Insurance of weather and climate-related disaster risk : inventory and analysis of mechanisms to support damage prevention in the EU : final report.
Date: 2017-09-22
Institutions: Directorate-General for Climate Action
Subjects: bad weather, damage, environmental risk prevention, insurance, man-made disaster, natural disaster, report

Insurance of weather and climate-
related disaster risk: Inventory and 
analysis of mechanisms to support 
damage prevention in the EU 

Final report 

August 2017 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Directorate-General for Climate Action 
Directorate A — International and Mainstreaming 
Unit A.3 — Adaptation 

Contact: Max Linsen 

E-mail:  Max.LINSEN@ec.europa.eu  

European Commission 
BE-1049 Brussels

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Insurance of weather and 
climate-related disaster risk: 
Inventory and analysis of 
mechanisms to support 
damage prevention in the EU 

Final report 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Luxembourg: Publications Office of the European Union, 2017 

PDF  

ISBN 978-92-79-73173-0  

doi: 10.2834/40222  

ML-04-17-809-EN-N 

© European Union, 2017 

Reproduction is authorised provided the source is acknowledged. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Date 

31 August 2017 

Made by 

Xavier Le Den, Matilda Persson, Audrey Benoist 
(Ramboll) 
Paul Hudson, Marleen de Ruiter, Lars de Ruig, 
Onno Kuik (IVM) 

Checked by 

Christian Nyerup Nielsen (Ramboll) 
Wouter Botzen (IVM) 

Acknowledgments  The authors are grateful for constructive 

comments and suggestions from the different 
organisations listed in the Annex. The contribution 
of the members of the focus group is particularly 
acknowledged 

i 

 
 
 
 
 
 
 
 
 
 
CONTENTS  

1. 
1.1 
1.2 
1.3 

2. 
2.1 
2.2 
2.3 
2.4 

3. 

3.1 
3.2 

4. 

4.1 
4.2 
4.3 
4.4 

5. 

5.1 
5.2 
5.3 

6. 

6.1 
6.2 
6.3 

7. 
7.1 
7.2 
7.3 
7.4 
7.5 

EXECUTIVE SUMMARY 

INTRODUCTION 
Background 
Purpose of this study 
Methodology 

PAST AND FUTURE EXTREME WEATHER LOSSES 
Observed losses between 1990 and 2015 
Projected losses for between 2030 and 2050 
Loss drivers of current and future impacts 
Chapter conclusions 

PRESENTATION OF CASE STUDIES AND OVERVIEW OF THEIR 
APPROACHES TO INSURE EXTREME WEATHER RISK 
Case study selection and description 
Chapter conclusion: Case study market structures 

IV 

1 
1 
2 
4 

5 
5 
13 
17 
21 

23 
23 
28 

OVERVIEW OF INSURANCE MARKET PERFORMANCE AND COST-
30 
EFFECTIVENESS OF INSURANCE MARKET APPROACHES 
30 
Definition of the process chain in extreme weather insurance 
Methodology: Definition of cost-effectiveness of insurance mechanisms  31 
34 
Summary of insurance market outcomes 
45 
Summary of mechanisms to incentivise risk reduction 

GOOD PRACTICE FOR THE COST-EFFECTIVENESS OF INSURANCE 
53 
MARKET APPROACHES 
How to determine good practice or the cost-effectiveness of insurance  53 
54 
Outcome of the MCA: best practice 
Outcome of the MCA: overall conclusions – an absence of risk reduction  64 

THE NEXT STEPS IN INSURING WEATHER AND CLIMATE-RELATED 
EXTREME EVENTS 
Outcomes from the MCA and policy recommendations 
Additional considerations from the stakeholder consultation 
Overview of policy themes and recommendations 

69 
69 
75 
78 

RECOMMENDATIONS TO THE EUROPEAN COMMISSION 
Increase reporting requirements and capacity building 
Promote risk awareness and reduction 
Closing the protection gap 
Support PPPs and cross-organisational collaboration 
Increase the role of cities and regions 

82 
83 
85 
87 
87 
89 

ii 

 
 
 
 
 
 
 
 
 
 
 
 
APPENDICES 

Appendix 1 
 References 

Appendix 2 
 Case study narratives and attribute tables 

Appendix 3 
 MCA Scoring 

Appendix 4 
 Stakeholder Consultation 

Appendix 5 
 Report on Final event (30.06.2017) 

Appendix 6 
 Distributed Agenda for the Final event 

iii 

 
 
 
 
 
 
 
 
 
 
EXECUTIVE SUMMARY 

Ramboll  and  the  Institute  for  Environmental  Studies  (IVM)  were  contracted  by  the  European 
Commission  (EC)  to  conduct  a  study  on  the  insurance  of  weather  and  climate-related  disaster 
risk,  and  to  create  an  inventory  and  analysis  of  mechanisms  to  support  damage  prevention in 
the  European  Union  (EU).  The  study  provides  an  overview  of  the  use  of  insurance  against 
natural disasters. It suggests general recommendations as well as specific recommendations on 
the  role  of  the  European  Commission  in  addressing  the  issues  uncovered,  and  encourages 
stakeholder’s efforts and best practices observed across the EU. 

Objectives of the study 

The  study  builds  on  the  EU  Adaptation  Strategy  (EC,  2013a)  and  the  Green  Paper  on  the 
insurance  of  natural  and  man-made  disasters  (EC,  2013b),  and  addresses  the  objective  of 
encouraging  the  use  of  insurance  to  manage  weather  and  climate-related  disaster  risk.  More 
specifically,  the  study  supports  the  EU  Adaptation  Strategy  through:  (1)  Increasing  the 
knowledge  base  by  collecting  and  providing  information  with  regards  to  insurance  coverage, 
mechanisms  and  cost-effectiveness,  including  preventive  capacity;  (2)  Increasing  awareness, 
promoting further action and defining the next steps in insuring extreme weather events.  

Methodology 

The  study  consists  of  the  following  tasks:  (1)  Stock-taking  of  insurance  mechanisms  covering 
weather-related  disaster  risks,  applied  in  (and  beyond)  the  EU,  (2)  Analysis  of  the  cost-
effectiveness of  insurance  mechanisms, including  preventive capacity and an analysis of which 
mechanisms incentivise prevention of risk and support damage reduction, and (3) Definition of 
next steps in insuring weather and climate-related extreme events. 

In Task 1, 12 case study countries were selected, based on a comprehensive overview of all EU 
countries of weather-related insurances for different sectors and perils (i.e. floods, droughts and 
windstorms), as well as the following criteria: geographical balance, wide coverage of extreme 
weather events, coverage of different styles of insurance provision, coverage of different sectors 
and data availability. The criteria were assumed to ensure that the conclusions from the 12 case 
studies  could  also  be  applied  to  the  Member  States  not  selected.  The  countries  studied  were: 
Bulgaria, Denmark, Germany, Spain, France, Italy, Hungary, Austria, Poland, Romania, Sweden 
and the United Kingdom.  

Task  2  defined  and  assessed  the  cost-effectiveness  of  insurance  mechanisms,  including  their 
preventive  capacity.  Firstly,  the  cost  effectiveness  of  weather-related  insurance  mechanisms 
was  determined,  and  secondly,  mechanisms  that  incentivise  prevention  of  risk  and  support 
damage reduction were analysed. For each natural disaster type, a systematic literature review 
was  conducted  and  supported  by  stakeholder  surveys  and  data  obtained  from  EM-DAT1, 
NatCatService2 and Sigma3 datasets. In addition, on a policyholder level, information on existing 
and innovative adaptation measures was collected per hazard type. The final views on the cost-
effectiveness  of  insurance  were  judged  by  comparing  qualitative  or  quantitative  outcomes 
against what various stakeholders consulted in this study viewed as the most important criteria 
for insurance: to perform well. This culminated in a multi-criteria analysis (MCA) of the selected 
national  insurance  schemes  in  the  private  property  and  agriculture  sectors..  The  commercial 
sector  was  not  selected  for  this  MCA  due  to  a  lack  of  available  data  and  because  it  shares 
similarities in insurance provision with the private property sector. 

1 http://www.emdat.be/ 
2 https://www.munichre.com/en/reinsurance/business/non-life/natcatservice/index.html 
3 http://institute.swissre.com/research/overview/ 

iv 

 
 
 
 
 
 
 
 
                                                
The  MCA  framework  was  based  on  five  evaluation  criteria:  insurance  penetration  rates,  risk 
signalling and risk reduction incentives, the affordability and availability of insurance, the speed 
of payments, and the overall solvency of the insurance mechanism. Using the MCA, elements of 
cost-effectiveness  of  weather-related  insurance  schemes  for  private  properties  and  agriculture 
that  are  present  in  the  12  selected  case  study  countries  were  assessed  and  evaluated. 
Thereafter,  it  focused  on  the  cases  that  scored  the  top  two  highest  scores  for  a  given  MCA 
weighting scheme. 

The weighting schemes were defined by different risk management objectives. For example, the 
same insurance product may fulfil a policy objective related to solidarity and coverage, while it 
does not score well from the point of view of risk management. The three key objectives studied 
were  based  on:  Weighting  scheme  1:  solidarity  and  coverage;  Weighting  scheme  3:  ability  of 
insurance  to  act  as  an  adaptation  signal  or  risk  management  incentive;  and  an  intermediary 
scheme, Weighting scheme 2: insurance as a private good, balancing the different objectives of 
the relevant stakeholders. 

Conclusions from taking stock of the innovative approaches and the case studies fed into  Task 
3.  A  questionnaire  was  circulated  to  stakeholders  within  the  case  study  countries  to  generate 
their  views  regarding  future  natural  disaster  insurance  and  how  this  can  support  damage 
prevention.  Parallel  to  the  case  studies  and  throughout  the  project,  interviews  with  a  broad 
range  of  stakeholders  were  conducted.  This  was  done  to  ensure  that  the  analysis  for the  next 
steps  goes  beyond  the  case  studies,  and  that  viewpoints  from  the  different  types  of 
stakeholders are taken into consideration. In order to get detailed feedback on the findings on 
the  case  studies  as  well  as  the  next  steps,  a  focus  group  of  recognised  experts  was  formed, 
which  held  two  meetings.  A  final  event  with  80  participants,  representing  a  broad  range  of 
stakeholders, provided an opportunity to communicate the results and recommendations of the 
study, and to receive valuable feedback.  

Main findings of the study 

An  examination  of  a  combination  of  available  loss  datasets  for  the  selected  European  cases 
shows  it is not yet very  clear whether there  is an  increase in  monetary impacts from  extreme 
weather events. The key loss drivers are increases in wealth in general, and increased exposure 
of assets and infrastructure in hazard-prone areas (EEA, 2017). The effect of climate change is 
by comparison more difficult to determine due to the natural variability of natural hazards and 
often unobserved changes in vulnerability, especially when the data used is primarily focused on 
monetary values. Under most climate change scenarios, the intensity and frequency of extreme 
weather events will increase in the future. This implies that probably the damage costs will also 
increase,  unless  more  proactive  disaster  risk  management  measures  are  taken.  This  is  a  key 
argument  for  policy-makers  and  other  actors  within  countries  and  the  Commission  involved  in 
adaptation  policies  and  actions,  including  insurance  mechanisms,  to  take  note  (EEA,  2017; 
Kurnik, 2017).  

The risk profile across the case  studies  and by extension  across the  EU  is changing  towards  a 
situation where extreme weather events are becoming more common, with an overall increase 
in  risk.  This  will  place  an  increasing  burden  on  the  budgets  of  policyholders,  insurers  and 
governments alike to absorb these impacts. 

Insurance  has  attracted  much  policy  attention  as  a  tool  for  building  resilience  to  extreme 
weather  events  by  providing  financial  compensation  for  losses  and  incentives  to  reduce  risk. 
Insurance  companies  share  financial  risk  across  policyholders,  and  risk-based  premiums  can 
incentivise  individual  policyholders  to  reduce  risk  (Hudson  et  al.,  2016).  However,  insurance 
becomes  less  attractive  for  high-risk  households  when  premiums  reflect  the  underlying  risk 

v 

 
 
 
 
 
 
 
 
(Botzen et al., 2009a). Although lower risk policyholders have a weaker incentive to reduce risk, 
they are more likely to buy insurance since premiums are more affordable.  

This trade-off between premium affordability and risk-reduction incentives is an important, yet 
difficult, challenge for insurance companies to balance, and is often influenced by different risk 
management  objectives  (Kunreuther,  1996;  Botzen  et  al.,  2009b;  Kunreuther  and  Michel-
Kerjan, 2009; Mechler et al., 2014; Penning-Rowsell and Pardoe, 2012; Surminski and Oramas-
Dorta,  2014).  The  differing  risk  management  objectives  of  the  stakeholders  show  that  there 
would  be  room  for  more  open  and  transparent  engagement  of,  and  collaboration  with,  the 
various stakeholders involved in the risk management process. 

On  the  whole  (across  extreme  weather  events),  insurance  at  affordable  rates, 
notwithstanding  individual  deviations  from  this,  is  available  in  the  countries  studied. 
Despite  the  relatively  good  performance,  there  is  still  room  for  improvement.  In  the  private 
property  sector  the  two  main  problems  to  overcome  are  insuring  localised  disasters,  such  as 
flooding,  and  promoting  households  to  buy  insurance  (the  two  may  be interconnected).  These 
two aspects are reflected in low private property insurance penetration rates. Households either 
do  not  fully  acknowledge  the  benefits  of  being  insured  against  extreme  weather  or  their 
willingness to pay is lower than the premiums charged.  

Generally,  the  countries  studied  do  not  perform  very  well  in  terms  of  providing 
incentives for risk reduction or signalling the risk. This comes from the overall reliance on 
deductibles  or  awareness  campaigns  for  achieving  risk  reduction.  These  incentives  provide 
indirect  risk  management  signals.  Awareness  campaigns  aim  to  readjust  risk  perceptions  by 
increasing  the  perceived  benefits  of  risk  management.  However,  unless  there  are  constant 
campaigns,  views  are  likely  to  revert  after  the  campaign  is  concluded.  Deductibles  by 
themselves are also unlikely to incentivise risk reduction (ex-ante) because  unless an extreme 
weather event occurs the deductible doesn’t provide a tangible incentive for the policyholder to 
act upon.  

There  are  several  reasons  why  this  relative  lack  of  focus  on  risk  reduction  has  occurred.  The 
first corresponds to the market’s focus on providing widespread or affordable coverage, 
which  can  reduce  the  focus  on  risk  reduction.  A  second  barrier  is  the  combination  of  the 
indemnity  principle  (i.e.  the  insured  is  compensated  for  what  is  lost)  and  competition 
between insurers. This can limit the extent to which insurers offer incentives or requirements 
for risk reduction as customers could instead take a policy with less stringent conditions. A third 
reason  is  that  there  could  be  information  asymmetries  or  transaction  costs  that  hamper 
offering  tangible  incentives  (such  as  premium  discounts)  for  a  wide  range  of  potential  risk-
reduction  measures.  The  costs  of  monitoring  the  employment  of  such  measures  could  be 
expensive. A final barrier can be seen as the lack of transparency in insurance premiums. 
It is quite common for insurance to be provided as a bundle of risks. This makes it difficult for a 
policyholder to identify the part of the premium that is charged for specific extreme weather risk 
and to know the degree of risk sharing across the insured population.  

Tables  1  and  2  highlight  the  consistent  characteristics  of  insurance  in  the  best  performing 
countries per sector. This provides an indication of the general features of a market that can be 
considered to be best practices, and highlights areas to focus upon. 

vi 

 
 
 
 
 
 
 
 
 
 
Table 1: Summary of features leading to high or low performances in the private property sector 

High-performing 

Low-performing 

  Multiple extreme weather risks are 

  Extreme weather risks are separately 

combined in a single policy 

insured 

  Lax enforcements of requirements to 

buy insurance 

  Low overall insurance coverage 

  Purchase of extreme weather insurance 
is connected to a far more commonly 
required and enforced product (e.g. 
mortgage contracts, fire insurance) 

  Collaboration between public and pri-
vate sectors with a commonly stated 
and understood objective  

This can be seen as a contract between 
the insurance sector and the govern-
ment, whereby each group takes ac-
tions that maintain the provision of in-
surance coverage. 

  Provision of a national pool or public 

reinsurance / support for catastrophic 
losses 

  Consumers are reliant on direct public 
compensation for extreme weather 
event losses 

Table 2: Summary of features leading to high or low performances in the agriculture sector 

High performing 

Low performing 

  The use of multi-risk insurance (with a 

  Only specific weather-event insurance 

focus on yield insurance) 

products are available  

  Requirements to insure all cultivated 

  Only land with a specific crop must be 

land 

insured 

  Premium subsidies to direct invest-

  The presence of ad-hoc government 

ment in multi-risk policies 

compensation not tied to insurance cov-
erage in the case of truly extreme 
events 

  Pool-like structures or public reinsur-
ance for systemic risks, such as 
droughts 

  A tradition of collaboration between 
the public and private sector risk 
managers 

Recommendations 

Policy  suggestions  to  overcome  these  barriers  can  come  in  many  forms,  and  the  stakeholder 
consultation highlighted several policy strategies to overcome them, which are listed below.  

A  first  one  could  be  to  place  the  responsibility  for  promoting  and  developing  risk  reduction 
strategies into the hands of an external body that collaborates with insurers. The exact nature 

vii 

 
 
 
 
 
 
 
 
 
 
 
of  such  an  external  body  is  difficult  to  determine  a  priori  across  countries;  however,  such  an 
organisation can operate directly or indirectly at various national levels (i.e. national, regional or 
city  level).  For  instance,  a  national  body  can  produce  investments  in  prevention  strategies  or 
larger scale risk-reduction strategies. These actions could facilitate a national minimum level of 
risk management and insurance  viability, upon which more localised bodies or agents can act. 
For  example,  a  city  can  collaborate  with  insurers  to  better  manage  their  risk  beyond  this 
minimum  level  imposed by the external  body. It  is difficult  to determine or propose  a uniform 
structure for such an organisation because EU Member States have varying levels of autonomy 
for cities or regions. Financial capacity for risk-reduction investments can be created by adding 
a  surcharge  to  insurance  premiums  into  a  fund  that  uses  the  money  raised  to  construct 
protection  and  other  large-scale  adaptation  measures,  or  to  subsidise  more  individual-level 
measures.  Potential  advantages  of  such  a  premium  surcharge  compared  with  financing  from 
general  taxation  is  that  such  funds  are  earmarked  for  risk  reduction,  and  that  the  surcharge 
acts as a signal of risk if premiums are at least partially risk-based. This fund can be a not-for-
profit  management  entity  in  which  insurers,  government  agencies  and  other  stakeholders  are 
involved. Moreover, such a management entity could be mainstreamed into a country’s overall 
climate change adaptation strategy. 

The second one is the improved use of insurer’s data and knowledge in developing zoning and 
building code regulations and construction requirements. Insurers often have good information 
on which areas are at high risk and which building-scale measures can lower risk, which is im-
portant  information  for  government  authorities  to  use  in  designing  zoning  and  building  code 
regulations.  An  advantage  is  that  such  measures  are  structural,  which  may  limit  information 
asymmetries  that  could  arise  with  non-structural  measures  that  policyholders  may  take  only 
temporarily. 

The  third 
insurance 
is  to  reconsider  regulations  that  require  policyholders  to  use 
reimbursements  after  a  disaster  for  reconstructing  their  property  to  the  same  state  as  before 
the disaster occurred. Introducing ‘build back better’ requirements could allow the recovery and 
repair  process  to  build  risk-reduction  measures  directly  into  buildings  when  awareness  of  the 
impacts of extreme weather events is strongest. 

Recommendations  derived  from  the  study  have  been  grouped  into  five  themes:  promote  risk 
awareness  and  reduction;  closing  the  protection  gap;  the  role  of  public  and  private  entities  in 
the  risk  cycle;  the  role  of  cities  and  regions;  and  integration  of  resilience,  including  insurance 
data,  in  relevant  policies.  It  should  however  be  noted  that  the  recommendations  are  in  many 
cases  cross-cutting  and  can  cover  several  themes.  Table  3  summarises  the  broader 
recommendations. 

Table 3: List of recommendations to the stakeholder community 

Sector 

Theme and recommendation 

Promote risk awareness and reduction 

Private property  

Low-income (following local definitions of low income or social hardship) 
households struggling to afford extreme weather insurance should have 
this pressure eased with insurance vouchers or tax credits if they buy 
insurance coverage. 

Minimum building standards, or build-back-better requirements, 
differentiated by risk levels can be required as a standard element of 
insurance contracts in order to gain coverage (with a focus on measures 
integrated into the building). 

Multi 

Research with the aim of defining and quantifying resilience to support risk 
awareness and reduction, and a focus on how insurance can enhance the 
economic resilience. 

viii 

 
 
 
 
 
 
 
 
 
Closing the protection gap 

Private property 

Promote the bundling of a complete extreme weather event insurance 
package with private property fire insurance policies (or a similar and 
often purchased product). 

Urge banks to require full and comprehensive insurance coverage when 
providing mortgage loans. 

Agriculture 

Redirect premium subsidies towards multi-peril (yield) crop insurance 
products to provide more extensive coverage. Each extreme weather 
event can contribute to the overall premium in line with its risk level. 

In order to reduce the presence of adverse selection in crop insurance and 
only insuring the high-risk land, a farmer should be compelled to insure all 
arable land as part of the terms and conditions of an insurance policy. 

Link access to wider agricultural sector subsidies (i.e. those relating to the 
common agricultural policy (CAP) or those offered at national level) to the 
purchase of sufficient insurance protection in order to develop a tradition 
of being insured. 

Support the use of farm income insurance by starting pilot initiatives in 
various Member States. 

Support public-private partnerships (PPPs) and cross-
organisational collaboration 

Use  a  surcharge  on  insurance  premiums  (either  newly  introduced  or 
redirected  current  taxes)  to  directly  finance  and  construct  risk-reduction 
infrastructure  or  to  directly  subsidise  household  level  risk-reduction 
measures.  

Create a national focal point or authority for developing and maintaining a 
legal framework through which extreme weather risks can be managed via 
a combination of risk reduction and/or transfer.  

Lay down the roles and responsibilities of all the stakeholders in a national 
platform, focal point or authority, in a clear and transparent framework.  

Develop an agricultural risk management association with a focus on 
protecting farmers against income variations due to crop yields, within a 
mutual or non-profit maximising organisation. 

Create a working group in the European Commission enabling cross-
Directorate-General (DG) collaboration, as well as coordination with 
national bodies. 

Increase the role of cities and regions 

Recommend cities assess their vulnerability in regard to insurance 
penetration rates, including for municipal infrastructure and extreme 
weather events covered, as well as reporting on how they use insurance 
as a mechanism for managing risks. 

Private property 

Agriculture 

Multi 

Cities and regions 

Cities and regions 

Promote the use of insurance disaster loss data in the municipalities’ risk-
assessment data. 

Promote the active and collaborative sharing of risk, hazard and impact 
data across stakeholders though the standardisation of metadata and the 
format of granular data that can be more efficiently and transparently 
shared across stakeholders productively. 

Cities and regions 

Promote the use of community rating systems for setting premiums  

Cities and regions 

Promote the spreading of risk by allowing cities to pool their insurance 

Cities and regions 

Increase capacity building with regard to insurance and climate resilience 

ix 

 
 
 
 
 
 
 
 
 
 
 
Member States 

Integration of resilience, including insurance data, in relevant 
policies 

Introduce a requirement for flood risk management plans, national 
adaptation strategies and applications for loans or national or EU funds to 
include insurance mechanisms for managing risk that cannot be (cost-) 
effectively prevented in order to further mainstream insurance into 
national adaptation conversations. 

The  table  below  is  based  on  the  general  recommendations  presented  previously  but  lists  the 
specific recommendations to the European Commission with the aim  of  being  in line with their 
mandate and role. 

Table 4: List of recommendations to the European Commission 

# 

Recommendation 

Recommendation 1 

Increase the requirements or recommendations for Member States to assess 
their vulnerability in regard to insurance penetration rates and events 
covered, as well as to report on how they use insurance as a mechanism for 
managing risks. 

Recommendation 2 

Include an ex-ante conditionality for the European Structural and Investment 
Funds  on  assessing  insurance  vulnerability  and  the  usage  of  insurance  as  a 
risk  management  tool.  Strengthen  reporting  requirements  in  EU  funding 
applications.  

Recommendation 3 

Fund a study with the aim to define and quantify resilience at EU level with a 
focus on how insurance can enhance economic resilience. 

Recommendation 4 

Promote the use of insurance mechanisms that will support damage 
prevention to Member States.  

Promote the bundling of a combination of extreme weather events with 
private property fire insurance policies (or similar purchased product). 

Inform stakeholders about the potentials of low-income households receiving 
subsidies for insurance in the form of insurance vouchers or tax credits if they 
buy insurance coverage. 

Foster discussions in relevant forums about minimum building standards or 
build-back-better requirements (after events), differentiated by risk levels, 
which may be required as a standard element of insurance contracts in order 
to gain better coverage. 

Recommendation 5 

Increase the proportion of funds to the second pillar of the CAP, focused on 
the risk-management toolkit, including insurance schemes for crops, animals 
and plants, as well as mutual funds and an income stabilisation tool. At a 
national level, the following conditions should be considered: 

- Redirect premium subsidies towards multi-peril (yield) crop insurance 
products to provide more extensive coverage. Each extreme weather risk can 
contribute to the overall premium in line with the threat posed by each 
extreme weather risk; 

- In order to reduce the presence of adverse selection in crop insurance, a 
farmer could be compelled to insure all arable land as part of the terms and 
conditions of an insurance policy; 

x 

 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
# 

Recommendation 

- Introduce farm income insurance as a potential tool through a pilot project. 

Recommendation 6 

Support  and  facilitate  the  creation  of  a  national  focal  point  or  authority  for 
developing  and  maintaining  an  institutional  and  legal  framework  through 
which  extreme  weather  risks  can  be  managed  via  a  combination  of  risk 
reduction and transfer. 

Support  and  facilitate  an  agricultural  risk  management  association  with  a 
focus on protecting farmers against income variations due to crop yields (i.e. 
supporting  multi-risk  yield  insurances  or  the  employment  of  risk-reducing 
measures) within a mutual or non-profit maximising organisation.  

Recommendation 7 

Create  a  working  group  in  the  European  Commission  that  enables  cross-DG 
collaboration,  awareness  raising  and  stakeholder  collaboration,  as  well  as 
coordination with national bodies.  

Recommendation 8 

Fund projects with the aim to increase the capacity of cities in order to use 
insurance as a risk management tool and insure infrastructure. 

Recommendation 9 

Create a dialogue between the insurance industry, municipalities and national 
bodies on how community rating systems and the pooling of city insurance 
can be developed 

xi 

 
 
 
 
 
 
 
 
 
 
 
1. 

INTRODUCTION 

Ramboll  and  the  Institute  for  Environmental  Studies  (IVM)  of  the  Vrije  Universiteit  (VU) 
Amsterdam  were  contracted  by  the  European  Commission  to  study  the  insurance  of  weather 
and  climate-related  disaster  risk,  and  to  create  an  inventory  and  analysis  of  mechanisms  to 
support damage prevention in the EU. 

1.1  Background 

The  impacts  of  natural  hazards  have  increased  over  recent  decades  in  several  regions  around 
the  world  (Bouwer,  2011)  and  extreme  weather  events  are  a  pressing  global  concern  (IPCC, 
2012, 2014). As an example, flooding is regarded as the natural disaster with the largest impact 
on  humanity  (UNISDR,  2011;  Swiss  Re,  2015).  In  May  2015,  floods  in  the  USA  caused 
USD 2.7 billion in damage, while the December 2015 floods across the United Kingdom, Ireland 
and Norway resulted in between USD 1.6 bn and 2 bn in damage (Munich Re, 2015; Swiss Re, 
2016). Europe has suffered an annual average loss of  USD 14bn between 1980 and 2010, due 
to the effects of extreme weather events (NMI, 2013).  

Climate  change  is  expected  to  increase  the  frequency  and  intensity  of  some  weather-related 
disasters  in  Europe  and  elsewhere  (IPCC,  2012,  2014).  The  economic  impacts  of  weather-
related  disasters  are  expected  to  increase  significantly  due  to  the  combined  effect  of  climate 
change  and  socio-economic  developments,  such  as  economic  growth,  increased  urbanisation 
and population growth in areas that are prone to disasters. Therefore, there is a growing need 
to enhance resilience to natural disasters and improve climate change adaptation mechanisms. 
To minimise the effects of natural disasters, pro-active management is required, as noted in the 
Sendai Framework for Disaster Risk Reduction for the period 2015-2030 (UNISDR, 2015a). One 
of the key priorities of the Sendai framework is to improve societal resilience (Mechler, 2016). 
While often contested (see e.g. Klein et al., 2003; Bockorjova, 2007; Rose, 2007), definitions of 
resilience  tend  to  focus  on  a  quick  recovery  from  the  impacts  of  a  disaster  event  at  low  cost. 
The  Sendai  framework,  therefore,  encourages  the  use  of  instruments  that  finance  recovery 
costs  while  offering  incentives  to  reduce  the  potential  impacts  of  weather-related  disasters 
(Mysiak et al., 2016).  

In  2013,  the  EU  presented  its  Adaptation  Strategy  (EC,  2013a),  which  outlines  objectives  and 
actions to contribute to a more climate-resilient Europe. The three objectives are as follows:  

1.  Promoting action by Member States; 
2.  Promoting better informed decision making ; and  
3.  Promoting adaptation in key vulnerable sectors.  

The  third  objective,  promoting  adaptation  in  key  vulnerable  sectors,  encourages  the  use  of 
insurance  against  natural  and  man-made  disasters. This  is  set  out  in  Action  8  in  the  strategy, 
which is to promote insurance and other financial products for resilient investment and business 
decisions.  The  Green  Paper  on  the  insurance  of  natural  and  man-made  disasters  (EC,  2013b) 
was  adopted  as  part  of  the  Adaptation  Strategy  package  in  2013.  The  objective  of  the  Green 
Paper was to encourage improvement in the way insurers help to manage climate change risks, 
to improve the market penetration of natural disaster insurance and to unleash the full potential 
of insurance pricing and other financial products. 

The  EU  recognises  that  disaster  risk  insurance  is  an  important  element  for  climate  adaptation 
since it does not only support risk sharing but is working throughout the risk management cycle 
(identify, analyse, plan, implement and evaluate) and  the disaster management cycle (prevent 
and protect, prepare, respond and recover).  

1 

 
 
 
 
 
 
 
 
 
In the European Commission’s Action Plan on the Sendai Framework for Disaster Risk Reduction 
2015-2030  (EC,  2016)  the  importance  of  insurance  is  highlighted  in  Implementation  Priority 
no 15 under key area 3: promoting EU risk-informed investments (Sendai Priority 3: Investing 
in disaster risk reduction for resilience), which is to promote the use of mechanisms for disaster 
risk  financing,  risk  transfer  and  insurance,  risk  sharing  and  retention.  The  study  under  which 
this report is prepared falls under activity 15.1 mentioned in EC (2016), which calls for a high-
level  round  table  with  the  insurance  sector  and  relevant  EC  services  to  explore  how  to 
strengthen the use of risk financing, risk transfer and insurance products as an incentive for risk 
awareness, prevention and mitigation as a follow-up to the Green Paper. 

1.2  Purpose of this study 

The present study builds on the EU Adaptation Strategy and the  Green Paper on the insurance 
of  natural  and  man-made  disasters,  and  addresses  the  objective  of  encouraging  the  use  of 
insurance against natural and man-made disasters. More specifically, the study will support the 
EU Adaptation Strategy through the following: 

1.  Increasing  the  knowledge  base  by  collecting  and  providing  information  with  regard  to 

insurance coverage, mechanisms and cost-effectiveness, including preventive capacity; 

2.  Increasing awareness, promoting  further  action  and defining the next steps in insuring 

weather and climate-related extreme events. 

To this purpose, the study consists of the following tasks, as defined in the tender specifications 
of the project: 

  Task  1:  Stock-taking  of  insurance  mechanisms  covering  weather  and  climate-related 

disaster risks, applied in (and beyond) the EU; 

  Task  2:  Analysis  of  the  cost-effectiveness  of  insurance  mechanisms,  including 

preventive capacity; 

-  Task 2a: Determining the cost-effectiveness of insurance mechanisms; 
-  Task  2b:  Analysis  of  the  mechanisms  that  incentivise  prevention  of  risk  and 

support damage reduction; 

  Task  3:  Definition  of  the  next  steps  in  insuring  weather  and  climate-related  extreme 

events. 

The  tasks  are  applied  to  12  case  studies  and  various  research  activities;  for  example,  data 
collection,  stakeholder  engagement  and  literature  reviews  have  been  used.  Figure  1.1  below 
provides an overview of the study objectives, tasks and research activities. 

2 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 1.1: Overview of the study objectives, tasks and research activities 

3 

Task 1:Stock-takingTask 2a:Cost-effectiveness(stock taking)Task 2b:Cost-effectiveness(analysis)Task 3:NextstepsIncreasing awareness, promotingfurther action and defining the nextsteps in insuring weather and climaterelated extreme events.Increasing the knowledge base bycollecting and providing information inregards to insurance coverage,mechanisms and cost-effectiveness,including preventive capacityStakeholderinterviews:collectdata andsuggestions for12 case studiesaswellasideasonnextsteps tobetakenAnalysis of 12case studies:identifybestpracticesandincreaseknowledgebaseFocusgroup:structureddiscussionsoncase studiesand innovativeideasonnextstepsProgressmeeting:present theprogress of thestudy andpreliminaryresultsFinal event:present anddiscuss thefindings of thestudy andrecommendationson the next stepsto be takenConclusionsonstock-takingandcase studies 
 
 
 
 
 
 
 
 
 
 
 
 
1.3  Methodology 

As  part  of  Task  1,  a  literature  review  was  conducted  to  create  an  overview  of  insurance 
mechanisms  covering  weather-related  disaster  risks  for  EU  countries  in  order  to  produce  the 
attribute  tables  with  characteristics  of  these  mechanisms  that  are  displayed  in  Appendix  2. 
Rather  than  using  company  or  policy  levels,  the  national  scale  has  been  selected  as  an 
appropriate  level  of  analysis.  The  national  scale  is  often  used  in  academic  literature 
investigating  insurance  market  structures  (e.g.  Paudel  et  al.,  2012;  Lamond  and  Penning-
Rowsell, 2014), it has been used by previous EC reports regarding various aspects of insurance 
(e.g. Diaz-Caneja et al., 2009; Maccaferri et al., 2012) and is most applicable to the objectives 
of this study.  

Task  2  assesses  the  cost-effectiveness  of  insurance  mechanisms,  which  is  defined  to  include 
their preventive capacity, among other characteristics. Firstly, the cost-effectiveness of extreme 
weather  insurance  mechanisms  is  determined;  secondly,  the  mechanisms  that  incentivise  the 
prevention of risk and support damage reduction are analysed. For each natural disaster type, a 
systematic literature review has been conducted, which is supported by stakeholder surveys and 
data obtained from EM-DAT, NatCatService and Sigma datasets. 

On  a  household  level,  information  on  existing  and  innovative  adaptation  measures  has  been 
collected per hazard type. The final views on the cost-effectiveness of insurance are judged by 
comparing qualitative or quantitative outcomes against what various stakeholders  consulted in 
the  study  view  as  being  the  most  important  criteria  for  insurance:  to  perform  well.  This  is 
similar to a multi-criteria analysis.  

Task  3  defines  the  next  steps  in  insuring  weather-related  extreme  events.  Conclusions  from 
taking stock of the innovative approaches and the case studies will feed into Task 3. Moreover, 
there is a section in the questionnaire that was circulated to stakeholders within the case study 
countries  to  gather  their  views  regarding  future  natural  disaster  insurance  and  how  this  can 
support damage prevention. Answers were collected in writing and via interviews. Parallel to the 
case studies and throughout the project progress, interviews with a broad range of stakeholders 
were made in order to ensure that the analysis for the next steps forward goes beyond the case 
studies.  Furthermore,  viewpoints  from  the  different  types  of  stakeholders  were  taken  into 
consideration. In order to get detailed feedback on the findings from the case studies as well as 
the next steps, a focus group was formed and two meetings  were held (one in person and one 
webinar).  A  final  event  with  80  participants  representing  a  broad  range  of  stakeholders  was 
held, which ensured that feedback from a wide range of stakeholders was received on both the 
results and the recommendations of this study.  

4 

 
 
 
 
 
 
 
 
 
 
2. 

PAST AND FUTURE EXTREME WEATHER LOSSES 

The purpose of the present chapter is to provide an overview of past (1990-2015) and projected 
(2030-2050)  extreme  weather-related  losses  and  their  potential  drivers  for  the  case  study 
countries  as  requested  by  the  project’s  specifications  (Task  1).  This  chapter  explores  this 
through a semi-qualitative analysis of extreme weather loss data and examples from the wider 
literature. 

The chapter consists of a section discussing observed losses from the extreme weather events 
that can be considered as natural disasters (section 2.1). The methodological approach for the 
first  sub-sections  is  a  simple  trend  analysis  of  non-normalised  and  normalised  disaster  losses 
aggregated across case study countries and extreme weather events (see the suggestion to use 
normalisation from EEA, 2017 among others). Section 2.2 presents a discussion of the projected 
changes  in  extreme  weather  risks,  which  is  supported  by  section  2.3,  which  provides  a 
discussion of the main drivers of changing extreme weather risk. The methodological approach 
taken for section 2.2 and section 2.3 is a review of the scientific and grey literature on the topic. 

