ﬁgures for 

different  sectors  in  the  EU28  +  Switzerland,  Norway  and  Iceland  (further  denoted 

herein as EU+). Results are presented in spatial maps as well as aggregated for five 

European regions to simplify interpretation, analogously to the presentation in the 

hazard chapter (Chapter 2): Southern (SEU), Western (WEU), Central (CEU), Eastern 

(EEU) and Northern (NEU) Europe (see Figure 1.2). For clarity of presentation, after 

the  description  of  the  methods  we  first  detail  the  impacts  on  the  present  stock  of 

critical economic infrastructures, which allow a better interpretation of the potential 

impacts  of  climate  change  on  the  current  socio-economic  system,  and  then  we 

synthesize the risk to EU regional investments. 

97 

 
 
 
5.2  Methods 

The  risk  assessment  focuses  on  direct  tangible  damages  related  to  the  impacts  to 

physical  assets,  the  disruption  of  productivity  and  loss  of  primary  sources  due  to 

climate  hazards.  Expected  Annual  Damages  are  obtained  from  the  combination  of 

climate  hazard,  exposed  infrastructures  and  investments,  and  the  vulnerability  of 

exposed  assets  (IPCC,  2012)  and  are  expressed  in  2010  €  assuming  no  socio-

economic  change  in  future  scenarios.  In  other  words,  we  have  modelled  the 

economic  effects  of  future  climate  change  on  the  current  economy  (quasi  static 

analysis).  

The  three  main  components  that  are  utilized here  for  the  risk  assessment,  namely 

climate hazard (Chapter 2), exposure (Chapter 4) and sensitivity of exposed assets 

(Chapter  3),  are  already  amply  discussed  in  their  respective  chapter.  They  are 

recalled  here  briefly  for  clarity  and  then  we  detail  how  they  are  combined  in  a 

coherent risk framework to translate impact scenarios into quantitative estimates of 

damage based on recorded loss data. 

5.2.1  Hazards 

The risk analysis focuses on seven critical climate hazards for Europe: heat and cold 

waves,  river  and  coastal  floods,  streamflow  droughts,  wildfires  and  windstorms. 

Climate hazard indicators were derived for the baseline (1981-2010), 2020s (2011-

2040),  2050s  (2041-2070)  and  2080s  (2071-2100)  for  an  ensemble  of  bias-

corrected  climate  projections  obtained  from  different  regional  climate  model 

simulations  under  the  A1B  emissions  scenario.  Hazard  magnitude  levels  (HL)  are 

classified  based  on  the  probability  of  occurrence  of  events  in  current  climatology; 

given TR as return period corresponding to HL, we assign the intensity class to the HL 

event according to the Table 5.1. 

98 

 
 
 
 
 
The fraction of a given area that is expected to be annually exposed to a hazard of HL 

magnitude – the Expected Annual Fraction Exposed (EAFE) – was derived for each 

scenario period. More details can be found in Chapter 2. 

Table 5.1 Classification of hazard intensity class based on probability thresholds 

Hazard intensity class (HL)  Probability threshold 
very high 
high 
moderate 
low 
very low 
no hazard 

TR ≥ 100yr 
100yr > TR ≥ 50yr 
50yr > TR ≥ 20yr 
20yr > TR ≥ 10yr 
10yr > TR ≥ 2yr 
2yr < TR 

5.2.2  Exposure 

Exposure is described by two sets of GIS layers that refer to critical infrastructures 

and EU regional investments, respectively. The first set represents the present stock 

of  transport, 

industry,  energy  and  social 

infrastructures;  the  second  set 

comprehends  EU  regional  investments  under  the  2007-2013  period  in  the  sectors 

transport, 

environment 

and 

tourism, 

energy, 

social, 

and 

information, 

communication and technology.  

In  order  to  allow 

intra-sector  comparability  between  different  types  of 

infrastructures  and  to  overcome  possible  incompleteness  in  the  source  databases, 

the  original  infrastructure  data  have  been  harmonized  by  assigning  sector-specific 

intensity measurement to each infrastructure. Intensity values were obtained from 

EUROSTAT  and  assumed  correlated  to  the  economic  value  of  the  asset  and  its 

productivity.  We  assume  no  changes  in  exposed  infrastructures  along  the  century. 

EU  regional  investments  are  already  expressed  in  a  common  unit  (€)  and  do  not 

require additional processing. More details are reported in Chapter 4. 

99 

 
 
 
 
 
 
 
5.2.3  Sensitivity 

A web-based survey was set up amongst specialists to assess the climate sensitivity 

of critical economic infrastructures and investments. Here, sensitivity refers to what 

degree the  asset  or system is  affected when  exposed to a  climate hazard.  For each 

sector a sample of ~50 experts (vs. potential 500 respondents) was collected from 

private  companies,  authors  and  editorial  boards  of  peer-reviewed  journals  in  the 

field of climate change  and sector-specific structural engineering. Experts assigned 

anonymously a degree of sensitivity (high, moderate, low, no) to infrastructures and 

investments with respect to each climate hazard. The distribution modes retrieved 

from the survey have been combined with vulnerability information obtained from 

literature in order to derive a representative set of sensitivities (S). More details are 

described in Chapter 3. 

5.2.4  Multi-hazard risk framework 

For  each  hazard  and  the  different  infrastructure  types  considered,  every  grid  cell 

where an infrastructure is located was labelled by a set of potential risk levels (very 

high,  high,  moderate,  low,  very  low,  no).  They  express  how  much  of  the 

infrastructure type in a particular cell is subject to certain levels of risk, which are 

based on the combination of the sensitivity of the infrastructure type and the hazard 

magnitude,  as  expressed  by  a  pre-defined  risk  matrix  (M)  (Figure  5.1).  The 

infrastructure  asset  expected  to  be  annually  exposed  to  a  given  risk  level  RL  -  the 

Expected Annual Asset Exposed EAAE – is then calculated as follows: 

[5.1] 

where the sum runs over all HL magnitudes that in combination with the sensitivity 

S  lead  to  the  risk  level  RL  in  the  risk  matrix  M.  I  refers  to  the  exposed  asset 

(harmonized infrastructure layer or EU regional investment). 

100 

LLLRSHEAFEMHLLIHEAFEREAAE, 
 
 
 
   
 
 
 
 
 
 
 
Figure 5.1 Risk matrix M 

The  cumulated  EAAE  under  very  high  and  high  risk  level  is  considered  linearly 

correlated  to  the  corresponding  damages.  Note  that  this  implies  that  for  highly 

sensitive infrastructures and investments, hazard events with a 50-yr return period 

or  higher  are  considered  to  contribute  to  the  damage,  whereas  for  moderately 

sensitive  infrastructures  and  investments  only  100-year  or  more  extreme  events 

result in damages. The EAAE-EAD relations are retrieved at sector and NUTS2 level 

for the  baseline (base) and propagated for the  future  scenarios (scen) (Figure  5.2) 

based on the following equation: 

[5.2] 

The  implementation  of  Equation  5.2  is  described  in  the  following  lines  for  the 

analysis of the impacts on critical infrastructures. After that we detail the application 

for the EU regional investments.  

EAAE  refers  to  the  amount  of  asset  at  risk  for  a  given  sector  and  is  expressed 

according  to  the  sector-specific  intensity  value  as  defined  by  the  harmonization 

process. As shown in Equation 5.1, EAAE values are obtained by combining exposure 

intensity  values  with  hazard  information.  The  latter,  expressed  by  EAFE,  varies 

amongst  climate  realizations,  which  explains  the  uncertainty  bounds  shown  in 

Figure 5.2.  

101 

basebasescenscenEADEAAEEAAEEAD 
 
 
 
  
  
 
 
 
 
 
 
 
 
 
Figure 5.2 Scheme of calculation for the EAAE-EAD relations in a two hazards case 

Baseline  annual  damages  (EADbase)  are  obtained  for  each  hazard  and  sector  at 

NUTS2  level  by  the  integration  of  multiple  disaster  datasets  (EMDAT,  Munich  RE) 

and  economic  statistics  (EUROSTAT).  More  than  1100  disaster  loss  records  for 

climate-related hazards that occurred in the 1981-2010 period have been collected 

from EMDAT. Information for each disaster include: hazard type, country, year and 

loss estimate (direct damages to asset and production interruption expressed in US 

$ in the  value  of the  year of occurrence). All data  have  been converted into €2010 

using the  country Harmonized Index of Consumer Prices derived from EUROSTAT. 

The resulting overall losses are compared with the estimates provided by Munich Re 

for Europe over the same period. Munich Re recorded an overall damage that is 30% 

larger than that derived from EMDAT, which can be imputable to minor events that 

are  not  reported  in  EMDAT.  The  30%  residual  damages  were,  for  each  European 

region, disaggregated amongst hazards based on the hazard damage shares (for that 

region)  derived  from  EMDAT  statistics.  Within  each  region  the  residual  minor 

damages were further distributed amongst countries based on national GDP values. 

As  such,  baseline  damage  values  were  obtained  for  each  of  the  7  hazards  for  each 

country.  

102 

 
 
 
 
It  is  important  to  note  that  the  baseline  damages  derived  per  country  for  each 

hazard  reflect  the  total  damage  (as  reported  in  the  EMDAT  and  Munich  Re 

databases)  in  that  country  due  to  the  specific  hazard.  This  total  damage  relates  to 

impacts  in  all  sectors,  hence  not  only  to  critical  infrastructures  in  the  sectors 

considered  in  this  study.  For  example,  for  droughts  a  considerable  share  of  the 

damages typically relate to agricultural losses, which are not considered herein. To 

account for this, the hazard-specific country baseline damages have been distributed 

over sectors in each country based on the national shares of the monetary value of 

sector-specific  capital  stock  and  gross  value  added,  and  their  sensitivity  to  the 

considered hazard. The shares of the total (reported) climate damages that relate to 

critical  infrastructures  considered  in  this  work  differ  strongly  between  hazards: 

28% for coastal flooding, 90% for cold, 45% for drought, 14% for fire, 28% for flood, 

heat for 67%, and 21% for windstorms. This means that for wildfires, for example, 

86% of the  damages  relate  to other assets/sectors than the  critical  infrastructures 

considered here, such as forests and nature (~35% of fire damages), or residential 

buildings  (~30%  of  fire  damages).  For  windstorms,  inland  and  coastal  flooding, 

large  shares  (up  to  50%)  also  relate  to  residential  infrastructures  that  are  not 

included in our study.  

Finally, hazard-specific damages to  each sector in a  country were  disaggregated to 

NUTS2  level  based  on  NUTS2  GDP  shares  within  a  country.  Baseline  EAD  for  EU 

regional investments have been obtained by rescaling the EAD values retrieved for 

the  sector-specific  critical  infrastructures  based  on  the  share  of  the  EU  regional 

investments to the total capital stock and gross value added for each sector. 

Baseline  and  future  EAD  values  were  calculated  separately  for  all  climate 

experiments,  scenario  periods  and  climate  hazards.  Multi-hazard  damages  are 

obtained  by  summing  up  single-hazard  multi-model  median  EAD  values  under  the 

assumption of static vulnerability (complete post-event recovery) and independent 

hazards (no hazard interrelations). 

103 

 
 
 
 
 
 
5.3  Results 

5.3.1  Impacts to critical infrastructures 

Here  we  present  how  climatic  extremes  are  projected  to  affect  critical 

infrastructures  in  the  energy,  transport,  industry  and  social  sector  in  the  coming 

decades up to the end of the century. Impacts are estimated for EU28 as well as for 

Switzerland,  Norway  and  Iceland,  together  further  referred  to  as  EU+.  Damages 

reported  further  herein  reflect  median  ensemble  results,  are  undiscounted  and 

expressed in 2010 €  assuming no socio-economic change  in future  scenarios  (only 

account for climate change). When reporting multi-hazard damages, for wind, where 

projections  of  hazard  are  not  available  for  2020  and  2050,  damages  for  these 

periods were obtained by linearly interpolating between the baseline and the 2080s. 

Further  note  that  the  modelling  domain  of  the  flood  and  drought  analysis  do  not 

include  Cyprus  and  Malta,  where  Cyprus  is  also  not  included  in  the  coastal  flood 

domain. Hence no damages are reported for these hazards in Cyprus and Malta.  

5.3.1.1 Multi-hazard damages in Europe 

Figure  5.3a  shows  the  evolution  in  time  of  the  multi-hazard  EAD  aggregated  at 

European  level  (EU+)  for  each  economic  sector  and  the  sectors  combined,  both  in 

terms of magnitude and relative change with respect to the baseline.  Results show 

that Europe will face a significant increase in multi-hazard multi-sector EAD in the 

coming decades. Baseline damages of 3.4 billion €/year are projected to triple by the 

2020s,  multiply  six-fold  by  mid-century,  and  rise  up  to  38  billion  €/year  by  the 

2080s.  These  numbers  reflect  only  the  (combined)  damages  from  the  7  climate 

hazards  to  the  sectors  considered  in  this  study,  hence  they  do  not  reflect  the  total 

damages of these hazards to society. Based on the shares of damages that relate to 

the infrastructures considered herein to the total  (reported) climate damage (28% 

for coastal flooding, 90% for cold, 45% for drought, 14% for fire, 28% for flood, 67% 

for  heat,  and  21%  for  windstorms,  see  first  paragraph  page  102  of  Section  5.2.4), 

and  assuming  that  changes  in  the  remaining  damages  follow  the  same  trend,  total 

damages from the 7 climate hazards to society could rise from currently 12 billion 

€/year to nearly 80 billion €/year by the end of this century. We further stress that 

104 

 
 
 
the myriad of climate change impacts go far beyond those of the 7 climate hazards 

considered herein; hence, it should be kept in mind that the damages presented here 

only reflect a fraction of the potential climate change damages to society.  

Multi-hazard damages  to  the  individual sectors evidently also show strong rises in 

time, with the actual degree and pace of change depending on the sector sensitivity 

to  the  different  hazards  and  the  rate  and  magnitude  of  change  of  the  latter.  The 

strongest  increase  in  multi-hazard  damages  is  projected  for  the  energy  sector,  for 

which  the  baseline  EAD  of  0.5  billion  €/year  could  rise  to  2,  4.4,  and  8.2  billion 

€/year (or increases in EAD of 394%, 860% and 1,612%) by the 2020s, 2050s and 

2080s,  respectively.  A  comparable  trend  can  be  observed  for  the  transport  sector, 

for  which  the  baseline  EAD  of  0.8  billion  €/year  is  expected  to  reach  nearly  12 

billion €/year (increase of 1,496%) by the end of this century. For industry, which 

shows  the  largest  damages  amongst  the  sectors  considered,  current  EAD  of  1.5 

billion €/year is estimated to surpass 16 billion €/year by the 2080s, corresponding 

to  a  10-fold  increase.  For  the  social  sector,  the  rising  trend  in  damages  is  less 

pronounced, but current EAD of 0.6  billion €/year could still  more  than  double by 

the end of this century due to climate change.  

105 

 
 
 
 
Figure 5.3 Multi-hazard risk aggregated at European level (EU+) for each time period and sector of 

critical  economic  infrastructures:  a)  Expected  Annual  Damages:  Bar  length  indicates  the  ensemble 

median – also reported in numerical labels in millions – where colours reflect the relative change in 

EAD with respect to the baseline; b) distribution of damages over the 7 hazards. 

The  hazard  contribution  (in  time)  to  the  total  and  sector  damages  is  presented  in 

Figure 5.3b. Aggregated over the four sectors, current climate hazard damages relate 

mostly  to  river  floods  (44%)  and  windstorms  (27%).  The  relative  contribution  to 

the  total  damage  of  these  two  hazards  diminishes  rapidly  in  time,  whereas  the 

106 

 
 
 
 
shares  of  drought  and  heat  damages  strongly  rises  due  to  their  more  pronounced 

increase in view of climate change compared to the other hazards. Heat waves and 

droughts will likely cover more than 70% of climate hazard damages by the end of 

the  century  (vs  12%  in  the  baseline  period).  This  suggests  that  drastic  changes  in 

damage scenarios could manifest not only in terms of the magnitude of damage, but 

also in typologies of impacts. The contribution of wildfires and coastal floods is low, 

albeit  that  a  strong  increase  in  coastal  flood  damages  is  projected  in  the  coming 

century. The low contribution of coastal flood damages may relate to the fact that in 

EMDAT there is no specific entry for coastal floods, and coastal flood events can be 

reported  under  storms  or  floods.  So  likely  part  of  the  coastal  flood  damages  is 

reflected  in  the  inland  flood  and  windstorm  damages.  The  contribution  of  cold 

waves is  also low and  cold-related damages in Europe could completely disappear 

with global  warming. Hazard-contributions vary amongst  the  different sectors. For 

the  social  sector,  damages  from  windstorms  and  inland  flooding  will  remain 

important. The industry and energy sector will be most impacted by droughts, with 

56% and 67%, respectively, of the sector damages relating to droughts by the end of 

this  century.  For  the  transport  sector,  on  the  other  hand,  heatwaves  will  largely 

dominate future damages (92% of total damages by 2080s). It is important to note 

that  a  decrease  in  the  share  of  the  damage  of  a  specific  hazard  to  the  total  multi-

hazard damage does not imply that in absolute terms the damage due to this hazard 

will be lower.  

5.3.1.2 Impacts in the energy, transport, industry and social sector 

Figure  5.4  shows  baseline  and  projected  EADs  both  in  terms  of  magnitude  and 

relative  change  aggregated  at  European  level  for  each  sector  and  climatic  hazard. 

Each horizontal bar reflects a different time period, while the whiskers describe the 

inter-model  spread  (i.e.,  reflects  the  climate  uncertainty).  It  is  complemented  by 

Figure 5.5 that displays the marginal contribution of infrastructures within a sector 

to the total hazard-induced damage (EADIMC) for that sector. 

107 

 
 
 
Figure 5.4 EAD to critical infrastructures aggregated at European level (EU+) for each hazard, time period and sector. Bar length indicates the ensemble median – 

also reported in numerical labels in millions – while the whiskers correspond to the 10 and 90 percentiles of the ensemble distribution. Colours reflect the relative 

change in EAD with respect to the baseline. 

108 

 
 
 
Results  suggest  that  energy  infrastructures  will  be  increasingly  affected  by  heat 

waves and droughts, up to 2,192 and 5,515 million €/year, respectively, by the end 

of  the  century,  or  approximately  4,700%  and  3,400%  of  baseline  damages.  Fossil-

fuel  and  nuclear  power  plants  could  be  seriously  affected  by  the  decrease  in 

efficiency  due  to  higher  air  and  water  temperatures  and  periods  of  lower  water 

volumes for cooling systems (Linnerud et al., 2011; Rübbelke and Vögele, 2011; van 

Vliet  et  al.,  2012).  Hydropower  production  could  also  be  negatively  impacted  by 

more frequent low flow conditions (Lehner et al., 2005). System deterioration due to 

excessive biological growth and clog water intakes and a reduction of the structural 

integrity due to melting permafrost and drought-induced subsidence could further 

endanger  physical  infrastructures  (Cruz  and  Krausmann,  2013;  Ebinger,  2011). 