2.1  Observed losses between 1990 and 2015 

As  part  of  the  stakeholder  engagement,  it  was  asked  which  extreme  weather  events  the 
stakeholders found most important. From the point of view of the engaged stakeholders (mainly 
insurers, see Appendix 3), we received an indication of the perceived importance of the various 
extreme  weather  events  studied;  i.e.  which  extreme  weather  event  is  the  most  important  in 
their  opinion.  Their  current  ranking  is  as  follows:  pluvial  and  fluvial  flooding,  windstorms, 
coastal flooding (for non-landlocked countries), droughts, heatwaves and hail. Pluvial flooding is 
indicated  as  the  most  important  disaster  because  it  is  generally  deemed  to  be  an  underrated 
extreme  weather  event.  It  is  underrated  in  the  sense  that  even  compared  to  other  types  of 
flooding,  the  risk  perception  of  potential  policyholders  is  low.  Therefore,  pluvial  flooding  may 
become  a  more  pressing  concern  if  urban  areas  are  not  correctly  adapted  to  cope  with  the 
predicted increase in precipitation intensity (IPCC, 2012). This finding may apply more widely to 
the various extreme weather events studied if the perception of risk is not in line with the actual 
development (IPCC, 2014). 

The engaged stakeholders also indicated which sector is most affected by each extreme weather 
event.  It  emerges  from  the  process  of  stakeholder  engagement  that  the  private  property  and 
commercial sectors are most affected by the various types of flooding and windstorms, and the 
agriculture sector is the most affected by heatwaves, droughts and hail.  

Box 1 Framework for modelling risk from extreme weather events 

The  impacts  of  extreme  weather  events  are  often  evaluated  based  on  their  risk,  consisting  of 
three  elements:  exposure,  vulnerability  and  hazard  (Kron,  2005).  Exposure  is  the  value  of 
assets and the population exposed to extreme weather events; vulnerability is the susceptibility 
to  damage;  and  hazard  is  the  extent  and  intensity  of  the  disaster  event  itself.  Current  and 
future impacts are often modelled according to the risk framework as presented in this box. The 
modelling  framework  expresses  risk  as  the  probability  weighted  average impact  of  all  possible 
events.  The  figure  in  this  box  highlights  that  the  assessment  of  current  and  future  impacts  is 
influenced  by  several  long  chains  of  intervening  concepts,  providing  many  sources  of 
uncertainty or areas to target for risk reduction. 

5 

 
 
 
 
 
 
 
 
 
Framework for modelling risk from extreme weather events (from Bouwer, 2013) 

The number of extreme weather events and monetary impacts for the selected case studies can 
also be estimated by using the following datasets: EM-DAT, NatCatService and Sigma. The data 
for  the  case  study  countries  not  only  cover  a  wide  geographical  spread,  but  also  cover  a 
substantial proportion of the  observed  data across the three  studied  datasets.  Over  the period 
1990-2015, the ensemble average number of extreme weather events within the 12 case study 
countries  is  on  average  75 %  of  the  events  in  Europe,  while  covering  about  90 %  of  the 
recorded monetary losses in Europe. Therefore, the case studies produce a representative view 
of the raw data available. It must be noted that each database had varying rules for an extreme 
weather event to be included in the dataset. This could produce an incomplete view of extreme 
weather  events  in  total,  but  a  more  representative  view  of  disastrous  events  (as  variously 
defined).  For  example,  EEA  (2017)  states  that  more  than  70 %  of  the  damage  suffered  was 
caused by of 3 % of the registered events (using the NatCatService database). 

Following the approach taken in the annual catastrophe reports on global natural disaster losses 
of  Munich  Re  and  Swiss  Re  (e.g.  Munich  Re,  2015;  Swiss  Re,  2016),  we  aggregated  national 
observations  to  their  totals  within  a  specific  year  (i.e.  all extreme  events  in  Sweden  in  2010). 
Moreover,  there  is  a  degree  of  uncertainty  in  the  datasets  between  the  recorded  number  of 
events and the specific impact. We shall therefore present the dataset ensemble average of the 
values reported in the three datasets used.  

2.1.1  Total monetary losses 

Observed impacts from natural disaster databases (EM-DAT, NatCatService and Sigma) for the 
years 1990 to 2015 

Figure 2.1 presents the dataset ensemble average number of reported extreme weather events 
(that can be considered as disasters) and indicates a slight upward movement in the number of 
reported  events  over  the  period  studied.  The  degree  of  annual  volatility  in  impacts  is  clearly 
visible  in  Figure  2.1,  but  it  is  difficult  to  spot  a  trend  in  losses  across  these  countries  and 
disasters (individually) due to the variability of losses. This would be expected because natural 
disasters  are  low-probability/high-impact  events  in  specific  regions.  However,  there  may  be  a 
reporting bias over time as reporting has improved. While this is a concern, Kron et al. (2012) 
argue that (for NatCatService at least) reporting bias may be fairly minor after 1980. 

6 

 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
Figure 2.1: Aggregated number of disastrous extreme weather events across the 12 case study coun-
tries (the ensemble average across the three datasets) 

s
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Year 

However, the number of disaster events itself does not provide a full picture of the impacts of 
disastrous  extreme  weather  events  on  society  as  a  whole.  Figure  2.2  presents  the  sum  of 
aggregated disaster losses for the 12 case studies, corrected for inflation and when normalised, 
from a range of extreme weather events. The normalisation process can be useful for comparing 
impacts of disaster events over time, due to changes in socio-economic and climate conditions 
that  determine  disaster  impacts.  Equation  (1)  shows  the  normalisation  process  based  on 
Neumayer and Barthel (2011), which accounts for inflation, population and wealth changes. 

𝐿𝑜𝑠𝑠𝑖,𝑡

𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑠𝑒𝑑,2015 =

𝑤𝑒𝑎𝑙𝑡ℎ𝑖,2015
𝑤𝑒𝑎𝑙𝑡ℎ𝑖,𝑡

𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖,2015
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖,𝑡

𝐺𝐷𝑃 𝑑𝑒𝑓𝑙𝑎𝑡𝑜𝑟𝑖,2015
𝐺𝐷𝑃 𝑑𝑒𝑓𝑙𝑎𝑡𝑜𝑟𝑖,𝑡

𝐿𝑜𝑠𝑠𝑖,𝑡 

(1) 

Figure  2.2  presents  the  aggregated  normalised  and  inflation-corrected  losses  across  the 
disastrous  extreme  weather  events  and  case  study  countries  studied,  with  2015  as  the  base 
year.  The  inflation-corrected  losses  do  not  display  a  consistent  trend  in  either  falling  or 
increasing  disaster  impacts  due  to  the  large  volatility  in  total  recorded  impacts.  However,  the 
normalised  disaster  loss  data  indicates  a  slight  decreasing  trend  in  disaster  losses  if  all  data 
points  are  included,  but  this  trend  is  not  robust  as  excluding  1990  eliminates  this  trend.  This 
trend  can  appear  to  be  different  to  those  presented  in  reports  such  as  Munich  Re  (2015)  or 
Swiss Re  (2016),  which is  likely  because our  focus is only on 12  countries  within the  EU  while 
the aforementioned studies take a more global perspective. The global perspective can include 
areas where exposure is growing faster than changes in vulnerability, resulting in a net increase 
in  potential  losses.  Figure  2.3  presents  the  average  inflation-corrected  or  normalised  loss  on 
average per event. The  inflation-corrected losses  do not present  a strong  trend,  similar to the 
findings  as  presented  in  EEA  (2017).  The  average  normalised  losses  per  event  are  (slightly) 
more robust than the results presented in Figure 2.2. 

7 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2.2: Normalised and inflation-corrected disaster losses aggregated across the 12 case study 
countries (EUR billion) 

60

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Inflation corrected

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Figure 2.3: Average normalised and inflation-corrected disaster losses aggregated across the 12 case 
study countries (EUR billion) 

3

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Inflation corrected

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Table  2.1  presents  5-year  averages  of  the  data  in  Figure  2.1  to  Figure  2.3.  It  highlights  a 
roughly  increasing  number  of  disaster  events,  while  the  disaster  impacts  remain  more  or  less 
constant,  even  with  a  slight  increase  in  the  number  of  disaster  events.  However,  it  must  be 
noted that these observations are based on draws from the range of possible impacts. A large 
annual  impact  can  be  due  to  a  series  of  small  events  or  a  single  larger  event.  The  values  in 
Table 2.1 could have altered quite extensively if a sufficiently low probability event had occurred 
somewhere in the dataset (see the sensitivity of Figure 2.2 to the year 1990). The earlier such 
an extreme event would appear in the data could  give the impression that impacts are falling, 
and  the  reverse  inference  may  be  made  when  such  an  event  would  occur  later  in  the  time 
period.  

8 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Moreover,  the  individual  datasets  employed  reveal  different  directions  in  how  losses  have 
evolved  for  the  12  studied  countries,  highlighting  the  uncertainty  in  measuring  the  impacts  of 
extreme weather events. Therefore, estimated risk may provide a more suitable metric to judge 
the pressure that extreme weather events may be placing on society rather than the recorded 
impacts  which  are  aggregated  across  countries  and  extreme  weather  events.  This  is  due  to  a 
relatively high degree of uncertainty of impacts, both in terms of what was recorded and what 
could have happened. 

Nevertheless,  we  can  learn  from  the  data  on  observed  losses  that  changes  in  exposure  have 
played an important role in changing losses, but so have been attempts to alter the vulnerability 
element of the natural hazard loss function. For example, Kienzler et al. (2015) report that after 
a  series  of  floods  in  Germany  the  overall  level  of  household  flood  preparedness  has  increased 
over time. Moreover, there can be investment in larger scale risk management activities by local 
or  national  governments,  which  are  hard  to  integrate  into  the  above  normalisation  framework 
due to the often lack of transparency regarding how this investment money is spent.  

Table 2.1: Summary of observed disaster losses in the 12 case study countries 

ATTRIBUTE 

Period average of the 
number of extreme 
weather events 

Period average of the 
annual loss due to 
disastrous extreme 
weather events 
(inflation, EUR billion) 

Period average of the 
average annual loss 
due to disastrous 
extreme weather 
events (inflation 
adjusted, EUR billion) 

1990-1994 

1995-1999 

2000-2004 

2005-2009 

2010-2015 

17 

16 

20 

23 

26 

Period average 

21 

9.4 

6.8 

12.5 

7.6 

9.8 

9.3 

0.5 

0.36 

0.56 

0.34 

0.4 

0.43 

Note:  Numbers  are  based  on  the  ensemble  average  of  the  recorded  events  across  datasets  in  2015  prices.  The  events 
included are those that correspond to  windstorms, hail storms, fluvial flooding, pluvial flooding, coastal flooding, droughts, 
and heatwaves (the extreme weather events studied in this report). The final period is extended from 5 to 6 years in order 
to prevent 2015 from being an orphaned year. 

The data presented does not provide convincing evidence of a trend in natural disaster-related 
losses  after  accounting  for  socio-economic  developments,  which  may  be  caused  by  general 
problems  that  plague  NatCat  datasets  (see  Bouwer,  2011)  and  normalisation  procedures 
(Estrada  et  al.,  2015).  For  example,  many  loss  records  exhibit  biases  and  there  is  much  less 
data available prior to 1980 when loss data were less reliably recorded. This data problem may 
have  been  solved  by  a  better-structured  data  collection.  For  example,  advancing 
communications technologies and crowdsourcing of impact information may lead to a reduction 
in bias and allow for the inclusion of smaller loss events that would otherwise not be included. 
However, additional work may be required on this topic as, for example, Thieken et al. (2016) 
report  that  (in  Germany)  the  procedures  and  standards  for  impact  data  collection  are  widely 
missing.  Present  impact  data  in  Germany  are  fragmentary,  heterogeneous,  incomplete  and 
difficult to access. 

9 

 
 
 
 
 
 
 
 
 
 
In  terms  of  observed  impacts,  Bouwer  (2011)  reviews  22  studies  of  normalised  disaster  loss 
trends covering a range of extreme weather events and countries. In doing so, Bouwer (2011) 
finds that 64 % of the studies did not discern a trend, 27 % found a positive trend and 9 % of 
studies found an inconclusive trend in disaster losses. Globally, studies conducted by the Global 
Facility  for  Disaster  Reduction  and  Recovery  (GFDRR)  contain  data  (e.g.  GFDRR,  2016)  that 
imply a 6 % annual increase in worldwide losses between 1990 and 2014, assuming a constant 
annual growth rate. Munich Re estimates worldwide natural catastrophe losses of USD 93 bn in 
2015,  of  which  about  USD 29 bn  was  insured.  The  observed  trend  in  losses  estimates  a  4 % 
annual  increase,  while  insured  losses  grew  at  about  6 %.  Normalised  disaster  losses  only 
indicated a trend when correcting for inflation.  

When  correcting  for  exposure,  no  trend  in  European  losses  can  be  found  (Neumayer  and 
Barthel, 2011). Europe, in 2015, accounted for 13 % of worldwide disaster events and monetary 
losses,  but  45 %  of  the  losses  were  insured.  Winter  storm  and  flooding  in  the  UK  caused 
EUR 3 bn of losses (with 66 % of insured impacts); storms in Italy and Spain  caused a loss of 
USD 700 m. A drought in Eastern Europe caused a loss of USD 1.5 bn (Munich Re, 2016). Bartel 
and  Neymayer  (2012)  investigated  normalised  insured  losses  for  the  years  1990  to  2008, 
controlling for inflation and changes in wealth. Controlling for inflation resulted in a trend, but 
no trends were found when controlling for inflation and changes in wealth. Bartel and Neymayer 
(2012)  also  note  that  there  is  a  large  variation  in  recorded  impacts  across  the  years.  A  3 % 
annual growth  rate in  monetary  impacts was found for the EU, between 1990 and 2008 (EEA, 
2010). Barredo (2009) found that flood losses over the period 1970-2006 displayed an upward 
trend;  however,  once  normalised  the  trend  was  not  present.  Barredo  (2009)  estimated  that 
there was an annual normalised impact of EUR 3.8 bn across Europe, the main driver of which 
was  socio-economic  development.  Barredo  (2010)  conducted  a  similar  study  for  windstorm 
losses over the period 1970-2008, finding a similar result to Barredo (2009). 

Kundzewicz et al. (2014) state that the economic losses caused by flood events have increased 
over time due to the increase in exposure of assets at risk. It has not been possible to attribute 
rain-generated  peak  streamflow  trends  to  anthropogenic  climate  change  over  the  past  several 
decades. The climate signal (i.e. climate change effects on changing patterns of precipitation) is 
less  spatially  coherent  and  less  statistically  significant  compared  to  observed  changes  in 
temperature  extremes.  However,  several  studies  of  a  range  of  regions  did  not  show  a  signal 
related to anthropogenic climate change in flood losses (Pielke and Downton, 2000; Downton et 
al.,  2005;  Barredo,  2009;  Hilker  et  al.  2009;  Neumayer  and  Barthel,  2011;  Barredo  et  al. 
2012).  A  few  other  studies  observed  a  partial  relationship  between  recent  increases  in  flood 
losses and (short-term) changes in intense rainfall events (Fengqing et al., 2005; Chang et al., 
2009; Barthel and Neumayer, 2012), and some studies find an increase in damages related to 
flash  floods  and  a  changing  incidence  in  extreme  precipitation  (Changnon,  2001).  However,  a 
Swiss  study  of  normalised  losses  from  flash  floods  and  landslides  failed  to  identify  any 
significant trends (Hilker et al., 2009).  

2.1.2  Comparison of insured and non-insured losses 

The  ratio  of  insured  to  total  monetary  losses  is  an  often-used  metric  for  measuring  the 
insurance ‘coverage gap’ (e.g. Munich Re, 2016; Swiss Re, 2016). However, this metric should 
be treated with caution as it includes impacts from people who cannot be insured (i.e. for whom 
insurance is not available), people who voluntarily choose not to be insured, the potential limits 
on the total amount of compensation  that can be  offered or the deductibles that policyholders 
choose.  Therefore,  a  ratio  of  1  is  not  always  an  optimal  target  since  some  elements  of  risk 
should be shared across the various participants in the insurance market. 

10 

 
 
 
 
 
 
 
 
The  two  datasets  provided  by  reinsurers4  have  data  points  that  associate  an  insured  loss  with 
the total damage suffered for a given disaster event. Therefore, the ratio of insured loss to total 
economic loss per disaster event can be produced. Figure 2.4 presents this ratio per year as one 
of  the  following  three  outcomes.  The  main  indicator  is  the  mean  ratio  per  disaster  event  that 
occurred across the EU Member States for a given year. 

In  addition  the  minimum  ratio  per  disaster  event  observed  for  a  given  year  as  well  as  the 
maximum  observed  ratio  are  shown  in  the  figure5.  The  three  indicators  are  produced  as  the 
average of that contained in the two reinsurance datasets. However, it must be noted that data 
points  on  insured  losses  were  rarer  than  observed  losses.  For  example,  1991  only  has  one 
observation  of insured losses  contained in  the Munich  Re  dataset  meeting  the  requirements of 
this  report.  Moreover,  the  data  is  biased  towards  countries  with  more  developed  insurance 
markets (e.g. Germany, France, Spain, and the UK). 

Figure 2.4: The ensemble ratio of insured natural disaster damage to economic damage  

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Notes: ‘mean’ represents the average ratio across extreme weather events studied; ‘max’ represents the highest ratio 

across extreme weather events; ‘min’ represents the smallest ratio across extreme weather events. 

The data presented in Figure 2.4 show that during the period 1990-2015 there was an average 
ratio of 0.43 for the mean ratio value, with an average annual maximum value of 0.71 and 0.13 
for the average annual minimum. The mean value appears to be relatively constant across the 
period, although it slightly increases over the period (an annual rate of 0.008 points). The range 
also slightly increases over the period studied at a trend rate of 0.008 points per year. However, 
some  of  this  increase  must  be  attributed  to  the  wider  coverage  in  terms  of  countries  studied 
after the year 2000.  

Table  2.2  presents  the  average  ratio  (across  all  EU  Member  States  during  the  period  1990-
2015)  for  the  different  disastrous  extreme  weather  events  that  could  be  isolated.  The  Sigma 
dataset  identifies  events  by  their  primary  extreme  weather  event.  The  NatCatService  dataset 
provided the main disastrous extreme weather events involved, but did not clearly state which 
was the prime event. 

4 The datasets are obtained through institutional agreements and subject to confidential use of the individual datasets.  
5 However, it must be noted that the events with both a total and an insured loss reported may tend to be larger events. However, 

these  events  may  be  where  the  benefits  of  insurance  are  most  strongly  felt  as,  proportionally,  less  of  the  loss  is  retained  by  the 

household or other affected agent. 

11 

 
 
 
 
 
 
 
 
 
  
                                                
 
 
 
Table 2.2: Average ratio of insured damage to economic damage per event, 1990-2015 

Extreme weather 
event 

Average ratio 

Average ratio 

Sigma 

NatCatService 

Drought (heatwave) 

Floods (various) 

Hail 

Storms 

0.33 

0.43 

0.53 

0.61 

Drought (heatwave) 

Flood 

Flood and storms 

Storm 

0.34 

0.35 

0.49 

0.8 

Both datasets provide a roughly similar ratio for droughts which stands at about one-third of the 
damage  suffered,  mainly  because  of  the  very  high  ratio  in  France  (over  0.8).  Floods  were  the 
most  common  disastrous  extreme  weather  event,  while  storms  had  the  highest  ratio  of 
insurance coverage.  

The damage associated with  storms is often  masked by the  simultaneous occurrence  of  floods 
(resulting in difficulty in attributing losses to specific extreme weather types). The high ratio for 
storms  is  observed  because  of  the  general  coverage  of  such  events  in  general  property 
insurance contracts with low deductibles. However, when the damage is also occurring together 
with floods, then less of the total damage will be covered. 

2.1.3  Initial conclusions regarding past losses 

This  chapter  provides  an  overview  of  natural  disaster  losses  between  1990  and  2015  for  total 
monetary losses and insurance claims. The inter-annual volatility in the size of events and their 
corresponding losses makes it difficult to spot long-term trends in losses over the 12 case study 
countries. This is due to the fact that it is hard to distinguish what is a trend vs. random chance 
related to natural variability. However, the ratio of insured to total losses appeared to increase 
slightly  over  time,  around  an  average  of  0.5  over  the  entire  period.  The  same  ratio,  but 
specified for the different hazard types, shows that hail and windstorms have the largest ratio, 
which  implies  that  most  of  these  losses  are  insured.  For  floods  and  droughts,  the  ratio  is 
significantly smaller. This difference is likely to be caused by the fact that  hail and windstorms 
are  less geographically bound to location-specific conditions and  thus are  able to  occur almost 
everywhere, while droughts and floods are more localised and systemic, affecting large numbers 
of policyholders at the same time. This finding also suggests that insurers face challenges with 
insuring localised and potentially systemic disastrous extreme weather events because for such 
events  the  conditions  for  a  viable  insurance  product  may  not  hold  (see  Chapter  4  for  more 
details on what these conditions are). 

There  was  (slightly)  stronger  evidence  presented  for  an  increasing  trend  in  the  number  of 
disastrous  extreme  weather  events  occurring  over  time.6  This  finding  highlights  a  potential 
problematic area  for the  future  insurance  of (disastrous) extreme weather  events. Models that 
estimate  insurance  premiums  commonly  use  three  variables  (e.g.  Hudson  et  al.,  2016):  the 
potential loss, the probability of loss, and a loading factor to cover the costs of doing business 
and  to  make  a  profit.  The  increasing  number  of  events  (even  if  it  is  difficult  to  establish  a 

6 Caution must be taken in interpreting this finding from a study of loss events rather than the raw hazard data. 

12 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                
corresponding increase in losses) could act as an indicator of an increasing probability of a loss 
which can either result in (1) increasing insurance premiums, or that (2) policyholders will more 
often  find  themselves  caught  in  their  insurance  policy’s  deductibles,  which  could  make  them 
question the value of insurance products. 

Risk  reduction  can  play  an  important  role  in  minimising  problems  with  the  affordability  of 
insurance  or  solvency  concerns.  By  implementing  adaptation  or  mitigation  measures,  the 
impacts  of  extreme  weather  events  would  be  significantly  reduced,  relieving  pressure  on  both 
policyholders and insurers. 

2.2  Projected losses for between 2030 and 2050 

Projection  of  future  losses  is  difficult  because  the  observed  loss  data  has  high  annual 
fluctuations  and  has  been  mainly  driven  by  socio-economic  factors  which  are  not  constant.  In 
addition,  loss  estimation  in  itself  is  highly  uncertain  (Bouwer,  2013).  As  explained  above,  the 
three drivers of risk (exposure, vulnerability and hazard) are not constant trends. For example, 
changes  in  vulnerability  (e.g.  improved  rates  of  disaster  risk  reduction  or  DRR)  and  exposure 
(e.g. higher asset values or population numbers) are highly uncertain and unpredictable trends. 
Large  variations  on  impacts  can  occur  because  vulnerability  and  exposure  are  highly  variable 
and spatially differentiated. Jongman et al. (2015) note that observed and predicted past impact 
estimates  have  deviated  due  to  the  fact  that  risk  models  often  cannot  account  for  human 
behaviour  when  projecting  future  impacts.  Therefore  it  is  difficult  to  produce  authoritative 
projections  of  future  impacts  as  drawn  from  risk  models  or  observed  trends  due  to  the  broad 
range of uncertain drivers (Bouwer, 2011). Moreover, insured losses also suffer from changes in 
insurance  penetration rates, or changes in policy  conditions like deductibles. This can result  in 
highly uncertain changes in impacts. 

Current  risk  models  that  aim  to  predict  future  natural  disaster  risk  often  focus  on  changes  in 
exposure  and  on  direct  monetary  losses  (Bouwer,  2013).  However,  there  is  disagreement  on 
how  future  impacts  should  be  modelled  and  what  factors  should  be  directly  included  in  the 
models  (Bouwer,  2013).  For  example,  indirect  or  intangible  impacts  are  more  difficult  to 
estimate in  monetary terms,  although  efforts  are  made  to  adopt  these  impacts  in  mainstream 
modelling  frameworks  (e.g.  Koks  et  al.,  2016;  Hudson  et  al.,  2017a).  Often  studies  project 
values  for  risk  rather  than  focusing  on  specific  events  (Bouwer,  2013).  Even  though  there  are 
scenario approaches that can be used to project extreme weather losses with a combination of 
climate,  socio-economic  and  vulnerability  scenarios,  it  should  be  noted  that  they  are  rare 
(Bouwer,  2013).  Vulnerability  is  another  important  driver  (Bouwer,  2013;  Jongman  et  al., 
2015). However, changes in vulnerability are often modelled as exogenous changes that occur 
at a single moment (e.g. Jongman et al., 2014) rather than as adaptive behaviour that occurs 
when risk develops. An exception is Haer et al. (2017), who present an example of such a study 
for fluvial flood risk. The model  results presented  in  the following sub-sections assume a fixed 
level of adaptation or changes in vulnerability that correspond to a policy change.  

Due  to  the  nature  of  studies  that  investigate  the  potential  of  increasing  losses  from  extreme 
weather  events,  it  is  difficult  to  present  the  outcomes  in  turn  across  the  case  studies.  Precise 
and  comparable  values  for  these  projections  are  not  consistently  available.  This  is  due  to  the 
different  modelling  frameworks  employed  as  well  as  different  assumptions  on  how  climate 
change and socio-economic drivers of risk will develop. However, it is expected that risk related 
to natural hazards will increase over time (EEA, 2017; IPCC, 2012). The next sections will give 
estimates of the total monetary losses for some of the case studies, followed by some estimates 
per disaster type, on a European scale. 

13 

 
 
 
 
 
 
 
 
 
Specific country estimates 
Localised projections were available for the following four countries while, subsequently, we 
present an overview of EU-wide studies.  

Austria 

Prettenthaler et al. (2015) study future fluvial flood risk for Austria. They estimate that between 
2016 and 2045 the annual average loss from fluvial flooding is  EUR 288 million to EUR 940 m, 
which was estimated to grow to between EUR 430 m and EUR 1.8 bn for the period 2036-2045. 

Germany  

Leckebush  et  al.  (2007)  estimate  that  by  mid-century  winter  storm  losses  could  increase  by 
40 %. This result is similar to a study of summer storms by the  German Insurance Association 
(GDV), which predicts an average increase in windstorm losses by 25 % over the period 2011-
2040  as  compared to  the period  1984-2008.  While comparing the  average  claim  in  the  period 
1984-2008  with  the  projected  outcome  in  the  period  2041-2070,  it  could  be  61 %  higher. 
However, this masks a large amount of spatial variation across regions of Germany. In addition, 
the GDV estimates that a 15 % increase in claim rates for general homeowners insured  during 
the period 2011-2040 compared to the period 1984-2008; between 2041 and 2070 it could be 
47 % higher under the SRES A1B scenario7  of  climate  change  (Munich  Re, 2016).  Te  Linde et 
al. (2011) estimate that the flood risk associated with the Rhine river risk could double by the 
end  of  the  century.  Huang  et  al.  (2015)  estimated  an  increase  in  droughts  for  the  Rhine  in 
Germany.  This  may  seem  conflicting  but  floods  and  droughts  could  occur  at  different  times, 
indicating potentially growing seasonal variation. Moreover, there is the potential for more flash 
floods when drainage conditions are subjected to more extreme rainfall conditions. 

United Kingdom 

Fluvial  and  coastal  risks  make  roughly  equal  contributions  to  future  risk,  while  surface  water 
flooding  contributes  less  (Sayers  et  al.,  2015).  Based  on  current  adaptation  trends,  when 
assuming no population growth, residential risks could grow by 6 % by the 2020s and by 26 % 
by the 2050s with a 2 °C increase in temperatures. A 4°C increase in temperature could result 
in residential risk increases of 35 % or 82 %, respectively. Positive population growth can result 
in larger increases in risk. For the agriculture sector, a 2 °C temperature increase could result in 
a  1 %  increase  in  the  land  exposed  to  flooding  (with  a  flood  probability  higher  than  1/75) 
growing to 15 % by the 2050s. A 4 °C temperature increase would increase the land exposed to 
flooding by 19 % and 39 %, respectively. Turning to non-residential (commercial) properties at 
risk  of  flooding,  the  2 °C  scenario  can  increase  the  number  of  non-residential  properties 
exposed  to  flooding  by  3 %  and  16 %  respectively.  With  a  4 °C  increase,  the  values  are 
increased  to  18 %  or  42 %.  Among  the  regional  studies  of  the  UK  is  a  scenario  analysis  of 
damage due to river and coastal flooding in England and Wales in the 2080s (Hall et al., 2005). 
This study combines four emissions scenarios with four scenarios of socio-economic change in a 
Special  Report  on  Emission  Scenarios-  (SRES)  like  framework.  In  all  scenarios,  flood  damages 
are predicted to increase unless current flood management policies, practices and infrastructure 
are improved. For a 2 °C temperature increase in a SRES  B1-type world8, by the 2080s annual 
damage is estimated to be GBP 5 bn as compared to GBP 1 bn today, while with approximately 

7 The A1B scenario describes very rapid economic growth, global population that peaks in mid-century and declines thereafter, and 
the rapid introduction of new and more efficient technologies (with a balanced development of fossil fuel and non-fossil fuel technolo-

gy). 

8 The SRES B1 scenario describes a world with growing and then falling population, combined with a rapid change towards a service 

and information-based economy with a higher focus on clean and efficient technologies.  

14 

 
 
 
 
 
 
 
 
 
 
 
 
                                                
 
the same climate change, damage is only GBP 1.5 bn in a B2-type world. In an A1-type world, 
with  a  temperature  increase  of  2 °C,  the  annual  damage  would  amount  to  GBP 15 bn  by  the 
2050s and GBP 21 bn by the 2080s (Hall et al., 2005; Evans et al.2004). 

The  impacts  on  agriculture  insurance  can  be  estimated  by  analysing  how  agricultural  yields 
change. If crops are insured on a yield basis, then potentially higher yields can result in higher 
premiums  or  indemnity  payments.  By  combining  national  datasets  on  historical  wheat  yields 
with climate data, a simple yield response to the temperature metric was developed and used to 
estimate  future  yield  changes  in  Knox  et  al.  (2012).  From  this,  potential  increases  of  wheat 
yields were projected to be a central estimate of 47 % (range 22 % to 76 %) by the 2020s for 
the  A1B  emissions  scenario,  which  increases  to  a  central  estimate  of  79 %  (range  36 %  to 
137 %) by the 2050s for one emissions scenario and a central estimate of 111 % (range 46 % 
to 212 %) by the 2080s for  the A1B emissions scenario.  However, it is important to note that 
these  projections  relate  to  changes  from  a  1961  to  1990  baseline.  A  more  recent  study  by 
Thomas  et  al.  (2011)  using  the  probabilistic  UKCP09  projections  suggested  mean  wheat  yield 
increases  of  11.5 %,  which  is  broadly  similar  to  those  reported  by  Semonov  (2009).  The 
assessment also considered the climate impacts on sugar  beet, projecting an increase in sugar 
beet  yield  of  23 %  by  the  2020s  for  the  medium  emissions  scenario,  rising  to  39 %  by  the 
2050s, and to a central estimate of 55 % (range 23 % to 105 %) by the 2080s for the medium 
emissions scenario (all from a 1961-1990 baseline).  

France 

A  study  for  France  indicates  that  the  losses  from  extreme  weather  events  will  increase  (river 
flooding,  coastal  flooding and  droughts).  Based on  the  Representative  Concentration  Pathways 
(RCP) 4.5 climate change scenario and an associated change in exposure scenario (assuming a 
constant  vulnerability),  the  monetary  losses  from  extreme  weather  events  are  projected  to 
double in France by 2050 as compared to 2015 (approximately  EUR 2 bn). The main drivers of 
this  increase  in  losses  are  changes  in  exposure  (accounting  for  80 %),  while  climate  change 
provides the remainder. For example, the annual costs charged to the Cat Nat insurance system 
could increase by an average of around 114 % in metropolitan France by 2050, of which climate 
change only contributes a 3.1 % increase in mean annual losses within metropolitan France as a 
whole (CCR, 2015). 

EU-wide studies 

Forzieri  et  al.  (2016)  discuss  the  projected  damages  for  the  EU’s  critical  infrastructure  due  to 
climate  change-related  disasters  and  estimate  that  damages  could  triple  by  the  2020s  and 
increase six-fold by 2050. Currently, river floods (44 %) and windstorms (27 %) are the hazard 
with the highest contribution to damages caused by climate change-related hazards. However, 
it  is  estimated  that droughts  and  heatwaves  are  likely  to  become the  most damaging  hazards 
(Forzieri et al., 2016). This section discusses projected losses due to climate change for each of 
the four climate change-induced hazards.  

Windstorms 

The  risk  of  storms,  especially  severe  winter  storms  and  possibly  severe  autumn  storms,  are 
projected to increase in many parts of Europe, especially in North Atlantic and northern, north-
western and central Europe (EEA, 2017). Bouwer (2013) reports that based on several studies, 
the  losses  from  windstorms  could  increase  by  11 %  to  120 %  by  2040,  depending  on  the 
country, with a median impact of 15 %. For this, exposure and vulnerability changes are more 
important than  climate change (Bouwer,  2013). One study estimates that the current EU-wide 
average annual loss for winter storms is  EUR 2.6 bn, growing to around EUR 11 m a year until 
2085  with  an  expected  loss  of  EUR 3.5 bn,  which  is  a  growth  rate  of  44 %  (Schwierz  et  al., 

15 

 
 
 
 
 
 
 
 
 
 
 
2010).  However,  a  separate  study  indicates  that  the  risk  from  storms  may  have  instead 
increased at a rate of  USD 120 m  a year (Mohleki and  Pielke, 2014). A  further  study  indicates 
that the northern parts of Europe are projected to have more rain and increased storm intensity 
(Dorland et al., 1999; Vousdoukas et al., 2016). In particular, a 2 % increase in storm intensity 
is predicted to increase associated losses by 80 %, and a 6 % increase in storm intensity could 
lead to a 500 % increase in losses (Dorland et al., 1999). It should be noted that there is a wide 
range of uncertainty in these estimates.  

Flooding  

For the Joint Research Centre’s (JRC) PESETA II report, Ciscar et al. (2014) used the LISFLOOD 
model  by  Rojas  et  al.,  (2013)  to  estimate  changes  in  both  frequency  and  severity  of  river 
flooding.  Current  estimates  suggest  that  direct  economic  damages  caused  by  floods  in  the  EU 
are around EUR 5 bn/year, which, driven by climate change, are projected to more than double 
by  2080  under  the  EU  Reference  Scenario  (Ciscar  et  al.,  2014).  These  damages  are  mostly 
incurred by the UK  and  Ireland,  where damages triple up to  EUR 3.3 bn/year,  and the  Central 
Europe South region where damages increase from EUR 2 bn to EUR 5.2 bn/year (Ciscar et al., 
2014).  The  JRC  PESETA  II  report  estimates  that  the  number  of  people  in  the  EU  affected  by 
floods on an annual basis is projected to increase from 160 000 to 240 000 people/year by the 
end of this century under the  EU Reference Scenario (Ciscar et al., 2014). A 2 metre sea-level 
rise  would  exacerbate  losses  by  67 %  for  coastal  flooding  and  increase  the  number  of  people 
affected  by  40 %  (Mokrech  et  al.,  2015).  The  combination  of  an  increase  in  precipitation  and 
sea-level  rise  make  it  very  likely  that  costal  floods  or  storm  surges  will  mostly  affect  regions 
connected to the North Sea, but there is a large degree of spatial variation in impacts (Paprotny 
et al., 2016). 

Hinkel  et  al.  (2010)  investigate  the  combined  impacts  of  coastal  and  river  flooding  under  two 
climate  and  socio-economic  development  scenarios,  with  and  without  climate  change 
adaptation. Without adaptation under the SRES A2 scenario, the combined risk is estimated to 
increase from EUR 3.2 bn in 2010 to EUR 4.8 bn in 2030 and EUR 6.4 bn in 2050. Assuming the 
presence  of  climate  change  adaptation,  risk  grows  from  EUR 1.8 bn  in  2010  to  EUR 2 bn  by 
2050.  Similarly,  under  the  SRES  B1  scenario,  risk  is  projected  to  increase  from  about 
EUR 3.3 bn in 2010 to EUR 8.1 bn in 2050, and with adaptation risk decreases from EUR 1.9 bn 
in 2010 to EUR 1.5 bn in 2050. A different study indicates that flood risk across Europe could be 
increasing at a rate of EUR 250 m per year (Mohleki and Pielke, 2014). 

Droughts and heatwaves  

The  JRC  PESETA  II  report  (Ciscar  et  al.,  2014)  presents  an  extensive  study  on  the  impacts of 
climate  change  on  agriculture  in  Europe.  Simulations  based  on  the  ‘warm’  climate  scenario 
suggest that yields in southern Europe are expected to increase as they are caused by changing 
rainfall  patterns.  However,  due  to  the  impacts  of  droughts,  EU  cropland  affected  by  this 
particular peril is expected  to increase  substantially under the  A1B climate  scenario,  especially 
in southern Europe, followed by southern central Europe (Ciscar et al., 2014).  

Crops  growing  in  the  Iberian  Peninsula  may  be  subjected  to  damagingly  high  temperatures 
during  the  sensitive  development  periods  of  flowering  and  grain  filling.  The  stress  placed  on 
crops will grow  most strongly for  summer  rather than  winter crops (Ruiz-Ramos et  al.,  2011). 
Therefore  it  is  likely  that  agricultural  impacts  from  droughts  and  heat  stress  will  increase; 
however, this potential for increased heat stress is not confined to the Mediterranean area.  

Across the EU, the IPCC (2012) reports that there is medium confidence that droughts will get 
worse  in  southern  Europe,  the  Mediterranean  area  and  central  Europe,  while  there  is  low 

16 

 
 
 
 
 
 
 
 
 
 
 
confidence in this prediction for the rest of Europe. The IPCC (2012) assigns a high confidence 
to  the  assertion  that  the  value  of  losses  has  increased.  Beniston  et  al.  (2007)  study  the 
potential  increase  in  heatwaves  across  Europe.  They  find  that  in  central  Europe  there  will  be 
more  hot  days,  while  in  the  countries  surrounding  the  Mediterranean  Sea,  droughts  will  occur 
earlier  in  the  year  and  will  last  longer.  Again,  these  projections  are  highly  influenced  by  the 
applied  scenario.  Furthermore,  droughts  are  projected  such  that  20 %  of  the  European  land 
mass  may  experience  significant  increases  in  moderate  and  severe  drought  months  between 
2011 and 2040 (Stagge et al., 2015).  

However,  due  to  climate  variability,  the  total  area  with  significant  increases  in  drought 
frequency  is  projected  to  expand  throughout  the  century,  with  an  increase  in  35-65 %  of  the 
total  area  subject  to  moderate  drought  frequency  and  an  increase  in  10-30 %  in  the  area 
subject  to  severe  drought  frequency  (Stagge  et  al.,  2015).  Model  agreement  is  strongest  for 
moderate  droughts;  the  duration  of  drought  events  is  not  projected  to  change  significantly  in 
the next 30 years, but may be significantly different for 10-30 % of the European land mass by 
the  period  2070-2100  under  the  RCP  8.5  scenario  (Stagge  et  al.,  2015).  Similar  to  the 
proportion  of  drought  months,  the  areas  with  significant  changes  in  drought  duration  are 
predominantly located in the Mediterranean and Atlantic coastal regions. Droughts are projected 
to  increase  throughout  the  Mediterranean,  including  the  eastern  Mediterranean  and  North 
Africa,  and  the  Atlantic  coastal  region,  including  France,  the  UK  and  the  Rhine  river  valley. 
These increases are due to a general shift towards a drier climate in the Mediterranean and an 
increase in precipitation variability along the Atlantic coast. This results in an overall increase in 
both moderate and severe drought frequency for the entire region, with significant increases in 
the  number  of  drought  events,  and  an  increase  in  maximum  drought  duration  for  the 
Mediterranean region (Stagge et al., 2015). 

2.2.1  Initial conclusions regarding future losses 

The  objective  behind  this  section  is  to  provide  an  overview  of  the  expected  losses  over  the 
period  2030-2050  for  both  total  monetary  loss  and  insurance  claims  over  the  same  period, 
where these forecasts were available.  

Providing a consistent approach to forecasting future losses was not available due to the varied 
combinations  of  modelling  approaches  and  differing  potential  scenarios  regarding  climate 
change  or  exposure  to  growth.  This  creates  a  great  deal of  uncertainty  in  the  way  that  future 
losses will evolve and how insurers or governments may have to respond to this. However, this 
is  not  necessarily  a  problem  in  itself.  The  broad  range  of  modelling  approaches  and  potential 
scenarios  used  can  help  prevent  a  new  systemic  risk  from  developing  whereby  all  the 
stakeholders are using the exact same approach with the same benefits and flaws. The range of 
approaches  can  create  a more  holistic  view  of  the  problem,  adding  value  to data  sources  that 
different actors may not have considered. 

The results are fairly consistent, however, in that they forecast an increase in extreme weather 
risk  on  the  whole  (which  may  or  may  not  materialise  within  a  given  year,  due  to  natural 
variability).  This  in  turn  highlights  the  role  that  risk  reduction  can  play  in  maintaining  the 
viability of extreme weather insurance  because  an increase in the number of extreme weather 
events can pose challenges for insurance.  

2.3 

Loss drivers of current and future impacts 

Given  the  uncertainty  in  climate  impacts  and  natural  variability  in  natural  hazards,  it  is 
impossible  to  attribute  individual  extreme  weather  events  to  anthropogenic  climate  change 
(IPCC, 2012). Changes in exposure, however, may be more easily observed, and are deemed to 

17 

 
 
 
 
 
 
 
 
be  the  main  drivers  of  changing  impacts  with  a  high  degree  of  confidence  (IPCC,  2012). 
Uncertainty  makes  it  difficult  to  formulate  a  meaningful  message  that  could  be  conveyed  to 
practitioners and decision-makers interested in implementing natural disaster risk management 
strategies  in  a  particular  locality,  yet  they  seem  useful  to  provide.  However,  despite  this 
difficulty,  a  portion  of  changing  risk  or  impacts  is  related  to  changes  in  weather  patterns. 
Nonetheless, there are indications that population and assets exposed to natural disasters have 
increased  more  rapidly  than  overall  population  or  economic  growth  (Bouwer  et  al.,  2007;  Di 
Baldassare et al., 2010; Bouwer, 2011; Jongman et al., 2012), even if there may have been an 
overall reduction in their vulnerability (Jongman et al., 2015). 

2.3.1  Changes in exposure 

Exposure was found to be the main driver of natural disaster impacts in many studies (Bouwer, 
2011). Over the past 50 years exposure changes have been the main driver for natural disaster 
losses  (Bouwer,  2013).  For  example,  socio-economic  growth  in  flood-prone  areas  is  a  major 
driver of various extreme weather risks (IPCC, 2012). As a result, there were more  properties 
exposed to extreme weather events and also more people reported impacts. A higher exposure 
to  these  extreme  events  will  result  in  more  reported  loss  events  and  larger  potential  damage 
(Stevens et al., 2013). An increase in exposure can result from rational individual decisions, for 
example  because  economic  productivity  is  higher  in  areas  at  risk  of  natural  hazards. 
Alternatively, individuals may underestimate natural disaster risk. If in that case an increase in 
disaster  losses  were  to  be  included  in  decision-making  processes,  investments  may  shift 
towards  adaptation  or  a  reallocation  of  assets  to  lower  risk  areas.  For  disaster  losses  to  be 
included in the decision processes, risk has to be approached rationally, which is often not the 
case  due  to  myopic  expectations  (the  actor  only  focuses  on  a  short  time  horizon,  which  is 
inappropriate  for  managing  extreme  weather events),  a  lack  of  information,  moral  hazard  and 
externalities. Estimates of exposure in the far future (e.g. after 2030) are likely to be unreliable 
due  to  these  behaviour  irrationalities  and  the  general  difficulty  in  projecting  complex  human 
behaviour. 

Flooding 

Kundzewicz  et  al.  (2014)  state  that  the  economic  losses  from  floods  have  been  generally 
increasing,  principally  driven  by  the  expanding  exposure  of  assets  at  risk.  Llasat  et  al.  (2014) 
argue  that  changes  in  population  density  and  land  use  are  the  most  probable  cause  of  the 
observed trend. Elmer et al. (2012) find that the main driver of risk in parts of Germany is the 
growing  amount  of  urban  areas.  Uncontrolled  or  unprotected  urban  development  will  be  a 
strong  driver  for  future  losses  (Bouwer,  2013).  Due  to  the  local  nature  of  pluvial  flood  risk, 
unplanned urban development could lead to a very sharp increase in pluvial flood risks.  Visser 
et  al.  (2014)  argue  that  changes  due  to  human  behaviour  are  the  largest  driver  of  losses. 
Winsemius  et  al.  (2016)  present  projections  for  flood  risk  across  Europe,  finding  that  across 
most of Europe exposure is the most important driver as compared to climate change, while in 
other regions it is a roughly equal driver. 

Droughts 

According to the  EC’s Water  Scarcity  and  Droughts Impact Assessment (2007), the number  of 
people  and  areas  affected  by  droughts  has  gone  up  by  almost  20 %  from  1976  to  2006.  The 
2003  drought,  which  was  amongst  the  most  widespread  one  at  the  time  of  writing,  affected 
over  100 million  people  and  damage  to  the  European  economy  was  estimated  to  be  at  least 
EUR 8.7 bn.  The  IPCC’s  5th  assessment  (2014)  acknowledges  the  difficulty  in  assessing  future 
trends  for  drought  risk  in  the  EU.  Nonetheless,  it  is  expected  that  southern  Europe  will 
experience longer meteorological dry spells (IPCC, 2014). Droughts are the second climate risk 
where  exposure  (demand)  changes  can  be  important.  The  effects  of  increasing  water  usage 
results in approximately the same impact as climate change. The impact of increasing demand 
for  water  on  the  occurrence  or  intensity  of  droughts  is  predicted  to  be  particularly  strong  in 

18 

 
 
 
 
 
 
 
 
eastern  Europe.  There  could  be  a  large  degree  of  regional  variation  as  intensive  water 
consumption could aggravate drought conditions by 10 to 30 % (by the end of the century) in 
southern,  western  and  central  Europe,  and  to  a  lesser  extent  also  in  the  United  Kingdom. 
Moreover,  some  regions may  be  subject to  small  impacts  of  climate  change,  which  are  turned 
negative due to intensive water use. This is the case for north-western Germany, north-western 
France and parts of the United Kingdom, as well as parts of Hungary and Romania. 

2.3.2  Changes in vulnerability  

Countries often have a significant regional variation in protection standards, similar to the large 
differences  between  countries  (Bouwer,  2013).  For  instance,  Scussolini  et  al.  (2016)  estimate 
that  Bulgaria  has  an  average  protection  standard  against  floods  with  a  20-year  return  period, 
while Poland has protection standards averaging a 200-year return period. Most risk models do 
not account for this variability in vulnerability between countries. Birkmann et al. (2010) argue 
that after disaster events there are  windows of  opportunity  that reinforce the  ability to reduce 
vulnerability.  Jongman  et  al.  (2015)  note  that  globally  there  has  been  a  general  reduction  in 
vulnerability given that observed impacts have deviated from predicted impacts.  

The  vulnerability  driver  is  important  and  can  have  noticeable  impacts  on  extreme  weather 
losses.  However,  including  adaptive  behaviour  in  risk  models  is  difficult  because  of  individual 
specific experiences and beliefs or political processes. There is currently insufficient information 
at the varying scales required to provide reliable information on how losses may change due to 
vulnerability changes. Bubeck et al. (2012) study the role of vulnerability  on impacts from the 
floods in 1993 and 1995 in the German part of the River Rhine. They note that the hydrological 
flood events that occurred were very similar, but the damage suffered by households located in 
this  area  was  smaller  in  1995  than  in  1993  due  to  the  greater  use  of  household-level  risk 
reduction measures. A second Germany-based study finds that after a series of floods in 2002, 
2005  and  2011,  the  average  level  of  flood  preparedness  tended  to  increase  as  well  as  the 
continuing development of early warning systems by the Government (Kienzler et al., 2015).  

However,  quantifying  such  reductions  in  vulnerability  is  difficult  due  to  incomplete  record 
keeping  of  protection  standards  (e.g.  policy  rules  vs.  actual  practice)  or  household-level  risk 
reduction  measures,  their  overall  effectiveness  and  how  the  employment  rates  of  these 
measures vary over time. The results shown in Table 2.1 and Figure 2.3 indicate, loosely, that 
vulnerability may  have  declined because losses remained constant, even with a slight increase 
in the number of recorded extreme natural disaster events. However, no firm conclusion about 
this can be drawn due to the relatively short time period and high annual variability of disaster 
losses. 

2.3.3  Changes in natural hazard(s) 

The  change  in  the  hazard  element  is  primarily  influenced  by  natural  variability  or  climate 
change, but may significantly vary regionally. Most studies conclude that the predicted changes 
in hazard characteristics are not the main driver of increased risk, although in places it may be 
a roughly equal driver to changes in exposure for specific risks. Caisse Centrale de Reassurance 
(CCR) (2015) highlights that climate change (on the whole, across weather risks) is projected to 
only  contribute  about  20 %  of  the  increasing  risk  in  France,  while  exposure  contributes  the 
remaining 80 %. Moreover, due to the temporal, spatial and inter-annual variability it is easy to 
view  a  trend  in  disaster  losses  as  a  sign  of  anthropogenic  climate  change,  while  in  reality  the 
trend is due to other causes (Bouwer, 2011). However, as shown in the previous sections, this 
may hold for the economic impacts but not necessarily for the number of events themselves. 

19 

 
 
 
 
 
 
 
 
 
Flooding 

Several  studies  examined  trends  in  flood  hazards.  For  example,  Stevens  et  al.  (2013)  use 
125 years of recorded flood events in the UK to establish an increasing trend in the number of 
floods reported. However, a consistent trend in the number of floods per capita during the 20th 
century  is  not observed  when  the  reported  number of  flood  events is  normalised  for  exposure 
using  population  and  number  of  dwelling  houses  in  the  UK  (Stevens  et  al.,  2013).  Increasing 
trends in heavy rains have been documented in some regions (Trenberth et al., 2007), but an 
ubiquitous increase in flood maxima is not evident (e.g. Kundzewicz et al., 2005). For example, 
an  analysis  of  the  time  evolution  for  the  Barcelona  rainfall  series  (1854-2000)  shows  that  no 
trend exists in terms of cloudburst, although impacts have altered over this time due to changes 
in  urban  planning  (Barrera  et  al.,  2006).  Llasat  et  al.  (2014)  study  the  evolution  of  floods  in 
Catalonia, and find an increase of one flood per decade due to extraordinary events that did not 
display  a  trend  in  themselves.  Hirsch  and  Archfield (2015)  also  use  precipitation  data to  show 
significant  increases  in  the  frequency  and  magnitude  of  heavy  precipitation  events  in  many 
areas, but cannot conclusively show that the size of floods is increasing. Although this trend is 
not  systematically  observed,  it  may  be  related  to  heat  waves  and  droughts  (Bouwer,  2011). 
Rouider et al. (2015) note that it is difficult to draw firm conclusions about trends in future flood 
losses  due  to  the  high  degree  of  spatial  variability.  Nevertheless,  Rouider  et  al.  (2015)  state 
that  the  hydrological  cycle  is  going  to  become  more  intense  across  Europe  due  to  climate 
change, leading to a general increase of flooding across the continent.  

Storms 

Feser  et  al.  (2014)  provide  a  comprehensive  review  of  studies  of  long-term  storminess  from 
observations, which include long-term records of wind speed, mean sea-level pressure and sea-
level  height.  Analyses  of  long-term  wind  records  in  the  UK  and  Ireland  (Hammond,  1990; 
Sweeney,  2000;  Hickey,  2003;  Ciavola  et  al.,  2011)  have  found  large  decadal  variations  in 
storminess,  but  no  significant  long-term  trends.  In  contrast,  Esteves  et  al.  (2011)  found  a 
significant decrease in storminess over the period 1929-2002 at Bidston Observatory (Wade et 
al.,  2015).  Consequently, to-date  observations  provide  no  conclusive  evidence  on  how  climate 
change affects storms. 

For  France,  it  has  been  observed  that  the  spatial  variability  of  trends  in  hail  records  from 
individual  stations  was  extremely  high.  Even  over  small  areas,  there  are  opposing  trends 
between  nearby  observation  stations.  Moreover,  a  potential  link  is  made  to  varying  trends 
across months of the year (Hermida et al., 2013). An analysis of hail damages and temperature 
series  for  peninsular  Spain  (Requejo  et  al.,  2011)  does  not  confirm  the  results  previously 
obtained  for  France  and  the  Netherlands,  which  relate  hail  damage  to  the  average  minimum 
temperature  (Botzen  et  al.,  2010).  This  is  a  complex  problem  since  the  available  time  series 
data of hail damage is non-homogeneous and non-continuous. Therefore, designing a consistent 
model  across  geographical  regions  is  very  difficult  (Piani,  2005;  Fraile  et  al.,  2003).  The  local 
conditions  of  hailstorm  formation  are  very  important  to  account  for  in  a  hailstorm  risk 
assessment,  which  implies  that  determining  hail  impacts  with  larger  scale  models  commonly 
used in risk management is problematic. In terms of insured losses for the Spanish agriculture 
sector between 1981 and 2007, the production of 79 % of winter cereals, 78 % of fruits, 39 % 
of  vineyards  and  14 %  of  olive  trees,  all  susceptible  to  hailstorm  damage,  were  covered  by 
insurance  (Requejo  et  al.,  2011).  Requejo et  al.  (2011)  find  similar  results to  those that  were 
discussed for France above with regard to the very large degree of spatial variation in trends in 
hail records. 

Droughts 

Lehner  and  Doll  (2001)  indicate  that  climate  change  projections  generally  imply  a  change  in 
drought frequencies across Europe. Northern and small parts of central Europe (e.g. Germany) 

20 

 
 
 
 
 
 
 
 
 
show  a  decreasing trend  in  future  drought  frequencies  by the  end of  the  century.  The  regions 
most affected by increased drought risk from climate change are southern Europe. For example, 
Spain,  western  France  and  parts  of  eastern-central  Europe  (e.g.  Poland)  may  experience  a 
drought  with  a  1 %  annual  probability  that  would  become  the  equivalent  of  a  drought  with  a 
10 %  annual  occurrence  probability.  Also,  for  areas  of  the  UK,  Italy  and  in  eastern-central 
Europe,  drought  risks  are  expected  to  increase  as  a  result  of  climate  change.  For  example, 
Forzieri  et  al.  (2014)  report  that  areas  that  consume  water  from  rivers,  may  find  that  the 
minimum  river  flows  may  fall  by  40 %  due  to  climate  change  in  areas  of  Spain,  France  and 
Italy,  increasing  the  likelihood  of  a  significant  drought  or  water-scarce  events  (EEA,  2017). 
Rouider  et  al.  (2015)  point  out  that  expected  trends  in  drought  may  not  be  very  robust.  The 
individual  climate  model  used  can  have  important  impacts  on  the  specific  drought  prediction 
since  the  model  can  be  very  spatially  and  temporally  specific.  Similar  uncertainty  results  from 
the  precise  set  of  climate  scenarios  used.  Moreover,  a  large  source  of  uncertainty  arises  from 
the hydrological modelling process. 

2.3.4  Initial conclusions regarding the drivers of risk and losses 

This section breaks down risk  into three  main  drivers: exposure,  vulnerability and  hazard. Out 
of  the  three,  exposure  is  recognised  as  the  main  driver  of  increases  in  predicted  losses  by 
natural hazards. Socio-economic development in hazard-prone areas is likely to continue in the 
coming decades and, furthermore, will put more people and assets at risk. 

The role of climate change and its role  in potential changes in hazards have proven difficult to 
account for in determining projected losses.  This is due to the extreme volatility of losses  and 
that a past record of accurate climate change measures are lacking, making it difficult to extend 
trends and projecting future  changes.  Still,  climate change is expected to influence  changes in 
hazards, even though associated impacts are uncertain.  

Vulnerability is an important driver although there are no clear projections for the near future. 
However,  risk  reduction  via  mitigation  or  prevention  can  be  important  in  maintaining  the 
insurability of extreme weather events. 

The two indicators that we have identified as being the more important, immediate,  drivers of 
losses  (based  on  current  trends)  are  exposure  and  vulnerability.  Compared  to  the  climate 
change element, these two elements are more directly under the control of the various national 
stakeholders.  Therefore,  there  is  a  role  for  an  increasing  focus  on  disaster  risk  reduction  or 
adaptation.  A  strong  focus  on  these  topics  can  limit  the  extent  to  which  changes  in  exposure 
and vulnerability lead to an overall threat from extreme weather events. 

2.4  Chapter conclusions 

An  examination  of  a  combination  of  available  loss  datasets  for  the  selected  European  cases 
shows it is not  yet  very clear whether there is an increase in monetary impacts from  extreme 
weather events. The key loss drivers are increases in wealth in general, and increased exposure 
of assets and infrastructure in hazard-prone areas (EEA, 2017). The effect of climate change is, 
by  comparison,  more  difficult  to  determine  due  to  natural  variability  of  natural  hazards  and 
often-unobserved changes in vulnerability, especially when the data used is primarily focused on 
monetary values. Under most climate change scenarios, the intensity and frequency of extreme 
weather events will increase in the future. This implies that probably the damage costs will also 
increase,  unless  more  proactive  disaster  risk  management  measures  are  taken.  This  is  a  key 
argument for an interest by policymakers and other actors within countries and the Commission 
in adaptation policies and actions, including insurance mechanisms (EEA, 2017; Kurnik 2017).  

21 

 
 
 
 
 
 
 
 
The risk profile across the case studies and by extension across the European Union is changing 
towards a situation where extreme weather events are becoming more common, with an overall 
increase  in  risk.  This  will  place  an  increasing  burden  on  the  budgets  of  policyholders,  insurers 
and governments to absorb these impacts. 

During  the  period  1990-2015  it  appears  that  there  has  been  a  slight  increase  in  the  total 
percentage  of  damages  due  to  an  extreme  weather  event  that  was  covered  by  insurance 
payouts, although this finding should be treated with caution due to data limitations. While this 
ratio is unlikely to reach 100 % due to risk being retained by policyholders (by not insuring or 
through deductibles), higher values do indicate an increase in risk transferred to insurers. 

One missing element of the conducted risk assessments, or in studying the past development of 
extreme  weather  events,  is  the  integration  of  changes  of  vulnerability  into  the  assessment 
frameworks. The authors observed that very few studies of future extreme weather risk include 
the behavioural elements of adapting to extreme weather risks as an integrated component of 
the modelling framework. Instead the studies made simplistic assumptions about adaptation to 
changing  natural  hazards,  for  example  through  assuming  fixed  or  overall  increases  in  large-
scale prevention measures.  

Given the potentially large impacts of individual extreme weather events (sections 2.1 and 2.2), 
it  is  imperative  to  design  adequate  risk  reduction  strategies  to  limit  future  losses.  The  active 
engagement  of  all  stakeholders  to  better  understand  risk  and  risk-reduction  measures  and  to 
act  to  reduce  threats  posed  by  extreme  weather  events  can  create  a  more  disaster-resilient 
society.  

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
3. 

PRESENTATION OF CASE STUDIES AND OVERVIEW OF THEIR AP-

PROACHES TO INSURE EXTREME WEATHER RISK 

Insurance  or  insurance-like  mechanisms  can  make  society  more  resilient  to  the  impacts  that 
emanate  from  extreme  weather  events  in  a  variety  of  ways.  The  first  way  is  that  insurance 
mechanisms can provide financial compensation for large disaster losses so that those affected 
can recover quicker. The quicker the recovery the smaller the impacts of a disaster are likely to 
be in the long run, which helps to make society more resilient. Moreover, underwriting and the 
design  of  insurance  schemes  requires  a  good  understanding  of  the  threats  that  are  insured 
against. Therefore insurance companies can play a large role in assessing and signalling risk so 
that  those  at  risk  can  have  a  better  understanding  of  the  threat  posed.  Moreover,  those 
stakeholders involved in  the insurance  sector can generate a framework in which they  provide 
incentives or requirements for risk management, which in turn can limit the potential impacts of 
an extreme weather event. While argued to be currently weak across Europe (Surminski et al., 
2015),  this link between insurance  and  risk  reduction can be  strengthened. Strengthening this 
risk  reduction  role  through  increased  prevention  and  adaptation  can  reduce  the  pressure  that 
extreme weather events place on society, which enhances disaster resilience (Botzen, 2013). 

The  purpose  of  this  chapter  is  to  highlight  the  selected  case  studies,  and  to  expand  upon  the 
justification  for  their  selection  in  terms  of  sector,  country  and  extreme  weather  event.  These 
cases  are  used  for  drawing  examples  of  ‘best  practice’  as  defined  in  later  chapters.  Moreover, 
this  chapter  provides  a  brief  description  of  the  cases  to  highlight  the  variety  of  approaches 
taken across the case studies.  This addresses Task  1 as defined in the Terms of Reference  for 
this project. 

This chapter starts with a presentation of the process by which case studies were selected and 
continues  with  a  brief  description  of  the  case  (section  3.1).  It  then  provides  some  initial 
observations  on  the  characteristics  of  extreme  weather  insurance  across  the  case  studies 
(section 3.2). 

3.1  Case study selection and description 

Here  we  describe  the  process  used  to  select  12  case  study  countries  and  provide  short 
descriptions  of  each  of  the  12  countries.  Please  see  Annex  1  for  more  detailed  per-country 
descriptions. 

3.1.1  Case study selection process 

The selection of the 12 case study countries was based on a wide-ranging exploratory literature 
review and stakeholder engagement to gather a substantial amount of information on different 
insurance markets and sectors across the EU. The Terms of Reference (ToR) of this study listed 
the  sectors  agriculture,  construction/building,  energy,  and  transport  as  requiring  study.  These 
sectors  were  grouped  under  the  labels  agriculture,  private  households/buildings  (private 
property  insurance),  and  commercial  insurance.  The  commercial  insurance  sector  consists  of 
policies  that  are  sold  to  cover  damage  to  business  property  (e.g.  damage  to  energy 
infrastructure or vehicles) and business interruption costs (e.g. loss of revenue from breakdown 
of production machinery and business equipment). Commercial insurance would therefore cover 
the major impacts of extreme  weather  events on businesses  such as energy and transport, as 
well as other business concerns. 

When investigating how insurance  provisions differ  across the three sectors for all EU  Member 
States, focus was put on the following  extreme weather events: cloudburst or extreme rainfall 
(pluvial),  fluvial flooding,  coastal flooding,  droughts and  heatwaves, and  windstorms. The only 

23 

 
 
 
 
 
 
 
 
deviation  between  the  extreme  weather  events  listed  above  and  in  the  ToR  is  that  heatwaves 
are treated as a subset of droughts because droughts would primarily only be a concern for the 
agricultural  sector.  It  should  be  noted  that  heatwaves  could  have  a  consequence  on  the 
transport or  energy sectors via business interruption (i.e. railway  tracks are no longer  useable 
or power plants are not as efficient). Moreover, heatwaves could have larger impacts on society 
outside of those identified, through higher rates of excess mortality (McMichael et al., 2006). As 
a result,  the  impacts of heatwaves are  more likely to  be covered by  health  and  life insurance, 
instead  of  insurance  for  extreme  weather  directly.  The  considerations  of  health  and  life 
insurance  are  different  to  those  of  non-life insurance  where  the  focus  is  predominantly  on  the 
identified primary disasters. Therefore the emphasis here is put on non-life insurance such that 
all  identified  insurance  schemes  arguably  have  the  same  focus  in  terms  of  potential  monetary 
impacts.  

Once the first stage of the exploratory study was completed, the cases were refined to a short 
list  of  potential  case  studies.  In  order  to  refine  the  list  of  cases,  this  study  employed  the 
following  selection  criteria:  geographical  balance,  wide  coverage  of  extreme  weather  events, 
coverage  of  different  styles  of  insurance  provision,  and  coverage  of  different  sectors.  An 
additional  selection  criterion  is  the  availability  of  sufficient  data  (as  compared  to  the  other 
potential case studies), which was used as the first selection criteria. Sufficient data availability 
was judged using the attributes as displayed in the attribute table (see Annex 1 for an example) 
for  extreme  weather  insurance.  The  table  was  completed  for  each  of  the  three  sectors  across 
the  different  extreme  weather  events  for  all  EU  Member  States.  Preference  for  inclusion  as  a 
case  study  was  given  to  countries  with  more  complete  attribute  tables  and  coverage  of  the 
majority  of  extreme  weather  events  as  listed  in  the  ToR  (collectively  across  sectors  within  a 
country). This step identified a potentially long list of viable cases.  

The next step was to select a subset of the initial long list to match the geographical spread as 
required  in  the  ToR.  The  wide  geographical  spread  was  achieved  by  splitting  the  EU  into  four 
quadrants  (north,  east,  south  and  west)  and  selecting  at  least  two  countries  located  in  each 
quadrant  to  act  as  case  studies  for  that  specific  geographical  sub-region.  A  short  summary  of 
case studies is shown in Table 3.1. 

24 

 
 
 
 
 
 
 
 
 
 
Table 3.1: Selection of case study areas 

Region 

Private property 

Commercial 

Agriculture 

Eastern Europe 

Northern Europe 

Western Europe 

Bulgaria 
Hungary 
Poland 
Romania 

Denmark  
Sweden 

Bulgaria 
Hungary 
Poland 

Denmark  
Sweden 

Bulgaria 
Hungary 
Poland 
Romania 

Denmark  
Sweden 

France 
United Kingdom 
Germany 
Austria 

France 
United Kingdom 
Germany 
Austria 

France 
United Kingdom 
Germany 
Austria 

Southern Europe 

Spain 
Italy 

Spain 
Italy 

Spain 
Italy 

Relevant extreme 
weather events 

Flooding (coastal, 
fluvial; pluvial) 
Storms (wind; hail) 

Flooding (coastal, 
fluvial; pluvial) 
Storms (wind; hail) 

Flooding (coastal, 
fluvial; pluvial) 
Storms (wind; hail) 
Droughts/heatwaves 

The selection criteria highlight positive  reasons for selecting each country (or  sector), recalling 
that cases have been selected to highlight differences in approaches. 

3.1.2  Case study description 

Brief summaries of the case study descriptions are presented below. More detailed versions can 
be found in Annex 1. 

Austria 

The opportunities for risk transfer in Austria are divided into two elements: a public mechanism 
in  the  form  of  a  catastrophe  fund  and  private  insurance;  these  interact  with  one  another.  In 
Austria,  many  extreme  weather  events  are  insured  on  a  voluntary  basis,  which  many 
households  in  Austria  have  as  a  matter  of  standard  practice.  The  exceptions  are  storms  and 
hail,  which  are  part  of  the  standard  property  insurance  (and  a  voluntary  extension  for 
businesses). The Austrian catastrophe fund provides compensation when other extreme weather 
events occur. 

Bulgaria 

Bulgaria  has  an  insurance  sector  that  is  slightly  less  developed  than  other  European  countries 
(the participation rate is 2.1 % of per capita income vs. 7.6 % on average across the EU). The 
Insurance Act of 1997 was revoked due to the adoption of a new Insurance Code, which came 
into  effect  on  1  January  2006.  Under  this  code,  insurance  companies  can  deal  with  extreme 
weather events by: setting higher solvency requirements; developing equalisation reserves; the 
use  of  reinsurance;  and  directly  excluding  extreme  weather  events  from  coverage.  There  is  a 
strong  international  presence  in  the  Bulgarian  insurance  market  on  the  whole  since  foreign 
investors  have  acquired  shares  in  most  of  the  Bulgarian  insurance  companies  or  provide 
reinsurance.  

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
Denmark 

An  increase  in  events  and  damages  has  made  it  important  to  establish  an  advance  risk 
management  system  combining  innovative  insurance  mechanisms,  the  participation  of  both 
public and private actors and their enhanced collaboration (as described during the stakeholder 
consultation).  Denmark  employs  a  mixture  of  compulsory  and  voluntary  insurance.  There  is  a 
tendency to move towards micro-rating insurance. After heavy floods in 2010 and 2011, many 
companies introduced restrictions on coverage. The penetration rate for private property storm 
insurance is over 90 % and for floods is 50-75 %.  

France 

France  is characterised by a public-private sector partnership in providing  affordable insurance 
and  high  penetration  rates  (~100 %  for  property  insurance  and  ~50-75 %  for  business 
interruption),  due  to  its  focus  on  national  solidarity  and  strong  public  sector  influence.  Since 
1982,  all  households  and  business  that  enter  into  a  contract  of  insurance  covering  damage  to 
property or business are also covered against the effects of most major natural disasters (flood, 
drought, earthquake, etc.). This CatNat scheme covers the majority of extreme weather events 
(except storm, snow and hail) for private property, businesses and agricultural enterprises. The 
Regime des Calamites Agricoles (Agricultural Calamity scheme) offers specific compensation for 
non-insurable assets of farmers through the National Fund  for the Management of Agricultural 
Risks. 

Germany 

Before the reunification of Germany there was a sharp divide between East and West Germany 
in  terms  of  insurance  for  extreme  weather  events.  In  East  Germany  there  was  a  state  run 
monopoly with fixed flat-rate premiums, the purpose of which was the socialisation of losses in 
order to provide widespread extreme weather insurance for both property and contents. In West 
Germany,  there  was  a  combination  of  statutory  public  bodies,  which  acted  as  monopoly  fire 
insurers, and private insurers. The premiums were permitted to be risk-based by the regulators, 
but  both  premium  calculations  and  the  terms  and  conditions  of  such  policies  were  strictly 
supervised. After the German reunification in 1990, the fear of adverse selection was no longer 
viable due to the large number of policies in force in East Germany. The presence of many East 
German  policies  allowed  for  the  creation  of  a  sufficient  risk  pool,  and  general  nationwide 
extreme weather insurance was allowed in 1991. After a major flooding in 2002 the estimated 
penetration rate of flood insurance (for example) was ~15 % (of private households). However, 
exposure  to  several  other  large-scale  events,  combined  with  attempts  to  increase  risk 
awareness, has led to a penetration rate of ~40 % of households. 

Hungary 

While the percentage of premiums as a proportion of gross domestic product (GDP) is rather low 
in  terms  of  coverage  against  extreme  weather  events,  ~75 %  of  households  with  general 
private property insurance have coverage against flooding, storm and hail as a standard policy. 
In  2009,  the  overall  size  of  the  premiums  collected  by  the  Hungarian  insurance  market  as  a 
ratio  to  GDP  was  3.3 %,  which  was  in  line  with  other  central  and  eastern  European  countries, 
though  smaller  than  the  EU  as  a  whole  (which  stood  at  8.9 %).  In  terms  of  agricultural 
insurance,  the  scheme  has  undergone  constant  innovation  in  order  to  better  develop  into  a 
viable solution for extreme weather risks. This has led to reform from a single compulsory policy 
to a partnership. 

Italy 

26 

 
 
 
 
 
 
 
 
 
 
 
 
The general property insurance market in Italy is dominated by two firms (Generali and Unipol) 
that,  between  them,  cover  70 %  of  the  market.  Natural  disaster  coverage  is  a  voluntary 
extension  of  homeowners’  insurance.  The  overall  penetration  rate  is  quite  low  due  to  low-risk 
perception and ad-hoc compensation. The majority of policies cover losses such as storms and 
hail,  while  flood  insurance  policies  are  less  common.  However,  a  flood  in  2014  in  Liguria  and 
Emilia Romagna caused  EUR 320 m  in damages, of which  EUR 140 m  was compensated.  Since 
1970,  agricultural  risk  management  has  been  coordinated  by  the  National  Solidarity  Fund 
(managed by the Ministry of Agriculture), the purpose of which is to support agriculture financial 
loss prevention. The National Solidarity Fund (NSF) has both ex-post and ex-ante roles. The ex-
post  role  is  to  provide  direct  compensation,  while  its  ex-ante  role  is  to  support  insurance 
coverage  or  provide  support  for  risk  reduction  (since  2004  this  subsidy  has  become  more 
decentralised, an example being the use of anti-hail nets). The NSF can contribute up to 80 % 
of the estimated insurance premium as a subsidy. 

Poland 

Due to the growing use of mortgages for new buildings, the insurance penetration rate reaches 
~63 %, even if the purchase of disaster insurance is voluntary. Policies generally cover a bundle 
of  extreme  weather  events  but  the  size  of  coverage  per  policy  may  be  limited.  Compulsory 
insurance  is,  however,  in  place  for  agricultural  farms  whereby  a  comprehensive  package  of 
insurance  (against  a  range  of  extreme  weather  events)  for  farm  buildings  in  addition  to  the 
crops allows the farmer access to other avenues of governmental support. Despite the growing 
insurance  coverage,  the  state  can  provide  ad-hoc  compensation  for  losses  from  extreme 
weather events resulting from repairing damaged infrastructure and homes. This compensation 
is a type of social assistance to satisfy basic living needs. 

Romania 

Romania can experience several natural perils, including earthquakes, floods and landslides. To 
provide  owners  of  dwellings  with  cover  for  these,  the  government  set  up  a  compulsory 
insurance  scheme  in  2007.  The  scheme,  called  PRAC  (Romanian  Program  for  Catastrophe 
insurance), is built on principles similar to several other national catastrophe insurance schemes 
but with special features of its own, which include an important role for the insurance industry 
and  the  local  authorities  strongly  involved  in  ensuring  compliance.  Since  2016,  the  insurance 
company  Die  Österreichische  Hagelversicherung  România  has  introduced  an  insurance  scheme 
for  farmers  who  are  particularly  affected  by  drought.  Currently  the  penetration  rate  is  low, 
which is partially explained by the company’s recent arrival on the Romanian insurance market. 