River and coastal flood impacts in the energy sector could amount up to 300 million 

€/year by the  2080s, or an increase  of 130% compared to  baseline flood damages 

(relative  increases  with  respect  to  baseline  of  ~85%  and  5,900%,  respectively,  for 

inland  and  coastal  flooding).  Impacts  on  electricity  transport  systems  will  be 

relevant  likely  due  to  short-circuiting,  power  failure  and  enhanced  corrosion 

(Brown  et  al.,  2013;  Cruz  and  Krausmann,  2013).  Structural  damages  to  energy 

production sites due to direct impacts of flood flows may be also critical (Brown et 

al.,  2013;  Chandramowli  and  Felder,  2014;  Cruz  and  Krausmann,  2013;  Ebinger, 

2011).  Windstorms  could  increasingly  endanger  energy  infrastructures,  with 

damages  reaching  162  million  €/year  by  the  2080s,  nearly  a  20%  increase  with 

respect  to  baseline  damages.  This  is  mostly  related  to  the  disruption  of 

transmission/distribution  electricity  lines  due  to  fallen  trees  or  wind  gusts  (94% 

EADIMC) (Ebinger, 2011; Sieber, 2013). Wildfire impacts on the energy sector could 

rise with 50% by the end of the century, amounting to 27 million €/year. Fires result 

in potential failures of the power system mainly due to damages to transmission and 

distribution  equipment  such  as  electricity  lines  and  gas  pipelines  (92%  EADIMC) 

(Bompard  et  al.,  2013).  Future  impacts  of  cold  waves  are  expected  to  decrease 

rapidly and they will fully disappear by 2080s. Residual damages along the twenty-

first century will prevalently disrupt the electricity transport network (93% EADIMC) 

due to ice and snow loads overhead distribution lines (Bompard et al., 2013; McColl 

et al., 2012).  

109 

 
 
Transport  infrastructures  could  be  strongly  affected  by  heat  haves  in  the  future, 

with damages showing a ~5,000% increase to amount to nearly 11 billion €/year by 

the end of the century. Roads and rails will likely be the most threatened (89% and 

11% EADIMC, respectively) due to melting of asphalt, increased rutting and softening 

of pavement and buckling of rails (Chinowsky et al., 2013; Dobney et al., 2009; Palin 

et  al.,  2013).  Drought-related  damages  in  the  transport  sector  could  rise  up  to  42 

million  €/year,  corresponding  to  6-fold  the  baseline  value,  due  to  the  reduced 

navigability  of  rivers  and  channels  (Jonkeren  et  al.,  2013,  2008,  2007).  Impacts  of 

river and coastal floods may increase up to 835 million €/year by 2080s, a doubling 

of the current damages, affecting prominently roads and rails due to the reduction of 

structural  integrity  of  surface  and  subgrade  material  deteriorated  by  the  wave 

action  and  induced-mass  movements,  scour  on  bridges  and  embankments, 

disruption of transport vehicles and facilities (Koetse and Rietveld, 2009; Suarez et 

al.,  2005;  Wright  et  al.,  2012).  Inland  waterways  could  be  increasingly  affected  by 

inland  floods  (9%  EADIMC)  due  to  a  reduced  navigability  of  rivers  and  channels 

(Koetse  and  Rietveld,  2009;  Love  et  al.,  2010),  while  port  infrastructures  are 

impacted by coastal floods (32% EADIMC) due to wave action and a deterioration of 

infrastructures  that  lack a  fouling-resistant design  against  salt water (Hallegatte et 

al., 2010). Projected damages of wind extremes on the transport sector rise by little 

over 10% to 74 million €/year and relate mainly to wind pressure or debris impact 

on  transport  facilities  and  transport  interruption  (Doll  et  al.,  2014;  Molarius  et  al., 

2013).  Damages  to  transport  systems  due  to  wildfires  could  rise  from  20  to  28 

million  €/year  and  will  affect  prevalently  roads  and  rails  (93%  and  7%  EADIMC, 

respectively) due to the deterioration of materials. Damages of cold waves, currently 

around 76 million €/year, will considerably fall with time and become negligible by 

the  end of this  century. They  affect pre-eminently roads due to bucking of asphalt, 

airport functioning due to icing of aircraft wings, and port infrastructures due to ice 

accumulation  on  vessels  and  deck,  as  well  as  the  freezing  of  sea  water  (Doll  et  al., 

2014;  Koetse  and  Rietveld,  2009;  Molarius  et  al.,  2013;  Pejovic  et  al.,  2009; 

Schweighofer, 2013). 

110 

 
 
Figure 5.5 Distribution of hazard impacts over infrastructures types per sector, calculated over 2011-2100. 

111 

 
 
 
Industry  infrastructures  could  be  strongly  affected  by  heat  waves  and  droughts  in 

the  future, with expected annual  damages rising up  to  5,197 (+4,600%) and 9,118 

(+3,300%) million  €/year by 2080s, respectively. Waste management  systems will 

be  largely  prone  to  increased  methane  production  and  potential  leachate  escape 

from brownfields and a reduction in decomposition rate leading to lower operability 

and  productivity  (Tannahill  and  Booth,  2012).  Water  management  systems  will  be 

likely  subject  to  prolonged  periods  of  water  shortage  and  increasing  freshwater 

degradation with corresponding higher costs for water and its treatment (van Vliet 

et al., 2012). River and coastal flood impacts are expected to double to 1,412 million 

€/year  for  the  industry  sector.  They  affect  predominantly  water  management 

systems due to an increasing cost for water treatment required to face the increased 

release  of  pollutants  from  flooded  brownfields  and  augmented  intrusion  of  salt 

water  (Delpla  et  al.,  2009;  Langeveld  et  al.,  2013;  Sir,  2012).  Chemical,  metal,  and 

mineral  industries  and  refinery  sites  are  also  affected  by  the  direct  contact  with 

flood water (Cozzani et al., 2010; Krausmann et al., 2011). Total windstorm damages 

are expected to slightly increase up to 501 million €/year. Wind pressure and debris 

impact  similarly  endanger  all  industry  infrastructures  (Krausmann  et  al.,  2011). 

Wildfires  impacts  will  likely  rise  to  9  million  €/year  by  2080s,  mainly  affecting 

waste management systems due to possible system failure. Cold waves mainly affect 

water management systems due to the possible disruption of water pipes vulnerable 

to icing (Whitehead et al., 2009) but results show a strongly decreasing trend in cold 

related damages to industry.  

For  the  social  sector  no  damages  are  obtained  for  heat  and  cold  waves,  as  the 

sensitivity  (derived  from  the  survey  and  literature)  of  education  and  health 

infrastructures to the considered hazards is low. Damages in the social sector for the 

other hazards show mostly similar marginal contributions for education and health 

infrastructures, with slightly larger impacts for the latter. Drought induced damages 

could rise significantly from 13 to 456 million €/year, mainly as a result of increased 

structural  damages  due  to  drought-induced  subsidence  (Corti  et  al.,  2011,  2009). 

River  and  coastal  flood  impacts  will  roughly  double  to  516  million  €/year  by  the 

2080s.  Damages  include  direct  damages  to  infrastructures  from  contact  with  flood 

water, including the reduced structural integrity of buildings due to wave action and 

112 

 
 
 
reduced  soil  stability  (Carmichael  et  al.,  2012;  Hallegatte  et  al.,  2010;  Oven  et  al., 

2012;  Radovic  et  al.,  2012).  Damages  related  to  windstorms  and  wildfires  show  a 

modest  increase  of  22%  (348  million  €/year)  and  36%  (19  million  €/year), 

respectively,  by  the  end  of  the  century.  Such  impacts  could  be  largely  related  to 

structural damages to social infrastructures due to wind pressure or debris impacts 

and fires-related failures (Stewart et al., 2011). 

The  inter-model  spreads  shown  by  whiskers  in  Figure  5.4  quantify  the  potential 

effects  of  climate  variability  on  the  resulting  uncertainty  of  EAD  estimates.  This  is 

largely  driven  by  the  influence  of  climate  variability  on  climate  hazard  indicators 

(see  Figure  2.1  in  Chapter  2  on  hazards)  and  further  modulated  by  the  spatial 

distribution of exposed infrastructures and their specific vulnerability. This results 

in  a  larger  spread  in  the  risk  scenarios  for  climate  hazards  whose  projections  are 

more uncertain, such as floods, especially in areas with many exposed assets and a 

high  sensitivity  to  the  hazard.  The  relevant  inter-model  spread  of  EAD  estimates 

reinforces  the  advantage  of an ensemble-based approach for  obtaining  statistically 

robust projections of future risk. 

5.3.1.3 Regional impacts to critical infrastructures 

The aggregated results presented above mask the strong geographical differences in 

impacts across Europe for different hazards. Regional impacts depend on the spatial 

variations  in  the  frequency  of  occurrence  and  magnitude  of  a  hazard,  the  latter 

connected in turn to regional climate conditions and changes therein, as well as on 

the  spatial  distribution  of  assets  and  regional  welfare.  Figure  5.6  displays  for  each 

hazard and the hazards combined the relative distribution of multi-sector EAD over 

European  regions  for  the  baseline  and  by  the  end  of  this  century.  The  upper  two 

plots represent region contributions based on absolute damages per region. Results 

show that in absolute terms, most of the current and future climate hazard damages 

are  largely  concentrated  in  Southern  (SEU),  Western  (WEU)  and  Central  (CEU) 

Europe  regions  (see  Figure  1.2).  This,  however,  is  in  large  part  due  to  the  areal 

extension  of  the  macro  regions  considered  herein,  with  the  Eastern  Europe  (EEU) 

113 

 
 
 
 
 
region  for  example  having  a  much  smaller  spatial  domain  compared  to  the  other 

regions. The bottom two plots in Figure 5.6 therefore represent region contributions 

based  on  damages  scaled  by  region  GDP.  They  show  that  current  multi-hazard 

damages  are  in  relative  terms  highest  in  EEU,  followed  by  SEU  and  CEU,  whereas 

WEU and the Northern Europe (NEU) region are relatively least impacted. The most 

noticeable  change  due  to  climate  change  is  the  strong  increase  in  relative  damage 

load in SEU in the coming decades. This relates mainly to the much stronger increase 

in  this  region  of  damages  from  heat  waves  and  especially  droughts  compared  to 

other  regions.  The  spatial  distribution  across  Europe  of  the  damages  of  the  other 

hazards are more stable under climate change, whereas cold-related damages tend 

to  disappear  everywhere  in  Europe.  Relative  windstorm  damages  are  and  will 

remain  highest  in  WEU,  CEU  and  NEU,  where  relative  fire  impacts  are  and  will 

remain by far the highest in SEU. For inland flooding the relative impacts are highest 

in EEU and CEU under current and future climate, whereas for coastal flooding EEA, 

NEU and SEU will show the highest relative impacts.   

The  hazard-specific  shares  at  regional  scale  mask  further  local  variations  in  risk 

patterns  and  dominant  hazards.  River  and  coastal  floods,  for  example,  will  remain 

the  most  critical  hazard  in  many  floodplains  and  coastal  stretches  of  Western, 

Central  and  Eastern  Europe,  including  the  British  Isles,  Poland,  Czech  Republic, 

Bulgaria, Romania and northern coastlines of the Iberian Peninsula. 

114 

 
 
 
 
 
 
 
 
 
 
 
 
 (a) Region contribution based on absolute region damages 
Baseline period 

(b) Region contribution based on absolute region damages 
2080s 

(c) Region contribution based on region damages scaled by GDP 
Baseline period 

(d) Region contribution based on region damages scaled by GDP 
2080s 

Figure  5.6  Distribution  of  multi-sector  EAD  by  regions  calculated  for  the  baseline  (left)  and  2080s 

(right). Upper row represents region contributions based on absolute damage per region, bottom row 

represents region contributions based on damages scaled by region GDP. 

More  detailed space-time variations  in impacts  are visualized in  the  pan-European 

maps of multi-hazard EAD for the different sectors and time windows displayed in 

Figure 5.7. Although all regions of Europe are projected to experience a progressive 

increase  in  multi-hazard  EAD,  a  prominent  spatial  gradient  of  increasing  EAD 

towards  southern  regions  of  Europe  becomes  apparent  with  time  for  all  sectors 

considered.  The  trend  first  manifests  mainly  in  the  Iberian  Peninsula,  south  of 

France,  Italy,  Slovenia  and  Croatia,  to  expand  towards  the  adjacent  regions  north, 

east  and  west  as  time  proceeds.  The  most  southern  regions  of  Europe  will 

progressively  be  much  larger  affected by  future  climate extremes  compared to  the 

rest  of  Europe.  This  is  exemplified  by  the  strong  increase  in  relative  and  absolute 

damage shares of SEU to the total European damages during this century (see Figure 

5.6).  A  large  part  of  the  north-south  gradient  in  damages  relates  to  droughts.  This 

hazard  will  strongly  intensify  in  southern  parts  of  Europe,  whereas  in  northern 

regions  droughts  will  become  less  severe  and  occur  less  frequent  with  climate 

115 

0%20%40%60%80%100%HEATCOLDDROUGHTFIRECOASTFLOODWINDMULTISEUWEUCEUEEUNEU0%20%40%60%80%100%HEATCOLDDROUGHTFIRECOASTFLOODWINDMULTISEUWEUCEUEEUNEU0%20%40%60%80%100%HEATCOLDDROUGHTFIRECOASTFLOODWINDMULTISEUWEUCEUEEUNEU0%20%40%60%80%100%HEATCOLDDROUGHTFIRECOASTFLOODWINDMULTISEUWEUCEUEEUNEU 
 
 
 
 
 
 
 
change. As such, for sectors sensitive to this hazard, namely the energy, industry and 

to a lesser extent the social sector, drought-induced damages will strongly increase 

in  the  south  and  decrease  in  the  north  of  Europe.  For  the  transport  sector  only 

inland  navigation  is  sensitive  to  droughts.  As  this  mode  of  transport  is  hardly 

utilized in southern and northern parts of Europe, the north-south damage gradient 

is somewhat less pronounced for the transport sector. Heatwaves also contribute to 

the north-south gradient but to a lesser extent than droughts, as heatwave damages 

are projected to rise significantly all over Europe.   

Figure 5.7 Multi-hazard  risk scenarios  for critical infrastructures in the energy, transport, industry 

and social sector.  

116 

 
 
 
 
 
Multi-hazard and multi-sector EAD values normalized by country GDP are shown in 

Table  5.2  for  European  countries.  Higher  shares  of  GDP  at  risk  indicate  larger 

impacts  on  the  country  overall  economy  and  a  higher  potential  of  cross-sectorial 

shocks.  Currently  (baseline  period),  for  EU+  the  share  of  GDP  at  risk  amounts  to 

0.03%, where for most countries less than 0.06% of GDP is at risk. Only for Slovenia 

(0.18%) and Croatia (0.15%) values are higher. Note that these figures do not reflect 

total potential climate damages to the economy, rather they represent the damages 

induced by the 7 climate hazards to critical infrastructures in the energy, transport, 

industry  and  social  sector.  For  EU+  the  share  of  GDP  at  risk  rises  progressively  to 

0.28% by the end of century. There are, however, strong variations in the shares of 

GDP at risk within Europe, confirming earlier observations of the spread of climate 

hazards damages across Europe. Countries in northern Europe (with the exception 

of Denmark), the Benelux, the British Isles, as well as Poland and the Czech Republic 

show the lowest share of GDP at risk, with damages remaining below 0.1% of GDP 

up to the end of the century. Impacts by the end of the century remain below 0.2% of 

GDP  in  Germany  (0.10%),  Denmark  (0.11%),  Austria  (0.14%),  Hungary  (0.15%), 

Lithuania  (0.15%)  and  Switzerland  (0.18%).  Also  Latvia  (0.2%),  France  (0.23%), 

Estonia  (0.25%)  and  Slovakia  (0.27%)  have  damage  shares  below  the  European 

average. Southern and south-eastern countries will be most impacted. For Bulgaria 

(0.40%), Romania (0.45%) and Italy (0.49%) damages remain  below 0.5% of GDP, 

whereas for Portugal (0.77%) and Spain (0.87%) damages could reach nearly 1% of 

GDP  by  the  2080s.  Most  alarming  are  projections  for  Slovenia  and  Croatia,  where 

2.15% and 3.33% of GDP could be annually at risk by the 2080s.  For Cyprus three 

(coastal  and  inland  floods,  and  droughts)  hazards  are  not  modelled  and  for  Malta 

two  (floods  and  droughts),  hence  no  damages  for  these  hazards  are  reported  for 

these countries. However, based on regional patterns of impacts it can be expected 

that the damages in these countries could represent a considerably larger share of 

the GDP as those reported in Table 5.2.  

117 

 
 
 
 
 
Table 5.2 Percentage of GDP at risk expressed by multi-hazard damage normalized by country GDP. 

*Note that for Cyprus (coastal and inland floods, and droughts) and Malta (floods and droughts) some 

hazards are not modelled hence no damages are reported for these countries.   

2050s 

0.07% 

0.03% 

0.17% 

0.11% 

0.03% 

0.06% 

0.06% 

0.07% 

0.20% 

0.45% 

0.04% 

0.13% 

0.51% 

1.13% 

0.10% 

0.03% 

0.05% 

0.28% 

0.14% 

0.02% 

0.20% 

0.12% 

0.02% 

0.02% 

0.06% 

0.45% 

0.21% 

0.04% 

0.69% 

0.11% 

0.03% 

0.15% 

2080s 

0.14% 

0.06% 

0.40% 

0.18% 

0.04% 

0.08% 

0.10% 

0.11% 

0.25% 

0.87% 

0.06% 

0.23% 

0.68% 

3.33% 

0.15% 

0.03% 

0.07% 

0.49% 

0.15% 

0.04% 

0.20% 

0.14% 

0.03% 

0.03% 

0.06% 

0.77% 

0.45% 

0.06% 

2.15% 

0.27% 

0.05% 

0.28% 

Country 

baseline 

AT 

BE 

BG 

CH 

CY* 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HR 

HU 

IE 

IS 

IT 

LT 

LU 

LV 

MT* 

NL 

NO 

PL 

PT 

RO 

SE 

SI 

SK 

UK 

EU+ 

0.02% 

0.02% 

0.04% 

0.03% 

0.00% 

0.05% 

0.02% 

0.03% 

0.04% 

0.04% 

0.01% 

0.02% 

0.02% 

0.15% 

0.05% 

0.01% 

0.01% 

0.03% 

0.03% 

0.01% 

0.05% 

0.00% 

0.01% 

0.01% 

0.06% 

0.03% 

0.08% 

0.01% 

0.18% 

0.03% 

0.02% 

0.03% 

2020s 

0.03% 

0.02% 

0.11% 

0.04% 

0.00% 

0.06% 

0.03% 

0.04% 

0.07% 

0.15% 

0.02% 

0.05% 

0.07% 

0.31% 

0.04% 

0.01% 

0.01% 

0.12% 

0.07% 

0.02% 

0.09% 

0.11% 

0.01% 

0.01% 

0.06% 

0.15% 

0.15% 

0.02% 

0.30% 

0.03% 

0.02% 

0.08% 

118 

 
 
 
 
5.3.2  Impacts on EU investments of 2007-2013 programming period 

Here  we  present  how  the  7  climate  hazards  are  projected  to  impact  EU  regional 

investments  in  the  transport,  energy,  environment  and  tourism,  ICT,  and  social 

sector in the coming decades up to the end of the century. Investments relate to the 

programming  period  2007-2013  in  the  EU27.  Damages  reflect  median  ensemble 

results,  are  undiscounted  and  expressed  in  2010  €  assuming.  Note  again  that 

impacts in Cyprus and Malta may be underestimated as they are not included in all 

model domains of the hazard analyses.  

5.3.2.1 Multi-hazard multi-sector impacts for EU27  

Figure 5.8 shows the evolution in time of overall climate risk of the EU27 Structural 

Investments for 2007-2013, with a breakdown of the total climate risk in (a) multi-

sector EAD per hazard and (b) multi-hazard EAD per sector. The EU27 baseline EAD 

amounts to 146 million €/year, or 0.04% of the total EU regional investments of 342 

billion € for the period 2007-2013. Damages rise rapidly due to climate change and 

reach 556 million €/year (382% baseline EAD) or 0.16% of total investments by the 

2020s. By the 2050s damages further climb to 1,109 million €/year (761% baseline 

EAD) or 0.32% of total investments, and by the end of this century the annual risk 

amounts  to  1,703  million  €/year  (1,168%  baseline  EAD)  or  0.49%  of  total 

investments.  