Spain 

There  are  two  bodies  primary  concerned  with  the  management  of  insurance  for  extreme 
weather  events.  For  private  and  commercial  property  there  is  the  Consorcio de  Compensacion 
de Seguros (CCS), while for agricultural insurance there is Agroseguro. Following reforms, there 
is currently a minimum level of protection required in an insurance policy against extraordinary 
risks (most extreme weather events but excluding hail, snow weight and common windstorms). 
The  CCS  is  able  to  provide  this  statuary  level  of  coverage  on  a  subsidiary  basis,  i.e.  if  the 
insurer does not provide this coverage, that element is passed on to the CCS for a surcharge on 
the  premium  and  a  5 %  commission  for  the  insurer.  Agroseguro  is  a  co-insurance  pool  of  29 
companies  including  the  CCS,  where  businesses  can  choose  to  integrate  into  the  pool  or  not. 
Within  the  pool  each  company  assumes  risk  in  proportion  to  the  share  of  total  capital  they 
contribute to Agroseguro. Unlike with the CCS, crop insurance is voluntary. 

Sweden 

27 

 
 
 
 
 
 
 
 
 
 
 
 
Sweden  relies  on  a  private  insurance  market  with  little  government  interference  to  provide 
extreme  weather  event  insurance.  The  Swedish  insurance  industry  currently  offers  good 
coverage  for  all  weather-related  claims.  Furthermore,  the  penetration  rate  is  high  with  one  of 
the  reasons  for  this  being  that  it  is,  in  effect,  compulsory  for  both  private  and  commercial 
mortgages. Insurance companies in Sweden have so far been able to give good protection for all 
climate-related events through their basic insurance products.  

United Kingdom 

The insurance market here is free from government interference outside of general regulation. 
There  have  only  been  very  rare  cases  of  limited  ad-hoc  government  compensation  after  a 
disaster.  Therefore  the  insurance  market  is  predominantly  driven  by  competition  between 
private  insurance  companies,  whereby  premiums  are  generally  risk-based.  However,  many 
banks or mortgage providers require properties to have full insurance coverage in order for the 
loan to be provided. Many companies are involved in the general property insurance market. 

3.2  Chapter conclusion: Case study market structures 

The  case  study  descriptions  in  the  previous  section  highlight  some  of  the  major  differences 
between  countries  in  terms  of  how  they  approach  insuring  extreme  weather  events  and  that 
there can be differences between sectors. 

On the whole, the case study insurance markets (across countries and sectors) can be divided 
into  three  broad  groups  following  Hudson  et  al.  (2017b):  voluntary  insurance,  semi-voluntary 
insurance  and  mandatory  purchase.  In  a  voluntary  insurance  market  a  potential  policyholder 
decides  if  they  will  buy  insurance  coverage  and  the  insurer  decides  if  they  will  provide  the 
coverage.  The  semi-voluntary  market  is  similar  in  that,  in  principle,  both  the  insurer  and 
policyholder  can  choose  as  to  whether  they  will  engage  in  the  extreme  weather  insurance 
market or not. However, in practice this freedom can be curtailed in the sense that there is an 
unofficial  compulsion  from,  for  example,  mortgage  providers  or  an  implicit  contract  between 
stakeholders  that  require  the  individual  to  take  part  in  the  insurance  market.  The  mandatory 
markets are where the insurer or policyholder has a legal compulsion to take part in the market. 
For  example,  an  insurer  may  be  legally  unable  to  refuse  coverage  against  extreme  weather 
events, while a policyholder may be compelled to buy fire insurance which has extreme weather 
insurance included.  

From the broad inventory of insurance market data collected (see Annex 1) it was often found 
that the more mandated (i.e. the less freedom participants had in choosing to buy/sell extreme 
weather  insurance)  an  insurance  market  was,  the  higher  the  degree  of  public  sector 
involvement  or  support  when  compared  to  more  voluntary  markets.  An  additional  structural 
feature tended to be that it  was more problematic to privately  insure  extreme  weather events 
that are more localised and less widespread across countries (river floods vs. windstorms). Thus 
several  markets  were  found  to  have  explicit  market  features  geared  towards  providing 
insurance for these truly extreme local events.  

These  divisions  hold  across  the  sectors  studied  and  can  potentially  result  in  quite  different 
outcomes for the individual markets. This is because the different approaches studied highlight 
the  different  rationales  behind  the  provision  of  extreme  weather  insurance.  For  example,  the 
compulsion to buy or to provide insurance can  result from the belief that coverage should first 
and  foremost  be  widespread  across  the  population  for  a  low  premium.  In  contrast,  more 
voluntary-based  markets  see  extreme  weather  insurance  as  a  tool  for  risk  management, 
providing  strong  incentives  for  a  policyholder  to  manage  their  own  risk.  The  following  chapter 

28 

 
 
 
 
 
 
 
 
 
presents selected market outcomes for the case study countries and sectors that will feed into 
the identification of best practices.  

29 

 
 
 
 
 
 
 
 
 
4.  OVERVIEW OF INSURANCE MARKET PERFORMANCE AND COST-

EFFECTIVENESS OF INSURANCE MARKET APPROACHES 

The main objectives of this chapter  are  as follows: to present our definition of insurance  cost-
effectiveness  and  the  main  criteria  upon  which  markets  will  be  judged  upon  (section  4.2);  to 
provide a detailed overview of the insurance market outcomes for the selected criteria (section 
4.3);  and  a  discussion  of  the  measures  that  can  be  undertaken  by  policyholders  to  limit  risk, 
and the mechanism by which insurers can promote their use (section 4.4). This addresses Tasks 
1, 2a and 2b as detailed in the Terms of Reference. 

This  chapter  builds  on  the  data  collected  and  presented  in  the  previous  chapters  and  also 
presented in Appendices 2 and 3. It provides a detailed description of the  findings used as the 
core input into the multi-criteria analysis conducted in Chapter 5, and for this reason it does not 
draw overall conclusions. 

4.1  Definition of the process chain in extreme weather insurance 

For extreme weather insurance  to be  provided,  the risk posed by the extreme weather events 
must meet the following conditions (Charpentier, 2008): 

1.  Actuarial insurability 

o  The extreme weather event occurs randomly (i.e.  a  certain  probability  within a 

year). 

o  The  maximum  possible  loss  should  be  reasonable  compared  to  an  insurer’s 

solvency conditions. 

o  The loss should be identifiable and quantifiable. 
o  The losses should be relatively uncorrelated so that risks can be pooled following 

the law of large numbers. 

2.  Economic insurability 

o  There should be limited consequences from information asymmetries (i.e. moral 

hazard and adverse selection). 

o  The  willingness  of  consumers  to  pay  for  an  insurance  policy  should  exceed  the 

premium for which insurers are willing to accept the risk transfer. 

When these conditions are met, a viable insurance market for an extreme weather event can be 
provided.  The  actuarial  insurability  criteria  result  in  the  insurer  understanding  the  risk  in 
monetary terms, while the economic insurability criteria allows the  market to find a ‘fair’ price 
that  results in  a  market equilibrium  of  supply  and  demand.  Meeting these  conditions  can  vary 
across  extreme  weather  events  and  locations.  Widespread  events  (e.g.  windstorms)  can  more 
easily meet the requirements of the law of large numbers as compared to more localised events 
(e.g. fluvial floods), which can result in highly correlated claims.  

The  actuarial  insurability  criteria  can  be  met  through  the  development  of  natural  disaster  risk 
modelling  to  better  understand  the  range  of  possible  losses  and  their  relative  likelihoods. 
Maintaining,  improving  and  sharing  knowledge  can  help  maintain  the  insurability  of  extreme 
weather events as the stakeholders are not ‘surprised’ by events and can plan accordingly. The 
applicability of the law of large numbers may be more difficult to maintain; however, structuring 
products  correctly  or  by  introducing  novel  mechanisms  can  help  create  a  suitable  pool  of 
policyholders.  

The economic insurability criteria can be hard to meet as a result of a potential mismatch in risk 
perceptions  between  an  insurer  and  a  potential  policyholder,  resulting  in  the  two  sets  of 

30 

 
 
 
 
 
 
 
 
 
 
stakeholders  not  having  sufficient  overlap  between  what  they  find  an  acceptable  premium  for 
the risk transfer to take place. The presence of information asymmetries creates problems with 
the insurability  criteria as it  means that  the  premiums charged do not reflect the  actual risk a 
policyholder  faces,  limiting  the  attractiveness  of  a  policy  for  a  policyholder  or  worsening  loss 
outcomes for an insurer. 

The  insurance  sector  by  itself  is  able  to  act  in  such  a  way  as  to  meet  some  of  the 
aforementioned criteria. However, the insurability criteria can be achieved through wide-ranging 
stakeholder  consultation  and  involvement  in  the  risk  management  process.  The  risk 
management process is split into risk transfer and risk reduction. The two are not independent 
of each other and can interact in either a positive or negative way (Hudson et al., 2017b). 

4.2  Methodology: Definition of cost-effectiveness of insurance mechanisms 

One proposed measure of the cost-effectiveness of insurance is the ratio of damage suffered to 
premiums  paid.  This  ratio  could  indicate  whether  it  is  cheaper  to  transfer  or  retain  risk. 
However, it is also an unreliable metric if based on observed data. This is because the observed 
losses are very volatile. In some years, insurance will appear very expensive when there are no 
extreme weather events or only small hail or windstorms (for example). While, for example, in 
years with major flood events, premiums would look relatively cheap. For instance, in 2015, EU-
wide total property insurance premiums9 equalled about EUR 92 bn, with reported 2015 extreme 
weather losses equalling about EUR 7 bn, which corresponds to a ratio of 8 %, while in 2013 it 
corresponded instead to about 30 % (Insurance Europe aisbl, 2016). 

Moreover, it should be noted that,  in the long run,  insurance  is more expensive than  the  total 
value  of  the  risk  that  is  transferred  (if  connected  to  risk).  While  in  theory  an  actuarially  fair 
insurance premium is equal to the annual expected damage, in reality the insurer must charge 
loading factors on top of the expected damage in order to remain viable (diverging from the risk 
faced). A risk-averse policyholder will still buy insurance if the premium loading is not too high 
because the financial certainty that insurance provides maximises welfare compared with facing 
the uncertain loss.  

Another  indicator  of  cost-effectiveness  could  be  the  premium  relative  to  the  impact  of  an 
extreme  weather  event.  This  would  show  the  relevant  benefit  of  being  insured  when  a  large 
event occurs. Table 4.1 indicates the percentage of the loss caused by a 1/500-year (yr) flood 
(shared  over the  population  affected  by  a  1/500-yr  flood) that  is  equal  to the  average  private 
property  insurance  premium  (for  households).  The  1/500-yr  flood  has  been  selected  as  it 
represents a large-scale flood that is not often directly protected against. The data in Table 4.1 
argues that while an insurance premium may be larger than the annual expected loss, insurance 
becomes truly important when very large disaster events occur. These are the events where it is 
often  not  economically  desirable  to  build  prevention  infrastructure  (i.e.  the  total  discounted 
welfare benefits are smaller than the total discounted investment and maintenance costs). While 
different extreme weather events may have different values associated with the 1/500-yr event, 
they  will  present  the  same  thinking  in  that  they  convert  the  possibility  of  a  large  random 
monetary loss for a known and fixed smaller loss. In this case, a comparison of premiums with 
the risk level may also be a suitable indicator if the pricing structure of the premium is relatively 
transparent, as the relative probability of extreme events is also an important influence. 

Insurance  has  attracted  much  policy  attention  as  a  tool  for  building  resilience  to  extreme 
weather  events  by  providing  financial  compensation  for  losses  and  incentives  to  reduce  risk. 

9 Total premiums are used due to the common practice of product bundling. 

31 

 
 
 
 
 
 
 
 
                                                
Insurance  shares  the  financial  risk  across  policyholders,  and  risk-based  premiums  can 
incentivise risk reduction by individual policyholders (Hudson et al., 2016). However, insurance 
becomes  less  attractive  for  high-risk  households  when  premiums  reflect  the  underlying  risk 
(Botzen et al., 2009a). Although lower risk policyholders have a weaker incentive to reduce risk, 
they can be more likely to buy insurance since premiums are more affordable.  

This trade-off between premium affordability and risk reduction incentives is an important, yet 
difficult, challenge for insurance companies to balance, and is often influenced by different risk 
management  objectives  (Kunreuther,  1996;  Botzen  et  al.,  2009b;  Kunreuther  and  Michel-
Kerjan, 2009; Mechler et al., 2014; Penning-Rowsell and Pardoe, 2012; Surminski and Oramas-
Dorta,  2014).  The  differing  risk  management  objectives  of  the  stakeholders  show  that  there 
would  be  room  for  more  open  and  transparent  engagement  of,  and  collaboration  with,  the 
various stakeholders involved in the risk management process. 

Table 4.1: The ratio of average private property insurance premiums to the losses included during a 
catastrophic flood event 

Average private property 
insurance premium per capita 
(EUR/yr) 

The ratio (as a percentage) of 
average private property 
insurance premium to the 
modelled damage caused by a 
1/500-yr flood per household 
exposed 

Bulgaria 

Denmark 

Germany  

Spain 

France 

Italy 

Hungary 

Austria 

Poland 

Romania 

Sweden 

United Kingdom 

90 

290 

225 

150 

250 

80 

100 

320 

30 

30 

250 

275 

80% 

92% 

74% 

60% 

87% 

31% 

66% 

99% 

19% 

32% 

89% 

92% 

Source: Attribute tables located in Appendix A. The 1/500-yr loss is taken from: http://wri.floods.org, last accessed on 18 
July 2017, converted into Euros from dollars. 

A range of risk management objectives could also be identified from stakeholder consultation(s) 
during the study10: 

10  These  views  are  drawn  from  our  own  stakeholder  consultation  in  addition  to  the  individual  consultation  responses  on  the  Green 
Paper  (that  were  published  in  English)  that  can  be  found  at:  http://ec.europa.eu/finance/consultations/2013/disasters-
insurance/contributions_en.htm 

32 

 
 
 
 
 
 
 
 
 
 
                                                
 

 

 

 

 

 

 

Insurance  can  make  two  key  contributions  to  risk  management  in  addition  to  risk 
transfer: increasing risk awareness and providing incentives for risk reduction.  

Insurance should function in a way that keeps claims and losses within acceptable limits 
(as subjectively defined by the various stakeholders). 

Policyholders  will  be  compensated  for  losses:  quickly,  efficiently,  reliably  and  fairly. 
Premiums and deductibles should be affordable and the policies should cover all aspects 
of an event’s consequences.  

Insurance  should  be  provided in a manner  that  creates a wide  pool of  policyholders to 
make premiums affordable. However, the use of insurance should also be accompanied 
with stricter building restrictions and/or standards to correct the imperfect risk signal of 
non-risk based premiums. 

Insurance can be used to promote preparedness for disaster events and to help limit the 
impacts afterwards. 

The insurance mechanisms in place should send policyholders a risk signal regardless of 
whether the decision to purchase insurance is voluntary or mandated. 

Insurance should be widely available, in a competitive market place, while being based 
on premiums that reflect risk. However, to achieve this outcome the state may have to 
act in a role that enables the industry to overcome barriers regarding premium setting 
and the general uncertainty surrounding natural disaster risks.  

Looking across the range of perspectives, the differences between risk management objectives 
highlight  the  complexity  of  comparing  different  insurance  schemes.  Countries  have  developed 
different  approaches  because  they  have  displayed  different  risk  profiles  and  priorities  in  the 
past, leading to their idiosyncratic development (Surminski et al., 2015). A comparative analysis 
of  schemes  is  therefore  required  that  focuses  on  cost-effectiveness,  the  extent  to  which  a 
scheme is able to achieve its objectives. 

Instead of basing cost-effectiveness on the size of premiums relative to losses or risk, the cost-
effectiveness  of  an  insurance  scheme  can  be  judged  on  affordability,  availability,  penetration 
rates  and  risk  reduction  incentives,  which  is  more  in  line  with  commonly  stated  objectives  of 
natural  disaster  insurance  mechanisms.  In  addition,  the  efficiency  of  insurance  should  be 
considered within the broader framework of disaster risk management as a tool for adaptation 
(based on  the  collective  input  from  the project’s opening  workshop).  The  framework  can  have 
several insurance-specific focuses. Examples could be: increasing the coverage against extreme 
weather events, the speed of compensation and the solvency of the sector. Cost-effectiveness in 
the  context  of  insurance  scheme  reforms  is  often  evaluated  by  the  penetration  rate,  that 
insurance  provides  a  signal  to  guide  risk  reduction  or  risk  management  more  generally,  the 
insurance industry is able to absorb large losses, and claim payments are certain and fast (see 
e.g. Schwarze and Wagner, 2007; Paudel et al., 2012; Poussin et al., 2013, DEFRA, 2011; EC, 
2013;  Michel-Kerjan  and  Kunreuther,  2011).  These  findings  are  combined  in  five  criteria  on 
which the cost-effectiveness of insurance schemes is analysed: 

 

 

 

the  insurance  penetration  rate  (i.e.  the  percentage  of  potential  policyholders  with  an 
insurance policy); 

the  ability  of  insurance  to  act  as  a  signal  of  risk  or  to  promote  risk  management  and 
preparedness; 

the  ability  of  the  insurance  sector  to  absorb  large  losses  (such  as  the  Solvency  II 
requirements); 

33 

 
 
 
 
 
 
 
 
 

 

the ability to provide quick and certain compensation payments after a disaster event; 

the affordability and availability of insurance policies / premiums. 

A  further  condition  for  the  cost-effectiveness  of  insurance  would  be  the  presence  of  moral 
hazard  and  adverse  selection  (or  charity  hazard).  These  factors  are,  however,  not  directly 
studied  as  consistent  information  is  not  available  across  the  case  studies.  Such  a  qualitative 
review is presented in section 4.3.6. 

4.3  Summary of insurance market outcomes 

The  different  market  structures  in  the  12  elected  case  studies  are  evaluated  according  to  five 
key  insurance  market  outcomes  that  are  later  used  as  the  key  criteria  for  identifying  best 
practices.  The  five  key  outcomes  are:  insurance  penetration  rates  (coverage  gaps),  risk 
signalling and risk reduction incentives, the affordability and availability of insurance, the speed 
of payments, and the overall solvency of the insurance mechanism. Additional key concerns are 
adverse selection, charity hazard and moral hazard, which can impact the successful operation 
of insurance mechanisms as a tool for managing natural disaster risk.  

4.3.1  Insurance penetration rates and coverage gaps 

Insurance penetration was defined to be the percentage of potential policyholders that holds the 
insurance  policy.  This  is  opposed  to  the  percentage  of  damage  covered  by  insurance  policies. 
This  definition  is  used  due  to  the  ubiquitous  presence  of  insurance  deductibles.  Therefore  the 
coverage  gap  is  defined  within  this  study  as  the  number  of  potential  policyholders  without 
insurance for private property insurance, or as the percentage of arable land that is not insured 
for agricultural insurance.  

Private property: There is a large variation in insurance penetration rates across Europe. It was 
noted  in  previous  chapters  that  the  case  studies  can  be  separated  into  three  broad  market 
classifications:  voluntary  (Germany,  Austria,  Italy  and  Bulgaria);  semi-voluntary  (Denmark, 
Sweden, Poland, Hungary and the UK); and mandatory (France, Spain and Romania). The case 
studies  are  allocated  to  a  market  classification  according  to  what  risks  are  automatically 
included in the insurance policy and the ‘mandate’ to buy insurance. Figure 4.1 (left) highlights 
the purchase requirement for the households (to buy private property insurance coverage) and 
Figure  4.2  (left)  highlights  the  insurance  penetration  rate  as  averaged  over  extreme  weather 
events.  For  instance,  a  flood  insurance  penetration  rate  of  80 %  and  a  windstorm  insurance 
penetration rate of 40 % result in an average penetration rate of 60 %. 

There  is  a  roughly  equal  spread  between  countries  with  a  formal  mandate 11  (three  cases),  an 
informal  mandate12  (five  cases)  or  voluntary  purchase  decisions  (four  cases).  There  are  four 
cases  where  the  average  penetration  rate  (across  extreme  weather  events)  is  over  85 %  of 
homeowners, five cases with an average penetration rate of between 50 % and 75 %, and three 
cases with an average penetration rate below 20 %. 

The  countries  with  the  highest  penetration  rate  tend  to  be  the  countries  with  mandatory  and 
semi-voluntary  insurance  markets;  the  voluntary  markets  tend  to  score  lower.  There  are 
exceptions however, as Romania with a ‘mandatory’ market has a very low penetration rate due 
to the lax enforcement of the regulations.  

11 In the case of a formal mandate neither insurers nor policyholders could refuse insurance coverage, i.e. there are compulsory 

extensions to offer insurance or legal requirements to buy. 
12 In the case of an informal mandate, in principle consumers can choose if they wish to buy insurance coverage, but many are in 

practice required to purchase coverage, for example due to mortgage conditions. 

34 

 
 
 
 
 
 
 
 
                                                
Commercial:  Information  on  the  penetration  rate  of  extreme  weather  insurance  in  the 
commercial  sector  is  much  scarcer.  In  terms  of  property  losses,  where  information  was 
available, it appears that storms are still the most commonly insured against extreme weather 
event,  as  is  also  the  case  with  private  property  insurance  for  households.  France  and  Spain 
achieve  roughly  comparable  penetration  rates  with  private  property  insurance  due  to  the 
compulsory extension on insurance coverage, while Austria has slightly lower penetration rates 
than was the case for private property insurance.  Similar to that observed for private property 
insurance, penetration rates for commercial extreme  weather insurance  are  likely to be higher 
when  the  decision  to  buy  or  provide  insurance  is  guided  by  (in)formal13  rules  on  insurance 
purchase.  

Agriculture: Hail is the most commonly insured against disaster event for the agriculture sector. 
Multi-peril  insurance  coverage  is  only  rarely  offered  in  the  case  study  countries.  The  vast 
majority  of  crop  insurance  markets  are  based  on  voluntary  insurance  purchases  (nine  cases, 
75 %).  Only  two  countries  have  mandatory  insurance  for  farms  over  a  certain  size  (Hungary 
and  Poland,  17 %).  Bulgaria  is  the  only  case  where  a  semi-voluntary  approach  could  be 
identified  because  without  insurance  coverage,  farmers  did  not  have  access  to  government 
subsidies.  

Figure 4.1: Summary of purchase requirements: private property (left) and agriculture (right) 

Hungary,  Austria  and  Spain  provide  multi-peril  insurance  coverage,  with  relatively  high 
penetration  rates.  Hungary’s  penetration  rate  is  over  80 %  due  to  its  mandated  nature,  while 
Austria  has  a  penetration  rate  of  60 %  (80 %  for  only  hail)  and  Spain  just  over  30 %  with 
voluntary  purchase  decisions.  We  therefore  do  not  see  a  strong  difference  between  the 
outcomes of a mandatory vs. voluntary market in these cases. 

The  remaining  countries  tend  to  only  have  substantial  coverage  for  hail  (e.g.  Germany  and 
Sweden  with  an  insurance  penetration  rate  of  about  60 %).  Romania  and  Bulgaria  also  have 
penetration  rates  of  approximately  50 %,  despite  the  different  market  classifications.  The  UK 
has the lowest level of coverage with a penetration rate of about 7 % for hail risks. 

13 A compulsion to buy coverage through legal requirements (e.g. compulsory extensions) or through other requirements (e.g. 

mortgage loan requirements). 

35 

ATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRPurchase requirements - Private PropertyLegendScheme TypeMandatorySemi-voluntaryVoluntaryNAATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRPurchase requirements - AgricultureLegendScheme TypeMandatorySemi-voluntaryVoluntaryNA 
 
 
 
 
 
 
 
                                                
 
 
It  appears  that  unlike  private  property  insurance,  the  decision  to  buy  agricultural  insurance  is 
not  strongly  driven  by  the  market  structure.  This  is  also  highlighted  by  the  observation  that 
several countries offer premium subsidies for crop insurance. The countries with no identifiable 
practice  of  subsidising  premiums  are  Bulgaria,  Denmark,  Sweden,  the  United  Kingdom  and 
Germany.  There  are  varying  penetration  rates  of  between  7 %  (UK)  and  60 %  (Sweden  and 
Germany). The remaining countries subsidise premiums with rates of between 40 % and 65 %, 
while  the  corresponding  penetration  rates  varied  between  24 %  (Poland)  and  60 %  (Austria). 
There is a great  deal  of  variety  between  and  within  the two groups of countries who do or  do 
not provide premium subsidies, which makes it hard to draw conclusions on the relative success 
of providing premium subsidies. 

Figure 4.2: The average insurance penetration rates across extreme weather events for private proper-
ty (left); the percentage of arable land that is insured against at least one extreme weather event 
(right) 

4.3.2   Risk signalling and risk-reduction incentives 

These  two  features  of  extreme  weather  insurance  are  important  in  determining  what  role 
insurance plays in adapting to changing patterns of risk (beyond the mechanism of transferring 
risk).  Risk  signalling  represents  the  ability  to  increase  awareness  and  understanding  of  the 
threats  facing  a  (potential)  policyholder,  while  the  ability  to  incentivise  risk  reduction  could 
promote policyholders to more proactively manage their risk profiles. 

These measures are described in more detail in the following section. 

Private  property  and  commercial:  There  are  specific  examples  of  where  individual  insurance 
markets  have  attempted  to  foster  risk  reduction  by  directly  promoting  resilience  through  risk 
reduction certificates (as with the GDV for example) or indirectly through discussion, which puts 
risk reduction on the agenda of stakeholders, and increasing risk awareness through information 
sharing platforms (such as HORA as led by the Austria Insurance Association).  

Figure  4.3  highlights  the,  effectively,  limited  spread  of  risk-based  premiums  as  only  four 
countries were identified as employing such premiums (33 % of cases). The remaining 67 % of 
cases employed flat rate premiums (or very limited risk differentiation). Additionally, even in the 
countries that used risk-based premiums there are cases where in very high flood risk areas the 
premiums are relaxed to flat rates. This is the case for policyholders ceded to Flood Re in the UK 

36 

ATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRIndication of insurance penetration - PrivatePropertyLegendPenetration rateshomeowners>85<10NAATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRIndication of insurance penetration - AgricultureLegendPenetration ratesagriculture>85<10NA 
 
 
 
 
 
 
 
 
 
or  the  Danish  storm  surge  insurance.  This  highlights  a  relatively  limited  ability  for  insurance 
premiums to directly signal risk. 

The  risk-reduction  incentives  most  systematically  present  are  policyholder  deductibles.  Nearly 
every  case  study  (83%)  displayed  the  use  of  deductibles  (Figure  4.4).  However,  for  the  most 
part, the deductibles found are small (e.g. EUR 380 in France per year). The deductible has the 
effect  that  the  policyholder  has  invested  money,  which  provides  a  financial  incentive  to  limit 
damage. Wind or hail storms tend to have the smallest deductible, while floods tend to have the 
largest.  

Neither Spain nor Romania provide direct policyholder incentives for risk reduction as  both see 
risk reduction as the role of the state (even though there are potential risk-reduction measures 
that can be employed), as revealed during the stakeholder consultation. The Spanish approach 
relies  on  education  to  make  policyholders  aware  of  their  risk  and  the  ways  that  they  can 
manage it. In both cases, this absence of incentives may be due to the strong solidarity focus of 
extreme weather insurance in these countries. 

Figure 4.3: Premium setting rule for private property insurance (left) and agricultural insurance (right) 

Agriculture:  Of  the  12  case  study  countries,  50 %  (or  six)  focused  risk  management  upon 
deductibles.  The  most  commonly  identified  deductible  amount  was  around  10 %  of  the  total 
insured  crop  value.  However,  the  deductible  for  various  crops  could  vary  quite  largely.  Cereal 
crops  tend  to  have  a  lower  deductible  (or  none  in  the  case  of  the  UK)  as  compared  to  higher 
deductibles for fruit or vegetables. Five of the countries (or 42 %) use both a deductible and a 
bonus-malus  system.  Therefore,  the  premiums  of  following  years  were  in  part  determined  by 
the  policyholders’  recent  claims.  This  promotes  risk  reduction  because  policyholders  with 
consistently  low  claims  records  would  be  rewarded  with  lower  premiums.  In  Spain,  both 
deductibles  and  a  bonus-malus  system  are  used  in  addition  to  imposing  certain  minimal  risk 
management  activities.  Moreover,  premium  discounts  are  given  in  Spain  for  hail  risk-reducing 
measures (i.e. hail nets) and other minimum standards to be insured are applied for hail. Figure 
4.3 indicates a roughly equal spread of premiums that are either risk differentiated or flat rates. 
Moreover,  there  is  a  roughly  equal  spread  of  countries  that  offer  subsidies  for  agricultural 
insurance premiums. 

37 

ATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRPremium setting rule - Private PropertyLegendPremium typeFlatRisk-basedNAATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRPremium setting rule - AgricultureLegendSubsidy availabilityYesNoNA 
 
 
 
 
 
 
 
 
 
Figure 4.4: Mechanisms linked to insurance to lower risk in private property insurance (left) and agri-
cultural insurance (right) 

4.3.3  Affordability and availability of insurance  

The affordability and availability of insurance are hard to  assess systematically across the case 
studies.  Affordability  is  a normative  concept  and  views  on  this  can  differ  quite  strongly  across 
the  case  study  areas.  Hudson  et  al.  (2016)  use  a  residual  income  approach  to  define  whether 
premiums  are  unaffordable  or  not.  In  that  case,  a  premium  is  judged  to  be  affordable  when 
disposable  income  minus  the  insurance  premium  and  expected  deductible  value  is  larger  than 
the national poverty line  (assuming a constant  distribution of  income  across a country). Other 
studies  proposed  definitions  that  take  into  account  the  expenditure  of  rent  or  a  simple 
percentage of income. However, a common point is that the definitions are based either on the 
ability  of  a  policyholder  to  pay  the  premium  or  the  burden  that  a  premium  places  on  their 
budget.  

Private property: In most cases, the full range of extreme weather events can be insured as an 
extension  of  general  property  insurance  across  the  case  studies.  The  cost  of  the  extension  is 
quite  low  (~EUR 50)  in  low-risk  areas.  Following  the  Hudson  et  al.  (2016)  definition  of 
unaffordability, it is likely that 20 % of the low-risk population will deem insurance (as a whole) 
unaffordable in the eastern European cases, 14 % in northern Europe, 16 % in western Europe 
and  21 %  in  southern  Europe  (Figure  4.5).  This  produces  an  average  rate  of  18 %  (with  a 
standard deviation 3.9%), with a minimum value of 13 % (Denmark) and a maximum value of 
26 % (Romania). 

However,  premiums  tend  to  differ  where  there  are  high  flood  risks  (of  various  types),  which 
implies premiums are more likely to be unaffordable for certain income groups in areas with  a 
high flood risk. The availability of insurance does not seem to be a pressing concern in low-risk 
areas,  but  in  higher  risk  areas  the  presence  of  higher  premiums  can  act  as  a  disincentive  for 
purchasing insurance. Not purchasing the insurance implies that the households who remain in 
the area bear the risk themselves. Therefore the above indicators can be considered as showing 
a minimum rate of unaffordability. 

The market structure or premium setting rule does not appear to bear a strong connection with 
the unaffordability of insurance. For example, the flat rates in Spain (23 % unaffordability rate) 
or  France  (14 %)  can  be  roughly  similar  to  overall  risk-based  premiums  in  the  UK  (17 % 

38 

ATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRIncentive types - Private PropertyLegendIncentivesDeductiblesDeductibles premiumdiscountsDeductibles premiumdiscounts standardsDeductibles standardsNoneNAATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRIncentive types - AgricultureLegendIncentivesDeductiblesDeductibles bonus-malusDeductibles bonus-malusstandardsNA 
 
 
 
 
 
 
 
 
 
unaffordability rate). This can indicate that the problem with insurance unaffordability is not the 
size of the premium itself but the low income and related purchasing power of a household. 

Figure 4.5: Indicators of affordability across the case study countries 

Note: percentage of households for whom private property insurance is unaffordable according to the Hudson et al. 2016 
definition. 

Commercial and agriculture: Debates on affordability of insurance usually focus on households. 
Commercial  insurance  premiums  are  often  unknown,  which  makes  it  difficult  to  judge  their 
affordability,  as  well  as  the  potential  requirement  for  affordability  to  be  defined  differently. 
Agricultural  insurance  seems  to  be  most  often  available  as  hail  insurance,  with  occasional 
extensions into other  hazard  events (i.e. the  multi-peril products available in Austria, Spain or 
Hungary). This could mean that in some cases it is not possible to insure against other extreme 
weather events (for example, flooding in the UK or Germany). The premiums as a percentage of 
the  total  insured  value  for  agriculture  insurance  appear  to  be  higher  than  those  for  private 
property insurance. Several studies (Mahul  and  Stutley, 2008) show that the transaction costs 
involved with this type of insurance are quite high, leading to an increase in overall premiums. 
This  may  result  in  problems  with  affordability,  but  many  countries  try  to  overcome  this  with 
rather  large  subsidies  for  agricultural  insurance  premiums  to  lower  the  effective  price  that 
farmers  pay,  namely  subsidies  are  between  40  and  65 %  of  the  premium  in  the  case  studies 
that offer such subsidies (e.g. in Italy). However, in order for a government to provide premium 
subsidies of 65 % an insurance policy must only start providing compensation for losses once a 
loss of at least 30 % of the historical average has been suffered. Lower levels of subsidies can 
relax this condition. 

4.3.4  Speed of payments 

The speed of payment corresponds to the speed at which compensation is paid to those affected 
by  an  extreme  weather  event.  This  includes  the  time  given  to  claim  processing  (i.e.  the 
insurance  company  receives  and  assesses  the  claim)  to  when  the  claim  is  confirmed  and  the 
indemnity transferred to the policyholder. 

Private property and commercial insurance: There is a large degree of variation between cases 
in the speed of insurance payments. The length of time from claim processing to payment tends 
to  be  within  15  to  30  days.  Despite  the  different  stylised  insurance  markets  across  the  case 

39 

ATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRAffordability - Private PropertyLegendPercentage ofunaffordability>26<13NA 
 
 
 
 
 
 
 
 
studies, there does not appear to be a strong link between how a market is structured and how 
quickly compensation is provided.  

Agriculture insurance: The majority of case studies aim to provide the compensation payment at 
around  harvest  time,  with  the  aim  of  providing  the  compensation  to  the  policyholder  within  1 
month after harvest at the latest.  

Overall, the various insurance markets across the case studies have a long tradition of providing 
insurance  and have developed  mature insurance  administration processes, or engage in cross-
border  business;  agriculture  hail  insurance  has  been  offered  since  the  19th  century  in  some 
cases. This means that the administrative processes for providing compensation once the claim 
has been processed can be rather quick. Nevertheless, some stakeholders raised concerns about 
the  speed  at  which  claims  are  processed  given  the  need  to  assess  and  adjust  the  damage 
suffered, because most insurance policies are indemnity-based.  

4.3.5  Ability to absorb large losses 

The  ability  to  absorb  large  losses  is  discussed  at  the  national  level  instead  of  across  sectors 
because  the  separate  insurance  lines  of  business  cannot  be  considered  independently  of  one 
another (Figure 4.6, however, presents the separate approaches graphically). It must be noted 
that  when  the  insurance  sectors’  ability  to  absorb  large  losses  is  discussed  that  due  to  the 
Solvency  II  regulations,  each  insurance  body  will  have  to  have  a  regulator  accepting  0.5 % 
annual  insolvency  probability  or  lower,  which  can  be  met  via  premiums,  capital  reserves  or 
reinsurance. The particular combination depends on the individual preferences of the company 
in question. Therefore the insurers should be able to absorb a loss of a return period of once in 
200 years. This should allow the industry to be financially stable. Loss events far in exceedance 
of the one in 200-year standard may require more government support to absorb the losses, as 
insurers are not required to plan for these events. 

Figure  4.6  presents  how  the  case  study  countries  support  the  financing  of  extreme  weather 
events. In eight (67 %) of the cases the industry is predominantly reliant on reinsurance for the 
private property sector. These  cases also tend  to  not  have a strong governmental presence  in 
their  markets;  the  UK  complements  private  reinsurance  with  a  specific  pool  and  levy  for  high 
flood-risk  households  (which  is  in  turn  privately  reinsured).  Denmark  also  has  a  pooling 
approach for storm surge insurance financed by a levy on fire insurance policies, though unlike 
the  UK  this  pool  has  been  offered  a  limited  guarantee  by the  Danish  Government.  France  and 
Spain  mainly  support  the  financing  of  large  losses  via  public  reinsurance  with  an  unlimited 
guarantee (to be called upon when the insurance body no longer has sufficient resources). The 
Spanish  approach  mainly  relies  on  the  equalisation  fund  of  the  CCS,  which  it  can  choose  to 
support with private reinsurance. However it has not yet done so. 

The  right  panel  of  Figure  4.6  highlights  the  various  approaches  for  absorbing  large  losses  for 
crop  insurance.  Again,  67 %  of  the  markets  are  reliant  on  only  private  reinsurance.  The 
remaining 33 % of countries have access to public support for all risks (Hungary, Spain through 
the CCS) or a specific risk (droughts in Poland).  

Figure  4.7  presents  the  countries  where  the  government  has  provided  compensation  after  an 
extreme weather event. The sample is equally split between those that do offer the potential for 
compensation and those that do not. Half of the countries which do offer compensation from a 
disaster  fund  did  so  on  an  ad-hoc  basis.  The  use  of  ad-hoc  (rather  than  predetermined 
strategies) can result in uncertain compensation arrangements, as their ad-hoc nature does not 
entail offering a victim of an extreme weather event a specific amount of compensation within a 
specific timeframe. 

40 

 
 
 
 
 
 
 
 
Figure 4.6: How case study countries support the financing of extreme weather events through rein-
surance (private property, left; agriculture, right) 

Figure 4.7: Case study access to government-provided disaster funds 

41 

ATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRGovernmental Disaster FundsLegendAd-hoc compensationYesNoNAATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRReinsurance types - Private PropertyLegendPrivate Propertyreinsurance typesPrivate reinsurancePrivate reinsurance,national poolPrivate reinsurance,national poolPrivate reinsurance,national pool, limited stategrantreePrivate reinsurance, publicreinsurance, state granteePrivate reinsurance, publicreinsurance, state granteeNAATBEBGCYCZDEDKEEELESFIHRHUIEITLTLULVMTNLPLPTROSESISKUKFRReinsurance types - AgricultureLegendAgriculture reinsurancetypesPrivate reinsruancePrivate reinsurancePrivate reinsurance,pooled co-insurance,government compensationPrivate reinsurance, publicreinsurance (Droughts)Private reinsurance, publicreinsurance, clamity fundPrivate reinsurance, publicreinsurance, insurer poolPublic insurance pillar (allrisks), private reinsruanceNA 
 
 
 
 
 
 
 
 
 
 
 
 
4.3.6  The presence of moral hazard, adverse selection and charity hazard 

While  not  formally  part  of  the  cost-effectiveness  assessment,  these  three  features  (moral 
hazard,  adverse  selection  and  charity  hazard)  are  important  considerations.  This  section 
discusses  their  likely  presence  in  the  case  study  countries.14  In  the  insurance  market  there  is 
often  asymmetric 
information  between  buyers  (policyholders)  and  sellers  (insurance 
companies).  In  such  a  market,  problems  of  adverse  selection  and  moral  hazard  can  occur. 
Adverse  selection  could  adversely  affect  the  penetration  rate,  and  hence  indirectly  their 
affordability.  Moral  hazard  may  adversely  affect  the  incentives  of  policyholders  to  take  private 
protection measures.  

Adverse selection:  

Adverse selection is a typical problem in insurance markets that may lead to the collapse of the 
market  or  even  prevent  the  market  being  established  in  the  first  place  (Akerlof,  1970; 
Rothschild and Stiglitz, 1976). In the case of  a natural disaster risk, a property owner may be 
better  informed  about  the  potential  damages  to  his  property  due  to  flooding,  windstorms  or 
cloudburst  than  the  insurance  company,  which  may  only  have  data  on  regional  average 
damages. In such a situation, property owners with far below average risks may be unwilling to 
buy insurance at the price of the community rating. If equilibrium exists, only high-risk property 
owners  will  purchase  insurance  at  high  premiums.  But  it  may  also  be  that  a  market  does  not 
exist  in  these  circumstances  (Rothschild  and  Stiglitz,  1976).  The  presence  of  such  private 
information is more likely for some extreme weather events than others. For example, the risk 
of  flooding  is  obviously  higher  for  people  living  on  flood  plains  than  for  people  living  outside 
such areas, and exposure to heatwaves is more likely in cities than in rural, green areas. 

Empirical  research  on  the  issue  of  adverse  selection  in  insurance  markets  has  focused  on  the 
observed  correlation  between  insurance  coverage  and  risk  claims,  controlling  for  all  relevant 
policyholder  characteristics  that  are  observable  to  the  insurer.  Cohen  and  Siegelman  (2010) 
conducted  a  comprehensive  review  of  empirical  studies,  testing  the  presence  of  adverse 
selection in different insurance markets. They concluded that the existence of adverse selection 
varies across individual insurance markets and even across segments of the same market. With 
respect to voluntary flood insurance in Europe, it has been argued that adverse selection is one 
of  the  reasons  for  low  market  penetration  rates  in  some  areas  (Schwarze  and  Warner  2007; 
Seifert  et  al.,  2013).  In  an  attempt  to  shed  more  light  on  this  issue,  Hudson  et  al.  (2017b) 
analysed field survey data of around 2 000 flood-prone households in the Elbe and Danube river 
catchments that were collected by Kreibich et al. (2005; 2011) after the flood events of 2002, 
2005  and  2006.  Statistical  analyses  showed  that  insured  households  had  a  higher  exposure 
(inundation  level)  and  higher  financial  damage  than  uninsured  households,  suggesting  the 
presence of adverse selection.  

Moral hazard:  

Unlike adverse selection, moral hazard has to do with ‘hidden action’ after the policy has been 
bought.  Moral  hazard  occurs  when  people  invest  less  in  risk  reduction  after  they  purchased 
insurance, and if financial incentives for policyholders to limit risk are insufficient. For example, 
moral hazard can arise when insured individuals expect compensation for damage regardless of 
their risk mitigation efforts. 

The recent literature has stressed that individuals are often not fully rational and act upon their 
own beliefs, which may be inaccurate. Moreover, private decision-making may not be limited to 
financial calculations alone, but may be influenced by social norms and feelings about risk, like 
minimising worry. More recent theoretical  work  has shown that if such behavioural drivers are 

14 As is encouraged under Task 2 of the Terms of Reference. 

42 

 
 
 
 
 
 
 
 
                                                
accounted  for,  moral  hazard  and  adverse  selection  may  not  arise  because  highly  risk-averse 
people  may  both  purchase  insurance  and  invest  in  risk  reduction  (de  Meza  and  Webb,  2001). 
Moral  hazard  has  been  investigated  in  the  academic  literature  across  natural  disasters  and 
countries.  Yamori  et  al.  (2009)  found  no  real  systematic  differences  between  the  degree  of 
policyholder self-protection between Japanese policyholders who had earthquake insurance, fire 
insurance, or where uninsured. Lindell and Hwang (2008) and Lindell et al. (2009) investigated 
the  correlation  between  holding  earthquake  insurance  and  employing  risk  reduction  measures 
for  US  households,  finding  a  positive  relationship.  Hudson  et  al.  (2017b)  and  Osberghaus 
(2015)  investigated  the  same  relationship  for  households  in  different  regions  of  Germany  and 
both found no evidence for moral hazard being present. Carson et al. (2013) and Petrolia et al. 
(2015) investigated the relationship between risk-reduction measures and windstorm insurance 
for the US, finding once again the absence of moral hazard.  

The results are highly consistent across different markets and countries, and therefore it is likely 
that  decisions  to  reduce  natural  disaster  risks  and  to  buy  insurance  are  jointly  taken  and  are 
mainly  driven  by 
low-
probability/high-impact  events  such  as  floods  (Hudson  et  al.,  2017b).  Such  risks  are  often 
misperceived,  and  individuals  often  do  not  use  traditional  rational  decision-making  models  in 
preparing for such risks. 

internal  (behavioural)  characteristics  of 

individuals  who 

face 

Charity hazard:  

One reason that has often been put forward to explain a low penetration rate of natural disaster 
insurance  in  voluntary  markets  is  the  so-called  ‘charity  hazard’,  i.e.  ‘[…]  the  tendency  of 
individuals to not to insure themselves against  possible natural  disasters because  they believe 
help  will  be  available,  e.g.,  from  friends,  family,  the  municipality,  charity,  or  state  emergency 
programs.’  (Browne  and  Hoyt,  2000,  p.  293).  The  bulk  of  the  literature  has  focused  on  post-
disaster government compensation as a source of charity hazard.  

Charity  hazard  has  also  been  identified  as  a  cause  for  lower  spending  on  private  protection 
measures by households (Kelly and Kleffner, 2003). Post-disaster government compensation is 
widespread  in  the  USA  and  the  EU.  Government  relief  programmes  include  the  EU’s  solidarity 
fund, or the Austrian catastrophe fund (Katastrophenfonds). Other countries, such as Germany 
and  Poland,  have  on  occasion  compensated  victims  of  natural  disasters  on  an  ad-hoc  basis 
(Raschky et al., 2013; Botzen et al., 2009). The theoretical case for an adverse effect of post-
disaster  compensation  is  clear  when  an  individual  believes that  government  compensation  will 
cover  (a  fair  part)  of  his  losses  in  the  case  of  a  disaster,  perceiving  it  as  a  ‘premium-free 
insurance’,  which  is  more  attractive  than  market  insurance  premium  or  costly  self-protection 
and insurance (e.g. Kelly and Kleffner, 2003; Raschky and Weck-Hannemann, 2007).  

The empirical evidence on charity hazard is less clear. Research in the USA (Kunreuther et al., 
1978;  Browne  and  Hoyt,  2000;  Petrolia  et  al.,  2011)  usually  rejects  the  charity  hazard 
hypothesis in the case of flood risks. This is not surprising because US households are obliged to 
purchase  flood  insurance  in  order  to  become  eligible  for  federal  disaster  assistance  after  they 
have experienced flood damage and were not insured. In Europe, some studies found evidence 
in favour of the hypothesis. Asseldonk et al. (2002) found evidence of charity hazard in a study 
that  elicited  the  willingness-to-pay  of  a  sample  of  Dutch  farmers  for  a  hypothetical  crop 
insurance  scheme.  Botzen  and  van  den  Bergh  (2012a;  2012b)  found  a  negative  effect  of 
government  compensation  on  the  willingness-to-pay  of  Dutch  homeowners  for  a  hypothetical 
flood insurance, and Botzen et al. (2009) also found that post-disaster compensation can reduce 
incentives  for  self-protection  (e.g.  sandbags,  water-resistant  floors).  Raschky  et  al.  (2013) 
argue that the design of post-disaster compensation schemes can make a big difference in their 
effect  on  charity  hazard.  To  test  this  presumption,  they  compare  the  institutional  scheme  for 
disaster  relief  in  Austria,  with  the  scheme  in  Germany.  The  scheme  in  Austria  assures  partial 

43 

 
 
 
 
 
 
 
 
compensation, while the scheme in Germany is more generous but uncertain (ad hoc). Raschky 
et  al.  (2013)  find  that  for  a  random  sample  of  households  in  Austria  and  Germany  in 
communities  that  were  affected  by  the  2005  ‘Alpine’  floods,  willingness-to-pay  for  flood 
insurance is much more negatively affected by the certain but incomplete Austrian scheme than 
by the complete but uncertain German scheme.  

Mitigating adverse selection, moral hazard and charity hazard:  

Adverse selection: Adverse selection occurs  due to informational asymmetries as the insurer is 
unable  to  fully  identify  high  and  low  risks,  which  causes  the  overall  pooling  mechanisms 
employed  to  collapse.  Adverse  selection  is  fundamentally  a  data  awareness  issue  and  can  be 
addressed through the improvement of risk modelling combined with the transparent sharing of 
data across all participants in  the  market.  These  actions will reduce  the information difference 
between the stakeholders that leads to adverse selection. Therefore platforms such as HORA or 
Zurs can address adverse selection by acting  as an  information platform. However, it must  be 
noted  that  the  sharing  of  information  is  of  key  importance  as  otherwise  the  informational 
asymmetry  falls  too  strongly  on  the  insurer  who  may  be  better  informed  than  the  potential 
policyholder. The agricultural sector for crop insurance tends to require one of the following two 
options:  either  a  farmer  must  insure  all  fields  of  the  same  crop  or  he  must  insure  all  of  his 
arable land. These  mechanisms force  the farmer to insure both high and low-risk areas  rather 
than only insuring the areas at highest risk. 

Moral hazard: Moral hazard is generally addressed in insurance markets due to the introduction 
of a deductible, as this is a payment that the policyholder must  make before compensation for 
damage  experienced  by  the  policyholder  is  provided.  Therefore  the  policyholder  has  invested 
money and thus has an incentive to manage potential impacts (Hudson et al., 2017b). However, 
Hudson  et  al.  (2017b)  provide  some  indication  that  deductibles  may  not  be  successful  at 
promoting  disaster  preparedness  unless  they  are  very  large.  Large  deductibles,  however,  can 
limit the effective insurance coverage provided or the attractiveness of such a policy. 

Charity hazard: Charity hazards can be prevented if governments do not provide direct disaster 
compensation  after  large  disaster  events.  However,  such  compensation  payments  can  be 
attractive  for  politicians,  in  election  years  (e.g.  Cole  et  al.,  2012;  Fuchs  and  Rodriguez-
Chamussy, 2015). Therefore rules must be in place to prevent governments from providing ad-
hoc disaster compensation. Formal rules or prohibitions can signal government commitment to 
no longer provide such ad-hoc compensation. Alternatively government compensation could be 
provided by loans instead of grants, because government compensation through loans has been 
shown  to  not  reduce  insurance  demand  (Kousky  et  al.,  2017).  An  additional  direction  for 
mitigating  charity  hazard  could  be  establishing  very  formal  and  published  rules,  allowing 
insurers  and  public  compensation  bodies  to  work  in  concert  by  each  providing  coverage  for 
different layers of damage by extreme weather events; such a policy is recommended in several 
studies (e.g. Botzen and van den Bergh, 2008; Mechler et al., 2014). 

4.3.7  Initial conclusions based on insurance market outcomes 

In reviewing the findings from the attribute tables located in Appendix A it is clear that there is 
no  single  country  that  performs  best  on  each  of  the  five  studied  outcomes.  Therefore,  when 
judging for best practice in the following chapter, the outcomes of a specific case study will have 
to  be  viewed  holistically  across  the  indicators  using  a  multi-criteria  analysis  (MCA).  Moreover, 
once  well-performing  cases  have  been  identified,  several  cases  may  need  to  be  compared  to 
one  another  so  that  we  can  find  the  common  characteristics  between  them.  In  doing  so 
conclusions on  what tends to  work  well  could  be identified,  as well  as possible limitations that 
the best-case countries overcame.  

44 

 
 
 
 
 
 
 
 
 
4.4  Summary of mechanisms to incentivise risk reduction 

Hudson  et  al.  (2016)  state  that  the  relevance  of  providing  financial  incentives  to  promote 
individual flood-risk adaptation can be found in the  observation that  few floodplain inhabitants 
voluntarily  invest  in  cost-effective  flood-risk  mitigation  measures  (e.g.  Kreibich  et  al.,  2005). 
Such behaviour can be explained by several individual decision-making processes (Kousky and 
Cooke,  2012),  for  example,  many  individuals  underestimate  flood  risk  and  the  benefits  of 
reducing it (e.g. Bubeck et al., 2013; Poussin et al., 2014). Offering premium discounts means 
that the decision to invest in disaster risk-reduction by policyholders is simplified to comparing 
the  costs  of  the  measure  with  the  premium  discounts  instead  of  the  perceived  risk-reduction 
benefits,  which  are  often  underestimated.  Moreover,  such  discounts  imply  that  the  benefits  of 
reducing  risks  are  transferred  to  the  policyholder  instead  of  accruing  mainly  to  the  insurance 
company in terms of lower claims. Yet, the effectiveness of such financial incentives has hardly 
been  studied  empirically  (Surminski,  2014).  However,  many  insurance  schemes  have  other 
mechanisms in place to promote policyholder risk management. 

4.4.1  Overview of insurance mechanism for promoting policyholder risk reductions 

Section 4.3.2 highlights the possible mechanisms that insurers have used systematically across 
the case study countries. In this section, these mechanisms are discussed in more detail with a 
judgement on their potential effectiveness as a risk-reduction mechanism. The focus is on how 
insurance  acts  as  a  signal  or  incentive  mechanism,  and  not  on  how  many  households  have 
undertaken measures because  of these  mechanisms due to limited data  availability  across the 
case studies.  

The  following  mechanisms  have  been  found,  during  the  inventory  of  insurance  markets,  to  be 
used by insurers to prompt their policyholder to conduct more active risk management (with the 
aim of lowering the risk faced): 

  Premium  discounts  –  This  means  that  the  insurance  company  provides  premium 
discounts when a policyholder has implemented risk-reduction measures to their insured 
property.  

- 

Likely  to  have  a  medium  effect  at  promoting  risk  reduction:  this  is  because  a 
policyholder  is  provided  with  a  tangible  value  for  the  benefit  of  employing  the 
risk-reduction measure.  This can  overcome  concerns regarding the behavioural 
heuristics  of  policyholders,  such  as  an  underestimation  of  the  benefits  of  risk 
reduction,  and  implies  that  the  benefits  of  risk  reduction  accrue  to  the 
policyholder  instead  of  only  to  the  insurance  company.  Therefore  policyholders 
could  more  often  employ  a  measure  where  it  is  cost-effective.  However,  the 
effectiveness is limited in practice because  setting up  and monitoring a system 
of  individualised  premium  discounts  in  return  for  the  constant  employment  of 
such  measures  can  entail  transaction  costs  that  may  render  such  incentives 
uneconomically viable.  

  Deductibles  –  The  policy  is  issued  with  a  fixed  deductible.  The  potential  residual  loss 
per  claim  is  assumed  to  promote  the  policyholder  to  manage  their  risk.  Additionally, 
deductibles  can  also  change  in  accordance  with  the  claims  made.  This  means  that  the 
deductible of the policyholder will be adjusted according to the implementation of risk-
reduction measures. This mechanism works two ways: on the one hand, the insurance 
company can increase the deductible when the policyholder doesn’t implement any risk 
reduction measures; on the other hand, the  deductible  can be decreased in  support of 
the  implementation  of  risk-reduction  measures  by  the  policyholder.  Moreover,  the 
presence of a deductible results in the policyholder having invested money and provides 
an incentive for continued risk reduction. 

45 

 
 
 
 
 
 
 
 
- 

Likely to be not effective (small deductible size, low awareness) to having a low 
effect  (large  deductible  size,  high  awareness).  The  deductible  is  not  a  strong 
incentive  for  risk  management  as  it  relies  on  the  expected  policyholder’s 
retained  loss  and  their  individual  coping  capacity  to  spark  a  risk  management 
decision.  Therefore  deductibles  may  be  large  to  provide  a  stronger  signal  to 
policyholders but this in turn lowers the attractiveness of the policy, although it 
could make the premium more affordable. 

  Risk-based  premiums:  The  price  of  the  premium  is  strongly  connected  to  the 
expected  annual  loss  from  the  range  of  extreme  weather  events  that  will  be  insured 
against. 

- 

Likely  to  have  a  low  effect.  Risk-based  premiums  by  themselves  can  raise  risk 
awareness,  resulting  in  indirect  risk  reduction  (if  the  premium  is  transparent 
regarding the  source  of  risk).  However,  they  can  cause  very  high  premiums  in 
high-risk  areas,  limiting  the  resources  available  for  employing  risk-reduction 
measures, unless premiums are reduced when such measures are implemented. 
The effectiveness of this mechanism will be enhanced if combined with premium 
discounts. 

  Terms  and  conditions  requirements  –  This  implicates  that  a  policyholder  can  only 
obtain  coverage  or  more  favourable  coverage  conditions  when  they  meet  certain  risk-
reduction conditions (i.e. the implementation of risk-reduction measures) as required by 
the insurance company.  

- 

Likely  to  have  a  medium  effect.  It  may  be  less  effective  in  locations  where 
insurance is voluntary since it imposes additional costs on the policyholder (i.e. 
they  are  required  to  retrofit  their  buildings).  Depending  on  the  size  of  these 
costs  it  may  reduce  the  attractiveness  of  insurance  coverage.  However,  such 
terms  and  required  conditions  could  be  integrated  in  overall  building  codes  or 
construction  practices,  so  that  newly  constructed  buildings  automatically 
comply. 

  Awareness  campaign  –  Informational  campaigns  (e.g.  using  hazard  maps  and  local 
risk  information)  that  are  aimed  at  increasing  the  level  of  risk  awareness  or  the 
perceived  benefits  from  insurance  or  undertaking  various  preparation  measures.  A 
successful campaign should lead to more agents preparing for extreme weather events. 
Moreover,  such  campaigns  can  allow  those  affected  to  ‘build  back  better’  if  they  are 
made  aware  of  the  appropriate  methods  for  reducing  risk  or  boosting  their  overall 
resilience.  

- 

Likely  to  have  a  low  to  medium  effect.  The  campaign  must  be  continuous  in 
order  to  prevent  behavioural  heuristics  returning expectations to  pre-campaign 
levels.  However,  the  effectiveness  is  still  reliant  on  the  subjective  decisions  of 
the target audience about risk reduction.  

  Bonus-malus  –  The  policyholder  is  rewarded  with  lower  premiums  for  a  period  of 

consistently low losses, while constantly high losses increase the premium. 

- 

Likely to have a low to medium effect. The main limitation of this mechanism is 
that it is difficult to connect specific risk management measures undertaken by 
the policyholder with changes in losses rather than a period of good fortune. The 
main  influence  of  this  incentivising  mechanism  is  likely  to  occur  in  the  end. 
Ultimately the inter-annual variability of losses is likely to average out, resulting 
in a lower average annual loss for those policyholders who manage their risk or 
exposure better than other policyholders. Therefore, the time frame over which 

46 

 
 
 
 
 
 
 
 
policyholder losses are included in the bonus-malus calculation is important  for 
the effectiveness of this mechanism.  

While these mechanisms are used across Europe, knowledge of their success is limited because 
there  are  few  records  of  how  systematically  these  measures  have  incentivised  a  reduction  in 
vulnerability.  Instead  there  are  a  limited  number  of  localised  studies  of  how  many  at-risk 
households have employed natural disaster risk-reduction measures (e.g. Kreibich et al., 2005; 
Bubeck et al., 2012; Poussin et al., 2014). Nevertheless, there is a nascent literature studying 
the potential of such mechanisms to incentivise policyholder risk reduction (e.g. Hudson et al., 
2016).  Instead  of  an  empirical  assessment  we  have  made  a  qualitative  ranking  of  how  likely 
they  are  to  be  effective.  In  doing  so  we  highlight  that  because  many  households  exposed  to 
extreme weather risks tend to underestimate the threat, mechanisms that rely  on the internal 
behaviour of an agent are unlikely to be very successful. For example, a deductible of EUR 380 
a  year  for  a  policyholder  who  expects  to  only  be  flooded  once  in  1 000 years  is  unlikely  to 
incentivise any additional risk management. However, more solid and tangible incentives, such 
as premium discounts or requirements, can overcome this problem as the decision to employ a 
risk-reduction measure is simplified and based on more tangible benefits and requirements than 
reduced  expectations  of  risk.  There  may  be  scope  for  a  reassessment  of  the  role  of  such 
incentivising  measures  in  the  future,  as  insurers  may  be  better  able  to  use  their  expertise  in 
assessing  and  transferring  risk.  This  can  be  done  in  collaboration  with  an  organisation(s)  that 
plays a stronger role in promoting policyholders to lower risk. For example, closer collaboration 
and  data/information  exchanges  between  major  stakeholders  could  allow  insurers  to  employ 
risk-based  premiums  to  signal  risk,  while  another  offers  direct  incentives  for  risk-reduction 
measures  (e.g.  building  codes  enforced  by  the  government),  that  are  then  reflected  in  lower 
insurance premiums. 

4.4.2  Potential measures for reducing extreme weather risk that can be taken by 

policyholders 

Table  4.2  provides  an  overview  of  the  preventive,  or  risk  reducing,  measures  per  hazard  that 
policyholders  could  implement  and  their  costs.  The  table  summarises  the  different  measures 
that  policyholders  could  employ  and  highlights  their  ability  to  reduce  risk;  it  is  followed  by  a 
brief discussion of the measures. The uptake of the measures listed below could be more or less 
successfully  promoted  by  insurer-provided  incentives,  based  on  the  strength  of  the  incentive 
and the level of risk a policyholder faces. The measures are often very specific for an area and 
are influenced by local hazard conditions. 

Table 4.2: A list of potential risk-reduction measures 

Strategy and examples  Description 

Investment 
costs 

Risk-reduction 
estimates 

€1 602 - €6 355 

Likely to have a low-to-
medium effect 

Wet-flood proofing 

Relocate furnace and 
other utilities 

Openings in basement 
or garage  

Attempts to limit water 
damage once floodwater has 
entered a building 

Furnace and other utilities 
are relocated above the 
expected flood level, 
resulting in no water damage 

Let water enter the building 
at the expected flood 
elevation level to prevent 
collapse of the structure 

47 

 
 
 
 
 
 
 
 
 
 
 
 
Strategy and examples  Description 

Investment 
costs 

Risk-reduction 
estimates 

Water-resistant 
materials 

Relocate furniture 

Apply water-resistant 
materials where floodwater is 
expected within the structure 

Household contents/furniture 
are moved above the 
expected water height 

Likely to be effective 

Dry-flood proofing 

Attempts to prevent water 
entering a building 

€6 176 - €9 369 

Likely to be highly 
effective 

Backflow valve 

Prevents water from entering 
a building through the sewer 
and drains 

External coating or 
covering 

Prevent floodwater entering 
through walls 

€14 - €41 per 
metre 

Shields for openings 

Reinforce walls 

Prevents floodwater entering 
through openings, such as 
doors and windows 

Protect walls from collapsing 
with the buoyancy force of 
floodwater 

€87 - €279 each 

Elevation of buildings 

Attempts to prevent water 
from reaching a building 

Likely to be highly 
effective 

Elevation of existing 
buildings 

Elevation of existing 
buildings using stilts, or 
elevation of staircases shafts 
etc. 

€24 763 - 
€27 802 

Elevation of new 
buildings 

Mounds 

Floodwalls 

Private floodwalls 

Redesign new buildings using 
stilts or additional filler/sand, 
so that they are elevated 
during their construction 

Elevate the land under the 
property above extreme 
storm elevation 

Construct a wall around the 
property to prevent water 
reaching the structure 

Build a floodwall with a 
height ranging between 
0.6 m and 1.8 m around the 
land. Might require a sump 
pump to remove seepage 
and internal drainage 

€812 – €5 705 

€225 – €477 per 
meter 

Mobile floodwalls 

Install the infrastructure for 
temporary floodwalls 

€157 per meter  

48 

Likely to have a medium 
effect 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Strategy and examples  Description 

Investment 
costs 

Risk-reduction 
estimates 

Likely to have a medium 
effect 

Likely to have a medium 
effect 

Wind proofing 

Bracing of gable-end 
roofs 

Roof tie-down clips 

Reinforced concrete 
roof deck 

Roof-to-wall 
connection: Clips  

Roof-to-wall 
connection: Single 
wraps 

Structural mitigation 
measures attempting to 
lower the physical 
vulnerability of buildings 
against windstorms 

Help to fortify vulnerable roof 
components and provide 
greater resistance to 
hurricane-force winds 

Roof tie-down clips connect 
the roof deck to a building’s 
framing; these clips provide 
a continuous load path from 
roof to foundation. This helps 
to increase the building’s 
resistance to hurricane-force 
winds 

A solid slab of concrete 
capping the top of a house 
increasing its storm wind 
resistance. 

Pieces of metal nailed into 
the side of the rafter/truss 
and into the side of the top 
plate or wall stud 

A single strap attached to 
side and/or bottom of top 
plate and nailed to the 
rafter/ truss 

Roof-to-wall 
connection: Double 
wraps 

Straps attached to side 
and/or bottom of top plate 
and nailed to the rafter/truss 

Shutters: Intermediate 
type 

Shutters strong enough to 
protect windows 

Hail-proofing 

Re-roof  

Structural mitigation 
measures attempting to 
lower the physical 
vulnerability of buildings 
against hailstorms, including 
placing thicker glass in 
greenhouses 

Re-roof buildings with hail-
impact-resistant material 

Protective shields 

Install protective shields for 
rooftop equipment 

Impact-resistant glass  Wind-resistant glass to 

49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Strategy and examples  Description 

Investment 
costs 

Risk-reduction 
estimates 

prevent windows breaking 
and causing water or wind 
damage indoors 

Hail-protection nets 

Nets to protect fruit trees 
from hail damage 

Drought-proofing 

Attempts to protect crops 
from the impacts of drought 

Likely to have a low-to-
medium effect 

Climate-resilient crop 
types 

Changing crop varieties to 
more climate resilient crop 
types  

Enhancing soil’s 
organic carbon 

Improved tillage 
practices 

Irrigation system 

Intercropping 

Improve the soil’s resilience 
by increasing the organic 
carbon 

Improve tillage practices to 
conserve soil water and to 
decrease levels of 
transpiration 

Reduces reliance on 
precipitation or natural 
reservoirs using irrigation  

Mix crop varieties to increase 
the resilience of crops and 
the soil 

Altering sowing time 

Change the growing season, 
limiting exposure to water 
and heat stress  

Up to 70 % of price and 
weather risk 

Stronger effect in 
southern Europe (around 
a 15 % increase in yield 
as compared to a baseline 
for most crops) than in 
northern Europe (around 
a 2 % increase), and for 
durum wheat 

Sources: Aerts et al. (2013); Cong et al. (2014); FEMA (2008); Howden et al. (2007); Hudson et al. (2016); Iglesias et al. 
(2009); National Drought Mitigation Center (1998); Rollins and Kinghorn (2013); Ye et al. (2016); 
http://www.hurricanescience.org 

Flooding (including pluvial, fluvial and coastal floods) 

Wet-flood  proofing:  these  measures  attempt  to  minimise  the  damage  inside  a  property  once 
floodwater has entered. Measures in this category are likely to have a low-to-medium effect, as 
they don’t prevent water from entering the building but aim to minimise the damage and aim to 
prevent the collapse of the building as a result of hydrostatic pressure. 

Dry-flood  proofing:  these  measures  attempt  to  prevent  water  from  entering  the  building. 
Measures in this category are likely to have a medium effect because they aim to prevent water 
from entering the property. The efficiency of measures in this category is limited by the extent 
to which the flood depth exceeds the height of dry proofing.  

50 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Elevation  of  buildings:  measures  in  this  category  attempt  to  prevent  water  from  reaching  the 
building and thus are likely to be highly effective. Flood damage can still occur if the flood depth 
exceeds  the  property  elevation  height,  but  measures  related  to  the  elevation  of  buildings  will 
still significantly decrease the overall damage.  

Floodwalls:  a  wall  constructed  around  the  property  to  prevent  floodwaters  from  reaching  the 
property.  Measures  in  this  category  are  likely  to  have  a  medium  effect  because  they  aim  to 
prevent water from entering the property. The efficiency of measures in this category is limited 
by the extent to which the flood depth exceeds the height of the floodwall and by the extent to 
which  the  wall  is  strong  enough  to  withstand  the  water  pressure.  Besides  the  wall  itself,  it  is 
necessary to take measures to control for seepage and to drain the area within the floodwalls. 

Storms 

Wind-proofing:  structural mitigation measures attempting to  lower the physical vulnerability of 
buildings  against  windstorms.  Measures  in  this  category  are  likely  to  have  a  medium  effect 
because they include relatively minor adjustments that strengthen the structure of the property, 
making it more resistant to substantial gusts of wind. 

Hail-proofing:  structural  mitigation  measures  attempting  to  lower  the  physical  vulnerability  of 
buildings  against  hailstorms.  Measures  in  this  category  are  likely  to  have  a  medium  effect 
because they include structural measures that aim to decrease the physical vulnerability of the 
property. Similar measures can be employed in order to protect agriculture. 

Droughts 
This category only applies to the agricultural sector.  

Drought-proofing:  measures  attempting  to  protect  crops  from  the  impacts  of  a  drought. 
Measures in this category are likely to have a low-to-medium effect because they require long-
term and very substantial changes in irrigation practices and crop- and soil-management.  

4.4.3  What preventive measures are associated with extreme weather event risk reduction 

at a larger scale? 

Insurance can provide incentives for policyholder-level risk reduction. However, there is also an 
interaction  with  larger  scale  risk-reduction  measures.  This  is  because  if  government-provided 
risk reduction lowers the potential impact of extreme weather events, then insurance premiums 
and/or deductibles should fall. Moreover, very high insurance premiums or the lack of available 
coverage in an area can provide an indication of the desirability of implementing risk-reduction 
measures in such an area. These measures can be most useful when there is a healthy degree 
of  interaction  between  the  private  and  public  sector  in  order  to  guide  larger  scale  risk-
management  attempts  to  where  it  is  required.  This  can  imply  a  ‘contract’  between  the 
government and the insurance sector. In this ‘social contract’, the government reduces risk (by 
investing  in  risk-reduction  measures  that  pass  a  cost-benefit  analysis)  and  the  remaining  risk 
can be transferred and shared by the insurance sector. 

For  example,  the  household-level  impacts  from  floods,  windstorms  or  hailstorms  could  be 
reduced  by  altering  building  codes  so  that  the  base  vulnerability  of  buildings  is  reduced. 
Similarly,  zoning  regulations  can  alter  land-use  patterns  so  that  land  in  high-risk  areas  is 
transferred  to  uses  that  result  in  less  damage  if  a  natural  hazard  occurs.  These  are  particular 
risk-reduction measures where governments and insurance sector stakeholders can collaborate.  

4.4.4  Initial conclusions regarding insurance-based incentives 

This  section  presents  the  finding  that  insurers  have  a  range  of  mechanisms  to  promote 
policyholder-level  risk  management,  and  the  policyholders  themselves  have  a  range  of 

51 

 
 
 
 
 
 
 
 
 
 
measures  that  they themselves can  employ. However,  it is seen  that while there  is a range of 
mechanisms  that  insurers  have  at  their  disposable,  there  is  a  very  strong  focus  on  providing 
indirect risk management signals (which  are  likely to be of low effectiveness themselves). The 
majority  of  markets  combine  deductibles  and  awareness  campaigns  to  highlight  the  risk  that 
policyholders still hold after buying an insurance policy. A wider range of incentivising strategies 
could be employed; however introducing such strategies could be quite costly (thus limiting the 
strategy’s  attractiveness),  or  go  against  the  disbursement  methods  or  channels  of  providing 
insurance  (and  hence  coverage)  but  limit  the  individuality  of  policies  (i.e.  off-the-shelf  online 
policies as compared to bespoke policies). Moreover, where incentives are provided it is difficult 
to  judge  how  well  the  incentives  have  worked  in  practice  due  to  limited  information  on 
policyholder-level preparedness. Nevertheless, it can be assumed that an insurance sector that 
employs  a  wider  range  of  mechanisms  is  relatively  more  successful  at  promoting  risk 
management strategies. 

However, while insurance policy-level incentives may not have appeared to be highly successful, 
close  collaboration  between  the  various  stakeholders  can  help  to  improve  overall  societal 
resilience. For example, an agreement to provide insurance so long as there is a certain level of 
risk  reduction  pre-existing  in  an  area.  In  the  next  chapter,  the  cost-effectiveness  of  insurance 
(including risk  reduction)  is discussed  and by means of  a  multi-criteria analysis,  the  ‘best’ and 
‘worst’ practices from the 12 case study countries are identified. 

52 

 
 
 
 
 
 
 
 
 
 
 
5.  GOOD PRACTICE FOR THE COST-EFFECTIVENESS OF INSURANCE 

MARKET APPROACHES 

This chapter evaluates and discusses elements of cost-effectiveness of the climate and weather-
related insurance schemes for households and agricultural enterprises that are present in our 12 
case  study  countries.  It  thereby  identifies  ‘best’  and  ‘worst’  cases  and  discusses  options  for 
improvement.  Thereafter,  we  focus  on  the  cases  with  the  two  highest  scores  for  a  given 
weighting  scheme  (representing  different  potential  objectives).  We  find  that  the  case  studies 
overall  (for  both  the  household  and  agriculture  sectors)  do  not  incentivise  risk  reduction  as 
strongly as they could do. This highlights a particular direction in which insurance markets could 
be reformed to further adapt to changing patterns of risk. 

5.1  How to determine good practice or the cost-effectiveness of insurance 

The  MCA  focuses  upon  the  household  and  agriculture  sectors  for  the  following  reasons.  The 
focus  emphasises  the  differences  between  the  two  styles  of  insurance.  While  commercial 
insurance  tends  to  be  a  bit  more  flexible  than  household  insurance  (for  which  we  had 
information),  their  characteristics  of  provision  seemed  quite  similar  overall.  Moreover,  the 
information  collected  on  the  commercial  sector  was  the  most  'patchy'  in  terms  of  content, 
preventing a full analysis in comparison to the household and agriculture sectors. 

These  criteria,  noted  in  the  previous  section,  are  used  to  evaluate  the  cost-effectiveness  of 
insurance  schemes:  overall  penetration  rate;  risk  signalling  or  risk  reduction;  ability  to  absorb 
large  losses;  provide  quick  and  certain  compensation;  affordability  and  availability.  The 
evaluation is based on the multi-criteria analysis (MCA) framework, as presented in Hudson et 
al.  (2017c).  An  MCA-based  approach  is  used  because  the  five  evaluation  criteria  cannot  be 
considered independent of each other in evaluating which scheme represents ‘best practice’. All 
evaluation criteria play a role and may be important for the evaluation. 