The  breakdown  per  hazard  (Figure  5.8a)  shows  that  currently  floods  is  the  most 

damaging hazard, accounting for about half (51%) of total hazard damages, followed 

by drought (26%) and heat waves (10%). Cold, fire and wind each have a share of 

4%,  whereas  coastal  floods  contribute  to  the  total  damages  for  0.5%.  Drought 

damages  increase  significantly,  from  38  million  €/year  now  to  888  million  €/year 

by the 2080s (2,315% baseline EAD), and form the largest share of future damages 

(52%  by  the  end  of  the  century).  The  strongest  relative  increase  in  damages, 

however,  is  projected  for  heatwaves  and  coastal  flooding  (increases  both  around 

+4,500%).  As  a  result  heatwaves  will  become  the  second  damaging  hazard  to  EU 

structural  investments  (40%  of  total  damages  by  2080s).  Damages  due  to  cold 

119 

 
 
 
 
waves  will  evanesce  in  the  coming  decades,  whereas  damages  due  to  wind,  floods 

and fires show more moderate increases, with absolute damages rising this century 

by 10%, 30% and 50%, respectively. Their relative contribution to the total damages 

logically reduces due to the pronounced increase in heatwave, drought and coastal 

flooding damages.  

Figure  5.8  Overall  climate  risk  of  2007-2013  EU27  Structural  Investments:  breakdown  of  total 

climate risk in (a) multi-sector EAD per hazard, and (b) multi-hazard EAD per sector. 

The  breakdown  of  damages  per  sector  (Figure  5.8b)  shows  that  currently  48%  of 

hazard  impacts  relate  to  the  transport  sector,  and  37%  to  the  environment  and 

tourism  sector.  Annual  damages  in  the  transport  sector  rise  by  722%  from  70 

million  €/year  in  the  baseline  to  573  million  €/year  by  the  2080s.  This  value 

corresponds  to  0.77%  of  total  transport  allocations.  For  the  environment  and 

tourism sector damages increase at a rate double as high (+1619%) from 55 million 

€/year now to 940 million €/year by the end of the century. Hence, this sector will 

under future climate account for more than half of the total hazard damages (55% vs 

34% for the transport sector) to EU investments because of its larger sensitivity to 

the dominating hazards (droughts and heatwaves). The damage level reached by the 

120 

02505007501,0001,2501,5001,750baseline2020s2050s2080sOverall climate risk (million €/year)(a)heatcolddroughtfirefloodcoastwind02505007501,0001,2501,5001,750baseline2020s2050s2080sOverall climate risk (million €/year)(b)transportenergyenv&tourICTsocial 
 
 
 
 
 
2080s represents 1.52% of the total EU regional investments in this sector. Damages 

in  the  energy  sector  increase  by  more  than  tenfold  (+1,077%),  with  energy  EAD 

rising  from  13  million  €/year  in  the  baseline  to  157  million  €/year  by  the  2080s. 

The latter corresponds to 1.49% of EU regional energy allocations. The rise for the 

energy  sector  is  similar  as  that  for  the  overall  losses,  and  as  such  the  share  of  the 

energy damages to the total damages (around 9%) remains fairly constant in time. 

Impacts  in  the  ICT  and  social  sector  are  smaller,  with  an  EAD  of  1.9  and  6  million 

€/year,  respectively,  for  the  baseline  period.  They  also  show  less  pronounced 

increases  compared  to  the  other  sectors.  Where  damages  to  ICT  investments  are 

expected to rise by 160% up to 5 million €/year by the 2080s, an increase of 363% 

up  to  28  million  €/year  is  projected  by  that  period  for  the  social  sector.  The 

damages  for  these  sectors  also  represent  a  smaller  share  of  the  ICT  and  social 

investments, with impacts by the end of the century totaling 0.03% and 0.17% of EU 

allocations for 2007-2013 in these sectors.  

Note that the rates of change in damage per sector can deviate from those observed 

for  the  critical  infrastructures.  This  is  because  EU  investments  may  be  aimed  at 

different  priority  themes  than  those  considered  in  the  spatial  layers  of  critical 

infrastructures. For the energy sector, for example, a large share of the damages for 

the critical infrastructures relates to fossil-fuel and nuclear power plants (see Figure 

5.5), to which EU regional investments are not targeted.   

5.3.2.2 Regional impacts on CPF investments 

The  spatial  distribution  of  the  absolute  impacts  across  the  EU27  is  presented  in 

Figure  5.9.  The  highest  absolute  damages  to  EU  regional  investments  under  the 

2007-2013 period are projected for the Iberian Peninsula, Southern Italy, the EU27 

countries in the Balkan Peninsula, and the Baltic States. In these regions damages to 

investments could amount to tens of millions on an annual basis, locally sometimes 

more than hundred million (e.g., regions of Andalucía and Galicia), by the end of this 

year. This  spatial pattern  is  largely  driven  by  the  large  allocation of funds to these 

regions (see Figure 4.2) and further modulated by the changes in hazard patterns.  

121 

 
 
 
 
Figure  5.9  Spatial  distribution  of  multi-hazard  multi-sector  EAD  to  2007-2013  EU27  regional 

investments: (a) baseline, (b) 2020s, (c) 2050s and (d) 2080s.  

122 

 
 
 
 
 
 
 
 
 
 
Impacts  expressed  as  a  share  of  the  investments  are  shown  in  Figure  5.10  for  the 

2080s. The multi-hazard multi-sector expected annual losses (Figure 5.10a), which 

for the whole EU27 correspond to 0.49% of total investments (see section 5.3.2.1), 

can reach up to 3% in isolated regions of the Iberian and Balkan Peninsulas. Hence, 

in these regions total damages accumulated over a 30-year period would nearly sum 

up  to  the  total  investments.  In  most  of  north  (apart  from  the  Baltic  States)  and 

central  (in  terms  of  latitude,  from  British  Isles  to  Poland)  Europe,  total  expected 

annual damages remain below 0.1% of total investments.  

When  looking  at  sector  level,  relative  impacts  can  even  be  substantially  higher  in 

some  parts  of  Europe.  For  the  transport  sector  (Figure  5.10b),  by  the  end  of  the 

century  expected  annual  losses  to  investments  rise  to  about  5%  of  the  transport 

investments  for  Latvia,  Romania,  Bulgaria  and  regions  of  Spain.  At  this  rate  the 

cumulated losses to the investment could equal the size of the investment after 20 

years.  For  the  energy  (Figure  5.10c)  and  the  environment  and  tourism  (Figure 

5.10d)  sectors,  annual  loss  shares  (with  respect  to  sector  investments)  could  go 

even up to 10% and higher, or an expected yearly loss of more than 1/10 of the total 

sector  investment,  for  regions  in  the  southwest  and  southeast  of  the  EU.  For  the 

social and ICT sectors, expected annual losses at regional scale remain mostly below 

1% and 0.5% of sector investments, respectively.  

123 

 
 
 
 
 
 
 
 
 
 
Figure  5.10  Spatial  distribution  by  2080s  of  multi-hazard  multi-sector  EAD  to  2007-2013  EU27 

regional investments expressed as a share of sector investments: (a) multi-sector, (b) transport, (c) 

energy, (d) environment and tourism.  

5.4  Main limitations and knowledge gaps  

We  recognize  that  our  risk  estimates  are  subject  to  uncertainty  due  to 

methodological  and  data  aspects  that  should  be  considered  in  addition  to  those 

described  for  the  climate  hazard,  sensitivity  and  exposure  components,  already 

discussed in Chapter 2, 3 and 4, respectively.  

124 

 
 
 
 
 
Our multi-hazard risk framework is built on the propagation of baseline damages to 

future scenarios according to variations in the frequency of extreme events and the 

spatial distribution of exposed assets/investments. At present, our understanding of 

long-term climate risks is limited by the lack of in-depth knowledge on the impacts 

of  climate  hazards  due  to  the  absence  of  harmonised  loss  data  recording.  Baseline 

damages  for  this  project  are  retrieved  from  hazard  loss  databases  (EMDAT  and 

MunichRe).  As  the  data  that  populate  these  databases  originate  from  different 

sources and are collected by multiple actors, their loss figures should be viewed in 

light of their potential biases (Gall et al., 2009). The databases may under-represent 

certain hazard types depending on their purpose and the audience, as well as on the 

type  of  impacts  and  how  they  are  perceived  (e.g.,  flood  impacts  are  much  more 

visible than heat impacts). A prominent example is the notoriously underestimation 

of  droughts  (Svoboda  et  al.,  2002),  being  an  insidious,  slow-onset  climate  hazard 

inducing  a  wide  range  of  impacts  that  affect  many  sectors  of  the  economy.  Also, 

coastal flooding events may be reported under floods or storms in general, therefore 

leading  to  an  underestimation  of  coastal  flood  impacts.  Furthermore,  different 

threshold criteria used across EMDAT and MunichRe for data recording may result 

in disparities in collected disaster information (e.g., exclusion of small-scale events 

in  EMDAT).  Changes  in  political  geography  may  affect  how  hazard  loss  data  are 

reproduced  in  space  and  time.  Loss  data  are  reported  in  EMDAT  and  MunichRe 

according to the political geography at the time of the event. Boundary changes that 

have occurred in the last three decades (e.g., Germany, Balkan countries) may have 

affected the spatial accuracy of baseline loss data.  

Disaster  risk  databases  mostly  record  direct  damages  at  the  time  of  occurrence  or 

direct  aftermath  of  the  hazard  event.  Possible  indirect,  inter-sectorial  effects  and 

intangible  damages,  which  may  considerably  amplify  the  impacts  of  hazards,  are 

typically  not  considered  in  damage  recording.  This  may  lead  to  potential 

underestimation of the impacts of climate extremes on the investigated sectors. The 

estimates  of  baseline  and  future  climate  risk  impacts  reported  herein  are  fully 

conditional on the reported country damages in EMDAT and the aggregated Munich 

Re  damage  figures.  Hence,  any  deviations  of  the  reported  damages  from  the  true 

impacts are inherently translated into our damage estimates.  

125 

 
 
 
The  national  recorded  hazard  damages retrieved from the  disaster  databases have 

been disaggregated across sectors and NUTS2 regions based on the regional societal 

and economic structure as represented by EUROSTAT statistics and the sensitivities 

to  the  specific  hazards  derived  from  the  survey  and  literature.  The  assumptions 

beyond  the  proposed  disaggregation  of  losses  represent  potential  sources  of 

uncertainty  resulting  from  the  incomplete  knowledge  about  the  true  sectorial-

specific  impacts  and  their  spatialization  (Meyer  et  al.,  2013).  Although  reasonable 

assumptions  have  been  formulated,  such  epistemic  uncertainties  are  difficult  to 

assess.  

In  this  study,  we  assume  independent  hazards  and  static  vulnerability.  However, 

hazards  may  induce  or  reinforce  other  hazards,  they  may  overlap  spatially  and 

temporally, as observed in Chapter 2, influencing not only the overall hazard level, 

but also the vulnerability of elements at risk through possible hazard interrelations 

or  cascade  effects  (Kappes  et  al.,  2012).  The  fragility  of  a  certain  asset  could  be 

largely  intensified  when  subject  to  simultaneous  or  cascade  impacts  of  several 

hazards (Lee and Rosowsky, 2006). While we recognize the relevance of the effects 

of overlapping hazards on the vulnerability of elements at risks, very few studies on 

this  topic  are  available  in  literature  (Kappes  et  al.,  2012).  The  scarcity  of 

observational  relations  linking  variations  in  multi-hazard  impacts  on  vulnerability 

does not allow a reliable integration of such effects in large-scale predictive systems.  

The  way  observed  damages  are  integrated  in  the  methodology  is  by  assuming  for 

the baseline that they relate to events that happen every 50-years or less frequent 

for  highly  sensitive  infrastructures  and  every  100-years  or  less  frequent  for 

infrastructures with medium sensitivity. For future time windows, we then translate 

changes in this high-end tail of the frequency distribution to project future damages. 

In principle, it is possible that for certain hazards more frequent (i.e., less extreme) 

events induce damages, or that impacts are avoided or minimized for more extreme 

events (e.g., very high flood protection standards in the  Netherlands). This  implies 

that  we  assume  that  the  changes  in  the  part  of  the  frequency  distribution  that  we 

126 

 
 
 
 
 
consider  to  be  linked  with  the  damages  are  representative  for  the  true  changes  in 

the frequency of damaging events.    

Climate-change  impact  uncertainties  are  quantified  in  this  study  solely  in  terms  of 

the spread induced by the climate-model projections, and do not account for all the 

sources of uncertainty detailed above. We recognize that the impact-model spread of 

our  damage  projections  can  be  comparable  to,  or  even  larger  than,  the  spread 

introduced  by  the  different  climate  models  considered  (Piontek  et  al.,  2014). 

However,  whereas  for  (some)  climate  hazards  (in  certain  regions)  there  exists 

uncertainty in both the direction and magnitude of change, uncertainty in the impact 

assessment only affects the magnitude of the impact and of the change therein.  

127 

 
 
 
 
   
 
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6  Climate resilient critical infrastructures and investments 

6.0  Key messages 

General 

  A  wide  range  of  adaptation  strategies  exists,  ranging  from  hard  (capital-

intensive,  large,  complex,  inflexible  technology  and  engineering)  to  soft 

(prioritization  of  natural  capital,  community  control,  simplicity  and 

appropriateness) measures. 

 

In view of uncertain climate change, preference for no- or low-regret measures, 

the inclusion of a safety margin and reversible strategies. 

  Quantitative  assessments  for  appraising  costs  and  effectiveness  are  limited, 

comprehensive frameworks largely absent.  

  There  is  large  uncertainty  about  the  costs  and  appropriateness  of  various 

measures and additional studies are needed to fill the existing gaps and explore 

costs  and  appropriateness  of  measures,  the  optimal  timing  of  action  and  the 

sensitivity  of  results  to  assumptions  and  uncertainties  about  future  extreme 

events and costing methods.  

Critical infrastructures 

 

Indicative  estimates  show  that  for  EU+  (EU28  +  Switzerland,  Norway  and 

Iceland)  the  total  accumulated  benefits  (or  avoided  damages)  amount  to  100 

billion  €  when  adapting  critical  infrastructures  against  short  term  climate 

changes  (up  to  2040),  with  an  accumulated  cost  of  39  billion  €.  Costs  incurred 

now  could  amount  to  12  billion  €,  or  0.1%  of  EU+  2010  GDP,  plus  a  yearly 

operational and maintenance (O&M) cost of nearly 1 billion  €. Expected annual 

benefits of these investments would amount to 3.3 billion €. 

  The  investments  for  adaptation  required  to  face  changes  in  climate  also  in  the 

medium term (including the 2050s) would amount to an upfront capital cost of 

133 

 
 
 
54 billion €, or 0.4% of EU+ 2010 GDP, and an annual O&M cost of 2.1 billion €, 

with expected annual benefits growing to 11.9 billion € by the 2050s. 

  For  making  infrastructures  climate  resilient  up  to  the  end  of  the  century,  the 

total cost rises to 461 billion €, of which 138 billion € capital cost to be incurred 

now  (about  1%  of  EU+  2010  GDP)  and  O&M  costs  of  nearly  3.6  billion  €/year. 

This would yield total accumulated benefits or avoided damages of 1,152 billion 

€ from now up to the end of this century, with expected annual benefits reaching 

23 billion € by the 2080s.   

  Adaptation  costs  will  not  fall  equally  across  Europe.  Some  countries  in  Europe 

will  potentially  have  to  invest  a  significant  share  of  their  current  GDP  to  abate 

the  future  impacts  from  climate  hazards  on  critical  infrastructures  –  notably 

Croatia, Slovenia, Spain, Portugal and Greece.  

EU Regional Investments 

  The  cost  of  making  CPF  investments  resilient  against  climate  up  to  2040  may 

amount  to  1.1%  of  total  CPF  allocations,  which  grows  to  6.2%  and  10.4%  for 

medium to long term climate changes, respectively.  

  There  are  considerable  variations  in  adaptation  requirements  for  different 

sectors, both in terms of overall magnitude and distribution across regions.  

  The sectors with the highest relative adaptation costs are the transport, energy, 

and environment/tourism sectors. For these sectors, several regions in southern 

and  south-eastern  Europe,  but  also  some  in  France,  can  face  short-term 

adaptation costs up to 10% of the sector investments, in localized regions in the 

Iberian and Balkan Peninsulas event up to 25% and more.  

134 

 
 
 
 
6.1  Introduction 

As  critical  infrastructures  and  key  economic  assets  typically  have  long  operational 

lifetimes  they  are  subject  not  only  to  the  existing  climate  but  also  to  climate 

variations over their life span. The hazard and risk parts of this report have shown 

that  much  of  the  existing  infrastructure  and  planned  future  investments  will  be 

increasingly put at risk by extreme climate events in many regions of Europe due to 

climate change. In order to mitigate impacts of climate change in the coming decades 

it is therefore needed to increase the resilience of both existing infrastructures and 

new investments to more frequent and intense climate hazards.  

The need to adapt for lowering the risks posed by unavoidable climate changes has 

in recent years received increased attention in the scientific and policy debate (e.g., 

EU  Strategy  on  Adaptation).  Adaptation  to  climate  change  can  be  a  challenging 

activity  as  it  must  consider  the  full  context  in  which  adaptation  takes  place.  Apart 

from the complexity and uncertainty of the factors that define (future) climate risks 

and  vulnerability,  adaptation  requires  technical  know-how,  substantial  funding, 

adequate institutional structures and political will that can be constrained by limited 

physical,  financial,  institutional,  political,  cultural,  and  individual  capacities  to  deal 

with climate-related changes and hazards.  

It  is  not  the  goal  of  this  report  to  provide  an  extensive  review  of  all  possible 

adaptation  options  for  key  infrastructures  in  the  different  sectors,  which  are 

bountiful. Rather, the aim of the CCMFF project is to take stock at a minimum of the 

available  evidence  on  the  costs  of  climate  proofing  critical  infrastructures  and 

investments  and  to  provide  a  methodology  and  first  assessment  of  the  additional 

investments needed to climate proof investments in different regions of Europe. 

135 

 
 
 
 
 
6.2  Climate hazard mitigation measures 

Adaptation  options  vary  depending  on  the  type  of  hazard  and  infrastructure.  In 

general, they can be grouped into nine categories as presented in Table 6.1 (Bouwer 

et al., 2014), although that other ways exist to classify the large variety of options.  

Table 6.1 Comprehensive Categories of climate hazard mitigation measures (from Bouwer 

et al., 2014).  

The  first  category  includes  risk  management  planning,  land-use  planning,  and 

climate adaptation aimed at reducing exposure and vulnerability. Land use planning 

and  management  is  increasingly  recognized  to  play  a  crucial  role  in  climate  risk 

reduction (Serrao-Neumann et al., 2015). Categories 2 (hazard modification) and 3 

(infrastructure)  target  a  reduction  of  the  hazard  with  measures  that  are  typically 

taken  at  national  or  regional  level  (e.g.,  flood  protection  measures  at  catchment 

scale).  Category  4  (mitigation  measures  stricto  sensu)  comprises  small-scale 

measures  applied at  the  local  (community,  company  or  household)  level  to  reduce 

vulnerability.  Communication  measures  (Category  5)  in  advance  of  an  event  can 

take place at all administrative levels. This includes, for example, flood risk maps by 

EU  member  states  in  compliance  with  the  EU  Flood  Directive  (EC,  2007),  but  also 

local  information  campaigns.  Monitoring  and  early  warning  systems  (Category  6) 

are  typically  operated  by  a  central  government  organization  that  coordinates  all 

related activities, for instance, for a coast or river basin. Supranational systems also 

136 

 
 
 
 
 
exist,  like  the  European  Drought  Observatory  and  European  Flood  Awareness 

System  that  have  been  developed  at  the  JRC.  Whereas  the  measures  under 

Categories 1 to 6 can be considered pre-event risk reduction measures, emergency 

response  and  evacuation  (Category  7)  take  place  during  and  in  the  immediate 

aftermath  of  the  event.  Finally,  financial  incentives  (Category  8)  can  be  used  as 

stimuli  for  pre-event  risk-reducing  measures,  whereas  risk  transfer  measures 

(Category 9) allow post-event compensation of losses (adapted from Bouwer et al., 

2014). 

For  critical  infrastructures  and  key  economic  investments  in  specific,  several 

documents provide overviews of possible risk reduction options for the energy and 

transport sector. For the energy sector, Table 10-1 and Table 10-2 of Chapter 10 of 

WGIIAR5  (Arent  et  al.,  2014)  describe  the  range  of  adaptation  options  for  energy 

supply, and pipelines and the  electricity grid,  respectively. In  general, more  robust 

design specifications will allow structures to withstand more extreme conditions. In 

some circumstances, it may also be necessary to consider relocating or retrofitting 

extremely vulnerable existing infrastructure. Furthermore, decentralized generation 

systems (renewables) may reduce the need for large facilities in high-risk areas. For 

the  transport  sector,  overviews  of  adaptation  challenges  and  options  across 

transport  modes  are,  for  example,  provided  in  “Adaptation  of  transport  to  climate 

change  in  Europe”  (EEA,  2014)  and  Doll  et  al.  (2011).  Adaptation  options  for 

transport  includes  engineering  (structural)  measures  (subsurface  conditions, 

material  specifications,  cross  section  and  standard  dimensions,  drainage  and 

erosion,  and  protective  engineering  structures),  as  well  as  non-engineering 

strategies  (maintenance  planning  and  early  warning,  alignment,  master  planning 

and  land  use  planning,  and  environmental  management).  For  other  critical 

infrastructures (e.g., industry and social infrastructures) information is more scarce 

and fragmented.  

In general, especially in view of uncertain climate change, it is suggested in literature 

that adaptation options preferably have the following characteristics: 

137 

 
 
 
 
  No or low regret strategies that yield development benefits regardless of the 

nature and extent of changes in climate. 

 

Include a safety margin to increase the robustness. 

  Should be reversible rather than irreversible. 

  Take  into  account  synergies  and  conflicts  between  different  options  and 

between adaptation and mitigation. 

  Soft  adaptation  measure  are  often  more  flexible  and  better  able  to  manage 

uncertainty than hard adaptation strategies. 

6.3  Costs and effectiveness of adapting to climate change 

There  are  many  approaches  to  arriving  at  a  priority  setting  for  alternative 

adaptation  options.  To  make  optimal  use  of  limited  resources  for  investments  in 

climate impact mitigation, information about the cost and the appropriateness and 

effectiveness of a range of adaptation options is needed.  

Whereas  many  studies  have  described  qualitatively  the  wide  range  of  potential 

adaptation  options, the literature  on  the  costing of these measures  and their value 

for  reducing  impacts  of  natural  hazards  (to  critical  infrastructures)  is  limited  and 

fragmented.  Comprehensive  frameworks  for  addressing  costs  and  benefits  of 

adaptation options are largely absent due to several reasons. Adaptation measures 

are  very  diverse  and  usually  take  place  at  the  local  level  with  diverse  regulatory, 

legal  and  governance  settings.  These  determine  the  type  of  measures  chosen  and 

level of investments as well as the scale at which the measures are implemented and 

the  associating  costing  framework  (Bouwer  et  al.,  2014).  Costs  and  benefits  in 

various studies are difficult to compare as they are represented in a variety of ways. 

They can cover (a combination of) direct, indirect and intangible aspects, and can be 

presented  on  an  annual  basis  or  accumulated  over  time,  discounted  or 

undiscounted, and in absolute terms or as share of GPD. Many studies on adaptation 

fail  to  provide  a  quantification  of  the  benefits,  rather  instead  assume  that  certain 

types  of  impacts  should  be  avoided.  Benefits  also  often  take  place  long  after  costs 

138 

 
 
 
 
are made and are more unpredictable as they depend on the possible occurrence of 

extreme events over the lifetime of the infrastructure.  

6.3.1  Indicative costs of adapting infrastructures to climate change 

The  decision  on  the  implementation  of  the  optimal  measure  in  a  specific  setting 

would require a detailed analysis of the costs and efficiency of a range of measures, 

as well as the consideration of non-monetary and non-market consequences, and of 

the  equity  impacts  of  alternative  actions  (Chambwera  et  al.,  2014).  A  number  of 

these items pose challenges for measurement and certainly for monetization. Within 

the  current  modelling  framework  it  is  not  possible  to  undertake  a  detailed  multi-

metric evaluation of potential adaptation measures across Europe. Hence, we do not 

provide  a  formal  cost-benefit  or  multi-criteria  analysis  but  instead  surveyed  the 

literature on cost and benefits of adaptation options related to infrastructures. Few 

studies  have  reported  figures  about  benefits  and costs of risk mitigation  strategies 

across  Europe,  covering  different  regions,  types  of  hazards,  infrastructures, 

measures,  accounting  and  appraisal  approaches.  These  studies  indicate  that  the 

uncertainty about the costs and benefits is large, but that many adaptation options 

could have high benefits when compared to costs, although capital investments can 

be large.  

The studies reviewed (see Table 6.2) provided a range of benefit to cost ratio (BCR) 

between 9 and 0.4, with an average value of  2.5. These BCR values have been used 

herein to provide indicative estimates (order of magnitude) of the potential cost of 

adaptation,  similar  as  done  by  Rojas  et  al.  (2013)  for  appraising  flood  adaptation 

costs.  Ideally,  adaptation  would  offset  the  increased  risk  from  climate  change, 

reducing future risk levels to equal current ones. In that case, the direct benefits of 

adaptation (apart from potential co-benefits such as enhancing development) equal 

the potential avoided adverse impacts, i.e., the increase in damages from the present 

to  the  future  in  the  case  of  no  adaptation.  However,  the  theory  and  the  evidence 

suggest  that adaptation cannot  generally overcome  all climate  change  impacts and 

that  some  adaptation  may  not  be  physically  possible  or  economically  worthwhile 

(Parry  et  al.,  2009).  We  assume  that  the  unavoidable  or  residual  damages  from 

139 

 
 
 
 
climate change that take place even with adaptation equal 25% of the increased risk, 

hence  the  benefits  of  adaptation  represent  75%  of  the  potential  avoided  damages. 

The latter are obtained as a difference in the damages to infrastructures between the 

future time period and the present (baseline).  

To  derive  indicative  costs  of  adaptation,  the  literature-based  BCR  values  were 

combined  with  the  projected  benefits.  Table  6.3  shows  the  indicative  estimates  of 

adaptation  costs  per  country  and  for  the  EU+  (EU28  +  Switzerland,  Norway  and 

Iceland) for the multi-hazard multi-sector analysis. Costs are provided for the three 

time  periods  and  should  be  interpreted  as  follows.  For  the  2020s  (period  2011-

2040), the values reflect adaptation costs assuming that adaptation would only take 

into  account  the  projected  changes  in  hazard  in  the  short  term  (up  to  2040,  or 

adapting  to  short  term  changes).  Cost  estimates  for  the  2050s  (2041-2070)  are 

based  on  expected  annual  damages  from  now  up  to  2070,  hence  assume  that 

adaptation  considers  also  medium  term  projected  climate  changes.  For  the  last 

period  (2080s),  adaptation  costs  take  into  consideration  the  changes  in  climate 

hazard and consequent damages up to the end of the century. Note that all estimates 

reported assume no discounting and no socio-economic changes. 

The total accumulated cost of adaptation (columns 2-4 of Table 6.3) reflects the total 

amount that should be invested in adaptation to achieve a 75% reduction in climate 

related  risk  (whereas  the  residual  25%  is  assumed  as  unavoidable  impacts  of 

climate  change).  For  the  EU+,  the  total  accumulated  cost  for  short-term  climate 

change  adaptation  amounts  to  39  billion  €,  which  grows  to  461  billion  €  when 

proofing infrastructures against long-term climate change. These costs will result in 

total accumulated benefits (or avoided damages) of 98 billion € by 2040 and 1,152 

billion € by the end of this century.   

140 

 
 
 
 
 
 
Table 6.2 Benefit to cost ratios (BCR) of adaptation measures reported in literature.  

Adaptation measure 
Infrastructures 
Prevention of storm damage to buildings 
Local structural protection 
Industry 
Awareness raising for companies 
Energy 
Adaptation of electricity grids 
High efficiency ventilation 
Transport 
Improved road pavement materials and design standards 
Adapting tracks to higher temperatures 
Adapting roads to higher temperatures 
Adapting roads to increase in precipitation 
Transport and spatial planning: general protection measures 
Transport and spatial planning: network redesign 
Infrastructure measures: incentives and information 
Infrastructure measures: supervision and maintenance 
Infrastructure measures: investments 
Vehicle technologies: detection and communication 
Vehicle technologies: vehicle engineering 
Vehicle technologies: maintenance 
Service operations: raising preparedness 
Service operations: co-operation strategies 
Service operations: system redesign 
Cross-cutting 
Building dykes and beach nourishment 
Storm retention reservoirs 
Action plan on Flood Defence for Rhine River 
Flood and coastal risk management in England 
Flood risk management plan in Belgium 
Early warning for flash floods 
Groins 
Beach nourishment 
Revetments and geotextiles 
Bulkheads 

BCR (avg and range) 

Region 

Reference 

Germany 
Austria 

EU 

Tröltzsch et al. (2012)  
Holub and Fuchs (2008) 

Hjerp et al. (2012) 

EU26, without Malta 
EU26, without Malta 

Hjerp et al. (2012) 
Hjerp et al. (2012) 

Germany and Austria 
EU 
EU 
EU 
EU 
EU 
EU 
EU 
EU 
EU 
EU 
EU 
EU 
EU 
EU 

Doll et al. (2014) 
Hjerp et al. (2012) 
Hjerp et al. (2012) 
Hjerp et al. (2012) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 
Doll et al. (2011) 

Germany 
EU 
River Rhine (Germany) 
UK 
Scheldt Estuary (Belgium) 
Germany 
Greece 
Greece 
Greece 
Greece 

Tröltzsch et al. (2012) 
Hjerp et al. (2012) 
Petrascheck (2003) 
EA (2009) 
Broekx et al. (2011) 
EWASE (2008) 
Kontogianni et al. (2014) 
Kontogianni et al. (2014) 
Kontogianni et al. (2014) 
Kontogianni et al. (2014) 

2.7 (1.3 – 4.8) 
1.7 

5.3 (1.0 – 9.7) 

5.1 
1.8 

3 
2 (0.34 - 9) 
0.4 (0.2 - 0.9) 
0.5 (0.1 - 1.9) 
1.3 
1.2 
2.4 
1.2 
1.5 
1.2 
1.9 
1 
1.4 
3.8 
0.7 

2.5 
3.5 (0.5-9.4) 
3.4 
7.5 (4 – 11) 
4.1 
9 
3.2 (1.6-4) 
2.1 (0.4-3.8) 
3.7 (3.3-3.9) 
3.3 (2.4-3.9) 

141 

 
 
The following two scenarios are then considered:  

1)  Scenario 1: adaptation costs reflect maintenance, operation and replacement 

costs  distributed  in  time  (assuming  capital  costs  are  distributed  equally  in 

time). Under this scenario two different strategies are further considered: a) 

strategy 1: incremental adaptation goals that only take  into account climate 

change  in  the  next  30-year  period;  and  b)  strategy  2:  long  term  adaptation 

goals taking into account long term climatic conditions; and  

2)  Scenario 2: capital costs are incurred upfront and operation and maintenance 

(O&M) costs are spread equally in time.  

Below results of the different combinations are detailed.  

Scenario 1 – strategy 1 

Columns  5-7  present  the  annual  costs  of  adaptation  for  the  three  different  time 

windows,  assuming  incremental  steps  of  adaptation  (only  accounting  for  marginal 

climate  impacts  in  next  30-year  period).  The  annual  cost  to  adapt  infrastructures 

against  short  term  climate  change  is  1.3  billion  €/year  for  EU+  in  the  period  from 

now up  to 2040. In the 2050s, 4.7  billion € would need to be  invested annually in 

adaptation  to  lower  risks  to  current  levels  (assuming  that  25%  of  the  projected 

increase  in  risk  is  unavoidable),  which  further  rises  to  9.3  billion  €/year  between 

2071 and 2100. There is a large variation in the cost of adaptation by country. These 

mirror the range of damage costs detailed in Chapter 5. Thus, countries with higher 

estimated  damages  will  face  higher  adaptation  costs. Expressed  as  a  share  of  GDP, 

annual costs for incremental steps of adaptation grow from 0.01% now up to 0.07% 

by the end of the century for EU+.  

Scenario 1 – strategy 2 

Column  8  shows  cost  estimates  assuming  that  adaptation  strategies  take  into 

account long term climate changes with costs of adaptation distributed evenly over 

the  90  years  (long  term  planning).  In  that  case,  annual  expenditure  for  adaptation 

142 

 
 
 
  
 
from now up to 2100 would amount to 5.1 billion €/year, or approximately 0.04% 

of  EU+  GDP.  Comparison  of  these  results  with  those  of  strategy  1  shows  that 

incremental steps of adaptation may result in lower investments in the short  term 

but higher investments on the longer term, hence the bulk of the burden is placed on 

future  generations.  This  strategy  may  only  be  justifiable  for  infrastructures  with 

short life times, for which impacts in the long run are not relevant. 

Scenario 2 

Adaptation  measures  often  involve  substantial  capital  expenses  (Sussman  et  al., 

2014), which under scenario 1 were considered as  part of the  annual costs. Under 

this scenario it is assumed that capital costs reflect 30% of the total adaptation cost 

over  its  lifetime  and  that  they  are  incurred  now,  whereas  O&M  costs  (70% 

remaining costs) are spread equally in time. It is important to note that depending 

on the adaptation measure the share of capital costs to the total cost of adaptation 

may be higher (hard structural measures) or lower (soft adaptation measures).  

Results  under  this  scenario  (columns  13  to  20  of  Table  6.3)  indicate  that  for  EU+, 

taking  into  account  short  term  projected  changes  in  climate,  costs  incurred  now 

would  equal  approximately  12  billion  €,  or  approximately  0.1%  of EU+  2010  GDP, 

plus  a  yearly  O&M  cost  of  nearly  1  billion  €.  Expected  annual  benefits  of  these 

investments  would  amount  to  3.3  billion  €.  This,  however,  would  only  make 

infrastructures  resilient  to  climate  up  to  2040.  The  investments  for  adaptation 

required to face changes in climate also in the medium term (including the 2050s) 

would amount to an upfront capital cost of 54 billion €, or 0.4% of EU+ 2010 GDP, 

and  an  annual  O&M  cost  of  2.1  billion  €.  Expected  annual  benefits  would  grow  to 

11.9  billion  €  by  the  2050s.  For  making  infrastructures  climate  resilient  up  to  the 

end  of  the  century,  the  capital  cost  rises  to  138  billion  €  (about  1%  of  EU+  2010 

GDP) and O&M cost grow to nearly 3.6 billion €/year, with expected annual benefits 

reaching 23 billion € by the 2080s.  

143 

 
 
 
 
 
While  it  is  obvious  that  adaptation  costs  and  impacts  will  not  fall  equally  across 

Europe, this does have important implications. The analysis of indicative adaptation 

costs  by  country  (if  incurred  now)  accounting  for  medium  to  long  term  climate 

effects  indicates  that  some  countries  in  Europe  would  potentially  have  to  invest  a 

significant  share  of  their  current  GDP  to  abate  the  future  impacts  from  climate 

hazards  on  critical  infrastructures  –  notably  Croatia,  Slovenia,  Spain,  Portugal  and 

Greece.  

It  is  stressed  that  these  indicative  costs  are  subject  to  many  factors,  such  as  the 

shape of the marginal cost curve for increasing resilience against increasing extreme 

hazard  intensity,  the  balance  between  soft  and  hard  options,  and  the  balance  of 

capital  and  operating  and  maintenance  costs,  among  others.  Nonetheless,  they 

suggest  that  adaptation  (i.e.,  enhanced  resilience)  could  be  a  highly  cost-effective 

strategy, but that costs to be incurred could be considerable for several countries in 

Europe. 

144 

 
 
 
 
Table 6.3 Estimated cost of adaptation for multi-hazard multi-sector analysis. No discounting, expressed in € 2010 constant prices or share of 2010 GDP, assuming 

no socio-economic change in future scenarios. Scenario 1, strategy 1; Scenario 1, strategy 2; Scenario 2.   