An example of the method is shown in Equation (2): 

i=5

S1

c,s   = ∑ ωi

1MCAc,i

i=1

(2) 

S1
c,s  is  the  overall  score  for  country  c  and  sector  s  under  risk  management  objective  1 
(superscript 1, weighting scheme 1). This score is  the  weighted sum  of  the individual  criterion 
1)  are  defined  per  risk  management  objective 
scores  (i)  for  country  c  (𝑀𝐶𝐴𝑐,𝑖).  The  weights  (𝜔𝑖
and  may  differ  per  criterion  or  over  three  different  weighting  schemes  to  highlight  potentially 
different  objectives.  For  example,  the  same  insurance  product  may  fulfil  a  policy  objective 
related  to  solidarity  and  coverage,  while  it  does  not  score  well  from  the  point  of  view  of  risk 
management.  The  three  key  objectives  to  study  will  be  based  on  the  following  weighting 
schemes shown in Table 5.1: 

  Weighting scheme 1: Solidarity and coverage. Providing a high degree of coverage and 

affordable insurance matching the idea of insurance as a social or public good15. 

  Weighting  scheme  2:  Balanced  objectives.  This  is  based  on  seeing  insurance  as  a 
private good16 and is a summary of the different objectives of relevant stakeholders17. 

15 Whereby insurance is seen as a method of protecting society as a whole by limiting the individual financial impacts, thereby pre-

venting economic damage from extreme weather events and should be accessible for all, or that there is a strong overall desire that 

all are covered. 

53 

 
 
 
 
 
 
 
 
 
 
 
                                                
  Weighting scheme 3: Risk management and adaptation incentives. This scheme focuses 

on the ability of insurance to act as an adaptation signal or risk management incentive. 

Table 5.1: The three weighting schemes to match the three key problems 

Weighting scheme 1  Weighting scheme 2  Weighting scheme 3 

              Objective 

Solidarity and coverage 

Objectives balanced 
across stakeholder 
views 

Risk management and 
adaptation incentives 

Criteria 

Insurance penetration 
rate 

Risk signalling 

Ability to absorb large 
losses 

Affordability and 
availability 

Quick and certain 
compensation 

0.35 

0 

0.15 

0.35 

0.15 

0.23 

0.22 

0.19 

0.19 

0.18 

0.125 

0.5 

0.125 

0.125 

0.125 

Notes: Schemes 1 and 3 are based on expect judgement; Scheme 2 is based on aggregating views from the stakeholder 

consultation. Numbers may not total to one due to rounding. 

The  individual  criterion  scores  (𝑀𝐶𝐴𝑐,𝑖)  are  scored  based  on  a  points  scheme  developed  by 
expert judgement, such that the better the case study’s performance on that outcome the more 
points are awarded to that sector (see Appendix 4). The next step regarding the 𝑀𝐶𝐴𝑐,,𝑖 scores is 
to standardise the scores to a more comparable basis. There  are  two different standardisation 
systems used, both of which are used to determine the set of countries that are determined to 
be best practice. These are noted in Appendix 4.  

5.2  Outcome of the MCA: best practice 

Table  5.2  highlights the overall  outcome  of the MCA  by  noting  the cases where they were the 
two  highest  scoring  countries  on  either  of  the  two  ranking  systems  of  the  household  and 
agriculture insurance sector18.  

16 Whereby insurance is seen as a method for protecting an individual financially from extreme weather events based on their own 

wishes. 
17 This was based on the stakeholder consultation process, whereby many stakeholders were asked to value the relative importance 

of the outcomes. Expert judgement was then used to aggregate these responses into an overall weighting scheme.  
18 The individual sector scores are presented in Tables 6 and 7. 

54 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
                                                                                                                                              
Table 5.2: Summary table of the ‘first’ and ‘second’ best outcomes in the household and agriculture 
sectors 

Household 

Agriculture 

First best 

Second best 

First best 

Second best 

France 

Spain/UK 

Austria 

France/Sweden 

Weighting scheme 
1 

Weighting scheme 
2 

France/UK 

Austria 

Spain 

Weighting scheme 
3 

Denmark/France/UK 

Spain 

Austria 

Notes: Shared boxes indicate either an equal score or that a country was in first or second place depending on the ranking 

system used. 

5.2.1  Household sector 

Denmark:  Denmark  is  placed  second  best  (together  with  France  and  the  UK)  on  weighting 
scheme 3 (under the relative ranking system) based on its ability to signal risk and incentivise 
climate adaptation. Denmark scored high on having risk-based premiums (other than for coastal 
flooding),  using  deductibles  as  well  as  incentivising  adaptation  through  a  number  of  different 
initiatives. However, as can be seen from the value presented in Table 5.6 and Table 5.7 under 
this  weighting  scheme  that  there  is  still  much  room  for  improvement,  the  performance  of 
Denmark  was  slightly  above  average  on  the  risk  reduction  aspect,  combined  with  a  high 
performance  on  the  other  four  evaluation  criteria  rather  than  acting  as  an  example  of 
outstanding risk incentivisation.  

An interesting feature of the Danish case is that it scores highly on the affordability criteria (with 
the lowest rate of unaffordability of all case studies) due to the combination of high incomes and 
the  structure  of  the  market  for  flood  insurance.  The  current  Danish  approach  was  developed 
because flood insurance (primarily coastal flooding) was considered unaffordable leading to the 
establishment of the Storm Council in 1990. The Storm Council decides whether a storm surge 
has  taken  place  and  thereafter  handles  cases  involving  compensation  following  flooding  from 
waterways and lakes, as well as subsidies for reforestation after windfall. It is appointed by the 
Danish Minister for  Business and  Growth  and consists of an independent chair and  eight other 
members.  Its  members  represent  insurance  companies,  citizens,  municipalities  and  ministries. 
This set-up provides inclusion of a broad range of stakeholder who can bring a holistic view on 
reducing risks, covering damages and avoiding moral hazard. The Storm Council’s compensation 
is financed by a yearly fee of EUR 4, which is included in the insured’s fire insurance policy. The 
fire  insurance  is  mandatory  when  owning  a  property,  which  also  explains  Denmark’s  high 
penetration  rate  and  scoring  under  this  criterion.  Furthermore,  with  a  formal  mechanism  and 
clear  definition  of  an  event  required  for  payouts,  Denmark  scores  high  on  the  criteria  of 
compensation payments. 

In  order  to  avoid  moral  hazard,  the  Storm  Council  is  based  on  two  principles.  The  property 
owner is responsible for making the necessary and reasonable investments in prevention and it 
is therefore important to incentivise these measures. The second is that the event is considered 
to  be  extraordinary  to  ensure  that  the  property  owner  manages  more  common  impacts.  It  is 
decided that a 1/20-year event is an appropriate level of coverage  

On the private insurance  market,  risk-based premiums  are  becoming more  and  more common 
in Denmark along with posing conditions to the  policyholder. What can be seen throughout the 

55 

 
 
 
 
 
 
 
 
 
 
insurance  scheme  and  initiatives  in  Denmark  is  that  there  is  a  broad  range  of  stakeholders 
involved on numerous different initiatives. An example of this is how insurance companies and 
municipalities  are  working  together,  both  in  the  Storm  Council  and  also  when  preparing  and 
implementing the climate adaptation plans. This ensures active stakeholders that have a strong 
buy-in  to  the  processes  and  who  can  streamline  the  different  initiatives,  such  as  payment  of 
damages from floods and the preparation of climate adaptation plans.  

France: The French household insurance sector ranks first in weighting scheme 1 as it provides 
a high insurance penetration rate and available and affordable insurance products. The property 
insurance  penetration  rate  reaches  100 %,  providing  coverage  to  all  households  against  all 
climate-related  risks.  The  average  annual  premium  for  a  basic  household  insurance  policy  is 
around  EUR 250  (including  around  EUR  25-30  for  CatNat).  The  insurance  is  unaffordable  for 
only  14 %  of  households  because  the  total  premium  paid  is  larger  than  what  low-income 
households can afford. The CatNat premium itself is rather small, but it is the combination with 
general household insurance that pushes up the rate of unaffordability. 

The  high  penetration,  availability  and  affordability  are  ensured  by  a  strong  influence  from  the 
public  sector,  through  a  regulatory  framework  that  provides  for  mandatory  insurance,  product 
bundling,  state-regulated  flat-rate  premiums,  and  a  state  guarantee  for  reinsurance.  The 
system  is  structured  such  that  private  insurers  are  obliged  to  provide  coverage  against  more 
general and spatially diverse risks of windstorms and hail, which is in practice bundled with the 
mandatory  property  insurance.  In  that  way,  a  high  penetration  rate  is  ensured  and  affordable 
premiums are provided as there is a sufficiently large pool of high and low risks. 

The  CatNat  regime  covers  uninsurable  extreme  weather  events  such  as  floods,  which  are 
generally  more  localised  in  specific  areas  or  regions  compared  to  windstorms,  for  example. 
CatNat  is  a  compulsory  extension  to  property  insurance,  provided  by  private  intermediaries 
through a flat-rate surcharge over existent policies against property damages  (i.e. 12 % of the 
property insurance premium). The government sets the level of both premiums and deductibles 
by decree, ensuring affordability and reflecting the national solidarity dimension of the regime. 
Therefore, France scores highly on the first scheme because the government selected the right 
structure mechanisms to provide affordable insurance in an enforceable manner. 

The French household insurance sector ranks first in weighting scheme 2 because it scores well 
on  all  other  cost-effectiveness  criteria.  A  key  element  of  the  second  scheme  is  the  ability  to 
provide  quick  and  certain  compensation,  and  absorb  large  losses.  France’s  relatively  good 
performance stems from the mixed system involving both public and private insurance sectors. 
The  system  relies  largely  on  the  private  insurance  sector  to  manage  and  pay  compensation 
claims  in  the  case  of  an  extreme  weather  event.  The  reason  is  private  insurers  are  the  main 
intermediary between those affected and indemnity payments, which provides a strong business 
case  for  providing  quick  compensation  due  to  potential  reputational  losses.  For  CatNat 
compensation  to  be  paid  to  an  insured  household,  the  extreme  weather  event  needs  to  be 
declared  as  a  natural  disaster  by  an  inter-ministerial  decree,  noting  the  causal  connection 
between  the  event  and  the  damage  suffered.  The  process  is  initiated  by  local  authorities  and 
submitted  to  the  government  representative;  the  decision  is  made  by  an  inter-ministerial 
commission, based on objective meteorological and geological data (as defined by law). Once a 
declaration has been made the insured has the responsibility to file a claim within 10 days; the 
insurer  must  make  a  first  deposit  within  2  months  and  disburse  the  claim  payment  within  3 
months  of  the  claim  or  decree.  The  current  framework  does  not  provide  all  the  necessary 
suitable guidance to ensure full certainty of compensation. In particular, the current framework 
does  not  provide  a  list,  and  the  definition  of  natural  hazards  covered  by  the  regime  and  the 
notion of uninsurable damages resulting from abnormal intensity of natural hazards leaves room 
for subjective interpretation. Nevertheless, there is likely to be a tendency to err on the side of 

56 

 
 
 
 
 
 
 
 
generosity rather than caution, due to the underlying political solidarity philosophy. There have 
been previous failed attempts to more clearly define the scope of the regime.  

Another key feature of the French regime is that it provides for a public reinsurance scheme in 
order  to  ensure  its  financial  sustainability.  Insurers  are  free  to  choose  a  reinsurance  scheme, 
but  the  Caisse  Centrale  de  Reassurance  (CCR)  benefits  from  the  state  guarantee  and  offers 
unlimited  reinsurance  coverage  in  the  framework  of  the  CatNat  regime.  So  far  the  insurance 
sector  has  shown  the  ability  to  absorb  large  losses.  The  equalisation  reserves  of  CCR  have 
proven  sufficient  to  play  a  buffer  role  and  the  French  Government  has  had  to  operate  its 
sovereign guarantee only once since 1982. However, the flat-rate surcharge was increased from 
9 %  to  12 %  in  2000  in  order  to  ensure  the  financial  viability  of  the  regime,  given  changing 
patterns of risk. 

The French home insurance sector ranks first in weighting scheme 3. This result is surprising, as 
the weakness of incentives for risk prevention is usually highlighted as the main shortage of the 
French system  for insurance  of extreme  weather events. However, the score is very similar to 
that  of  the  second  best  and  remains  relatively  low  (0.58  under  continuous  scoring,  compared 
with  0.80  and  0.70  under  the  first  and  second  schemes  respectively),  thus  highlighting  an 
overall  finding  that  there  is  an  insufficient  linkage  with  risk  reduction  overall.  Therefore,  by 
being  roughly  in  line  with  other  countries  in  the  risk-reduction  aspect  but  with  a  higher 
performance on the other outcomes, the French case is pushed into first place, and not because 
it is the best practice in terms of risk-reduction incentivisation.  

The  main  source  of  risk  reduction  evolves  outside  the  sphere  of  the  policyholder  and,  to  a 
certain degree, the insurance industry, with mixed effects. This is because (similar to Spain) the 
focus in not in providing a risk signal per se but in providing insurance as a social good, which 
limits the strength of potential risk-management incentives. The first avenue is the PPR (Plan de 
Prevention des Risques), which can mandate certain activities limiting risk to  be undertaken in 
areas  exposed  to  extreme  weather  events.  However,  the  extent  to  which  these  plans  can 
require risk reduction measures is limited (Poussin et al., 2013). A second external mechanism 
is that a small part of the CatNat premium (12 % of the insurance premium) contributes to the 
Fonds de Prévention des Risques Naturels Prévisibles (FPRNM, the so-called Barnier Fund). The 
fund,  in  principle,  finances  preventive  measures,  generally  within  the  scope  of  voluntary  local 
prevention  action  plans  against  flood  (Programmes  d’Action  de  Prévention  des  Inondations, 
PAPI).  The  Barnier  Fund  was,  however,  criticised  for  not  really  financing  hard  prevention 
measures  (such  as  resilient  infrastructure  investments)  and  for  its  lack  of  transparency  (the 
fund  was initially designed to finance  expropriation but  is currently being used for many other 
purposes). 

The problem of moral hazard is inherent in the compulsory extension of cover, which is provided 
at  a  flat  premium  that  is  not  linked  to  the  level  of  loss  prevention,  although  this  is  hard  to 
quantify. The main mechanism employed by French insurers is a deductible that changes, based 
on past flood experiences, from EUR 380 to EUR 3 050 (i.e. the more events within a set period 
of time the higher the deductible), dependent on the presence of a PPR (Poussin et al., 2013). 
However, these two processes are restricted and subject to centralised procedures, so in reality 
these prerogatives are hardly applied. Moreover, these deductibles fall over time if no claimable 
event has occurred, further limiting their potential effectiveness. 

Spain:  The Spanish system  only scores highly  under weighting scheme 1 due to this  scheme’s 
focus on the penetration rate and the affordability of insurance. The system scores high on the 
penetration  rate  for  two  reasons.  The  first  is  that  private  insurers  provide  hail  and  windstorm 
protection  as  a  standard  part  of  insurance  policies,  while  uninsurable  risks  (e.g.  flooding)  are 
transferred  to  the  CCS  for  a  small  fixed  surcharge  per  mile  of  coverage  purchased.  Therefore, 
policies cannot in effect refuse to provide coverage against extreme weather events. However, 

57 

 
 
 
 
 
 
 
 
despite  the  fact  that  coverage  against  extreme  events  is  a  compulsory  element  of  insurance 
policies,  there  is  no  legal  compulsion  to  buy  a  general  household  insurance  policy.  The  high 
penetration rate is achieved due to the presence of an overall developed insurance market and 
the  compulsion  to  buy  insurance  from  mortgage  lenders.  Moreover,  the  overall  protection 
provided  by  the  insurance  is  also  high  as  coverage  is  provided  against  a  bundle  of  extreme 
weather events, rather than having separate polices covering separate risks.  

The  compulsory  bundling  of  risks  also  aids  in  improving  the  affordability  of  extreme  weather 
insurance. The compulsory extension and coverage against the range of extreme weather risks 
creates  a  large  cross  subsidy  between  high-  and  low-risk  policyholders  (or  within  high  or  low 
risks).  This  cross  subsidy  helps  in  making  coverage  against  localised  extreme  weather  events 
(such  as  flooding)  insurable  at  low  cost.  However,  it  may  only  be  viable  when  combined  with 
mandated purchases.  

Less  important,  though  still  significant  under  the  first  weighted  scheme,  the  Spanish  market 
also  scores  highly  on  the  ability  to  absorb  large  losses  by  the  development  of  its  own 
equalisation fund through retained premiums from previous years. Moreover, the CCS also has 
the potential to buy private reinsurance coverage if deemed necessary, as well as having access 
to  a state grantee for its losses  that exceed its ability  to pay (though this has not been called 
upon yet).  

The Spanish approach  in  providing  insurance  matches the average  score  in terms of  providing 
quick  and  certain  compensation.  The  CCS  does  not  require  a  disaster  to  be  formally  declared 
and is obliged to follow the terms of the original insurance contract or conditions and regulations 
laid down in law. Therefore, policyholders have certainty regarding whether they will or will not 
receive  compensation.  Moreover,  the  CCS  manages  the  process  of  receiving  claims,  loss 
adjustment and indemnity payments, resulting, in principle, in a smooth payout process with a 
target of paying out 90 % of claims within 60 days. 

Therefore, it can be seen that the Spanish case is successful in providing insurance as a social 
good,  because  of  its  ‘compulsory’  nature  creating  a  large pool of  policyholders  that  can  cross-
subsidise  high  and  low  risks  due  to  the  comprehensive  coverage  provided  by  the  policy.  The 
insured  (nor  insurers,  effectively)  do  not  have  a  choice  over  which  disasters  to  be  insured 
against,  allowing  systemic  localised  risks  to  become  insurable  as  adverse  selection  pressures 
are  removed. Such  an approach allows events that would otherwise be uninsurable to become 
insurable, given the risk management incentive. However, it is at the expense of removing the 
risk-signalling  role  of  insurance  and  imposes  a  burden  on  households  who  are  not  exposed  to 
these highly localised disasters. Adverse selection could also be solved by mandating insurance 
coverage, without premium subsidies. 

The  Spanish  approach  to  household  insurance  only  scores  second  place  under  scheme  1.  The 
Spanish  approach  did  not  score  as  highly  under  weighting  schemes  2  and  3.  This  is  because 
compared  to  weighting  scheme  1,  greater  importance  is  placed  on  sending  a  risk-reduction 
signal. This can be clearly seen in that the MCA score drops by 41 % from scheme 1 to scheme 
3. The CCS does not provide direct signals for risk reduction due to the consideration that risk 
reduction is the role of the state and not the insurer per se, whose role is to provide affordable 
insurance. Therefore, when risk reduction is considered the Spanish scheme appears to be less 
successful. 

UK: The UK scores result in it being identified as a best practice on weighting schemes 2 and 3. 
The  UK  results  are  driven  by  its  high overall penetration  rate,  and  above-average  attempts  of 
risk  management.  However,  it  must  be  noted  that  there  is  still  room  for  improvement  as  the 
MCA scores range between 0.58 (weighting scheme 3) to 0.76 (weighting scheme 1).  

58 

 
 
 
 
 
 
 
 
The UK  has a long-developed insurance market where comprehensive insurance is required by 
mortgage providers. In the UK, insurance is provided by private insurers, whereby the full range 
of  extreme  weather  events  was  included  in  homeowner’s  insurance  policies  as  a  standard 
feature.  The  UK  insurance  industry  and  government  have  struck  several  agreements,  starting 
with the gentlemen’s agreement, which led to the statement of principles whereby flooding was 
included as a standard part of household insurance policies if the area had a 1/75 annual chance 
of  flooding  or  smaller  (in  effect  it  was  bundled  with  other  extreme  weather  risks).  Therefore, 
many homeowners in effect find it compulsory to buy complete coverage against disasters even 
if  neither  insurers  nor  homeowners  were  legally  required  to  buy  or  provide it.  However,  these 
agreements  between  the  insurance  industry  and  the  government  were  conditional  on  the 
continued development and protection of flood protection infrastructure. The rapid succession of 
major flood events and the  perceived  underinvestment in protection  infrastructure led  to  rapid 
changes  in  premiums  and  deductibles.  This  rapid  change  could  have  led  to  flood  insurance 
becoming a non-viable commodity in very high-risk areas. To this end, the insurance industry in 
collaboration  with  the  government  created  Flood  Re.  Flood  Re  formally  cross-subsidises  high 
and low flood-risk households in order to cap flood risk premiums in very high risk areas (about 
2 %  of  households)  along  with  the  deductibles  that  can  be  charged.  Flood  Re  therefore  has  a 
strong  focus  on  availability  and  affordability  rather  than  risk  reduction.  Without  this 
development the UK would have scored less well regarding the affordability, availability and the 
penetration  rate  of  extreme  weather  insurance.  Flood  Re  is  also  able  to  absorb  large  losses 
through  its  pool-like  structure:  the  ability  to  buy  private  reinsurance  coverage  as  well  as  the 
ability  to  establish  a  second  levy  on  insurers  to  make  up  for  shortfalls  in  resources.  This  last 
feature is an ability that could be considered as a tax-like ability, which required parliamentary 
approval and state aid approval. This process required an intense period of negotiation between 
the  insurance  industry  and  the  UK  Government.  This  highlights  a  potential  problem  in 
introducing  similar  structures  due  to  pre-existing  state  aid  and  competition laws:  even  though 
there are possible legal exceptions for natural disasters, it may be legally difficult to replicate. 

The  Flood  Re  structure  represents  a  suitable  structure  for  providing  affordable  insurance  by 
using a formal and transparent subsidy to cap the flood insurance premiums. The current cap on 
premiums is based on a property tax that takes into account either the ability of a household to 
pay or fine changes in risk. This could be improved by connecting the premium cap instead to 
income  or  sub-classification  of  the  high-risk  zone  so  that  an  element  of  risk  differentiation  is 
maintained (as argued in Hudson et al., 2017c).  

The second important outcome under these two weighting schemes is the ability of an insurance 
market  to  signal  risk.  The  UK  market  scores  highly  on  this  indicator  due  to  its  overall 
employment of risk-based pricing (outside of those properties ceded to Flood Re), the standard 
presence  of  deductibles  and  the  presence  of  awareness  initiatives  led  by  the  Association  of 
British  Insurers  (among  others).  Flood  Re  doesn’t  focus  on  risk  reduction  directly;  only  on 
affordable  risk  transfer.  An  attempt  to  manage  future  risk  is  that  buildings  in  high-risk  zones 
constructed after 2009 will not be able to be transferred into the Flood Re pool. Therefore, it is 
hoped that construction in high-risk areas  will be limited; however, it is too early to tell if this 
will  be  successful  or  not.  A  further  consideration  is  that  commercial  properties  are  included  in 
the  pool.  Flood  Re  is  only  a  temporary  structure  that  aids  in  improving  the  affordability  and 
availability of  flood  insurance.  Five-year  reviews  are  planned to  monitor  the  progress that  can 
be  made  towards  fully  risk-based  premiums  over  the  programme’s  expected  25-year  lifespan. 
Additionally, Flood Re aims to produce a plan to strengthen its risk management role, which is 
to  be  published  within  a  year.  Given  the  problems  that  occurred  to  spur  the  creation  of  Flood 
Re, unless either the insurance industry  or the state provide a greater focus on risk mitigation 
or prevention, the same problem may occur (or be larger due to a projected increase in risk). 

59 

 
 
 
 
 
 
 
 
Low-performing  countries:  The  low-performing  cases  highlight  the  features  of  the  previously 
discussed  cases.  These  countries  tend  to  rely  on  the  voluntary  purchase  of  extreme  weather 
insurance,  or  that  the  enforcement  of  purchase  requirements  is  lax  to  the  point  where  the 
decision  to  be  insured  is  effectively  voluntary.  Moreover,  when  combined  with  low  awareness 
levels of the benefits of insurance or the peril of extreme weather events, a low penetration rate 
is  produced.  The  low  awareness  can  be  further  compounded  by  the  presence  of  ad-hoc 
government  compensation,  which  further  lowers  the  benefits  of  being  insured,  relative  to  not 
being insured.  

An additional common point is that insurers tend to be reliant on their own capital reserves or 
private  reinsurance,  which  can  be  suitable  for  meeting  regulatory  requirements  (such  as 
Solvency II requirements) or more common place events, but they can prove insufficient if very 
large  and  widespread  disasters  occur  (as  is  commonly  argued  to  be  a  problem  with  flood 
insurance in the Netherlands, for example). 

Table 5.3: Summary of features leading to high or low performances in the household sector 

High performing 

Low performing 

Multiple extreme weather risks are combined in a 
single policy 

Extreme weather risks are separately insured 

The purchase of extreme weather insurance is 
connected to a far more commonly required and 
enforced product (e.g. mortgage contracts, fire 
insurance) 

Lax enforcements of requirements to buy insurance 

Collaboration between public and private sectors 
with a commonly stated and understood objective  

Low overall insurance coverage 

This can be seen as a contract between the 
insurance sector and the government, whereby each 
group takes actions that maintain the provision of 
insurance coverage. 

Provision of a national pool or public reinsurance / 
support for catastrophic losses 

Consumers are reliant on direct public compensation 
for extreme weather event losses 

5.2.2  Agriculture 

Austria:  The  Austrian  crop  insurance  sector  scores  in  either  first  or  second  position  in  each 
weighting scheme. Due to its high overall performance (i.e. providing compensation 4 days after 
a loss has been settled for a target total duration of 2 weeks) on most of the individual criterion 
points  that  are  above  the  case  study  average,  the  dominant  provider  of  crop  insurance  in 
Austria is the Austrian Hail Insurance Company, which is a mutual insurance company that aims 
to provide a broad risk management framework for the agricultural sector. The mutual structure 
provides  a  non-profit  maximising  focus  that  supports  the  objective  as  providing  the  best  risk 
management  infrastructure  for  the  agricultural  sector.  Moreover,  the  Austrian  Government 
collaborates  with  insurers  to  control  and  distribute  EU  subsidies.  This  allows  the  insurers  to 
receive  data  on  several  factors  (i.e.  area,  type  of  crops,  yields)  to  develop  more  suitable 
insurance  products.  Government  contributions  (i.e.  premium  subsidies)  are  provided  to 
stimulate farmers to adopt risk management strategies.  

The most impressive element of crop insurance in Austria is the level of protection. The Austrian 
insurance  market  provides  possibly  the  most  extensive  form  of  coverage  against  extreme 
weather events as about 60 % of cultivated land has multi-peril insurance coverage (while 80 % 
is protected against hail).  However, it  must be noted that  the  coverage  provided in  the  multi-

60 

 
 
 
 
 
 
 
 
 
 
peril  policy  differs  across  perils.  Hail  coverage  matches  the  insured  value  (with  an  8 % 
deductible) while the other  risks  are  provided  at  EUR 175 per hectare  (droughts must  cause  a 
yield less than 50 % of the long run  average  per  field to trigger compensation). This is a high 
penetration  rate  for  a  market  where  insurance  purchase  is  voluntary;  the  only  market  with  a 
similarly  comprehensive  degree  of  coverage  is  the  compulsory  pillar  of  the  Hungarian 
agriculture  risk-management  system.  The  Austrian  crop  insurance  market  has  likely  achieved 
such a high penetration rate because the dominant provider of crop insurance has a long history 
of  providing  multi-peril  crop  insurance  (since  1995,  longer  than  many  other  countries)  that 
promotes  great  familiarity  with  the  product  and  its  related  benefits.  Moreover,  the  mutual 
structure and the relatively small differences between insurance against only hail vs. multi-peril 
risks  can  strengthen  the  attractiveness  of  more  comprehensive  insurance  coverage.  Moreover, 
farmers  must  insure  all  arable  land  in  order  to  gain  coverage  rather  than  specific  fields.  This 
feature provides a mechanism that both increases the percentage of land covered by insurance 
and  addresses  adverse  selection  as  farmers  cannot  only  insure  specific  high-risk  parcels  (this 
has been in place since 1987). This blanket insurance coverage can be seen as the foundation of 
introducing the multi-peril insurance  policies, in  addition  to the government policy  that ad  hoc 
government compensation can only  be provided if  an extreme weather event was not covered 
by the multi-peril insurance product.  

Austria is placed  in  second place  under  the third weighting  scheme,  even though the  market’s 
attempts at promoting risk reduction are roughly average. Austria is awarded this place due to 
the  market’s  higher  performance  on  the  other  factors,  which  results  in  a  high  overall 
performance.  One  main  mechanism  is  the  bonus-malus  system,  which  rewards  policyholders 
with a bonus of 30 % if there is no damage and a loss ratio below 0.75 (there is no direct malus 
element). However, the Austrian Hail Insurance Company also offers early warning mechanisms 
for  thunderstorms  (via  SMS)  or  hail  (via  an  app)  that  can  allow  the  insured  to  change  to 
employing temporary risk management strategies in advance of an extreme weather event. In 
addition to general climate change awareness, the company sponsors awards for climate change 
journalism, scientific programmes and general attempts at lowering climate impacts. 

France: France reaches second position in the first scheme, meaning that it provides relatively 
good coverage (in terms of risks covered in principle) and affordable insurance products relative 
to  the  other  countries.  Crop  insurance  is  not  mandatory  in  France  and  the  penetration  rate  is 
about  30 %  of  land  and  farms  (similar  to  Spain)  (FFA,  2016;  L’Argus  de  l’Assurance,  2015). 
This,  according  to  stakeholders,  remains  largely  insufficient  in  a  context  of  increasing  climate 
hazards,  but  is  a  relatively  satisfactory  outcome  compared  with  other  case  study  countries. 
However,  as  is  common,  farms  are  mostly  insured  against  hail,  with  other  weather  extremes 
lacking coverage. The main driving force behind the high score is the ability of France overall to 
support  the  compensation  of  high  losses  and  the  overall  speed  at  which  insurers  provide 
compensation. 

The  French  system  relies on  private  insurance  complemented  by  a  solidarity fund  called  fonds 
national de gestion des risques en agriculture (FNGRA). The fund is financed through additional 
contributions to insurance premiums and a state contribution. In order to receive compensation 
from FNGRA a farmer must possess (at least) a fire insurance policy.  

FNGRA is the main instrument to ensure a minimum level of protection for all farmers. The fund 
serves three purposes. The first section of the fund contributes to the financing of compensation 
for economic losses related to the occurrence of an outbreak of an animal or plant disease or an 
environmental  incident  by  mutual  funds  approved  by  the  administrative  authority.  The  second 
section of the fund contributes to the financing of aid for the development of insurance against 
damage  to  farms.  To  this purpose,  the  fund  subsidises  a  share  of  the  insurance  premiums  for 
certain  agricultural  risks.  In  principle,  subsidies  are  paid  on  a  flat-rate  basis  though  they  can 

61 

 
 
 
 
 
 
 
 
vary  according  to  the  magnitude  of  the  risk  and  the  type  of  production.  To  be  eligible  for 
subsidy,  the  insurance  cover  should  include  drought,  hail,  frost  and  flood.  In  an  attempt  to 
boost the insurance market for crops and increase the penetration rate, the French Government 
has  recently  increased  subsidies  to  up  to  65 %  of  the  insurance  premium,  i.e.  the  maximum 
authorised  by  the  Common  Agricultural  Policy  (CAP)  (Enjolras  and  Santeramo,  2016).  The 
subsidy  now  takes  the  form  of  a  reduced  premium  on  the  so-called  Contrat  socle  d’assurance 
récolte,  a  multi-risk  insurance  policy  for  the  agricultural  sector.  Since  2016,  the  Contrat  socle 
covers losses on production costs rather than losses on turnover only, allowing for quicker and 
more  certain  compensation.  The  subsidy  is  partly  financed  by  the  CAP  and  partly  financed  by 
FNGRA.  

The third section of the fund contributes to the compensation of so-called agricultural calamities 
and  is  used  for  unharvested  crops  and  for  crops  not  insured  or  not  excluded  from  the  fund. 
Agricultural calamities are damages that result from risks other than those considered insurable. 
Of exceptional  importance  are  the  agricultural calamities  caused by  abnormal variations in  the 
intensity  of  a  natural  climatic  agent  (drought,  flood,  frost,  etc.),  which  are  recognised  by 
ministerial  decree.  In  this  scheme,  compensations  for  natural  disasters  have  to  satisfy  two 
conditions:  an  agricultural  calamity  must  be  recognised  by  a  decree  from  the  ministry  of 
agriculture  and  the  farm  business  affected  must  be  covered  by  an  insurance  policy  (e.g.  fire, 
crop  or  livestock  insurance).  The  list  of  insurable  risks  (events  and  crops),  and  therefore 
excluded  from  FNGRA  compensation,  is  set  by  the  inter-ministerial  decree  of  29  December 
2010. Insurable risks include hail, as well as all climatic risks to cereals, oilseeds, protein crops, 
industrial plants, including the seeds of these crops, and on vines. 

Spain: The Spanish approach to crop insurance  is based around the entity known Agroseguro. 
Agroseguro  is  a  management  entity  that  handles  the  entire  insurance  process  from  issuing 
claims  to  loss  adjustment,  with  a  self-stated  objective  to  manage  agricultural  risk  as  a  whole. 
Agroseguro is a co-insurance pool that manages crop insurance on behalf of its stakeholders (a 
range  of  companies  involved  in  providing  agricultural  insurance).  Agroseguro  also  acts  as  a 
body  that  the  Spanish  Government  can  interact  with  in  an  efficient  manner,  rather  than 
interacting  with  separate  insurers.  The  scores  awarded  to  Agroseguro  result  in  it  representing 
the  second  best  practice  on  the  second  weighting  scheme  and  the  best  practice  on  the  third 
weighting scheme. 

The  Spanish  insurance  market  scores  the  highest  score  on  weighting  scheme  3  as  the  risk 
reduction  element  is  given  a  weight  four  times  as  large  as  the  other  criteria.  The  Spanish 
approach scores highest (by between 0.2 and 0.3 points) in terms of its ability to signal risk as 
Agroseguro  employs  a  range  of  risk-reduction  incentives  (e.g.  required  standards,  premium 
discounts,  bonus-malus,  information  campaigns)  that  operate  at  varying  scales.  Moreover, the 
Spanish  case  provided  the  highest  absolute  score  of  all  the  agricultural  cases  under  this 
weighting  scheme.  This  mixture  of  mechanisms  can  be  quite  effective  in  helping  to  promote 
policyholder-level  risk  management.  In  this  sense  Agroseguro  represents  best  practice  in  this 
regard  as  four  direct  risk-reduction  strategies interact  with  one  another.  First,  to  be insured  a 
farmer  must  meet  certain  pre-set  conditions  regarding  their  vulnerability  to  extreme  events, 
setting vulnerability to an acceptable level. The second is the possibility of receiving a premium 
discount  in  return  for  employing  risk-reduction  measures,  e.g.  hail  nets,  which  reduce  a 
farmer’s vulnerability past the acceptable base level. This feature is an important addition as it 
allows Agroseguro to signal measures known to be effective and to allow farmers the option to 
employ  risk-reduction  measures  in  a  manner  that  is  cost  effective  for  a  particular  farmer.  The 
third mechanism is a bonus-malus system. The bonus-malus system results in lower premiums 
if claims have not been made within a certain period. In this sense farmers can be rewarded for 
undertaking  measures  or  farming  strategies  that  result  in  a  lower  long-run  vulnerability  to 
extreme  weather  events.  This  mechanism  is  useful  as  it  allows  for  a  certain  degree  of 

62 

 
 
 
 
 
 
 
 
information  asymmetry  between  Agroseguro  and  the  policyholder,  in  that  Agroseguro  cannot 
observe and evaluate all the actions that a farmer may take but it can monitor claims as a proxy 
measurement.  However,  as  noted  in  Chapter  4,  the  time  frame  over  which  the  bonus-malus 
payments  are  considered  is  important  as  better  extreme  weather  management  strategies  will 
only be noticeable in the long rather than short run. Therefore, while it can help to provide risk-
management incentives, it can be less effective compared to the other mechanisms.  

The Spanish system scores second best on weighting scheme 2, as there is a slight focus upon 
both  the  penetration  rate  and  risk-reduction  incentives.  This  slight  focus  on  risk-reduction 
incentives  slightly  increased  the  score  (as  can  be  seen  in  the  increase  of  the  MCA  score  from 
0.61  to  0.64).  The  percentage  of  cropland  insured  is  in  line  with  the  average  score  awarded; 
however,  the  coverage  provided  is  quite  extensive.  This  is  because  it  is  provided  as  yield 
insurance that protects against the range of extreme weather events studied. Moreover, farmers 
must  insure  all  land  for  the  specific  crop  insured.  The  Spanish  approach  also  scores  above 
average in terms of the ability to absorb large losses due to its pool structure along with access 
to  both  private  and  public  reinsurance.  Additionally,  in  the  case  of  truly  exceptional  losses, 
farmers  with  an  insurance  policy  are  eligible  to  receive  assistance  from  the  government  while 
uninsured  farmers  cannot.  The  remaining  scores  are  roughly  average.  Therefore,  we  see  that 
the  main  reason  why  Spain  provides  the  second  best  score  is  because  of  the  risk-reduction 
incentives and the comprehensive nature of coverage provided. 