Accumulated total cost  
(in million €) 
2050 
1,462 
610 
646 
3,683 
43 
92 
11,374 
1,201 
245 
51,749 
587 
24,901 
11,067 
4,591 
472 
313 
38 
49,296 
350 
38 
318 
138 
644 
433 
203 
8,997 
2,323 
1,136 
2,087 
522 
2,232 
181,789 

2080 
4,644 
1,962 
1,838 
9,634 
110 
450 
29,518 
2,912 
517 
132,710 
1,453 
63,325 
24,483 
17,465 
1,395 
633 
98 
115,411 
655 
144 
569 
219 
1,570 
1,150 
453 
20,998 
6,629 
2,551 
8,526 
1,988 
7,158 
461,166 

2020 
260 
7 
223 
543 
1 
26 
1,839 
249 
40 
11,605 
127 
5,304 
1,095 
628 
0 
26 
0 
13,335 
89 
3 
72 
67 
34 
35 
43 
2,072 
847 
122 
407 
14 
214 
39,297 

Country 
AT 
BE 
BG 
CH 
CY 
CZ 
DE 
DK 
EE 
ES 
FI 
FR 
GR 
HR 
HU 
IE 
IS 
IT 
LT 
LU 
LV 
MT 
NL 
NO 
PL 
PT 
RO 
SE 
SI 
SK 
UK 
EU+ 

Annual total cost  
(in million €) 
2080 
2050 
106 
40 
45 
20 
40 
14 
198 
105 
2.2 
1.4 
12 
2.2 
605 
318 
57 
32 
9.1 
6.8 
2,699 
1,338 
29 
15 
1,281 
653 
447 
332 
429 
132 
31 
17 
11 
10 
2.0 
1.3 
2,204 
1,199 
10 
8.7 
3.5 
1.2 
8.4 
8.2 
2.7 
2.4 
31 
20 
24 
13 
8.3 
5.3 
400 
231 
144 
49 
47 
34 
215 
56 
49 
17 
164 
67 
9,313 
4,750 

2080 
52 
22 
20 
107 
1.2 
5.0 
328 
32 
5.7 
1,475 
16 
704 
272 
194 
16 
7.0 
1.1 
1,282 
7.3 
1.6 
6.3 
2.4 
17 
13 
5.0 
233 
74 
28 
95 
22 
80 
5,124 

2020 
8.7 
0.2 
7.4 
18 
0.0 
0.9 
61 
8.3 
1.3 
387 
4.2 
177 
36 
21 
0 
0.9 
0.0 
445 
3.0 
0.1 
2.4 
2.2 
1.1 
1.2 
1.4 
69 
28 
4.1 
14 
0.5 
7.1 
1,310 

Annual total cost  
(share GDP) 
2080 
2050 
0.036 
0.014 
0.012 
0.005 
0.108 
0.038 
0.045 
0.024 
0.012 
0.007 
0.008 
0.001 
0.023 
0.012 
0.024 
0.013 
0.062 
0.046 
0.250 
0.124 
0.015 
0.008 
0.064 
0.033 
0.198 
0.147 
0.954 
0.294 
0.031 
0.017 
0.006 
0.006 
0.020 
0.013 
0.137 
0.075 
0.036 
0.031 
0.009 
0.003 
0.047 
0.046 
0.041 
0.036 
0.005 
0.003 
0.007 
0.004 
0.002 
0.001 
0.222 
0.128 
0.113 
0.039 
0.013 
0.009 
0.593 
0.155 
0.073 
0.025 
0.009 
0.004 
0.069 
0.035 

2080 
0.018 
0.006 
0.056 
0.024 
0.006 
0.003 
0.013 
0.013 
0.039 
0.136 
0.009 
0.035 
0.120 
0.431 
0.016 
0.004 
0.011 
0.080 
0.026 
0.004 
0.035 
0.037 
0.003 
0.004 
0.001 
0.130 
0.058 
0.008 
0.262 
0.033 
0.004 
0.038 

2020 
0.003 
0.000 
0.020 
0.004 
0.000 
0.001 
0.002 
0.003 
0.009 
0.036 
0.002 
0.009 
0.016 
0.046 
0.000 
0.001 
0.000 
0.028 
0.011 
0.000 
0.013 
0.034 
0.000 
0.000 
0.000 
0.038 
0.022 
0.001 
0.037 
0.001 
0.000 
0.010 

Capital cost 
(in million €) 
2050 
439 
183 
194 
1,105 
13 
28 
3,412 
360 
73 
15,525 
176 
7,470 
3,320 
1,377 
142 
94 
11 
14,789 
105 
11 
95 
42 
193 
130 
61 
2,699 
697 
341 
626 
157 
670 
54,537 

2080 
1,393 
589 
551 
2,890 
33 
135 
8,856 
874 
155 
39,813 
436 
18,997 
7,345 
5,239 
419 
190 
29 
34,623 
196 
43 
171 
66 
471 
345 
136 
6,299 
1,989 
765 
2,558 
596 
2,147 
138,350 

2020 
78 
2 
67 
163 
0 
8 
552 
75 
12 
3,482 
38 
1,591 
328 
188 
0 
8 
0 
4,001 
27 
1 
22 
20 
10 
10 
13 
622 
254 
37 
122 
4 
64 
11,789 

145 

Capital cost 
(share GDP) 
2050 
0.15 
0.05 
0.53 
0.25 
0.07 
0.02 
0.13 
0.15 
0.50 
1.44 
0.09 
0.37 
1.47 
3.06 
0.14 
0.06 
0.11 
0.92 
0.38 
0.03 
0.53 
0.63 
0.03 
0.04 
0.02 
1.50 
0.55 
0.09 
1.73 
0.23 
0.04 
0.40 

2080 
0.47 
0.16 
1.50 
0.66 
0.17 
0.09 
0.34 
0.36 
1.05 
3.68 
0.23 
0.95 
3.25 
11.64 
0.43 
0.12 
0.29 
2.16 
0.70 
0.11 
0.95 
0.99 
0.07 
0.11 
0.04 
3.50 
1.57 
0.21 
7.06 
0.89 
0.12 
1.02 

2020 
0.03 
0.00 
0.18 
0.04 
0.00 
0.00 
0.02 
0.03 
0.08 
0.32 
0.02 
0.08 
0.15 
0.42 
0.00 
0.00 
0.00 
0.25 
0.10 
0.00 
0.12 
0.30 
0.00 
0.00 
0.00 
0.35 
0.20 
0.01 
0.34 
0.01 
0.00 
0.09 

Annual O&M 
cost (in million €) 
2080 
2020 
6.1 
36 
0.2 
15 
5.2 
14 
13 
75 
0.0 
0.9 
0.6 
3.5 
43 
230 
5.8 
23 
0.9 
4.0 
271 
1,032 
3.0 
11 
124 
493 
26 
190 
15 
136 
0 
11 
0.6 
4.9 
0.0 
0.8 
311 
898 
2.1 
5.1 
0.1 
1.1 
1.7 
4.4 
1.6 
1.7 
0.8 
12 
0.8 
8.9 
1.0 
3.5 
48 
163 
20 
52 
2.9 
20 
10 
66 
0.3 
15 
5.0 
56 
917 
3,587 

 
 
 
 
 
6.3.2  Climate proofing EU investments  

The  above  shows  that  the  implementation  of  adaptation  measures,  whether  it  be 

physical  adjustments 

to 

infrastructures  and  buildings,  or  organizational, 

institutional  and  other  soft  response  actions,  may  require  substantial  resources 

(Bouwer and Aerts 2006), especially in regions that will be most affected by climate 

change. The Cohesion Policy Funds (CPF) are a key financing instrument of the EU, 

distributing about a third (347 billion € for the period 2007-2013) of the EU budget 

across regions, based on  economic and social  indicators, with the  aim to stimulate 

development especially in less prosperous regions in order to achieve economic and 

social cohesion. Even though that adaptation only became a serious policy concern 

for the EU after the definition of the thematic priorities of the CPF 2007-2013, some 

of the thematic priorities can be linked with adaptation. Especially since funding is 

available not only for hard infrastructures but also for human capital, institution and 

capacity  building  as  well  as  technological  development,  which  may  be  tailored 

towards increasing the adaptive capacity of regions.  

We  build  on  the  work  of  Hanger  et  al.  (2013)  to  appraise  the  amount  of  funding 

under  CPF  2007-2013  that  can  be  considered  relevant  to  supporting  adaptation-

related  aims.  From  the  86  thematic  priorities,  adaptation-related  expenditure  is 

assigned based on  the  Rio marker methodology developed by the  Organization  for 

Economic  Cooperation  and  Development  (OECD)  for  reporting  climate-related 

development  assistance  (OECD,  2011).  This  method  distinguishes  between  three 

categories  of  priority  themes:  the  first  category  (climate  change  as  principal 

objective) is weighted 100%, the second (climate change as significant objective) at 

40%  and  the  third  (climate  change  not  an  objective)  are  assigned  no  weight.  For 

priority themes of the first two categories that are also related to climate mitigation 

an equal share is assigned to mitigation and adaptation. Table 6.4 shows the priority 

themes that were retained in this analysis, with an indication of their relevance for 

climate  change  mitigation  and/or  adaptation.  The  sum  of  these  contributions 

represents  the  climate  adaptation  potential  of  the  EU  CPF  2007-2013  investments 

(CPF-A).  

146 

 
 
 
 
 
 
Table 6.4 CPF priority themes marked according to OECD ‘Rio marker’ system as ‘climate change as 

principal  objective’  (100  %)  or  ‘climate  change  as  significant  objective’  (40  %);  together  with  a 

classification  into  adaptation  relevant  (A),  mitigation  relevant  (M),  or  mitigation  and  adaptation 

relevant (M&A), and the share of total CPF funding (in percentage of total “Structural and Cohesion 

Funds, SCF”) – from Hanger et al. (2013).  

147 

 
 
 
 
 
The  spatial  distribution  of  CPF-A  is  presented  in  Figure  6.1.  It  shows  highest 

adaptation-related  allocations  in  regions  of  Spain,  Portugal,  Italy,  Hungary,  Poland 

and  the  Baltic  States.  Similar  as  for  the  distribution  of  CPF  investments  at  risk 

(Figure  5.9)  this  pattern  is  largely  driven  by  the  total  amount  of  allocations,  as 

adaptation  was  not  high  on  the  agenda  when  the  distribution  amongst  priority 

themes  were  agreed.  Aggregated  over  EU27  CPF-A  amounts  to  6.2  billion  €,  or 

approximately 1.8% of  the  total allocations for 2007-2013. It  should  be  noted that 

the EU allocations are indicative, proposed by the Member States and approved by 

the  European  Commission,  hence  may  differ  from  actual  spending.  Ultimately  the 

effectiveness of spending under each thematic priority can only be evaluated at the 

project  level,  for  which  no  comprehensive  data  exist  at  the  European  level. 

Furthermore,  the  amounts  earmarked  for  adaptation  under  the  CPF  2007-2013 

refer largely to environmental measures, which may not necessarily be effective in 

reducing the impacts to all the investments at risk.  

148 

 
 
 
 
Figure 6.1 Adaptation relevant allocations (CPF-A, in million €) under the Cohesion policy during the 

2007-2013 programming period (based on Hanger et al., 2013).  

Specific  investment  allocation  data  for  the  on-going  programming  period  (2014-

2020) were not available during the writing of this report. However, in recent years 

the EU has made strong efforts for augmenting the profile of climate change in their 

budget and policies. This is expressed by the following recent actions.  

  The European Council has set as political objective to earmark at least 20% of 

the entire EU budget for climate-relevant actions in the period 2014-202014.  

14 Conclusions of the European Council (7/8 February 2013) as regards the Multiannual Financial Framework. 

149 

 
 
 
 
 
 
                                                        
  The current programming period is the first in which climate considerations 

have been included. Major projects funded by ESIF will need to be screened 

against  climate-related  vulnerabilities  during 

the  preparation  and 

implementation  phase  and  necessary  adaptation  measures  need  to  be 

reported15.  

  For  European  and  Structural  Investment  Funds  (ESIF)  there  is  now  the 

specific  requirement  that  adaptation  to  climate  change  is  part  of  the 

horizontal principle of sustainable development16.  

  One of the 11 thematic objectives under the new ESIF interventions includes 

specific measures for adaptation (Thematic Objective 5  – Promoting climate 

change adaptation, risk prevention and management)17.  

To optimally allot funds for adaptation across the EU it is important to  understand 

the  regional  distribution  of  impacts  and  adaptation  costs,  such  that  higher 

adaptation  efforts  can  be  targeted  towards  the  most  impacted  regions.  To  provide 

an indication of expenditures required for climate proofing CPF investments across 

the  EU27,  we  map  in  Figure  6.2  adaptation  costs  expressed  as  a  share  of  the  total 

2007-2013  CPF  allocations.  These  adaptation  costs  relate  to  the  risks  to  EU 

investments and not  to the  total stock of infrastructure. The  adaptation costs have 

been derived as follows.  The difference in multi-hazard damages (EAD) between the 

baseline and 2020s have been accumulated over a 30-year period. This reflects the 

total increased climate risk over the next 30 years compared to the baseline. Hence, 

it  is  assumed  here  that  the  life  span  of  the  investment  is  limited  to  30  years,  and 

therefore only climate changes in the short term are accounted for. This means that 

for  investments  and  infrastructures  with  longer  life  spans  the  risks  may  be 

underestimated.  Similar  as  in  the  analysis  for  critical  infrastructures,  we  further 

assume that with adaptation we can avoid 75% of the increase in risk (= benefits of 

adaptation) and apply a benefit-to-cost ratio (BCR) of 2.5 to get indicative numbers 

15 Article 101 of Regulation (EU) No 1303/2013 of the European Parliament and of the Council of 17 December 2013; Commission 
Implementing Regulation (EU) 2015/207 – Annex II, Section F.8. 
16 Article 8 of Regulation (EU) No 1303/2013 of the European Parliament and of the Council of 17 December 2013. 
17 Article 9 of Regulation (EU) No 1303/2013 of the European Parliament and of the Council of 17 December 2013. 

150 

 
 
 
 
 
                                                        
of the adaptation costs. In Figure 6.2 the latter are scaled by the total CPF allocations 

under the 2007-2013 programming period.  

For EU27 the cost of proofing CPF investments against climate up to 2040 amounts 

to  1.1%  of  total  CPF  allocations.  This  implies  that  the  total  adaptation  relevant 

allocations under the 2007-2013 programming period as derived by the method of 

Hanger et al. (2013) should be sufficient to cover adaptation costs to face short term 

climate  changes.  When  climate  change  in  the  medium  term  (including  the  2050s, 

results not shown here) is also accounted for, however, total costs for EU27 rise to 

6.2%,  whereas  proofing  investments  against  climate  in  the  2080s  would  require 

10.4%  of  total  allocations.  The  maps  in  Figure  6.2  show  that  there  is  a  large 

heterogeneity  across  the  EU  in  adaptation  costs,  which  follow  the  distribution  of 

risks  to  CPF  investments  as  described  in  Chapter  5.  For  regions  in  the  south  and 

southeast  the  costs  of  climate  proofing  all  investments  against  short  term  climate 

change  can  reach  5%  of  total  allocations  (Figure  6.2a).  There  also  exists 

considerable  variation  in  adaptation  requirements  for  different  sectors,  both  in 

terms  of  overall  magnitude  and  distribution  across  regions.  The  sectors  with  the 

highest 

relative 

adaptation 

costs 

are 

the 

transport, 

energy, 

and 

environment/tourism  sectors,  with  EU27  aggregated  costs  for  short-term  climate 

change adaptation between 2 and 3% of sector investments (for longer term climate 

change  adaptation  cost  could  rise  to  20-30%  of  sector  investments).  For  these 

sectors,  several  regions  in  southern  and  south-eastern  Europe,  but  also  some  in 

France, can face short-term adaptation costs up to 10% of the sector investments, in 

localized  regions  in the Iberian and Balkan Peninsulas event up  to 25% and  more. 

The ranking of the three most impacted sectors in terms of adaptation requirements 

can be different for each region, depending on the amount of sector allocations, the 

prevalence of climate hazards and the sensitivity of the investment to the hazards. 

The costs for climate proofing are considerably lower for the ICT and social sectors, 

amounting  for  the  EU27  to  0.05%  and  0.28%,  respectively,  of  sector  investments 

when only considering short-term climate change. For the social sector, however, we 

see that locally in Spain but also in France short-term adaptation costs could reach 

nearly 5% of sector investments.  

151 

 
 
 
 
 
Figure 6.2 Adaptation costs as share of CPF investments: (a) multi-sector, (b) transport, (c) energy, 

(d) environment and tourism, (e) ICT, and (f) social sector. Estimated adaptation costs only take into 

account short term climate changes.  

152 

 
 
 
 
 
We  stress  that  the  reported  adaptation  costs  can  be  subject  to  large  uncertainty. 

However,  while  these  estimates  are  only  indicative,  they  do  highlight  some 

important  issues.  The  distribution  in  space  and  amongst  sectors  provides  an 

indication  of  the  regions  and  sectors  that  may  face  substantial  efforts  for  climate 

proofing  EU  investments.  The  disproportionate  distribution  of  economic  costs  – 

both adaptation and impacts – suggests that a better understanding of the regional 

and thematic distribution of costs could aid in targeting further EU investments such 

that  cohesion  policy  also  gains  meaning  as  a  burden  sharing  instrument  for 

adaptation to climate change. 

153 

 
 
 
 
 
 
 
 
 
 
 
 
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Broekx, S., Smets, S., Liekens, I., Bulckaen, D., de Nocker, L., 2011.  Designing a long-
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156 

 
 
 
 
 
 
Annex I 

Structure of CPF and CI risk maps 

Maps  representing  (current  and  future)  impacts  of  individual  hazards  on  EU 

investments  and  critical  economic  assets  are  organized  with  the  following  tree 

structure 

  CPF map collection 

  Single hazard maps 

1.  Hazard 1 

1.  Sector 1 

1.  Time step 1 

…

4.  Time step 4 

  Namefile 

2.  Sector 2 

…

6.  Sector 6 

2.  Hazard 2 

…

7.  Hazard 7 

  Multi-hazard maps 

8.  Multi-hazard 

1.  Sector 1 

1.  Time step 1 

…

2.  Time step 4 

  Namefile 

2.  Sector 2 

…

6.  Sector 6 

Figure Annex.3 CPF folder structure 

157 

 
 
 
 
  
 
 
 
 
 
 
 
   
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  CI map collection 

  Single hazard maps 

1.  Hazard 1 

1.  Sector 1 

1.  Time step 1 

…

5.  Time step 4 

  Namefile 

2.  Sector 2 

…

5.  Sector 5 

2.  Hazard 2 

…

7.  Hazard 7 

  Multi-hazard maps 

8.  Multi-hazard 

1.  Sector 1 

1.  Time step 1 

…

2.  Time step 4 

  Namefile 

2.  Sector 2 

…

6.  Sector 6 

Figure Annex.4 CI folder structure 

158 

 
 
 
 
  
 
 
 
 
 
 
 
   
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The  CPF  folder  refers  to  Cohesion  Fund  investments  (2007-2013)  at  risk,  with 

damages  expressed  in  Euro  per  NUTS2  region.  A  collection  of  maps  have  been 

produced for each single hazard as well as for the aggregation of all the hazards. The 

CPF  single  hazard  folder  contains  maps  that show  the  CPF  themes  at  risk  for  each 

hazard and time slice. Maps are in different folders per hazard and for each sector 

(See Figure Annex.1) 

The nomenclature of the files is as follows:  

Namefile  =  Hazard  number  +  Sector  number  +  Time  step  number  +  “CPF”  +  Time 

step name + Hazard abbreviation + Sector abbreviation 

  Hazard number/name = “1” for Cold wave, “2” for Drought, “3” for Wildfire, 

“4”  for  River  floods,  “5”  for  Heatwave,“6”  for  Coastal  Flood  and  “7”  for 

Windstorm. 

  Sector number/name = “1” for Energy, “2” for Environment and Tourism, “3” 

for  Information,  Communication  and  Technology,  “4”  for  Social,  “5”  for 

Transport,   and “6” for Total. 

  Time step number/name = “1” for ts1 (1981-2010, except Windstorm hazard 

that  has  used  1961-1990  as  baseline),“2  for  ts2  (2011-2040),  “3”  for  ts3” 

(2041-2070) and “4” for ts4 (2071-2100)  

  Hazard  abbreviation  =  “C”  for  Cold  wave,  “D”  for  Drought,  “F”  for  Wildfire, 

“RF”  for  River  floods,  “H”  for  Heatwave,  “CF”  for  Coastal  Flood  and  “W”  for 

Windstorm. 

  Sector abbreviation = “ENER” for Energy, “ENV_TOUR” for Environment and 

Tourism,  “ICT”  for  Information,  Communication  and  Technology,  “SOCIAL” 

for Social, “TRANS” for Transport,   and “TOTAL” for the aggregation of all the 

sectors. 

For instance, the namefile: “1.1.1 CPF_ts1_C_ENER” corresponds to the single map of 

annual  damages  in  EUR  of  CPF  Energy  allocations  to  Cold  waves  in  the  time  step 

1981-2010. 

159 

 
 
 
 
 
 
The  CPF  multi-hazard  folder  contains  maps  that  show  CPF  themes  at  risk  for  the 

sum  of  all  the  hazards  for  each  time  slice.  Maps  are  stored  in  different  folders  for 

each hazard.  

The nomenclature of the files is as follows:  

Namefile  =  Hazard  number  +  Sector  number  +  Time  step  number  +  “CPF”  +  Time 

step name + Hazard abbreviation + Sector abbreviation 

  Hazard number/name = “8” for Multi-hazard 

  Hazard abbreviation = “M” for Multi-hazard 

For instance, the  namefile  =  7.2.4  CPF_ts1_M_ENV_TOUR  corresponds to  the  multi-

hazard  map  of  annual damages  in  EUR  of  CPF  Environment  and  Tourism  to  Multi-

hazard in the time step 2071-2100. 

The  CI  folder  refers  to  the  Critical  Infrastructures  (or  Critical  Economic  Assets  – 

CEA) under risk, with damages expressed in Euro per NUTS2 region. A set of maps 

have been created for each single hazard and the aggregation of all the hazards.  

The CI single hazard folder contains maps that show the  CI at risk for each hazard 

and time slice. Maps are stored in different folders for each hazard first and secondly 

for each sector (See Figure Annex.2) 

The nomenclature of the files is as follows:  

Namefile  =  Hazard  number  +  Sector  number  +  Time  step  number  +  “CEA”  +  Time 

step name + Hazard abbreviation + Sector abbreviation 

  Hazard number/name = “1” for Cold wave, “2” for Drought, “3” for Wildfire, 

“4”  for  River  floods,  “5”  for  Heatwave,“6”  for  Coastal  Flood  and  “7”  for 

Windstorm. 

160 

 
 
 
 
 
 
 
 
  Sector number/name = “1” for Energy, “2” for Industrial, “3” for Social ,“4” for 

Transport,   and “5” for Total. 

  Time step number/name = “1” for ts1 (1981-2010, except Windstorm hazard 

that takes 1961-1990),“2 for ts2 (2011-2040), “3” for ts3” (2041-2070) and 

“4” for ts4 (2071-2100)  

  Hazard  abbreviation  =  “C”  for  Cold  wave,  “D”  for  Drought,  “F”  for  Wildfire, 

“RF”  for  River  floods,  “H”  for  Heatwave,  “CF”  for  Coastal  Flood  and  “W”  for 

Windstorm. 

  Sector abbreviation = “ENER” for Energy sector, “INDUSTRIAL” for Industrial 

facilities, “SOCIAL” for Social facilities, “TRANS” for Transport infrastructures,   

and “TOTAL” for the sum of all the sectors. 

For instance, the namefile “1.2.1 CEA_ts1_C_INDUSTRIAL” corresponds to the single 

map of annual damages in EUR of CEA Industrial allocations to Cold waves  hazard 

from 1981 to 2010. 

The CI multi-hazard folder contains maps that show CI at risk for the sum of all the 

hazards for each time slice. Maps are stored in different folders for each hazard.  

The nomenclature of the files is as follows:  

Namefile  =  Hazard  number  +  Sector  number  +  Time  step  number  +  “CEA”  +  Time 

step name + Hazard abbreviation + Sector abbreviation 

Hazard number/name = “8” for Multi-hazard 

Hazard abbreviation = “M” for Multi-hazard. 

For  instance,  the  namefile  =  8.5.4  CEA_ts4_M_TOTAL  corresponds  to  the  multi-

hazard map of annual damages in EUR of CEA Total investments to Multi-hazard in 

the time step running from 2071 to 2100. 

161 

 
 
 
 
 
 
 
 
 
Annex II  CPF and CI risk maps 

1A: CPF single hazard maps 

Namefile  

Caption 

1.1.1.CPF_ts1_C_ENER 

1.1.2.CPF_ts2_C_ENER 

1.1.3.CPF_ts3_C_ENER 

1.1.4.CPF_ts4_C_ENER 

1.2.1.CPF_ts1_C_ENV_TOUR 

1.2.2.CPF_ts2_C_ENV_TOUR 

1.2.3.CPF_ts3_C_ENV_TOUR 

1.2.4.CPF_ts4_C_ENV_TOUR 

1.3.1.CPF_ts1_C_ICT 

1.3.2.CPF_ts2_C_ICT 

1.3.3.CPF_ts3_C_ICT 

1.3.4.CPF_ts4_C_ICT 

1.4.1.CPF_ts1_C_SOCIAL 

1.4.2.CPF_ts2_C_SOCIAL 

1.4.3.CPF_ts3_C_SOCIAL 

1.4.4.CPF_ts4_C_SOCIAL 

1.5.1.CPF_ts1_C_TRANS 

1.5.2.CPF_ts2_C_TRANS 

1.5.3.CPF_ts3_C_TRANS 

1.5.4.CPF_ts4_C_TRANS 

1.6.1.CPF_ts1_C_TOTAL 

1.6.2.CPF_ts2_C_TOTAL 

1.1.1  Annual Damages in Euros to CPF Energy allocations from 
Cold wave hazard in 1981-2010 
1.1.2  Annual Damages in Euros to CPF Energy allocations from 
Cold wave hazard in 2011-2040 
1.1.3  Annual Damages in Euros to CPF Energy allocations from 
Cold wave hazard in 2041-2070 
1.1.4  Annual Damages in Euros to CPF Energy allocations from 
Cold wave hazard in 2071-2100 
1.2.1  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Cold wave hazard in 1981-2010 
1.2.2  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Cold wave hazard in 2011-2040 
1.2.3  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Cold wave hazard in 2041-2070 
1.2.4  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Cold wave hazard in 2071-2100 
1.3.1  Annual Damages in Euros to CPF I.C.T. allocations from 
Cold wave hazard in 1981-2010 
1.3.2  Annual Damages in Euros to CPF I.C.T. allocations from 
Cold wave hazard in 2011-2040 
1.3.3  Annual Damages in Euros to CPF I.C.T. allocations from 
Cold wave hazard in 2041-2070 
1.3.4  Annual Damages in Euros to CPF I.C.T. allocations from 
Cold wave hazard in 2071-2100 
1.4.1  Annual Damages in Euros to CPF Social allocations from 
Cold wave hazard in 1981-2010 
1.4.2  Annual Damages in Euros to CPF Social allocations from 
Cold wave hazard in 2011-2040 
1.4.3  Annual Damages in Euros to CPF Social allocations from 
Cold wave hazard in 2041-2070 
1.4.4  Annual Damages in Euros to CPF Social allocations from 
Cold wave hazard in 2071-2100 
1.5.1  Annual Damages in Euros to CPF Transport allocations 
from Cold wave hazard in 1981-2010 
1.5.2  Annual Damages in Euros to CPF Transport allocations 
from Cold wave hazard in 2011-2040 
1.5.3  Annual Damages in Euros to CPF Transport allocations 
from Cold wave hazard in 2041-2070 
1.5.4  Annual Damages in Euros to CPF Transport allocations 
from Cold wave hazard in 2071-2100 
1.6.1  Annual Damages in Euros to CPF Total allocations from 
Cold wave hazard in 1981-2010 
1.6.2  Annual Damages in Euros to CPF Total allocations from 
Cold wave hazard in 2011-2040 

162 

 
 
 
 
 
 
1.6.3.CPF_ts3_C_TOTAL 

1.6.4.CPF_ts4_C_TOTAL 

2.1.1.CPF_ts1_D_ENER 

2.1.2.CPF_ts2_D_ENER 

2.1.3.CPF_ts3_D_ENER 

2.1.4.CPF_ts4_D_ENER 

2.2.1.CPF_ts1_D_ENV_TOUR 

2.2.2.CPF_ts2_D_ENV_TOUR 

2.2.3.CPF_ts3_D_ENV_TOUR 

2.2.4.CPF_ts4_D_ENV_TOUR 

2.3.1.CPF_ts1_D_ICT 

2.3.2.CPF_ts2_D_ICT 

2.3.3.CPF_ts3_D_ICT 

2.3.4.CPF_ts4_D_ICT 

2.4.1.CPF_ts1_D_SOCIAL 

2.4.2.CPF_ts2_D_SOCIAL 

2.4.3.CPF_ts3_D_SOCIAL 

2.4.4.CPF_ts4_D_SOCIAL 

2.5.1.CPF_ts1_D_TRANS 

2.5.2.CPF_ts2_D_TRANS 

2.5.3.CPF_ts3_D_TRANS 

2.5.4.CPF_ts4_D_TRANS 

2.6.1.CPF_ts1_D_TOTAL 

2.6.2.CPF_ts2_D_TOTAL 

2.6.3.CPF_ts3_D_TOTAL 

2.6.4.CPF_ts4_D_TOTAL 

1.6.3  Annual Damages in Euros to CPF Total allocations from 
Cold wave hazard in 2041-2070 
1.6.4  Annual Damages in Euros to CPF Total allocations from 
Cold wave hazard in 2071-2100 
2.1.1  Annual Damages in Euros to CPF Energy allocations from 
Drought hazard in 1981-2010 
2.1.2  Annual Damages in Euros to CPF Energy allocations from 
Drought hazard in 2011-2040 
2.1.3  Annual Damages in Euros to CPF Energy allocations from 
Drought hazard in 2041-2070 
2.1.4  Annual Damages in Euros to CPF Energy allocations from 
Drought hazard in 2071-2100 
2.2.1  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Drought hazard in 1981-2010 
2.2.2  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Drought hazard in 2011-2040 
2.2.3  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Drought hazard in 2041-2070 
2.2.4  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Drought hazard in 2071-2100 
2.3.1  Annual Damages in Euros to CPF I.C.T. allocations from 
Drought hazard in 1981-2010 
2.3.2  Annual Damages in Euros to CPF I.C.T. allocations from 
Drought hazard in 2011-2040 
2.3.3  Annual Damages in Euros to CPF I.C.T. allocations from 
Drought hazard in 2041-2070 
2.3.4  Annual Damages in Euros to CPF I.C.T. allocations from 
Drought hazard in 2071-2100 
2.4.1  Annual Damages in Euros to CPF Social allocations from 
Drought hazard in 1981-2010 
2.4.2  Annual Damages in Euros to CPF Social allocations from 
Drought hazard in 2011-2040 
2.4.3  Annual Damages in Euros to CPF Social allocations from 
Drought hazard in 2041-2070 
2.4.4  Annual Damages in Euros to CPF Social allocations from 
Drought hazard in 2071-2100 
2.5.1  Annual Damages in Euros to CPF Transport allocations 
from Drought hazard in 1981-2010 
2.5.2  Annual Damages in Euros to CPF Transport allocations 
from Drought hazard in 2011-2040 
2.5.3  Annual Damages in Euros to CPF Transport allocations 
from Drought hazard in 2041-2070 
2.5.4  Annual Damages in Euros to CPF Transport allocations 
from Drought hazard in 2071-2100 
2.6.1  Annual Damages in Euros to CPF Total allocations from 
Drought hazard in 1981-2010 
2.6.2  Annual Damages in Euros to CPF Total allocations from 
Drought hazard in 2011-2040 
2.6.3  Annual Damages in Euros to CPF Total allocations from 
Drought hazard in 2041-2070 
2.6.4  Annual Damages in Euros to CPF Total allocations from 
Drought hazard in 2071-2100 

163 

 
 
 
 
3.1.1.CPF_ts1_F_ENER 

3.1.2.CPF_ts2_F_ENER 

3.1.3.CPF_ts3_F_ENER 

3.1.4.CPF_ts4_F_ENER 

3.2.1.CPF_ts1_F_ENV_TOUR 

3.2.2.CPF_ts2_F_ENV_TOUR 

3.2.3.CPF_ts3_F_ENV_TOUR 

3.2.4.CPF_ts4_F_ENV_TOUR 

3.3.1.CPF_ts1_F_ICT 

3.3.2.CPF_ts2_F_ICT 

3.3.3.CPF_ts3_F_ICT 

3.3.4.CPF_ts4_F_ICT 

3.4.1.CPF_ts1_F_SOCIAL 

3.4.2.CPF_ts2_F_SOCIAL 

3.4.3.CPF_ts3_F_SOCIAL 

3.4.4.CPF_ts4_F_SOCIAL 

3.5.1.CPF_ts1_F_TRANS 

3.5.2.CPF_ts2_F_TRANS 

3.5.3.CPF_ts3_F_TRANS 

3.5.4.CPF_ts4_F_TRANS 

3.6.1.CPF_ts1_F_TOTAL 

3.6.2.CPF_ts2_F_TOTAL 

3.6.3.CPF_ts3_F_TOTAL 

3.6.4.CPF_ts4_F_TOTAL 

4.1.1.CPF_ts1_RF_ENER 

4.1.2.CPF_ts2_RF_ENER 

3.1.1  Annual Damages in Euros to CPF Energy allocations from 
Wildfire hazard in 1981-2010 
3.1.2  Annual Damages in Euros to CPF Energy allocations from 
Wildfire hazard in 2011-2040 
3.1.3  Annual Damages in Euros to CPF Energy allocations from 
Wildfire hazard in 2041-2070 
3.1.4  Annual Damages in Euros to CPF Energy allocations from 
Wildfire hazard in 2071-2100 
3.2.1  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Wildfire hazard in 1981-2010 
3.2.2  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Wildfire hazard in 2011-2040 
3.2.3  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Wildfire hazard in 2041-2070 
3.2.4  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Wildfire hazard in 2071-2100 
3.3.1  Annual Damages in Euros to CPF I.C.T. allocations from 
Wildfire hazard in 1981-2010 
3.3.2  Annual Damages in Euros to CPF I.C.T. allocations from 
Wildfire hazard in 2011-2040 
3.3.3  Annual Damages in Euros to CPF I.C.T. allocations from 
Wildfire hazard in 2041-2070 
3.3.4  Annual Damages in Euros to CPF I.C.T. allocations from 
Wildfire 'Multi CEA'!M16hazard in 2071-2100 
3.4.1  Annual Damages in Euros to CPF Social allocations from 
Wildfire hazard in 1981-2010 
3.4.2  Annual Damages in Euros to CPF Social allocations from 
Wildfire hazard in 2011-2040 
3.4.3  Annual Damages in Euros to CPF Social allocations from 
Wildfire hazard in 2041-2070 
3.4.4  Annual Damages in Euros to CPF Social allocations from 
Wildfire hazard in 2071-2100 
3.5.1  Annual Damages in Euros to CPF Transport allocations 
from Wildfire hazard in 1981-2010 
3.5.2  Annual Damages in Euros to CPF Transport allocations 
from Wildfire hazard in 2011-2040 
3.5.3  Annual Damages in Euros to CPF Transport allocations 
from Wildfire hazard in 2041-2070 
3.5.4  Annual Damages in Euros to CPF Transport allocations 
from Wildfire hazard in 2071-2100 
3.6.1  Annual Damages in Euros to CPF Total allocations from 
Wildfire hazard in 1981-2010 
3.6.2  Annual Damages in Euros to CPF Total allocations from 
Wildfire hazard in 2011-2040 
3.6.3  Annual Damages in Euros to CPF Total allocations from 
Wildfire hazard in 2041-2070 
3.6.4  Annual Damages in Euros to CPF Total allocations from 
Wildfire hazard in 2071-2100 
4.1.1  Annual Damages in Euros to CPF Energy allocations from 
River Flood hazard in 1981-2010 
4.1.2  Annual Damages in Euros to CPF Energy allocations from 
River Flood hazard in 2011-2040 

164 

 
 
 
 
4.1.3.CPF_ts3_RF_ENER 

4.1.4.CPF_ts4_RF_ENER 

4.2.1.CPF_ts1_RF_ENV_TOUR 

4.2.2.CPF_ts2_RF_ENV_TOUR 

4.2.3.CPF_ts3_RF_ENV_TOUR 

4.2.4.CPF_ts4_RF_ENV_TOUR 

4.3.1.CPF_ts1_RF_ICT 

4.3.2.CPF_ts2_RF_ICT 

4.3.3.CPF_ts3_RF_ICT 

4.3.4.CPF_ts4_RF_ICT 

4.4.1.CPF_ts1_RF_SOCIAL 

4.4.2.CPF_ts2_RF_SOCIAL 

4.4.3.CPF_ts3_RF_SOCIAL 

4.4.4.CPF_ts4_RF_SOCIAL 

4.5.1.CPF_ts1_RF_TRANS 

4.5.2.CPF_ts2_RF_TRANS 

4.5.3.CPF_ts3_RF_TRANS 

4.5.4.CPF_ts4_RF_TRANS 

4.6.1.CPF_ts1_RF_TOTAL 

4.6.2.CPF_ts2_RF_TOTAL 

4.6.3.CPF_ts3_RF_TOTAL 

4.6.4.CPF_ts4_RF_TOTAL 

5.1.1.CPF_ts1_H_ENER 

5.1.2.CPF_ts2_H_ENER 

5.1.3.CPF_ts3_H_ENER 

5.1.4.CPF_ts4_H_ENER 

4.1.3  Annual Damages in Euros to CPF Energy allocations from 
River Flood hazard in 2041-2070 
4.1.4  Annual Damages in Euros to CPF Energy allocations from 
River Flood hazard in 2071-2100 
4.2.1  Annual Damages in Euros to CPF Environment and 
Tourism allocations from River Flood hazard in 1981-2010 
4.2.2  Annual Damages in Euros to CPF Environment and 
Tourism allocations from River Flood hazard in 2011-2040 
4.2.3  Annual Damages in Euros to CPF Environment and 
Tourism allocations from River Flood hazard in 2041-2070 
4.2.4  Annual Damages in Euros to CPF Environment and 
Tourism allocations from River Flood hazard in 2071-2100 
4.3.1  Annual Damages in Euros to CPF I.C.T. allocations from 
River Flood hazard in 1981-2010 
4.3.2  Annual Damages in Euros to CPF I.C.T. allocations from 
River Flood hazard in 2011-2040 
4.3.3  Annual Damages in Euros to CPF I.C.T. allocations from 
River Flood hazard in 2041-2070 
4.3.4  Annual Damages in Euros to CPF I.C.T. allocations from 
River Flood hazard in 2071-2100 
4.4.1  Annual Damages in Euros to CPF Social allocations from 
River Flood hazard in 1981-2010 
4.4.2  Annual Damages in Euros to CPF Social allocations from 
River Flood hazard in 2011-2040 
4.4.3  Annual Damages in Euros to CPF Social allocations from 
River Flood hazard in 2041-2070 
4.4.4  Annual Damages in Euros to CPF Social allocations from 
River Flood hazard in 2071-2100 
4.5.1  Annual Damages in Euros to CPF Transport allocations 
from River Flood hazard in 1981-2010 
4.5.2  Annual Damages in Euros to CPF Transport allocations 
from River Flood hazard in 2011-2040 
4.5.3  Annual Damages in Euros to CPF Transport allocations 
from River Flood hazard in 2041-2070 
4.5.4  Annual Damages in Euros to CPF Transport allocations 
from River Flood hazard in 2071-2100 
4.6.1  Annual Damages in Euros to CPF Total allocations from 
River Flood hazard in 1981-2010 
4.6.2  Annual Damages in Euros to CPF Total allocations from 
River Flood hazard in 2011-2040 
4.6.3  Annual Damages in Euros to CPF Total allocations from 
River Flood hazard in 2041-2070 
4.6.4  Annual Damages in Euros to CPF Total allocations from 
River Flood hazard in 2071-2100 
5.1.1  Annual Damages in Euros to CPF Energy allocations from 
Heatwave hazard in 1981-2010 
5.1.2  Annual Damages in Euros to CPF Energy allocations from 
Heatwave hazard in 2011-2040 
5.1.3  Annual Damages in Euros to CPF Energy allocations from 
Heatwave hazard in 2041-2070 
5.1.4  Annual Damages in Euros to CPF Energy allocations from 
Heatwave hazard in 2071-2100 

165 

 
 
 
 