The  Spanish  system  provides  two  sources  of  inspiration  regarding  reforms  for  crop  insurance 
markets  across  Europe.  The  first  is  that  a  public-private  partnership  can  be  centred  on  a 
management  entity  that  provides  a  fixed  locus  for  the  relevant  stakeholders  to  engage  with 
when required. A single representative agent can minimise the transaction costs in developing a 
suitable adaptation policy. Moreover,  by providing Agroseguro with the responsibility for policy 
insurance  or  claims  processing,  those  involved  have  an  interest  in  enabling the  success  of  the 
body. Arguably, Flood Re represents a similar type of body (though there are differences). Flood 
Re  is  a  not-for-profit  insurer  owned  by  the  insurance  industry  and  has  a  formal  legislative 
stance,  indicating  that  similar  structures  may  be  possible  to  replicate  in  other  countries  or 
sectors.  

The Spanish approach  fails  to score the first  or  second position on  weighting scheme 1 due to 
the low overall penetration rate compared to Austria or Sweden (though this is mainly covering 
hail).  Moreover,  the  premiums  charged  are  slightly  higher  due  to  their  comprehensive  nature, 
while the premiums are about 50 % subsidised. While, this high subsidy can limit the degree to 
which  the  final  price  reflects  risk,  it  can  help  to  overcome  the  relatively  high  premiums  rates 
(for  instance,  agricultural  rates  are  whole  percentage  points  of  the  insured  value,  while  for 
households the premium rates are fractions of a percentage point of the insured value). 

Sweden: The insurance of crops in Sweden has been ranked second best together with France 
on  Scheme  1,  based  on  its  penetration  rate  as  well  as  affordability  and  to  a  lesser  extent 
availability. However, the main driver is the very high penetration rate. Private hail damage and 
reseeding  insurance  is  the  only  crop  insurance  available,  which  about  60 %  of  Swedish  crops 
are  covered  by.  Even  though  only  two  perils  are  actively  insured  against  it  still  represents  a 
broader  degree  of  protection  than  several  other  countries  where  hail  is  the  main  insured 
extreme  weather  event.  Moreover,  the  Swedish  insurance  market  is  dominated  by  two  firms 
(one with a ~75 % market share, and  the other with ~15 %)  so there is a focus on providing 
insurance  that  leads  to  a  relative  absence  in  risk-management  incentives  for  crop  risk 
management. 

The  main  driving  force  behind  the  high  penetration  rate  can  be  argued  to  be  the  historical 
system of compulsory crop insurance, which existed until 1994 and was provided by the state. 
Similarly, countries such as Bulgaria and Romania also had previous state monopoly providers, 

63 

 
 
 
 
 
 
 
 
achieving similar penetration rates, even after the state monopoly and compulsion was ended. 
However,  this  approach  was  removed  as  part  of  the  requirements  of  the  European  Common 
Agricultural  Policy.  Furthermore,  this  approach  was problematic  in  the  sense  that  the  rules  for 
compensation  were  unclear,  leaving  it  too  expensive  and  difficult  to  determine  who  should  be 
covered  by  the  payouts.  However,  despite  this  it  did  create  a tradition  of  being  insured  and  a 
general  awareness  of  the  benefits  of  being  insured  among  farmers,  which  leads  to  the  high 
coverage rate. 

Low-performing countries: Unlike the household sector, the importance of mandating insurance 
purchase  (formally  or  informally)  is  not  as  strongly  noticeable  with  regards  to  the  crop 
insurance  sector.  Rather  the  main  concern  is  the  presence  of  multi-risk  insurance  policies  or 
named  peril  only.  The  countries  that  ranked  as  low  performing  tended  to  only  have  coverage 
being  offered  for  hail-induced  losses,  leaving  other  events  unprotected.  Moreover,  the  low 
penetration  rate  can  exacerbate  problems  with  adverse  selection  and  transaction  costs  (loss 
adjustment  is  conducted  at  the  field  level)  as  only  the  land  at  highest  risk  will  tend  to  be 
insured.  Therefore  there  may  be  an  effective  minimum  penetration  rate,  which  may  act  as  a 
threshold for when insurance can become more widespread. 

Table 5.4: Summary of features leading to high or low performances in the agriculture sector 

High performing 

Low performing 

The use of multi-risk insurance (with a focus on 
yield insurance) 

Only specific weather event insurance products are 
available  

Requirements to insure all cultivated land 

Only land with a specific crop must be insured 

Premium subsidies to direct investment in multi-risk 
policies 

Presence of ad-hoc government compensation 
untied to insurance coverage in the case of truly 
extreme events 

Pool-like structures or public reinsurance for 
systemic risks, such as droughts 

Tradition of collaboration between the public and 
private sector risk managers 

5.3  Outcome of the MCA: overall conclusions – an absence of risk reduction  

The scores in the previous table highlight the features of the top performing countries per sector 
in  terms  of  systematic  behaviour.  This  provides  an  indication  of  general  features  of  a  market 
that  can  be  considered  to  be  best  practice  (overall).  However,  looking  at  the  scores  can  also 
highlight areas to focus upon. 

Table 5.5: Average MCA scores across the 12 case study countries 

Private property 

Agriculture 

Weighting scheme 1 

0.56 (0.19) 

0.58 (0.17) 

Weighting scheme 2 

0.5   (0.15) 

0.5   (0.14) 

Weighting scheme 3 

0.4   (0.13) 

0.41 (0.13) 

Notes:  Values  outside  parenthesis  are  mean  values  (from  the  continuous  ranking  scheme);  those  within  parenthesis  are 

standard deviations (from the continuous ranking scheme). 

64 

 
 
 
 
 
 
 
 
 
 
 
Scheme  1  focuses  on  the  penetration  rate  and  affordability,  while  scheme  3  focuses  on  risk 
signalling (with scheme 2 acting as an intermediate weighting). The average score across the 12 
countries (for both sectors)  is highest  for  scheme 1 and lowest under scheme 3. Moreover, as 
we  move  from  scheme  1  to  scheme  3  we  see  that  the  standard  deviation  falls,  indicating  a 
smaller difference in overall outcomes across countries.  

This indicates that on the whole (across extreme weather events) the case studies are relatively 
successful  at  providing  insurance  at  affordable  rates,  despite  individual  deviations  from  this. 
Regardless  of  the  relatively  good  performance,  there  is  still  room  for  improvement.  In  the 
household sector, the two main problems to overcome are insuring localised disasters, such as 
flooding,  and  promoting  households  to  buy  insurance  (the  two  may  be interconnected).  These 
two  aspects  of  insurance  are  responsible  on  the  whole  for  falls  in  the  household  insurance 
penetration rate, as households either do not fully acknowledge the benefits of being insured or 
it  becomes  very  expensive  to  insure,  compared  to  what  the  household  is  willing  to  pay  for 
insurance. The average score under the solidarity and coverage weighting appears to be slightly 
higher in the agricultural sector compared to the household sector. However, it should be kept 
in  mind  that  the  average  penetration  rate  as  a  value  tends  to  be  lower  (an  average  of  40 %, 
compared to the 60 % in the household sector). 

The case studies on the whole score low on incentives for risk reduction or risk signalling. This 
comes  from  the  overall  reliance  on  deductibles  or  awareness  campaigns.  These  incentives 
provide  indirect  risk  management  signals.  Awareness  campaigns  aim  to  readjust  risk 
perceptions and increase the perceived benefits of risk management. However, unless there are 
constant  campaigns,  views  are  likely  to  revert  after  the  campaign  is  concluded.  Germany 
provides  an  example  of  where  continuous  awareness  campaigns  and  the  development  of 
different  tools  have  led  to  a  sustained  increase  in  the  overall  penetration  rate  of  extreme 
weather insurance. Deductibles by themselves are also unlikely to incentivise risk reduction (ex-
ante)  as  unless  an  extreme  weather  event  occurs  the  deductible  doesn’t  provide  a  tangible 
incentive for the policyholder to act upon.  

There are several reasons for why this relative lack of focus on risk reduction has occurred. The 
first corresponds to the market’s focus. A focus on providing widespread or affordable coverage 
can reduce the focus on risk reduction (i.e. France, Spain). A second barrier is the combination 
of  the  indemnity  principle  (i.e.  the  insured  is  compensated  for  what  is  lost)  and  competition 
between insurers. This can limit the extent to which insurers offer incentives or requirements for 
risk  reduction  as  customers  could  instead  take  a  policy  with  less  onerous  demands  (this  was 
noted  to  be  the  case  in  Bulgaria).  A  third  reason  is  that  there  could  be  informational 
asymmetries  or  transaction  costs  that  make  offering  tangible  incentives  (such  as  premium 
discounts)  for  a  wide  range  of  potential  risk-reduction  measures  unattractive.  The  costs  of 
setting  up  and  monitoring  the  employment  of  such  measures  could  be  expensive,  limiting  the 
overall benefit. A final barrier can be seen as the lack of transparency in insurance premiums. It 
is  quite  common  for  insurance  to  be  provided  as  a  bundle  of  risks,  making  it  difficult  for  a 
policyholder to identify what part of the premium is for what risk and the degree to which their 
risk has been shared across the insured population.  

Deductibles and awareness campaigns are mechanisms that allow insurers to overcome some of 
these  barriers  as  it  places  the  primary  responsibility  on  the  policyholder  to  manage  their  risk 
and are easily integrated into pre-existing insurance structures.  

Policy  suggestions  to  overcome  these  barriers  can  come  in  many  forms.  A  stakeholder  focus 
group highlighted several policy strategies to overcome such barriers. One is the improved use 
of  insurer  knowledge  in  developing  zoning  requirements,  as  insurer  knowledge  of  which 
measures  lower  risk  as  part  of  general  building  codes.  Moreover,  such  measures  would  be 

65 

 
 
 
 
 
 
 
 
structural,  which  may  limit  information  asymmetries,  as  once  employed  a  policyholder  is  less 
likely to stop employing the risk-reduction measure. 

The second is to reconsider limitations whereby insurers must return a policyholder to the state 
they  were  in  before  the  event  and  no  better  off.  Introducing  ‘build  back  better’  requirements 
could  allow  the  recovery  and  repair  process  to  build  risk-reduction  measures  directly  into 
buildings  when  awareness  of  the  impacts  of  extreme  weather  events  is  strongest.  In  addition, 
building  and  construction  codes  should  be  applied  and  followed  for  new  infrastructure  (to  be 
potentially incentivised as a condition for EU funding, for example). 

A  third  could be to  place  the  responsibility  for  risk reduction  in  the  hands of  an  external  body 
dedicated  to  promoting  and  developing  risk-reduction  strategies,  which  can  collaborate  with 
insurers.  The  exact  nature  of  such  an  external  body  is  difficult  to  determine  a  priori  across 
countries;  however,  such  an  organisation  can  operate  directly  or  indirectly  at  various  national 
levels  (i.e.  national,  regional  or  city  level).  For  instance,  a  national  body  can  produce 
investments  in  prevention  strategies  or  larger  scale  risk-reduction  strategies.  These  actions 
could  facilitate  a  national  minimum  level  of  risk  management  and  insurance  viability,  upon 
which  more  localised  bodies  or  agents  can  act  upon.  For  example,  a  city  can  collaborate  with 
insurers to better manage their risk past this minimum level imposed by the external bodies. It 
is difficult to determine or propose such structures as the different Member States have varying 
levels  of  autonomy  for  cities  or  regions.  This  could  be  achieved  by  adding  a  surcharge  to 
insurance premiums into a fund(s) so that the money raised can be used to construct protection 
measures,  general  adaptation  measures  (e.g.  water  retention  areas),  or  to  subsidise  more 
individual-level measures. This fund can be a management entity whereby insurers, government 
agencies,  etc.  are  involved  in  a  not-for-profit  manner.  Moreover,  such  a  management  entity 
could be easier to mainstream into a country’s overall climate change adaptation strategy. 

66 

 
 
 
 
 
 
 
 
 
 
Table 5.6: MCA scores following the continuous ranking system 

Household 

Scheme 2: 
Private 
good/ 
PPP 
working 
together 

Scheme 1: 
Solidarity 
and 
coverage 

Scheme 3: 
Adaptation 
signal / risk 
incentive 

Scheme 1: 
Solidarity 
and 
coverage 

Agricultural 

Scheme 2: 
Private 
good/ PPP 
working 
together 

Scheme 3: 
Adaptation 
signal / risk 
incentive 

Bulgaria 

Romania 

Hungary 

Poland 

Demark 

Sweden 

France 

UK 

Germany 

Austria 

Spain 

Italy 

0.22 

0.38 

0.56 

0.66 

0.51 

0.39 

0.81 

0.76 

0.56 

0.74 

0.78 

0.34 

0.25 

0.35 

0.45 

0.56 

0.54 

0.33 

0.71 

0.69 

0.53 

0.56 

0.68 

0.33 

0.24 

0.23 

0.32 

0.43 

0.5 

0.26 

0.59 

0.58 

0.54 

0.38 

0.46 

0.29 

0.41 

0.5 

0.63 

0.52 

0.72 

0.78 

0.4 

0.43 

0.54 

0.44 

0.56 

0.6 

0.7 

0.59 

0.22 

0.72 

0.81 

0.61 

0.52 

0.2 

0.63 

0.68 

0.64 

0.51 

0.37 

0.35 

0.45 

0.32 

0.38 

0.41 

0.45 

0.17 

0.5 

0.53 

0.7 

0.38 

Notes: Yellow represents first best, orange represents second best; values are rounded to two decimal places. 

67 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.7: MCA scores following the relative ranking system 

Household 

Scheme 2: 
Private 
good/ 
PPP 
working 
together 

Scheme 1: 
Solidarity 
and 
coverage 

Scheme 3: 
Adaptation 
signal / risk 
incentive 

Scheme 1: 
Solidarity 
and 
coverage 

Agricultural 

Scheme 2: 
Private 
good/ PPP 
working 
together 

Scheme 3: 
Adaptation 
signal / risk 
incentive 

Bulgaria 

Romania 

Hungary 

Poland 

Demark 

Sweden 

France 

UK 

Germany 

Austria 

Spain 

Italy 

0 

0.27 

0.41 

0.68 

0.62 

0.38 

0.82 

0.79 

0.38 

0.73 

0.77 

0.26 

0.07 

0.28 

0.31 

0.59 

0.67 

0.26 

0.72 

0.76 

0.38 

0.49 

0.7 

0.24 

0.17 

0.18 

0.24 

0.49 

0.68 

0.23 

0.68 

0.72 

0.58 

0.36 

0.5 

0.28 

0.33 

0.5 

0.59 

0.45 

0.68 

0.8 

0.79 

0.04 

0.66 

0.84 

0.63 

0.53 

0.35 

0.41 

0.52 

0.39 

0.51 

0.6 

0.66 

0.05 

0.62 

0.71 

0.71 

0.51 

0.33 

0.32 

0.42 

0.25 

0.31 

0.38 

0.48 

0.03 

0.49 

0.55 

0.81 

0.38 

Notes: Yellow represents first best, orange represents second best; values are rounded to two decimal places. 

68 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6. 

THE NEXT STEPS IN INSURING WEATHER AND CLIMATE-RELATED 

EXTREME EVENTS 

The purpose of this chapter is to identify the next steps in insuring weather and climate-related 
extreme events. To this purpose, the  chapter outlines the results from the MCA, together with 
findings  from  the  stakeholder  consultation  process,  and  in  line  with  this,  provides  general 
recommendations  for  steps  forward  within  the  subject  of  insurance  and  weather-related 
disasters. 

The recommendations presented in this section should not all be specifically implemented by the 
European Commission but can be seen as a framework that can be used as a starting point for 
discussion at national level. They can be applied in other cases as we state the general principle 
of  the  policy  recommendation  and  leave  the  general  implication  of  the  reform  to  be  decided 
upon by the relevant stakeholders. This is because the different countries of the European Union 
operate within different legal and cultural contexts. Therefore while their final approaches may 
be similar, they are unlikely to be exactly the same to account for these differences. 

The  chapter  begins  with  recommendations  based  on  the  outcomes  of  the  MCA  (section  6.1), 
then  additional  recommendations  based  on  findings  from  the  stakeholder  consultation  process 
are  made  (section  6.2).  These  are  then  combined  in  the  consistent  set  of  recommendations 
around five policy themes (section 6.3). Specific recommendations to the European Commission 
follow in Chapter 7. 

6.1  Outcomes from the MCA and policy recommendations 

This  section  presents  the  policy  implications  and  recommendations  that  are  directly  derived 
from the MCA presented above. 

A  point  to  keep  in  mind  when  considering  these  outcomes  is  the  relative  importance  of  flood 
risks  compared  to  windstorm  or  hail  risks.  The  inventory  of  insurance  markets  across  Europe 
identified  relatively  few  problems  with  the  insurance  of  windstorms  or  hail  because  of  their 
widespread  nature  allowing  them  to  be  more  easily  pooled  by  the  insurance  sector  (even  if 
demand was low, i.e. in crop insurance). Flooding (of the various types) represents a different 
problem,  which  many  of  the  insurance  markets  have  struggled  at  various  points  to  provide or 
maintain ‘high’ scoring outcomes.  

6.1.1  Private property sector 

Policy  recommendation  1a:  Promote  the  bundling  of  a  complete  extreme  weather 
event  insurance  package  with  private  property  fire  insurance  policies  (or  a  similarly 
and more often purchased product). 

Policy  recommendation  1b:  Urge  banks  to  require  full  and  comprehensive  insurance 
coverage when providing mortgage loans. 

The  high  penetration  rates  and  coverage  levels  are  provided  when  a  market  has  an  effective 
compulsion  to  buy  insurance.  In  such  a  market,  policyholders  are  obliged  to  buy  coverage 
against  a  complete  bundle  of  extreme  weather  risks.  This  can  be  illustrated  by  the  high 
penetration rate in  France  compared  to  the  low  penetration rate in Bulgaria.  Moreover, simply 
mandating the purchase of extreme weather insurance may not  be sufficient to achieve a high 
penetration  rate.  Romanian  flood  insurance  represents  an  example  of  this  situation  as  it 
mandates the purchase of insurance though achieves a penetration rate of approximately 20 %, 
due to its lax enforcement.  

69 

 
 
 
 
 
 
 
 
The  connection  of  extreme  weather  insurance  to  general  private  property  insurance  or  fire 
insurance  in  areas  with  developed  insurance  markets  can  lead  to  a  high  average  insurance 
penetration  rate  as  it  is tied  to  a  product  with  a larger  perceived  value.  Moreover,  in  order  to 
support a high market penetration rate, current government programmes or desires to provide 
ad-hoc  compensation  after  a  disaster  should  either  be  stopped  or  be  formalised  so  that  there 
are  very  clear  rules  that  imply  insurance  demand  is  not  reduced.  Alternatively,  compensation 
should  be  provided  in  loans  or  potential  beneficiaries  should  pay  a  contribution  towards  the 
fund.  The  second  common  feature  of  the  markets  that  leads  to  widespread  and  affordable 
coverage  is  the  bundling  of  major  risks  into  a  single  policy.  This  reduces  the  presence  of 
adverse selection and spreads localised risks over a wider area so that premiums become more 
affordable.  Therefore  it  appears  that  a  policy  direction  is  to  remove  the  choice  of  (potential) 
policyholders  over  whether  to  insure  which  kind  or  perils.  Policyholders  can  be  left  with  the 
choice of to what degree to insure in order to allow deductibles and coverage limits to match the 
risk preferences  and  financial capacities  of policyholders.  Offering  a comprehensive  package of 
extreme weather events is desirable (i.e. not separating wind/hail from floods or landslides, for 
example).  This  approach  could  be  more  successful  than  relying  on  measures  to  increase  risk 
awareness alone as awareness campaigns will  have to be constant and  widespread  in order to 
maintain high levels of risk awareness. Nevertheless, such campaigns proved to be successful in 
Germany.  

Moreover, high penetration rates were observed in countries with an effective compulsion to buy 
insurance  in  order  to  protect  a  bank’s  asset.  This  helps  to  explain  in  part  a  rapid  increase  in 
insurance  penetration  rates  in  eastern  Europe  (especially  in  Hungary  and  Poland).  Therefore, 
the  banking  sector  through  its  loan  terms  and  conditions  could  also  play  a  role  in  closing  the 
protection gap. The combination  of  policy  recommendation 1a and 1b can,  in effect,  create an 
informal  ‘compulsion’  to  buy  insurance.  This  is  a  different  focus  to  the  cases  with  a  current 
formal  compulsion  to  buy  insurance,  as  in  these  cases  insurance  tends  to  be  provided  at  risk 
rates that do not act as a risk signal. The informal compulsion can help to sidestep this issue. 

Policy recommendations 1a and 1b can achieve  higher penetration rates; however, their exact 
provision  (i.e.  insurance  regulation,  through  a  public-private  partnership)  will  have  to  be 
decided upon at national level. 

Policy  recommendation  2:  Low-income  (following  local  definitions  of  low  income  or 
social  hardship)  households  struggling  to  afford  extreme  weather  insurance  should 
have this pressure eased with insurance vouchers or tax credits if they buy insurance 
coverage.  

The scores awarded to each of the markets regarding affordability indicate that no sector scored 
more than two points on the affordability criterion out of a maximum of four. The premiums also 
range from between EUR 30 to EUR 320 before considering high-risk areas (in cases where risk-
based premiums are used). This indicates that the source of unaffordability originates from the 
buying power of insurance premiums rather than the value of insurance premiums themselves. 
For  example,  the  average  premium  in  Bulgaria  in  2015  was  estimated  to  be  approximately 
EUR 90  with  an  unaffordability  rate  of  ~23 %,  or  Romania  with  premiums  of  approximately 
EUR 30 EUR and an unaffordability rate  of  26 %. Therefore,  another policy recommendation  is 
the  introduction  of  mechanisms  external  to  the  insurance  industry  to  address  the  problems  of 
unaffordability. Moreover, this would allow insurance premiums to more clearly signal risk, while 
allowing public  support  to  be  more  carefully  targeted.  Examples  of  such  mechanisms  could  be 
tax  credits  or  temporary  vouchers  for  low-income  households  if  they  are  insured  (see  e.g. 
Kousky  and  Kunreuther,  2014).  The  mechanisms  to  address  unaffordability  will  have  to  take 
into  account  the  possible  development  of  socially  vulnerable  communities  in  high-risk  areas. 
However, if the previous policy suggestion of ‘compulsory’ bundled insurance is employed then 

70 

 
 
 
 
 
 
 
 
the  mechanisms  to  address  unaffordability  will  be  spread  across  the  entire  population  rather 
than  only  high-risk  areas.  Moreover,  these  measures  from  outside  of  insurance  allow the  risk-
management  signals  provided  by  insurance  to  be  strengthened.  Moreover,  such  mechanisms 
may provide governments with a stronger incentive to more actively manage extreme weather 
risks, as it may be cheaper to lower risk in the long run rather than paying a stream of vouchers 
to low-income households in high-risk areas. 

Vouchers  and  tax  credits  are  used  in  policy  recommendation  2  as  an  example  of  mechanisms 
that  could  be  employed.  However,  the  exact  mechanism,  thresholds  and  provider  can  be 
decided  upon  on  a  country-by-country  basis,  even  if  there  is  a  common  principle  behind  this 
dialogue.  

Policy  recommendation  3a:  Direct  incentivisation  of  risk  reduction  post-insurance 
purchase  is  currently  not  very  effective  in  practice.  Therefore,  minimum  building 
standards,  or  build-back-better  requirements,  differentiated  by  risk  levels,  can  be 
required as a standard element of insurance contracts in order to gain coverage (with 
a focus on measures integrated into the building). 

Policy  recommendation  3b:  Use  a  surcharge  on  insurance  premiums  (either  newly 
introduced  or  redirected  current  taxes)  to  directly  finance  and  construct  risk-
reduction  infrastructure  or  to  directly  subsidise  household-level  risk-reduction 
measures (i.e. retrofitting buildings).  

The  majority  of  the  case  studies  did  not  score  very  highly  regarding  the  risk-reduction 
incentives  offered,  systematically,  in  their  area.  Those  that  did  score  high  enough  to  be 
considered best practice was because of their high performances on other outcomes, in addition 
to  performing  above-average  on  risk  reduction.  Moreover,  engagement  with  the  insurance 
sector stakeholders reveals that their focus on risk reduction (as organisations) tends to occur 
at a different level to that studied in the MCA. There is a focus on preventative measures rather 
than  the  actions  individual  policyholders  can  conduct.  This  is  because,  for  the  most  part,  an 
individual policyholder cannot alter the likelihood of an extreme weather event or its magnitude, 
although he can influence the damage. The actions a single policyholder can undertake to limit 
risk face informational asymmetries and transaction costs from the perspective of the insurance 
provider.  The  process  of  mandating  such  incentives  on  a  large  scale  (as  compared  to 
commercial  policies)  could  be  problematic  given  the  standardisation  of  contracts.  Risk-based 
premiums were not commonly employed across the 12 case studies, and there are fewer cases 
of  where  risk-based  premiums  were  combined  with  premium  discounts.  Developing  such  a 
system  with  risk-based  premiums  and  discounts  for  risk  reduction  could  contribute  to  risk 
management by a non-negligible amount (e.g. Hudson et al., 2016; Hudson et al., 2017c). The 
employment of one without the other would be less effective. Nevertheless, it may be possible 
to promote situations where property after an  event  is built back better or redirect  building  to 
lower risk areas in  order  to limit  future (or recurring)  risk.  This may  entail  a slight increase in 
premiums in order to account for both the compensation provided and the overall improvement 
(depending on the measures that are expected to reduce vulnerability). 

Moreover, insurance premiums are a product of a risk calculation. The more frequent events are 
the  higher  the  premium  and  so  a  focus  on  prevention  (outside  the  scope  of  individual 
policyholders) is  required  to  make  an  insurance  product  a  viable  product.  Therefore  it  may be 
worthwhile  to  use  premiums  as  an  indicator  for  where  investments  in  prevention  are  needed. 
While  it  can  be  argued  that  only  risk-level  data  is  needed  to  guide  these  investments, 
presenting the potential impacts of such investments by a reduction in premiums or the burden 
premiums  place  on  policyholders  could  be  useful.  The  presentation  of  the  benefits  from  risk 
reduction  in  terms  of  lower  premiums  could  frame  the  benefits  in  a  more  tangible  or  familiar 
way.  Additionally,  a  small  surcharge  on  premiums  could  be  used  to  finance  such  protection 

71 

 
 
 
 
 
 
 
 
infrastructure or to directly subsidise household-level risk-reduction measures. Mechanisms like 
this exist in Switzerland and France. The organisation responsible will need to have the ability to 
directly  finance  and  construct  such  measures  (or  advertise  subsidies  at  a  household  level)  to 
overcome  the  hurdles  or  disincentives  for  local  authorities  investing  in  risk-reduction 
infrastructures. However, collaboration may still be required so that all of the stakeholders’ local 
knowledge can be used to identify the most productive use of resources. 

Indicators  of  unaffordability  can  be  used  to  guide  investment  decisions  so  that  premiums  can 
become  more  affordable,  allowing  risk  to  be  better  spread  and  for  those  at  (higher  levels  of) 
risk  to  be  better  able  to  cover  their  risk.  This  could  be  seen  as  an  element  of  risk  sharing, 
whereby  those  exposed  to  the  extreme  weather  event  still  contribute  to  pre-funding  their 
compensation, and low-risk households who help to prevent such impacts in the first place. This 
can  allow  for  insurance  to  act  as  a  sufficiently  accurate  signal  of  risk,  which  stimulates  an 
external response to lower risk.  

Policy recommendation 4a: Introduce a requirement for flood risk management plans, 
national  adaptation  strategies  and  applications  for  loans  or  national  or  EU  funds  to 
include  insurance  mechanisms  for  managing  risk  that  cannot  be  (cost-)  effectively 
prevented  in  order  to  further  mainstream  insurance  into  national  adaptation 
conversations.  

Policy  recommendation  4b:  Create  a  national  focal  point  or  authority  for  developing 
and  maintaining  a  legal  framework  through  which  extreme  weather  risks  can  be 
managed via a combination of risk reduction and/or transfer. The focal point can be a 
pre-existing  or  a  newly  formed  body.  However,  the  focal  point  should  consider  the 
views  of  a  range  of  stakeholders:  local  and  national  government  risk  managers, 
insurance  bodies,  policyholders  or  consumer  representatives,  to  be  decided  on  a 
country-by-country basis. 

Policy  recommendation  4c:  Lay  down  the  roles  and  responsibilities  of  all  the 
stakeholders in a national platform, focal point or authority, in a clear and transparent 
framework.  This  could  be  considered  as  a  ‘social  contract’  between  the  various 
sectors  involved  in  managing  risk.  This  can  help  commit  the  actors  to  action  and  to 
underline 
the  coordinating  authority  (see 
recommendation 4b). Discussions should, amongst others, cover whether data can be 
shared  between  stakeholders,  especially  public  bodies  receiving  loss  data  from 
insurance companies, for the planning of infrastructure. 

roles  and 

functions 

their 

for 

Another  common  feature  of  the  four  case  studies  identified  as  best  practice  had  a  degree  of 
successful  and  active  collaboration  between  the  insurance  sector  stakeholders  and  the 
government. For instance, the French insurance industry’s contribution to extreme weather risk 
management is fairly well integrated, addressing risk transfer, disaster risk reduction financing, 
and data sharing for a better governance. The public and private sectors have a long-standing 
cooperation, put in place by the French Association for Disaster Risk Reduction (AFPCN), a non-
profit  organisation  founded  in  2001  as  the  successor  to  the  French  Committee  for  the 
International  Decade  for  Natural  Disaster  Reduction,  and  which  is  supported  by  government 
departments.  This  body  aims  to  bring  together  the  DRR  community  to  promote  a  coordinated 
approach.  Its  activities  include  stakeholder  dialogue,  exchange  of  good  practice  and  research. 
This Danish case has flood insurance provision being focused though the Storm Council, which is 
a  body  that  brings  stakeholders  together  and  frames  their  interaction  within  a  single  common 
goal  (i.e.  provision  of  storm  surge  and  fluvial  flood  insurance).  Moreover,  the  Storm  Council 
over recent years has been benefiting from the greater involvement of private sector insurers. 
Similarly in the UK, the universal provision of flood insurance can be characterised as a series of 
negotiations  between  the  British  Government  and  the  insurance  industry,  and  what  the 

72 

 
 
 
 
 
 
 
 
individual  roles  of  the  two  should  be.  The  exact  nature  of  this  relationship  has  changed  over 
time.  While the  relationship  may  be  troubled  (see  the  discussions  around,  and  the  process  of, 
forming Flood Re), the very nature of this relationship shows that the groups acknowledge each 
other’s  role  and  require  each  other  to  take  suitable  action.  This  is  not  always  the  case  in  the 
other case study countries, as the roles and willingness of the public and private sectors to act 
productively  together  is  not  always  clearly  established.  For  instance,  in  Sweden  or  Hungary 
insurance  does  not  have  an  active  role  in  developing  or  being  a  part  of  national  adaptation 
plans.  While  in  countries  such  as  Germany  and  Austria,  the  presence  of  government 
compensation  actively  hampers  a  larger  role  for  the  insurance  sector  in  insuring  catastrophic 
extreme  weather  events.  Another  potential  problem,  as  highlighted  in  the  case  of  Bulgaria,  is 
that  sometimes  the  coordination  between  the  responsible  institutions  is  not  on  the  required 
level.  Moreover,  there  can  be  frequent  changes  to  the  legal  framework  in  the  sector,  and  the 
lack  of  effective  coherence,  consistency  and  continuation  of  the  responsible  structures  has 
negative consequences for disaster risk reduction. This leads to a reduction in the administrative 
capacity  at  national,  regional  and  local  levels,  as  well  as  to  a  loss  of  expertise  and  past 
experience. 

Policy  recommendation  4a  represents  a  holistic  approach  for  acknowledging  the  role  that 
insurance  can  play  in  managing  risk  because  insurance  is  a  key  adaptation  tool  for  risks  that 
cannot  be  cost-effectively  prevented.  Moreover,  the  inclusion  of  such  thinking  in  a  report  can 
create more openness to reveal and share information across stakeholders.  

Policy recommendation 4b as presented can be broadly understood. This is because at its heart 
it  is  promoting  an  organisational  body  (current  or  future)  that  can  coordinate  stakeholders 
across  various  levels  of  government  and  the  public  in  order  to  overcome  hurdles  that  may 
inhibit proactive action (i.e. federally structured countries may see governments not wishing to 
be  involved  in  what  is  considered  as  a  role  for  another  level  of  government).  This  can  offer  a 
focal point or a national platform in line with the Sendai Framework. The structure and authority 
can  be  decided  upon  at  the  local  level  but  it  should  always  have  the  aim  of  coordinating  all 
stakeholders that have a stake in risk management or insurance provision. For example, in one 
country  it  may  be  decided  that  the  focal  point  is  the  ministry  of  finance  (following  an  OECD 
recommendation), while another creates a working group with decentralised powers. There are 
informal  examples  of  such  bodies  across  the  world  (e.g.  MRN  in  France,  the  institute  for 
catastrophic loss reduction in Canada), which can be further formalised to promote cooperation 
and synergies across the stakeholders’ actions. 

6.1.2  Agriculture sector 

Policy recommendation 5: Redirect premium subsidies towards multi-peril (yield) crop 
insurance products to provide more extensive coverage. Each extreme weather  event 
can contribute to the overall premium in line with its risk level. 

Currently  the  majority  of  the  crop  insurance  products  that  are  both  available  and  commonly 
bought cover hail damage only. The Spanish and Austrian insurance markets most often score 
highest  and  in  both  of  these  markets  there  is  a  very  large  presence  of  multi-risk  insurance 
products  compared  to  other  markets.  Moreover,  in  both  cases  where  multi-risk  premiums  are 
more expensive, countries also employ premium subsidies of about 50 %. Therefore, one policy 
recommendation  is  the  expansion  of  multi-peril  yield  insurance,  as  this  would  offer 
comprehensive  coverage  against  both  annual  yield  variations  in  addition  to  extreme  weather 
events. This could be achieved by redirecting premium subsidies so that they are only available 
for  comprehensive  multi-peril  yield  insurance  (as  is  the  case  in  France).  The  redirection  of 
premium  subsidies  would  increase  the  opportunity  cost  of  not  buying  more  comprehensive 
coverage. Additionally, lowering standard policies to include a clause that says that losses must 

73 

 
 
 
 
 
 
 
 
exceed a loss of at least 30 % would allow the premium subsidy to equal 65 % of the premium, 
while providing a sufficient deductible to promote risk management among farmers. 

Policy recommendation 6: In order to reduce the presence of adverse selection in crop 
insurance and only the high risk land being insured, a farmer should be compelled to 
insure all arable land as part of the terms and conditions of an insurance policy. 

In order to reduce the presence of adverse selection and to increase the amount of land covered 
by insurance the Austrian example can be used, which requires that all arable land is insured in 
order  to  gain  insurance  coverage,  rather  than  specific  fields.  The  blanket  approach  was 
introduced  in  Austria  in  1987  and  formed  the  basis  upon  which  the  1995  multi-peril  crop 
insurance was provided. This trajectory could therefore be applied to other countries to aid the 
transition to widespread multi-peril crop insurance.  

Policy recommendation 7a: Link the access to wider agricultural sector subsidies (i.e. 
those relating to the common agricultural policy or those offered at the national level) 
to  the  purchase  of  sufficient  insurance  protection  in  order  to  develop  a  tradition  of 
being insured. 

A  second  common  characteristic  between  the  Spanish  and  Austrian  approaches  is  that  the 
majority of insurance coverage is provided for by a single overarching body that has the overall 
strategic aim of improving agricultural risk-management strategies. In Austria this is done by a 
mutual insurance company and in Spain by the members of the insurance pool. The benefits of 
a single organising body can result in easier access to reinsurance or capital in the case of large 
agricultural  disasters  or  general  economies  of  scale,  facilitating  the  development  of  risk 
reduction  or  management  strategies.  Moreover,  a  single  body  acts  as  a  locus  for  the  relevant 
stakeholders to converge upon. A single body could help to lower the barriers to mainstreaming 
risk reduction into overall adaptation planning, as the organisation must take a comprehensive 
view of risk management in its own sector.  

The remaining case  studies on the whole tend to be dominated by a few insurance  companies 
offering crop insurance. Therefore, having a single body may not be sufficient; for example, in 
the  UK,  the  National  Farmers  Mutual  Insurance  Company  is  quite  dominant  but  does  not  rank 
first  or  second.  Also  Sweden  performs  second  best  on  some  weighting  schemes  with  a  very 
dominant  firm  providing  insurance.  These  bodies  have  slightly  different  focuses  regarding 
agricultural  risk  management.  For  instance,  the  Austrian  hail  insurance  company  only  offers 
products managing agricultural output, while the Farmers Mutual Insurance Company in the UK 
has diluted this focus by expanding into more general insurance products for farmers. This focus 
on crop risk management is then supported by a tradition of buying insurance. This is the case 
in Sweden, Romania, Bulgaria where there were long-lasting systems of compulsory insurance, 
which  can  create  familiarity  with  the  products  that  best  suit  a  farmer’s  needs  and  overall 
familiarity  with  the  benefits  of  being  insured.  The  Swedish  system  of  compulsory  insurance 
coverage  was  removed  in  part  because  of  CAP  regulations  and  because  it  was  considered  too 
expensive,  but  it  was  sufficient  to  start  a  tradition  of  being  insured.  The  same  is  true  for 
Bulgaria,  which  has a far  higher penetration rate  for the agricultural sector  when  compared  to 
the  private  property  sector,  due  to  the  experience  with  farmers  with  crop  insurance  as  a 
measure  for  commercial  protection.  Therefore,  supporting  the  development  of  a  non-profit 
maximising  body  concerned  solely  with  minimising  farmers’  financial  risks  could  help  facilitate 
the development of weather risk transfer arrangements. Moreover, this could be complemented 
with an obligation to insure all of their farmland, and be combined with access to governmental 
or EU subsidies that are conditional on them buying sufficient insurance coverage. This provides 
a very tangible opportunity cost for farmers who are not insured, strengthening the incentive for 
buying insurance. Moreover, by tying the purchase decision to a subsidy outside the insurance 

74 

 
 
 
 
 
 
 
 
market,  allows  the  premiums  to  still  be  risk-based  and  provide  an  indication  of  risk  and  risk-
management incentives. 