5.2.1.CPF_ts1_H_ENV_TOUR 

5.2.2.CPF_ts2_H_ENV_TOUR 

5.2.3.CPF_ts3_H_ENV_TOUR 

5.2.4.CPF_ts4_H_ENV_TOUR 

5.3.1.CPF_ts1_H_ICT 

5.3.2.CPF_ts2_H_ICT 

5.3.3.CPF_ts3_H_ICT 

5.3.4.CPF_ts4_H_ICT 

5.4.1.CPF_ts1_H_SOCIAL 

5.4.2.CPF_ts2_H_SOCIAL 

5.4.3.CPF_ts3_H_SOCIAL 

5.4.4.CPF_ts4_H_SOCIAL 

5.5.1.CPF_ts1_H_TRANS 

5.5.2.CPF_ts2_H_TRANS 

5.5.3.CPF_ts3_H_TRANS 

5.5.4.CPF_ts4_H_TRANS 

5.6.1.CPF_ts1_H_TOTAL 

5.6.2.CPF_ts2_H_TOTAL 

5.6.3.CPF_ts3_H_TOTAL 

5.6.4.CPF_ts4_H_TOTAL 

6.1.1.CPF_ts1_CF_ENER 

6.1.2.CPF_ts2_CF_ENER 

6.1.3.CPF_ts3_CF_ENER 

6.1.4.CPF_ts4_CF_ENER 

6.2.1.CPF_ts1_CF_ENV_TOUR 

6.2.2.CPF_ts2_CF_ENV_TOUR 

5.2.1  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Heatwave hazard in 1981-2010 
5.2.2  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Heatwave hazard in 2011-2040 
5.2.3  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Heatwave hazard in 2041-2070 
5.2.4  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Heatwave hazard in 2071-2100 
5.3.1  Annual Damages in Euros to CPF I.C.T. allocations from 
Heatwave hazard in 1981-2010 
5.3.2  Annual Damages in Euros to CPF I.C.T. allocations from 
Heatwave hazard in 2011-2040 
5.3.3  Annual Damages in Euros to CPF I.C.T. allocations from 
Heatwave hazard in 2041-2070 
5.3.4  Annual Damages in Euros to CPF I.C.T. allocations from 
Heatwave hazard in 2071-2100 
5.4.1  Annual Damages in Euros to CPF Social allocations from 
Heatwave hazard in 1981-2010 
5.4.2  Annual Damages in Euros to CPF Social allocations from 
Heatwave hazard in 2011-2040 
5.4.3  Annual Damages in Euros to CPF Social allocations from 
Heatwave hazard in 2041-2070 
5.4.4  Annual Damages in Euros to CPF Social allocations from 
Heatwave hazard in 2071-2100 
5.5.1  Annual Damages in Euros to CPF Transport allocations 
from Heatwave hazard in 1981-2010 
5.5.2  Annual Damages in Euros to CPF Transport allocations 
from Heatwave hazard in 2011-2040 
5.5.3  Annual Damages in Euros to CPF Transport allocations 
from Heatwave hazard in 2041-2070 
5.5.4  Annual Damages in Euros of CPF Transport allocations 
from Heatwave hazard in 2071-2100 
5.6.1  Annual Damages in Euros to CPF Total allocations from 
Heatwave hazard in 1981-2010 
5.6.2  Annual Damages in Euros to CPF Total allocations from 
Heatwave hazard in 2011-2040 
5.6.3  Annual Damages in Euros to CPF Total allocations from 
Heatwave hazard in 2041-2070 
5.6.4  Annual Damages in Euros to CPF Total allocations from 
Heatwave hazard in 2071-2100 
6.1.1  Annual Damages in Euros to CPF Energy allocations from 
Coastal flood hazard in 1981-2010 
6.1.2  Annual Damages in Euros to CPF Energy allocations from 
Coastal flood hazard in 2011-2040 
6.1.3  Annual Damages in Euros to CPF Energy allocations from 
Coastal flood hazard in 2041-2070 
6.1.4  Annual Damages in Euros to CPF Energy allocations from 
Coastal flood hazard in 2071-2100 
6.2.1  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Coastal flood hazard in 1981-2010 
6.2.2  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Coastal flood hazard in 2011-2040 

166 

 
 
 
 
6.2.3.CPF_ts3_CF_ENV_TOUR 

6.2.4.CPF_ts4_CF_ENV_TOUR 

6.3.1.CPF_ts1_CF_ICT 

6.3.2.CPF_ts2_CF_ICT 

6.3.3.CPF_ts3_CF_ICT 

6.3.4.CPF_ts4_CF_ICT 

6.4.1.CPF_ts1_CF_SOCIAL 

6.4.2.CPF_ts2_CF_SOCIAL 

6.4.3.CPF_ts3_CF_SOCIAL 

6.4.4.CPF_ts4_CF_SOCIAL 

6.5.1.CPF_ts1_CF_TRANS 

6.5.2.CPF_ts2_CF_TRANS 

6.5.3.CPF_ts3_CF_TRANS 

6.5.4.CPF_ts4_CF_TRANS 

6.6.1.CPF_ts1_CF_TOTAL 

6.6.2.CPF_ts2_CF_TOTAL 

6.6.3.CPF_ts3_CF_TOTAL 

6.6.4.CPF_ts4_CF_TOTAL 

7.1.1.CPF_ts1_W_ENER 

7.1.4.CPF_ts4_W_ENER 

7.2.1.CPF_ts1_W_ENV_TOUR 

7.2.4.CPF_ts4_W_ENV_TOUR 

7.3.1.CPF_ts1_W_ICT 

7.3.4.CPF_ts4_W_ICT 

7.4.1.CPF_ts1_W_SOCIAL 

7.4.4.CPF_ts4_W_SOCIAL 

6.2.3  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Coastal flood hazard in 2041-2070 
6.2.4  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Coastal flood hazard in 2071-2100 
6.3.1  Annual Damages in Euros to CPF I.C.T. allocations from 
Coastal flood hazard in 1981-2010 
6.3.2  Annual Damages in Euros to CPF I.C.T. allocations from 
Coastal flood hazard in 2011-2040 
6.3.3  Annual Damages in Euros to CPF I.C.T. allocations from 
Coastal flood hazard in 2041-2070 
6.3.4  Annual Damages in Euros to CPF I.C.T. allocations from 
Coastal flood hazard in 2071-2100 
6.4.1  Annual Damages in Euros to CPF Social allocations from 
Coastal flood hazard in 1981-2010 
6.4.2  Annual Damages in Euros to CPF Social allocations from 
Coastal flood hazard in 2011-2040 
6.4.3  Annual Damages in Euros to CPF Social allocations from 
Coastal flood hazard in 2041-2070 
6.4.4  Annual Damages in Euros to CPF Social allocations from 
Coastal flood hazard in 2071-2100 
6.5.1  Annual Damages in Euros to CPF Transport allocations 
from Coastal flood hazard in 1981-2010 
6.5.2  Annual Damages in Euros to CPF Transport allocations 
from Coastal flood hazard in 2011-2040 
6.5.3  Annual Damages in Euros to CPF Transport allocations 
from Coastal flood hazard in 2041-2070 
6.5.4  Annual Damages in Euros to CPF Transport allocations 
from Coastal flood hazard in 2071-2100 
6.6.1  Annual Damages in Euros to CPF Total allocations from 
Coastal flood hazard in 1981-2010 
6.6.2  Annual Damages in Euros to CPF Total allocations from 
Coastal flood hazard in 2011-2040 
6.6.3  Annual Damages in Euros to CPF Total allocations from 
Coastal flood hazard in 2041-2070 
6.6.4  Annual Damages in Euros to CPF Total allocations from 
Coastal flood hazard in 2071-2100 
7.1.1  Annual Damages in Euros to CPF Energy allocations from 
Windstorm hazard in 1981-2010 
7.1.4  Annual Damages in Euros to CPF Energy allocations from 
Windstorm hazard in 2071-2100 
7.2.1  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Windstorm hazard in 1981-2010 
7.2.4  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Windstorm hazard in 2071-2100 
7.3.1  Annual Damages in Euros to CPF I.C.T. allocations from 
Windstorm hazard in 1981-2010 
7.3.4  Annual Damages in Euros to CPF I.C.T. allocations from 
Windstorm hazard in 2071-2100 
7.4.1  Annual Damages in Euros to CPF Social allocations from 
Windstorm hazard in 1981-2010 
7.4.4  Annual Damages in Euros to CPF Social allocations from 
Windstorm hazard in 2071-2100 

167 

 
 
 
 
7.5.1.CPF_ts1_W_TRANS 

7.5.4.CPF_ts4_W_TRANS 

7.6.1.CPF_ts1_W_TOTAL 

7.6.4.CPF_ts4_W_TOTAL 

7.5.1  Annual Damages in Euros to CPF Transport allocations 
from Windstorm hazard in 1981-2010 
7.5.4  Annual Damages in Euros to CPF Transport allocations 
from Windstorm hazard in 2071-2100 
7.6.1  Annual Damages in Euros to CPF Total allocations from 
Windstorm hazard in 1981-2010 
7.6.4  Annual Damages in Euros to CPF Total allocations from 
Windstorm hazard in 2071-2100 

1B: CPF Multi-hazard maps 

Namefile  

Caption 

8.1.1 CPF_ts1_M_ENER 

8.1.2 CPF_ts2_M_ENER 

8.1.3 CPF_ts3_M_ENER 

8.1.4 CPF_ts4_M_ENER 

8.2.1 CPF_ts1_M_ENV_TOUR 

8.2.2 CPF_ts2_M_ENV_TOUR 

8.2.3 CPF_ts3_M_ENV_TOUR 

8.2.4 CPF_ts4_M_ENV_TOUR 

8.3.1 CPF_ts1_M_ICT 

8.3.2 CPF_ts2_M_ICT 

8.3.3 CPF_ts3_M_ICT 

8.3.4 CPF_ts4_M_ICT 

8.4.1 CPF_ts1_M_SOCIAL 

8.4.2 CPF_ts2_M_SOCIAL 

8.4.3 CPF_ts3_M_SOCIAL 

8.4.4 CPF_ts4_M_SOCIAL 

8.5.1 CPF_ts1_M_TRANS 

8.5.2 CPF_ts2_M_TRANS 

8.5.3 CPF_ts3_M_TRANS 

8.1.1  Annual Damages in Euros to CPF Energy allocations from 
Multi-hazards in 1981-2010 
8.1.2  Annual Damages in Euros to CPF Energy allocations from 
Multi-hazards in 2011-2040 
8.1.3  Annual Damages in Euros to CPF Energy allocations from 
Multi-hazards in 2041-2070 
8.1.4  Annual Damages in Euros to CPF Energy allocations from 
Multi-hazards in 2071-2100 
8.2.1  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Multi-hazards in 1981-2010 
8.2.2  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Multi-hazards in 2011-2040 
8.2.3  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Multi-hazards in 2041-2070 
8.2.4  Annual Damages in Euros to CPF Environment and 
Tourism allocations from Multi-hazards in 2071-2100 
8.3.1  Annual Damages in Euros to CPF I.C.T. allocations from 
Multi-hazards in 1981-2010 
8.3.2  Annual Damages in Euros to CPF I.C.T. allocations from 
Multi-hazards in 2011-2040 
8.3.3  Annual Damages in Euros to CPF I.C.T. allocations from 
Multi-hazards in 2041-2070 
8.3.4  Annual Damages in Euros to CPF I.C.T. allocations from 
Multi-hazards in 2071-2100 
8.4.1  Annual Damages in Euros to CPF Social allocations from 
Multi-hazards in 1981-2010 
8.4.2  Annual Damages in Euros to CPF Social allocations from 
Multi-hazards in 2011-2040 
8.4.3  Annual Damages in Euros to CPF Social allocations from 
Multi-hazards in 2041-2070 
8.4.4  Annual Damages in Euros to CPF Social allocations from 
Multi-hazards in 2071-2100 
8.5.1  Annual Damages in Euros to CPF Transport allocations 
from Multi-hazards in 1981-2010 
8.5.2  Annual Damages in Euros to CPF Transport allocations 
from Multi-hazards in 2011-2040 
8.5.3  Annual Damages in Euros to CPF Transport allocations 
from Multi-hazards in 2041-2070 

168 

 
 
 
 
 
 
 
8.5.4 CPF_ts4_M_TRANS 

8.6.1 CPF_ts1_M_TOTAL 

8.6.2 CPF_ts2_M_TOTAL 

8.6.3 CPF_ts3_M_TOTAL 

8.6.4 CPF_ts4_M_TOTAL 

8.5.4  Annual Damages in Euros to CPF Transport allocations 
from Multi-hazards in 2071-2100 
8.6.1  Annual Damages in Euros to CPF Total allocations from 
Multi-hazards in 1981-2010 
8.6.2  Annual Damages in Euros to CPF Total allocations from 
Multi-hazards in 2011-2040 
8.6.3  Annual Damages in Euros to CPF Total allocations from 
Multi-hazards in 2041-2070 
8.6.4  Annual Damages in Euros to CPF Total allocations from 
Multi-hazards in 2071-2100 

2A: CI single hazard maps 

Namefile  

Caption 

1.1.1 CI_ts1_C.ENER 

1.1.2 CI_ts2_C.ENER 

1.1.3 CI_ts3_C.ENER 

1.1.4 CI_ts4_C.ENER 

1.2.1 CI_ts1_C.INDUSTRIAL 

1.2.2 CI_ts2_C.INDUSTRIAL 

1.2.3 CI_ts3_C.INDUSTRIAL 

1.2.4 CI_ts4_C.INDUSTRIAL 

1.3.1 CI_ts1_C.SOCIAL 

1.3.2 CI_ts2_C.SOCIAL 

1.3.3 CI_ts3_C.SOCIAL 

1.3.4 CI_ts4_C.SOCIAL 

1.4.1 CI_ts1_C.TRANS 

1.4.2 CI_ts2_C.TRANS 

1.4.3 CI_ts3_C.TRANS 

1.4.4 CI_ts4_C.TRANS 

1.5.1 CI_ts1_C.TOTAL 

1.5.2 CI_ts2_C.TOTAL 

1.1.1  Annual Damages in Euros to Critical Energy 
Infrastructures from Coldwaves hazard in 1981-2010 
1.1.2  Annual Damages in Euros to Critical Energy 
Infrastructures from Coldwaves hazard in 2011-2040 
1.1.3  Annual Damages in Euros to Critical Energy 
Infrastructures from Coldwaves hazard in 2041-2070 
1.1.4  Annual Damages in Euros to Critical Energy 
Infrastructures from Coldwaves hazard in 2071-2100 
1.2.1  Annual Damages in Euros to Critical Industry 
Infrastructures from Coldwaves hazard in 1981-2010 
1.2.2  Annual Damages in Euros to Critical Industry 
Infrastructures from Coldwaves hazard in 2011-2040 
1.2.3  Annual Damages in Euros to Critical Industry 
Infrastructures from Coldwaves hazard in 2041-2070 
1.2.4  Annual Damages in Euros to Critical Industry 
Infrastructures from Coldwaves hazard in 2071-2100 
1.3.1  Annual Damages in Euros to Critical Social 
Infrastructures from Coldwaves hazard in 1981-2010 
1.3.2  Annual Damages in Euros to Critical Social 
Infrastructures from Coldwaves hazard in 2011-2040 
1.3.3  Annual Damages in Euros to Critical Social 
Infrastructures from Coldwaves hazard in 2041-2070 
1.3.4  Annual Damages in Euros to Critical Social 
Infrastructures from Coldwaves hazard in 2071-2100 
1.4.1  Annual Damages in Euros to Critical Transport 
Infrastructures from Coldwaves hazard in 1981-2010 
1.4.2  Annual Damages in Euros to Critical Transport 
Infrastructures from Coldwaves hazard in 2011-2040 
1.4.3  Annual Damages in Euros to Critical Transport 
Infrastructures from Coldwaves hazard in 2041-2070 
1.4.4  Annual Damages in Euros to Critical Transport 
Infrastructures from Coldwaves hazard in 2071-2100 
1.5.1  Annual Damages in Euros to Total Critical Infrastructures 
from Coldwaves hazard in 1981-2010 
1.5.2  Annual Damages in Euros to Total Critical Infrastructures 
from Coldwaves hazard in 2011-2040 

169 

 
 
 
 
 
 
 
1.5.3 CI_ts3_C.TOTAL 

1.5.4 CI_ts4_C.TOTAL 

2.1.1 CI_ts1_D.ENER 

2.1.2 CI_ts2_D.ENER 

2.1.3 CI_ts3_D.ENER 

2.1.4 CI_ts4_D.ENER 

2.2.1 CI_ts1_D.INDUSTRIAL 

2.2.2 CI_ts2_D.INDUSTRIAL 

2.2.3 CI_ts3_D.INDUSTRIAL 

2.2.4 CI_ts4_D.INDUSTRIAL 

2.3.1 CI_ts1_D.SOCIAL 

2.3.2 CI_ts2_D.SOCIAL 

2.3.3 CI_ts3_D.SOCIAL 

2.3.4 CI_ts4_D.SOCIAL 

2.4.1 CI_ts1_D.TRANS 

2.4.2 CI_ts2_D.TRANS 

2.4.3 CI_ts3_D.TRANS 

2.4.4 CI_ts4_D.TRANS 

2.5.1 CI_ts1_D.TOTAL 

2.5.2 CI_ts2_D.TOTAL 

2.5.3 CI_ts3_D.TOTAL 

2.5.4 CI_ts4_D.TOTAL 

3.1.1 CI_ts1_F.ENER 

3.1.2 CI_ts2_F.ENER 

3.1.3 CI_ts3_F.ENER 

3.1.4 CI_ts4_F.ENER 

1.5.3  Annual Damages in Euros to Total Critical Infrastructures 
from Coldwaves hazard in 2041-2070 
1.5.4  Annual Damages in Euros to Total Critical Infrastructures 
from Coldwaves hazard in 2071-2100 
2.1.1  Annual Damages in Euros to Critical Energy 
Infrastructures from Drought hazard in 1981-2010 
2.1.2  Annual Damages in Euros to Critical Energy 
Infrastructures from Drought hazard in 2011-2040 
2.1.3  Annual Damages in Euros to Critical Energy 
Infrastructures from Drought hazard in 2041-2070 
2.1.4  Annual Damages in Euros to Critical Energy 
Infrastructures from Drought hazard in 2071-2100 
2.2.1  Annual Damages in Euros to Critical Industry 
Infrastructures from Drought hazard in 1981-2010 
2.2.2  Annual Damages in Euros to Critical Industry 
Infrastructures from Drought hazard in 2011-2040 
2.2.3  Annual Damages in Euros to Critical Industry 
Infrastructures from Drought hazard in 2041-2070 
2.2.4  Annual Damages in Euros to Critical Industry 
Infrastructures from Drought hazard in 2071-2100 
2.3.1  Annual Damages in Euros to Critical Social 
Infrastructures from Drought hazard in 1981-2010 
2.3.2  Annual Damages in Euros to Critical Social 
Infrastructures from Drought hazard in 2011-2040 
2.3.3  Annual Damages in Euros to Critical Social 
Infrastructures from Drought hazard in 2041-2070 
2.3.4  Annual Damages in Euros to Critical Social 
Infrastructures from Drought hazard in 2071-2100 
2.4.1  Annual Damages in Euros to Critical Transport 
Infrastructures from Drought hazard in 1981-2010 
2.4.2  Annual Damages in Euros to Critical Transport 
Infrastructures from Drought hazard in 2011-2040 
2.4.3  Annual Damages in Euros to Critical Transport 
Infrastructures from Drought hazard in 2041-2070 
2.4.4  Annual Damages in Euros to Critical Transport 
Infrastructures from Drought hazard in 2071-2100 
2.5.1  Annual Damages in Euros to Total Critical Infrastructures 
from Drought hazard in 1981-2010 
2.5.2  Annual Damages in Euros to Total Critical Infrastructures 
from Drought hazard in 2011-2040 
2.5.3  Annual Damages in Euros to Total Critical Infrastructures 
from Drought hazard in 2041-2070 
2.5.4  Annual Damages in Euros to Total Critical Infrastructures 
from Drought hazard in 2071-2100 
3.1.1  Annual Damages in Euros to Critical Energy 
Infrastructures from Wildfire hazard in 1981-2010 
3.1.2  Annual Damages in Euros to Critical Energy 
Infrastructures from Wildfire hazard in 2011-2040 
3.1.3  Annual Damages in Euros to Critical Energy 
Infrastructures from Wildfire hazard in 2041-2070 
3.1.4  Annual Damages in Euros to Critical Energy 
Infrastructures from Wildfire hazard in 2071-2100 

170 

 
 
 
 
3.2.1 CI_ts1_F.INDUSTRIAL 

3.2.2 CI_ts2_F.INDUSTRIAL 

3.2.3 CI_ts3_F.INDUSTRIAL 

3.2.4 CI_ts4_F.INDUSTRIAL 

3.3.1 CI_ts1_F.SOCIAL 

3.3.2 CI_ts2_F.SOCIAL 

3.3.3 CI_ts3_F.SOCIAL 

3.3.4 CI_ts4_F.SOCIAL 

3.4.1 CI_ts1_F.TRANS 

3.4.2 CI_ts2_F.TRANS 

3.4.3 CI_ts3_F.TRANS 

3.4.4 CI_ts4_F.TRANS 

3.5.1 CI_ts1_F.TOTAL 

3.5.2 CI_ts2_F.TOTAL 

3.5.3 CI_ts3_F.TOTAL 

3.5.4 CI_ts4_F.TOTAL 

4.1.1 CI_ts1_RF.ENER 

4.1.2 CI_ts2_RF.ENER 

4.1.3 CI_ts3_RF.ENER 

4.1.4 CI_ts4_RF.ENER 

4.2.1 CI_ts1_RF.INDUSTRIAL 

4.2.2 CI_ts2_RF.INDUSTRIAL 

4.2.3 CI_ts3_RF.INDUSTRIAL 

4.2.4 CI_ts4_RF.INDUSTRIAL 

4.3.1 CI_ts1_RF.SOCIAL 

4.3.2 CI_ts2_RF.SOCIAL 

3.2.1  Annual Damages in Euros to Critical Industry 
Infrastructures from Wildfire hazard in 1981-2010 
3.2.2  Annual Damages in Euros to Critical Industry 
Infrastructures from Wildfire hazard in 2011-2040 
3.2.3  Annual Damages in Euros to Critical Industry 
Infrastructures from Wildfire hazard in 2041-2070 
3.2.4  Annual Damages in Euros to Critical Industry 
Infrastructures from Wildfire hazard in 2071-2100 
3.3.1  Annual Damages in Euros to Critical Social 
Infrastructures from Wildfire hazard in 1981-2010 
3.3.2  Annual Damages in Euros to Critical Social 
Infrastructures from Wildfire hazard in 2011-2040 
3.3.3  Annual Damages in Euros to Critical Social 
Infrastructures from Wildfire hazard in 2041-2070 
3.3.4  Annual Damages in Euros to Critical Social 
Infrastructures from Wildfire hazard in 2071-2100 
3.4.1  Annual Damages in Euros to Critical Transport 
Infrastructures from Wildfire hazard in 1981-2010 
3.4.2  Annual Damages in Euros to Critical Transport 
Infrastructures from Wildfire hazard in 2011-2040 
3.4.3  Annual Damages in Euros to Critical Transport 
Infrastructures from Wildfire hazard in 2041-2070 
3.4.4  Annual Damages in Euros to Critical Transport 
Infrastructures from Wildfire hazard in 2071-2100 
3.5.1  Annual Damages in Euros to Total Critical Infrastructures 
from Wildfire hazard in 1981-2010 
3.5.2  Annual Damages in Euros to Total Critical Infrastructures 
from Wildfire hazard in 2011-2040 
3.5.3  Annual Damages in Euros to Total Critical Infrastructures 
from Wildfire hazard in 2041-2070 
3.5.4  Annual Damages in Euros to Total Critical Infrastructures 
from Wildfire hazard in 2071-2100 
4.1.1  Annual Damages in Euros to Critical Energy 
Infrastructures from River Flood hazard in 1981-2010 
4.1.2  Annual Damages in Euros to Critical Energy 
Infrastructures from River Flood hazard in 2011-2040 
4.1.3  Annual Damages in Euros to Critical Energy 
Infrastructures from River Flood hazard in 2041-2070 
4.1.4  Annual Damages in Euros to Critical Energy 
Infrastructures from River Flood hazard in 2071-2100 
4.2.1  Annual Damages in Euros to Critical Industry 
Infrastructures from River Flood hazard in 1981-2010 
4.2.2  Annual Damages in Euros to Critical Industry 
Infrastructures from River Flood hazard in 2011-2040 
4.2.3  Annual Damages in Euros to Critical Industry 
Infrastructures from River Flood hazard in 2041-2070 
4.2.4  Annual Damages in Euros to Critical Industry 
Infrastructures from River Flood hazard in 2071-2100 
4.3.1  Annual Damages in Euros to Critical Social 
Infrastructures from River Flood hazard in 1981-2010 
4.3.2  Annual Damages in Euros to Critical Social 
Infrastructures from River Flood hazard in 2011-2040 

171 

 
 
 
 
4.3.3 CI_ts3_RF.SOCIAL 

4.3.4 CI_ts4_RF.SOCIAL 

4.4.1 CI_ts1_RF.TRANS 

4.4.2 CI_ts2_RF.TRANS 

4.4.3 CI_ts3_RF.TRANS 

4.4.4 CI_ts4_RF.TRANS 

4.5.1 CI_ts1_RF.TOTAL 

4.5.2 CI_ts2_RF.TOTAL 

4.5.3 CI_ts3_RF.TOTAL 

4.5.4 CI_ts4_RF.TOTAL 

5.1.1 CI_ts1_H.ENER 

5.1.2 CI_ts2_H.ENER 

5.1.3 CI_ts3_H.ENER 

5.1.4 CI_ts4_H.ENER 

5.2.1 CI_ts1_H.INDUSTRIAL 

5.2.2 CI_ts2_H.INDUSTRIAL 

5.2.3 CI_ts3_H.INDUSTRIAL 

5.2.4 CI_ts4_H.INDUSTRIAL 

5.3.1 CI_ts1_H.SOCIAL 

5.3.2 CI_ts2_H.SOCIAL 

5.3.3 CI_ts3_H.SOCIAL 

5.3.4 CI_ts4_H.SOCIAL 

5.4.1 CI_ts1_H.TRANS 

5.4.2 CI_ts2_H.TRANS 

5.4.3 CI_ts3_H.TRANS 

5.4.4 CI_ts4_H.TRANS 

4.3.3  Annual Damages in Euros to Critical Social 
Infrastructures from River Flood hazard in 2041-2070 
4.3.4  Annual Damages in Euros to Critical Social 
Infrastructures from River Flood hazard in 2071-2100 
4.4.1  Annual Damages in Euros to Critical Transport 
Infrastructures from River Flood hazard in 1981-2010 
4.4.2  Annual Damages in Euros to Critical Transport 
Infrastructures from River Flood hazard in 2011-2040 
4.4.3  Annual Damages in Euros to Critical Transport 
Infrastructures from River Flood hazard in 2041-2070 
4.4.4  Annual Damages in Euros to Critical Transport 
Infrastructures from River Flood hazard in 2071-2100 
4.5.1  Annual Damages in Euros to Total Critical Infrastructures 
from River Flood hazard in 1981-2010 
4.5.2  Annual Damages in Euros to Total Critical Infrastructures 
from River Flood hazard in 2011-2040 
4.5.3  Annual Damages in Euros to Total Critical Infrastructures 
from River Flood hazard in 2041-2070 
4.5.4  Annual Damages in Euros to Total Critical Infrastructures 
from River Flood hazard in 2071-2100 
5.1.1  Annual Damages in Euros to Critical Energy 
Infrastructures from Heatwave hazard in 1981-2010 
5.1.2  Annual Damages in Euros to Critical Energy 
Infrastructures from Heatwave hazard in 2011-2040 
5.1.3  Annual Damages in Euros to Critical Energy 
Infrastructures from Heatwave hazard in 2041-2070 
5.1.4  Annual Damages in Euros to Critical Energy 
Infrastructures from Heatwave hazard in 2071-2100 
5.2.1  Annual Damages in Euros to Critical Industry 
Infrastructures from Heatwave hazard in 1981-2010 
5.2.2  Annual Damages in Euros to Critical Industry 
Infrastructures from Heatwave hazard in 2011-2040 
5.2.3  Annual Damages in Euros to Critical Industry 
Infrastructures from Heatwave hazard in 2041-2070 
5.2.4  Annual Damages in Euros to Critical Industry 
Infrastructures from Heatwave hazard in 2071-2100 
5.3.1  Annual Damages in Euros to Critical Social 
Infrastructures from Heatwave hazard in 1981-2010 
5.3.2  Annual Damages in Euros to Critical Social 
Infrastructures from Heatwave hazard in 2011-2040 
5.3.3  Annual Damages in Euros to Critical Social 
Infrastructures from Heatwave hazard in 2041-2070 
5.3.4  Annual Damages in Euros to Critical Social 
Infrastructures from Heatwave hazard in 2071-2100 
5.4.1  Annual Damages in Euros to Critical Transport 
Infrastructures from Heatwave hazard in 1981-2010 
5.4.2  Annual Damages in Euros to Critical Transport 
Infrastructures from Heatwave hazard in 2011-2040 
5.4.3  Annual Damages in Euros to Critical Transport 
Infrastructures from Heatwave hazard in 2041-2070 
5.4.4  Annual Damages in Euros to Critical Transport 
Infrastructures from Heatwave hazard in 2071-2100 

172 

 
 
 
 
5.5.1 CI_ts1_H.TOTAL 

5.5.2 CI_ts2_H.TOTAL 

5.5.3 CI_ts3_H.TOTAL 

5.5.4 CI_ts4_H.TOTAL 

6.1.1 CI_ts1_CF.ENER 

6.1.2 CI_ts2_CF.ENER 

6.1.3 CI_ts3_CF.ENER 

6.1.4 CI_ts4_CF.ENER 

6.3.1 CI_ts1_CF.INDUSTRIAL 

6.3.2 CI_ts2_CF.INDUSTRIAL 

6.3.3 CI_ts3_CF.INDUSTRIAL 

6.3.4 CI_ts4_CF.INDUSTRIAL 

6.4.1 CI_ts1_CF.SOCIAL 

6.4.2 CI_ts2_CF.SOCIAL 

6.4.3 CI_ts3_CF.SOCIAL 

6.4.4 CI_ts4_CF.SOCIAL 

6.5.1 CI_ts1_CF.TRANS 

6.5.2 CI_ts2_CF.TRANS 

6.5.3 CI_ts3_CF.TRANS 

6.5.4 CI_ts4_CF.TRANS 

6.6.1 CI_ts1_CF.TOTAL 

6.6.2 CI_ts2_CF.TOTAL 

6.6.3 CI_ts3_CF.TOTAL 

6.6.4 CI_ts4_CF.TOTAL 

7.1.1 CI_ts1_W.ENER 

7.1.4 CI_ts4_W.ENER 

5.5.1  Annual Damages in Euros to Total Critical Infrastructures 
from Heatwave hazard in 1981-2010 
5.5.2  Annual Damages in Euros to Total Critical Infrastructures 
from Heatwave hazard in 2011-2040 
5.5.3  Annual Damages in Euros to Total Critical Infrastructures 
from Heatwave hazard in 2041-2070 
5.5.4  Annual Damages in Euros to Total Critical Infrastructures 
from Heatwave hazard in 2071-2100 
6.1.1  Annual Damages in Euros to Critical Energy 
Infrastructures from Coastal Flood hazard in 1981-2010 
6.1.2  Annual Damages in Euros to Critical Energy 
Infrastructures from Coastal Flood hazard in 2011-2040 
6.1.3  Annual Damages in Euros to Critical Energy 
Infrastructures from Coastal Flood hazard in 2041-2070 
6.1.4  Annual Damages in Euros to Critical Energy 
Infrastructures from Coastal Flood hazard in 2071-2100 
6.3.1  Annual Damages in Euros to Critical Industry 
Infrastructures from Coastal Flood hazard in 1981-2010 
6.3.2  Annual Damages in Euros to Critical Industry 
Infrastructures from Coastal Flood hazard in 2011-2040 
6.3.3  Annual Damages in Euros to Critical Industry 
Infrastructures from Coastal Flood hazard in 2041-2070 
6.3.4  Annual Damages in Euros to Critical Industry 
Infrastructures from Coastal Flood hazard in 2071-2100 
6.4.1  Annual Damages in Euros to Critical Social 
Infrastructures from Coastal Flood hazard in 1981-2010 
6.4.2  Annual Damages in Euros to Critical Social 
Infrastructures from Coastal Flood hazard in 2011-2040 
6.4.3  Annual Damages in Euros to Critical Social 
Infrastructures from Coastal Flood hazard in 2041-2070 
6.4.4  Annual Damages in Euros to Critical Social 
Infrastructures from Coastal Flood hazard in 2071-2100 
6.5.1  Annual Damages in Euros to Critical Transport 
Infrastructures from Coastal Flood hazard in 1981-2010 
6.5.2  Annual Damages in Euros to Critical Transport 
Infrastructures from Coastal Flood hazard in 2011-2040 
6.5.3  Annual Damages in Euros to Critical Transport 
Infrastructures from Coastal Flood hazard in 2041-2070 
6.5.4  Annual Damages in Euros to Critical Transport 
Infrastructures from Coastal Flood hazard in 2071-2100 
6.6.1  Annual Damages in Euros to Total Critical Infrastructures 
from Coastal Flood hazard in 1981-2010 
6.6.2  Annual Damages in Euros to Total Critical Infrastructures 
from Coastal Flood hazard in 2011-2040 
6.6.3  Annual Damages in Euros to Total Critical Infrastructures 
from Coastal Flood hazard in 2041-2070 
6.6.4  Annual Damages in Euros to Total Critical Infrastructures 
from Coastal Flood hazard in 2071-2100 
7.1.1  Annual Damages in Euros to Critical Energy 
Infrastructures from Windstorm hazard in 1981-2010 
7.1.4  Annual Damages in Euros to Critical Energy 
Infrastructures from Windstorm hazard in 2071-2100 

173 

 
 
 
 
7.2.1 CI_ts1_W.INDUSTRIAL 

7.2.4 CI_ts4_W.INDUSTRIAL 

7.3.1 CI_ts1_W.SOCIAL 

7.3.4 CI_ts4_W.SOCIAL 

7.4.1 CI_ts1_W.TRANS 

7.4.4 CI_ts4_W.TRANS 

7.5.1 CI_ts1_W.TOTAL 

7.5.4 CI_ts4_W.TOTAL 

7.2.1  Annual Damages in Euros to Critical Industry 
Infrastructures from Windstorm hazard in 1981-2010 
7.2.4  Annual Damages in Euros to Critical Industry 
Infrastructures from Windstorm hazard in 2071-2100 
7.3.1  Annual Damages in Euros to Critical Social 
Infrastructures from Windstorm hazard in 1981-2010 
7.3.4  Annual Damages in Euros to Critical Social 
Infrastructures from Windstorm hazard in 2071-2100 
7.4.1  Annual Damages in Euros to Critical Transport 
Infrastructures from Windstorm hazard in 1981-2010 
7.4.4  Annual Damages in Euros to Critical Transport 
Infrastructures from Windstorm hazard in 2071-2100 
7.5.1  Annual Damages in Euros to Total Critical Infrastructures 
from Windstorm hazard in 1981-2010 
7.5.4  Annual Damages in Euros to Total Critical Infrastructures 
from Windstorm hazard in 2071-2100 

2B: CI Multi-hazard maps 

Namefile  

Caption 

8.1.1 CI_ts1_M.ENER 

8.1.2 CI_ts2_M.ENER 

8.1.3 CI_ts3_M.ENER 

8.1.4 CI_ts4_M.ENER 

8.2.1 CI_ts1_M.INDUSTRIAL 

8.2.2 CI_ts2_M.INDUSTRIAL 

8.2.3 CI_ts3_M.INDUSTRIAL 

8.2.4 CI_ts4_M.INDUSTRIAL 

8.3.1 CI_ts1_M.SOCIAL 

8.3.2 CI_ts2_M.SOCIAL 

8.3.3 CI_ts3_M.SOCIAL 

8.3.4 CI_ts4_M.SOCIAL 

8.4.1 CI_ts1_M.TRANS 

8.4.2 CI_ts2_M.TRANS 

8.4.3 CI_ts3_M.TRANS 

8.1.1  Annual Damages in Euros to Critical Energy 
Infrastructures from Multi-hazards in 1981-2010 
8.1.2  Annual Damages in Euros to Critical Energy 
Infrastructures from Multi-hazards in 2011-2040 
8.1.3  Annual Damages in Euros to Critical Energy 
Infrastructures from Multi-hazards in 2041-2070 
8.1.4  Annual Damages in Euros to Critical Energy 
Infrastructures from Multi-hazards in 2071-2100 
8.2.1  Annual Damages in Euros to Critical Industry 
Infrastructures from Multi-hazards in 1981-2010 
8.2.2  Annual Damages in Euros to Critical Industry 
Infrastructures from Multi-hazards in 2011-2040 
8.2.3  Annual Damages in Euros to Critical Industry 
Infrastructures from Multi-hazards in 2041-2070 
8.2.4  Annual Damages in Euros to Critical Industry 
Infrastructures from Multi-hazards in 2071-2100 
8.3.1  Annual Damages in Euros to Critical Social 
Infrastructures from Multi-hazards in 1981-2010 
8.3.2  Annual Damages in Euros to Critical Social 
Infrastructures from Multi-hazards in 2011-2040 
8.3.3  Annual Damages in Euros to Critical Social 
Infrastructures from Multi-hazards in 2041-2070 
8.3.4  Annual Damages in Euros to Critical Social 
Infrastructures from Multi-hazards in 2071-2100 
8.4.1  Annual Damages in Euros to Critical Transport 
Infrastructures from Multi-hazards in 1981-2010 
8.4.2  Annual Damages in Euros to Critical Transport 
Infrastructures from Multi-hazards in 2011-2040 
8.4.3  Annual Damages in Euros to Critical Transport 
Infrastructures from Multi-hazards in 2041-2070 

174 

 
 
 
 
 
 
 
8.4.4 CI_ts4_M.TRANS 

8.5.1 CI_ts1_M.TOTAL 

8.5.2 CI_ts2_M.TOTAL 

8.5.3 CI_ts3_M.TOTAL 

8.5.4 CI_ts4_M.TOTAL 

8.4.4  Annual Damages in Euros to Critical Transport 
Infrastructures from Multi-hazards in 2071-2100 
8.5.1  Annual Damages in Euros to Total Critical Infrastructures 
from Multi-hazards in 1981-2010 
8.5.2  Annual Damages in Euros to Total Critical Infrastructures 
from Multi-hazards in 2011-2040 
8.5.3  Annual Damages in Euros to Total Critical Infrastructures 
from Multi-hazards in 2041-2070 
8.5.4  Annual Damages in Euros to Total Critical Infrastructures 
from Multi-hazards in 2071-2100 

175 

 
 
 
 
 
 
 
 
 
 
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JRC Mission 

As the Commission’s  
in-house science service,  
the Joint Research Centre’s  
mission is to provide EU  
policies with independent,  
evidence-based scientific  
and technical support  
throughout the whole  
policy cycle. 

Working in close  
cooperation with policy  
Directorates-General,  
the JRC addresses key  
societal challenges while  
stimulating innovation  
through developing  
new methods, tools  
and standards, and sharing  
its know-how with  
the Member States,  
the scientific community  
and international partners. 

Serving society  
Stimulating innovation  
Supporting legislation 

doi:10.2788/171858 
ISBN 978-92-79-54003-5 

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