Policy recommendation 7b: Develop an agricultural risk management association with 
a  focus  on  protecting  farmers  against  income  variations  due  to  crop  yields  (i.e. 
supporting multi-risk yield insurances or the employment of risk-reducing measures), 
within a mutual or non-profit maximising organisation.  

In  accordance  with  policy  recommendation  4b,  recommendation  7b  can  also  be  broadly 
understood in regards to the form it should take. There are bodies in transnational regions (like 
the International Association of Agricultural Production Insurers) or national bodies in Spain and 
Austria  that  are  already  in  place,  which  can  be  used  as  an  example  but  would  have  to  be 
adapted to the national conditions. What would be common for the associations is the objective 
of  supporting  the  take-up  of  insurance  in  the  agriculture  sector  and  risk-reducing  measures. 
Members  would  mainly  be  representatives  from  the  agricultural  business  (national  agriculture 
organisations,  for  example),  representatives  from  the  insurance  industry  and  representatives 
from the government.  

6.2  Additional considerations from the stakeholder consultation 

This  section  presents  additional  policy  recommendations  gathered  from  the  stakeholder 
consultation in parallel  with the research and analysis on the 12 selected case studies and  the 
MCA. 

Policy  recommendation  8:  Research  with  the  aim  of  defining  and  quantifying 
resilience to support risk awareness and reduction, and a focus on how insurance can 
enhance the economic resilience. 

Many of the recommendations in this study are based on effective risk-reduction activities and 
dependant  on  the  stakeholders’  consensus.  If,  for  example,  premiums  are  to  be  lowered  as  a 
result of a specific risk-reduction activity then stakeholders need to agree  as to what level the 
increased resilience should be achieved to change the premium. Furthermore, finding a way to 
define and  quantify resilience  would provide a strong basis for policy, planning and adaptation 
decisions  at  EU  level,  down  to  the  individual  private  property  level.  Although  a  quantitative 
measuring  of  resilience  may  or  may  not  be  possible,  it  would  be  an  opportunity  to  create  a 
common language for stakeholders. On a pan-European level, the European Commission has an 
important  role  that  can  create  consensus  and  engage  stakeholders  in  the  discussion. 
Additionally,  a  study  helping  to  understand  and  quantify  resilience  can  also  investigate  and 
evaluate  new  measures  for  risk  management  and  their  effectiveness.  This  investigation  can 
focus on areas  that  have  been neglected in  the  current research.  For example,  the  impacts to 
private property insurance are larger than that to crops and as such, risk management methods 
for  property  have  been  thoroughly  studied.  However,  relatively  few  agricultural  risk-
management methods have been studied so far and this can be done in a new, wider study. 

Policy recommendation 9: Support the use of farm income insurance by starting pilot 
initiatives in various Member States. 

By introducing farm income insurance, a farmer would be protected from a fall in income from 
any  source  (i.e.  storms,  droughts,  etc.)  and  the  insurance  would  thus  offer  him/her  a 
comprehensive  package  of  protection.  This  type  of  insurance  removes  the  focus  of  insurance 
away  from  extreme  weather  risks  to  a  more  tangible  focus  (annual  income),  which  limits  the 
influence  of  behavioural  heuristics  regarding  extreme  weather  events.  Risk  reduction  would 
have  to  be  encouraged  by  minimum  risk  management  /risk  reduction  standards  to  gain 
coverage  (more  active/effective  risk  management  allows  the  farmer  to  gain  higher  levels  of 

75 

 
 
 
 
 
 
 
 
coverage). Drawbacks of the method is that this could encourage the farmer to expand into less 
productive/more risky areas but it could be limited to trends in long-term income, thus reducing 
the  incentive  to  expand  into  more  risky  land  (paying  for  income  insurance  that  does  not  take 
into  account  the  new  land).  While  there  is  a  well-known  example  of  income  insurance  in  the 
USA,  it  is  not  commonly  provided  in  Europe;  therefore  both  potential  customers  and  insurers 
are not familiar with the product and may be hesitant to provide it. This could be supported by 
the EU through subsidies for income insurance or the establishment of an EU-wide reinsurer for 
agricultural  income  insurance.  However,  it  is  believed  that  further  research  is  necessary  to 
design and implement such an insurance. 

Policy  recommendation  10:  Create  a  working  group  in  the  European  Commission 
enabling  cross-Directorates-General  collaboration  as  well  as  coordination  with 
national bodies. 

The  area  of  insurance  and  weather-related  disaster  risk  reduction  is  one  that  affects  multiple 
agencies  and  Directorates-General  (DGs)  in  the  European  Commission,  and  in  order  to  avoid 
silos  and  ensure  horizontal  coordination,  the  creation  of  a  working  group  on  insurance  and 
climate  risk  reduction  is  recommended.  The  working  group  should  have  representatives  from 
the  DG  for  European  Civil  Protection  and  Humanitarian  Aid  Operations  (ECHO),  the  DG  for 
Agriculture and Rural Development (AGRI), the DG for Financial Stability, Financial Services and 
Capital  Markets  Union  (FISMA)  and  be  led  by  the  DG  for  Climate  Action  (CLIMA).  It  would  be 
responsible for the important role of the European Commission to foster innovation, engage and 
educate  stakeholders,  and  provide  platforms  for  multi-stakeholder  initiatives.  This  would 
involve, amongst other things, the coordination of national bodies, communication with external 
stakeholders and ensure collaboration with initiatives, like for example the Geneva Association, 
UNEP PSI, ClimateWise, MCII, etc. 

Policy recommendation 11: Recommend that cities assess their vulnerability in regard 
to  insurance  penetration  rates,  including  cover  for  municipal  infrastructure  and 
extreme  weather  events,  as  well  as  to  report  on  how  they  use  insurance  as  a 
mechanism for managing risks. 

The  role  of  cities  and  regions  has  not  been  addressed  in  the  12  case  studies  since  they  were 
carried  out  at  national  level.  However,  with  the  importance  of  cities  and  regions  in  regards  to 
preparing and implementing their climate adaptation strategies in general, further steps need to 
address their capacity and needs. The national strategies for adaptation, including the insurance 
mechanisms  for  managing  risks  that  cannot  be  (cost-effectively)  prevented  in  the  city’s 
adaptation  strategy,  do  not  only  increase  risk  awareness,  but  can  also  guide  investment  in 
adaptation measures (such as investments in damage preventive structures or defining building 
zones).  This  would  include  an  assessment  of  their  vulnerability  in  regards  to  the  penetration 
rate, including municipal assets.  

Policy  recommendation  12a:  Promote  the  use  of  insurance  disaster  loss  data  in  the 
municipalities’ risk-assessment data. 

Policy  recommendation  12b:  Promote  the  active  and  collaborative  sharing  of  risk, 
hazard  and  impact  data  across  stakeholders  though  the  standardisation  of  metadata 
(to understand what the data contains) and the format (i.e. GIS files) of granular data 
so  that  they  can  be  more  efficiently  and  transparently  shared  across  stakeholders 
productively. 

Using  insurance  disaster  loss  data  when  developing  risk  maps  and  prioritising  risk  zones 
significantly  increases  the  municipalities’  ability  to  make  better  climate  adaptation  decisions. 
Norway  and  Denmark  have  both  used  insurance  data  for  risk  modelling  in  a  number  of 

76 

 
 
 
 
 
 
 
 
municipalities in  order to  better  improve  their  attempts  at  managing extreme  weather events. 
The  financial  benefits  of  using  insurance  data  are  currently  evaluated  in  both  countries.  In 
Denmark, the insurance companies got more favourable contracts with re-insurance companies 
since  they  could  prove  that  the  municipalities  had  significantly  reduced  risk  through  the 
planning and implementation of adaptation strategies.  

A major challenge in the use of the insurance loss data are data protection acts and especially 
the European General Data Protection Regulation (GDPR) (Regulation (EU) 2016/679), adopted 
in April 2016,  which is planned to be  enforceable  from  25 May 2018. This makes the  rules on 
valid  consent  in  regards  to  insurance  data  collected  and  the  purposes  data  is  used  for. 
Furthermore,  the  General  Data  Protection  Rule  puts  the  ‘right  to  be  forgotten’  on  a  statutory 
footing. This means data processors must, for example, erase personal data when the data is no 
longer necessary in relation to the purpose for which it was collected or when the data subject 
withdraws their consent.  

In  Denmark,  the  issue  of  data  protection  was  addressed  in  such  a  way  that  individual 
households  or  companies  could  not  be  identified.  Furthermore,  the  municipalities  were  only 
allowed  to  hold information  for  a  specific  amount  of  time.  In  Norway,  insurance  loss  data  was 
combined  with  other  loss  data  from  the  municipalities  and  it  was  ensured  that  the  data  could 
not be traced back to individual insurance companies. The Norwegian Data Protection Authority 
could  issue  an  exemption  to  the  law  that  policyholders/claimants  have  to  agree  to  the  use  of 
data  based  on  two  reasons:  1)  the  data  was  not  sufficiently  sensitive  to  be  protected,  and  2) 
the data was essential for the municipality to be able to ensure risk mapping. 

The  main  difficulty  in  the use  of  insurance  loss  data  for  risk  modelling  would be  to  access  the 
data from the insurance industry, as this is one of the assets that they build their business on 
but this would make them reluctant to release it. An analysis of the cases where this has been 
done and a communication of these best practices (through, for example Climate ADAPT) could 
possibly  alter  this  reluctance.  Furthermore,  such  collaboration  would  require  a  great  deal  of 
trust  between  the  partners.  The  EU  may  play  a  role  in  regards  to  creating  platforms  for 
stakeholders  to  meet  (as  described  above).  In  the  cases  of  both  Denmark  and  Norway,  trust 
between  partners  has  been  indicated  as  one  of  the  key  success  factors  but  it  should  be 
mentioned  that  the  majority  stakeholders  in  Denmark  already  had  a  relationship  through  the 
Danish Storm Council. The creation of a national body would be a first step in fostering a strong 
relationship  between  stakeholders,  which,  for  example  would  facilitate  collaboration  regarding 
loss data. 

This  sharing  of  data  is  important  because  there  are  elements  of  a  public  good  in  the  various 
sources  of  data  regarding  extreme  weather  events  (i.e.  meteorological  data,  insurer  loss  data 
and  scientific  hazard  maps).  The  data  currently  rests  in  different  silos  with  different  levels  of 
access  and  cost  available.  The  barriers  (formal,  informal,  monetary  or  otherwise)  should  be 
reduced. This is because the wider the data access there is, the greater the ‘competition’ for the 
most productive use of the data. This process can add value to the data that a stakeholder may 
have  in  ways  that  they  may  not  see  for  themselves.  Moreover,  data  produced  as  part  of 
scientific  studies  (see  recommendation  8)  should  be  made  accessible  to  all  interested  parties. 
This  is  in  line  with  Key  Area  1  in  the  European  Commission's  Sendai  Action  Plan,  which  is  to 
‘promote the collection and sharing of baseline loss and damage data’. 

Policy  recommendation  13:  Promote  the  use  of  community  rating  systems  for 
premium setting. 

One way to make the relationship between the premium and climate adaptation measures more 
transparent  is  community  rating  systems,  which  are  implemented  in  the  USA.  The  principle 
behind  the  community  rating  system  (CRS)  in  the  National  Flood  Insurance  Program  (NFIP)  is 

77 

 
 
 
 
 
 
 
 
that  communities  involved  in  the  NFIP  can  earn  premium  discounts  for  their  citizens  if  they 
score  enough  points  on  a  range  of  criteria:  from  awareness  campaigns  to  the  construction  of 
levees. It is unclear how well the CRS works in lowering risk due to the wide range of activities 
that can earn premium discounts, but the principle could be applied in Europe, with a stronger 
focus  on  activities  that  directly  lower  risk,  i.e.  improved  drainage,  retention  areas,  building 
codes. There would, however, be differing levels of autonomy for local governments or regions 
across  Europe,  creating  difficulties  where  cities/regions  may  not  have  the  official  ability  to 
develop  risk-reduction  measures.  Therefore,  the  level  of  implementation  could  not  be  pan-
European but would work on a national level.  

Policy  recommendation  14:  Promote  the  spreading  of  risk  by  allowing  cities  to  pool 
their insurance. 

Another  potential  area  that  should  be  further  explored  is,  for  example,  allowing  cities  to  pool 
their insurance, which is an effective way to spread risk. With the natural diversification of cities 
and risks across Europe, cities would create an insurance pool to provide compensation in case 
of a disaster event in order to aid recovery. With implementation at a pan-European level, the 
Commission  would  have  a  role  in  creating  and  sustaining  dialogue  between  cities,  Member 
States and insurance companies. This could, for example, be done through the national bodies 
that are recommended in the study or through the Covenant of Mayors for climate and energy. 

Policy  recommendation  15:  Increase  capacity  building  with  regards  to  insurance  and 
climate resilience. 

Capacity  building  of  cities  and  regions  on  aspects  such  as  vulnerability  assessments  and 
identifying areas where insurance can promote resilience to the use of insurance loss data is a 
crucial success factor for the above-recommended initiatives. This could, for example, be done 
through  the  Covenant  of  Mayors  and/or  national  bodies  (under  recommendation  4a  above), 
should  they  be  established.  The  LIFE  Programme  for  Climate  Action  can  be  used  to  provide 
financial  support  for  the  activities  required  to  build  the  capacity  of  Member  States,  including 
municipalities.  While  national  bodies  can  provide  an  excellent  platform  for  cities  to  share  their 
experience on national issues and conditions and gain further capacity building within this area, 
the Covenant of Mayors enables the cities to do so on a pan-European level.  

An example of such a project is the LIFE DERRIS project (project ref. LIFE14 CCA/IT/000650), 
which aims to transfer knowledge from insurance companies to public administrations and SMEs 
in  terms  of  risk  assessment  and  risk  management  for  catastrophic  weather  events. 
Furthermore,  it  focuses  on  implementing  innovative  forms  of  PPPs  for  climate  catastrophes, 
involving SMEs, public administration bodies and insurance companies,  which is consistent with 
the  EU  Adaptation  Strategy  and  the  Green  Paper  on  the  insurance  of  natural  and  man-made 
disasters. 

6.3  Overview of policy themes and recommendations 

Recommendations  concluded  from  the  study  have  been  grouped  into  five  themes  which  are: 
promote risk awareness and reduction; closing the protection gap; the role of public and private 
entities in the risk cycle; the role of cities and regions; and increase reporting requirements and 
knowledge sharing. It should however be noted that the recommendations are, in many cases, 
cross-cutting and can cover several themes.  

The policy themes and recommendations foster and/or improve the use of insurance to increase 
resilience  and  can  be  considered  as  a  framework  that  can  be  used  at  the  national  level  to 
provoke a discussion on  how these  recommendations can be taken  up  and adapted to  fit  local 
circumstances  and  preferences.  The  adjustment  of  these  recommendations  to  more  localised 

78 

 
 
 
 
 
 
 
 
variants can be an important step in providing the stakeholders with a sense of ownership over 
the reforms, and improving their ability to manage extreme weather risks more generally.  

While  there  are  a  number  of  themes  and  recommendations  presented  in  this  study,  they  only 
make  up  a  part  of  the  many  mechanisms  to  consider  in  order  to  reduce  the  risk  to  weather-
related events. Additional mechanisms have not been included in the recommendations as they 
have not been relevant on a pan-European level or concluded from the 12 case studies.  

Table 6.1 below provides an overview of the policy themes and recommendations uncovered by 
the  study.  The  theme  is  listed  with  the  sector  it  falls  under  which  are:  private  property, 
agriculture,  city  or  Member  States.  It  should  be  noted  that  the  number  of  recommendations 
under each sector does not indicate the importance of the sector. 

79 

 
 
 
 
 
 
 
 
 
 
Table 6.1: Overview of policy themes and recommendations 

Sector 

Theme and recommendation 

Promote risk awareness and reduction 

Private property  

2: Low-income (following local definitions of low income or social hardship) 
households struggling to afford extreme weather insurance should have 
this pressure eased with insurance vouchers or tax credits if they buy 
insurance coverage. 

3a: Minimum building standards, or build-back-better requirements, 
differentiated by risk levels, can be required as a standard element of 
insurance contracts in order to gain coverage (with a focus on measures 
integrated into the building). 

Multi 

8: Research with the aim of defining and quantifying resilience to support 
risk awareness and reduction and a focus on how insurance can enhance 
the economic resilience. 

Closing the protection gap 

Private property 

1a: Promote the bundling of a complete extreme weather event insurance 
package with private property fire insurance policies (or a similar and 
more often purchased product). 

1b: Urge banks to require full and comprehensive insurance coverage 
when providing mortgage loans. 

Agriculture 

5: Redirect premium subsidies towards multi-peril (yield) crop insurance 
products to provide more extensive coverage. Each extreme weather 
event can contribute to the overall premium in line with its risk level. 

Private property 

6: In order to reduce the presence of adverse selection in crop insurance 
and where only the high-risk land is insured, a farmer should be compelled 
to insure all arable land as part of the terms and conditions of an 
insurance policy. 

7a: Combine access to wider agricultural sector subsidies (i.e. those 
relating to the common agricultural policy or those offered at the national 
level) to the purchase of sufficient insurance protection in order to develop 
a tradition of being insured. 

9: Support the use of farm income insurance by starting pilot initiatives in 
various Member States. 

Support PPPs and cross-organisational collaboration 

3b:  Use  a  surcharge  on  insurance  premiums  (either  newly  introduced  or 
redirected  current  taxes)  to  directly  finance  and  construct  risk-reduction 
infrastructure  or  to  directly  subsidise  household-level  risk-reduction 
measures  

4b:  Create  a  national  focal  point  or  authority  for  developing  and 
maintaining  a  legal  framework  through  which  extreme  weather  risks  can 
be managed via a combination of risk reduction and/or transfer.  

4c: Lay down the roles and responsibilities of all the stakeholders in a 
national platform, focal point or authority, in a clear and transparent 
framework. 

Agriculture 

7b: Develop an agricultural risk management association with a focus on 
protecting farmers against income variations due to crop yields, within a 
mutual or non-profit maximising organisation. 

Multi 

10: Create a working group in the European Commission to enable cross-
DG collaboration as well as coordination with national bodies. 

80 

 
 
 
 
 
 
 
 
 
 
 
 
 
Sector 

Theme and recommendation 

Increase the role of cities and regions 

Cities and regions 

11: Recommend cities assess their vulnerability in regard to insurance 
penetration rates, including for municipal infrastructure and extreme 
weather events covered, as well as to report on how they use insurance as 
a mechanism for managing risks. 

Cities and regions 

12a: Promote the use of insurance disaster loss data in the municipalities’ 
risk-assessment data. 

12b: Promote the active and collaborative sharing of risk, hazard and 
impact data across stakeholders though the standardisation of metadata 
and the format of granular data so that it can be more efficiently and 
transparently shared across stakeholders productively. 

Cities and regions 

13: Promote the use of community rating systems for premium setting. 

Cities and regions 

14: Promote the spreading of risk by allowing cities to pool their 
insurance. 

Cities and regions 

15: Increase capacity building in regards to insurance and climate 
resilience. 

Member States 

Integration of resilience, including insurance data, in relevant 
policies 

4a: Introduce a requirement for flood risk management plans, national 
adaptation strategies and applications for loans or national or EU funds to 
include insurance mechanisms for managing risk that cannot be (cost-) 
effectively prevented in order to further mainstream insurance into 
national adaptation conversations. 

81 

 
 
 
 
 
 
 
 
 
 
7.  RECOMMENDATIONS TO THE EUROPEAN COMMISSION 

While the previous section provides recommendations on steps forward, not all of them  are  in 
the  European  Commission’s  mandate  or  interest  to  execute.  Therefore,  many  of  the  policy 
suggestions  and  themes  are  to  be  discussed  at  national  level  and  adapted  thereafter.  The 
overview of Article 196 in the Treaty on the Functioning of the European Union (TFEU) sets forth 
that  the  EU  shall  foremost  support  and  complement  Member  States  actions,  and  promote 
cooperation and consistency. This was reflected in the European Parliament’s reply to the Green 
Paper  on  the  insurance  of  natural  and  man-made  disasters,  which  rejected  the  idea  of  a  one-
size-fits-all  solution  to  be  implemented  across  Europe  and  for  the  EU  to  intervene  on  the 
national insurance markets.  

This  study  therefore  recommends  that  the  EU  takes  the  role  of  facilitating  discussions  and 
provides  platforms  for  multi-stakeholder  collaboration,  promoting  the  use  of  insurance  to 
increase resilience to weather-related events and most importantly, increase risk awareness and 
risk reduction. 

This section is based on the general recommendations presented previously but lists the specific 
recommendations  for  the  European  Commission.  These  recommendations  are  presented  along 
with comments on their implementation with the following points of focus: 

  A justification for the necessity of implementing the suggested change 

  Risks and potential mitigation strategies 

  Conditions for the implementation  

  Principal actors that need to be involved in the implementation  

  Timing (short/medium/long-term/ongoing) 

  Any potential budget implications 

Note that these recommendations are not presented in order of importance; instead, they follow 
the structure of the policy themes.  

82 

 
 
 
 
 
 
 
 
 
 
7.1 

Increase reporting requirements and capacity building  

Recommendation 1 

Recommendation 1 

Increase  the  requirements  or  recommendations  for  Member 
States  to  assess  their  vulnerability  in  regard  to  insurance 
penetration  rates  and  events  covered,  as  well  as  to  report  on 
how they use insurance as a mechanism for managing risks.  

Member  States  should  report in  their  national  adaptation  strategies  on 
insurance coverage in the country, as well as on how insurance is used 
as a mechanism to promote climate adaptation.  

Furthermore,  and  in  line  with  the  national  adaptation  strategies,  there 
would a requirement for cities to also include insurance coverage and/or 
insurance payment mapping in their adaptation strategies.  

Mandatory  reporting  under  the  Flood  Directive  (2007/60/EC)  should 
take the role of insurance into consideration in the flood risk maps and 
the  flood  risk  management  plans.  The  flood  risk  map,  which  should 
indicate the potential economic damage,  should include  information on 
penetration rates and events covered, while the flood risk management 
plans  should  consider  the  use  of  insurance  mechanisms  for  managing 
risks.  

Sector 

Member States 

Justification 

Risks 

Conditions 

Action  8  in  the  EU  adaptation  strategy  promotes  the  use  of  products 
and  services  by  insurance  and  financial  markets.  This  is  subsequently 
addressed in point 8e on the adaptation preparedness scoreboard which 
states  ‘adaptation  is  mainstreamed  in  insurance  or  alternative  policy 
instruments,  where  relevant,  to  provide  incentives  for  investments  in 
risk prevention’. By including information on the insurance conditions in 
the national adaptation  strategies,  this increases the  risk awareness of 
the  Member  States,  as  well  as  making  the  European  Commission 
informed on what the insurance status is in the EU. 

The three priorities under the EU adaptation strategy are:  

1.  Promoting action by Member States 
2.  Better informed decision-making 
3.  Key vulnerable sectors. 

By  increasing  reporting  requirements  and  recommendations,  it  is 
believed  that  all  these  priorities  will  be  met  in  regards  to  using 
insurance  to  promote  climate  adaptation.  Member  States  will  be 
encouraged  to  act  with  an  increased  awareness  of  their  risk  and  how 
insurance  can  be  used  to  manage  it.  With  increased  risk  awareness, 
both  the  Commission  and  the  Member  States  will  be  able  to  make 
better informed decision-making and be able to approach the sectors or 
regions that have been identified as vulnerable in the reports.  

There is a risk that increased reporting requirements could be met with 
resistance  if  the  benefits  of  the  requirements  are  not  clearly 
communicated and promoted. 

Member  States  and  municipalities  should  have  the  capacity  to  asses 
vulnerability  based  on  insurance  coverage,  as  well  as  how  to  use 
insurance  as  a  risk  management  tool.  This  could  be  ensured  by  the 
capacity  building  measures,  which  are  introduced  in  the  following 
recommendations. A roadmap or guidelines provided to Member States 
or  cities through,  for  example,  the  Covenant  of  Mayors  or  through the 
Climate-ADAPT  website  would  provide  additional  support  to  the 
preparation  of  the  vulnerability  assessments  and  risk-management 

83 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Principal actors 
and responsibilities 

Recommendation 1 

strategies, as well as the implementation of the strategies.  
A dialogue with the Member States is also important in order to create 
consensus on the new reporting requirements. 

The  actors  would  include  Member  States,  municipalities,  project 
applicants and the European Commission. It would be the responsibility 
of  the  European  Commission  to  impose  the  new  requirements  or  the 
recommendations 
and 
management,  and  for  the  other  actors  mentioned  to  prepare  the 
reporting.  

vulnerability 

report  on 

insurance 

to 

Budget implication 

The new requirements and recommendations would involve an increase 
in administrative costs.  

Timing  

The  second  review  of  flood  hazard  and  risk  maps  are  to  be  prepared 
before  December  2019,  and  the  second  review  of  the  Flood  Risk 
Management Plans is due in December 2021. 

As  the  EU  strategy  on  adaptation  to  climate  change  is  currently  under 
review, this poses an opportunity to consider the inclusion of insurance 
data  and  information  about  how  insurance  is  used  as  a  mechanism  to 
manage  risk  in  the  national  adaptation  strategies  as  well  as  city 
adaptation plans. 

Recommendation 2 

Recommendation 2 

Include  an  ex-ante  conditionality  for  the  European  Structural 
and Investment Funds  on assessing insurance vulnerability and 
the usage of insurance as a risk management tool. 

the 
The  European  Commission's  Sendai  Action  Plan  promotes 
risk  prevention  and 
consideration  of  disaster 
management  in  the  European  Structural  and  Investment  (ESI)  Funds 
implementation.  This  study  recommends  that  the 
programmes' 
promotion 
the 
implementation. 

insurance  vulnerability  and  usage 

resilience  and 

includes 

in 

Furthermore,  while  climate  and  disaster  proofing  is  built  into  the 
appraisal of major projects for cohesion policy support (through the ESI 
Funds),  the  role  of  insurance  should  be  stressed  by  requiring  that  the 
insurance  of  the  investment  is  part  of  the  vulnerability  and  risk 
assessment,  and  that  the  use  of  insurance  is  considered  in  the 
adaptation planning. 

Sector 

Member States 

Justification 

Risks 

Conditions 

Similar  to  the  recommendation  given  above  on  adaptation  strategies 
and  flood-risk  planning,  an  increase  in  reporting  requirements  would 
support  the  use  of  insurance  as  a  risk  management  tool  and  also 
increase  the  Commission’s  awareness  on  the  coverage  and  usage  of 
insurance as a risk management tool in the EU. 

There is a risk that increased reporting requirements could be met with 
reluctance  if  the  benefits  of  the  requirements  are  not  clearly 
communicated and promoted. 

The  increased  requirements  on  using  insurance  as  a  risk  management 
tool  and  reporting  on  it  demands  an  increase  in  the  capacity  of  the 
Member  States on  this.  The  usage  of  insurance  in  the  preparation  and 
implementation  of  projects  should  therefore  be  a  part  of  the  capacity-

84 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Recommendation 2 

building  activities  recommended  to  the  Member  States.  Furthermore, 
and in line with the recommendation above, information and guidelines 
should be provided through Climate ADAPT. 

Principal actors 
and responsibilities 

Member States, European Commission, European Investment Bank 

Budget implication  New  requirements  and  recommendations  would  involve  an  increase  in 

administrative costs 

Timing  

Short to medium term 

7.2  Promote risk awareness and reduction 

Recommendation 3 

Recommendation 3  Fund a study with the aim to define and quantify resilience at EU 
level  with  a  focus  on  how  insurance  can  enhance  economic 
resilience. 

Sector 

Multi 

Justification 

A  project  that  would  aim  to  define  and  quantify  resilience  would 
possibly  create  an  understanding  of  which  risk-reducing  measures  are 
most  cost-effective  and  thereby  create  stakeholder  consensus  on,  for 
example, to what degree a specific action should lower  premiums. The 
European Commission has an important role as a neutral arbitrator that 
can,  on  a  pan-European  level,  create  consensus  on  risk-reducing 
measures.  

Risks 

This is a highly complex task, both in regards to defining resilience and 
in  creating  stakeholder  consensus.  The  results  of  the  study  might  be 
limited.  

Conditions 

As  with  many  initiatives  and  projects  suggested,  a  multi-stakeholder 
approach is required. 

Principal actors 
and responsibilities 

It is the responsibility of the European Commission to fund and manage 
the  project  and  as  a  service  provider  to  implement  it.  The  additional 
actors  involved  are  the  stakeholders  to  be  consulted  on  the  projects, 
which  could,  for  example,  be  the  insurance  industry,  academia,  cities 
and regions.  

Timing of 
implementation 

The study would be expected to have a 1-year duration and if there are 
funds  available,  it  could  be  initiated  at  rather  short  notice  by  the 
European Commission. However, while the implementation of the study 
on resilience could be done in a short period of time, the end objective 
of  stakeholder  consensus  knowledge  sharing  should  be  seen  from  a 
medium-term perspective.  

Budget implication 

The  project budget  would  vary  greatly,  depending on  the  scope  of  the 
project  and  the  number  of  sectors  to  study.  A  project  budget  could 
roughly be estimated at EUR 400 000. 

85 

 
 
 
 
 
 
 
 
 
 
 
 
Recommendation 4 

Recommendation 4  Promote  the  use  of  insurance  mechanisms  that  will  support 

damage prevention to Member States.  

Promote  bundling  of  a  combination  of  extreme  weather  events  with 
private property fire insurance policies (or a similar product). 

Inform  stakeholders  about  the  potentials  of  low-income  households 
receiving  subsidies  for  insurance  in  the  form  of  insurance  vouchers  or 
tax credits if they buy insurance coverage. 

Foster  discussions 
forums  about  minimum  building 
events), 
standards 
differentiated  by  risk  levels,  which  may  be  required  as  a  standard 
element of insurance contracts in order to gain better coverage. 

in  relevant 
build-back-better 

requirements 

(after 

or 

There  are  a  number  of  channels  for  information  and  platforms  for 
discussions  that  can  be  used  by  the  European  Commission  in  order  to 
reach  the  relevant  stakeholders.  The  current  study  is  one  of  them. 
Should  a  focal  point  for  these  types  of  discussions  be  created  or 
organised in a Member State (unless already existing), this provides an 
excellent opportunity for a platform in which these types of mechanisms 
and their potential in the country can be discussed.  

Sector 

Private property  

Justification 

By informing Member States about the benefits of these measures and 
discussing  how  they  could  be  implemented  at  a  national  level,  the 
Commission  would  be  able  to  promote  risk  awareness  and  reduction 
tailored to the national legislations and conditions.  

Risks 

The  Member  States  and  the  private  sector  might  be  opposed  to 
interference,  as  this  may  reduce  their  income  from  taxation,  and 
hamper private business models. 

Conditions 

Should  a  Member  States  not  realise  the  benefits  of  an  increased 
penetration rate, they would most probably be opposed to subsidies for 
low-income households.  

The Commission is merely involved in sharing information and initiating 
discussions  and  not  in  the  actual  implementation  of  the  insurance 
mechanisms  in  the  Member  States.  The  condition  for  this  is  that  the 
Commission  can  identify  the  right  channels,  forums  and  platforms  in 
which  this  can  be  discussed.  By  creating  a  working  group  in  the 
Commission involving several DGs it is believed that more channels and 
forums  can  be  accessed  and  stakeholders  reached  (this  is  further 
discussed below). 

Principal actors 
and responsibilities 

Government bodies, insurance sector, European Commission  
It  is  the  responsibility  of  the  European  Commission  to  initiate  the 
discussions.  

Timing 

The initiation of discussions and knowledge sharing can be done  in the 
short term but the implementation of recommended mechanisms would 
be done with a long-term perspective, depending on the Member States 
and the insurance industry. 

Budget implication 

The  recommended  actions  to  improve  awareness  of  the  recommended 
measures  will  be  approximately  cost-neutral  as 
they  will  be 
incorporated in ongoing discussions and consulting processes.  

86 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7.3  Closing the protection gap 

Recommendation 5 

Recommendation 5 

Increase the proportion of funds to the second pillar of the CAP, 
focused  on  the  risk-management  toolkit,  including  insurance 
schemes for crops, animals and plants, as well as mutual funds 
and an income stabilisation tool.  

At national level, the following conditions should be considered: 

Redirect  premium  subsidies  towards  multi-peril  (yield)  crop  insurance 
products  to  provide  more  extensive  coverage.  Each  extreme  weather 
risk can contribute to the overall premium in line with the threat posed 
by each extreme weather risk. 

In order to reduce the presence of adverse selection in crop insurance a 
farmer could be compelled to insure all arable land as part of the terms 
and conditions of an insurance policy. 

Introduce  farm  income  insurance  as  a  potential  tool  through  a  pilot 
project. 

Sector 

Agriculture 

Justification 

Risks 

Conditions 

While  carefully  organising  subsidies  to  not  exclude  private  insurance 
instruments  from  the  market,  the  EU  has  an  important  role  to  play  in 
increasing  the  tradition  of  being  insured  in  the  agriculture  sector. 
Fostering the purchase of insurance should, however, be combined with 
risk-reduction activities. 

There would potentially be resistance to moving to subsidised insurance 
rather than income, and possibly hesitation about changing products to 
something stakeholders are not familiar with.  

Extensive awareness raising and the promotion of the new subsidisation 
as  well  as  insurance  schemes  would  be  a  prerequisite  for  stakeholder 
engagement and acceptance.  
The  new  instruments  would  call  for  a  strong  private  and  public 
partnership,  which  could  be 
the  agricultural  risk-
management association described in the recommendation below. 

initiated 

in 

Principal actors 
and responsibilities 

European Commission, DG AGRI, Member States, insurance sector 

Timing 

Long term 

Budget implication 

There  would  be  no  increase  in  the  budget  for  the  CAP  as  it  is  a 
redirection of existing  subsidies  but there are,  however,  administration 
costs in redirecting them. 

7.4  Support PPPs and cross-organisational collaboration 

Recommendation 6 

Recommendation 6  Support  and  facilitate  the  creation  of  a  national  focal  point  or 
authority  for  developing  and  maintaining  an  institutional  and  legal 
framework through which extreme weather risks can be managed via a 
combination of risk reduction and transfer. 

87 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Recommendation 6 

Support  and 
facilitate  an  agricultural  risk  management 
association  with  a  focus  on  protecting  farmers  against  income 
variations due to crop yields (i.e. supporting multi-risk yield insurances 
or  the  use  of  risk-reducing  measures),  within  a  mutual  or  non-profit 
maximising organisation.  

Sector 

Multi 

Justification 

By  centralising  the  responsibility  for  natural  disaster  risk  to  a  national 
body  in  the  Member  State,  there  will  be  a  natural  platform  for  the 
various  stakeholders  to  meet.  The  role  here  of  the  EU  as  facilitator 
would  be  important  in  order  to  share  information  on  best  practice, 
bringing  together  stakeholders  and  assist  with  the  definitions  of  roles 
and responsibilities, as well as ensuring that the momentum is not lost. 
As  can  be  seen  in  examples  presented  in  the  MCA  outcomes,  such 
platforms  have  from  previous  experience  opened  up  for  additional 
collaboration  initiatives  such  as  joint  research  actions  and  a  voluntary 
sharing of data.  

Risks 

It is important that members of the body are committed to the initiative 
and  that  stakeholders buy  in  to  the  initiative  as  well  because  the  risk-
reduction  measures  would  be  funded  by  a  surcharge  on  the  insurance 
premiums.  

Conditions 

Clear and transparent discussions of the roles and responsibilities of all 
the stakeholders should be clearly stated and noted.  

Principal actors 
and responsibilities 

Insurance  associations,  agricultural  associations,  insurance  companies, 
responsible ministries, local governments, the European Commission 

Timing 

Medium to long term 

Budget implication 

The  responsibility  and  costs  for  the  creation  of  the  bodies  are  covered 
at  national  level  but  there  might  be  a  need  for  the  Commission  to 
coordinate and motivate these bodies and therefore a need for a budget 
to cover an increase in administration costs. 

Recommendation 7 

Recommendation 7  Create  a  working  group  in  the  European  Commission  enabling 
cross-DG  collaboration,  awareness  raising  and  stakeholder 
collaboration, as well as coordination with national bodies.  

The  body  would  have  representatives  from  DG  ECHO,  DG  FISMA,  DG 
CLIMA  (lead),  DG  AGRI,  DG  Competition  (COMP)  and  other  interested 
DGs,  with  part  of  their  responsibilities  being  to  document  and  share 
good  practices,  demonstrating  socio-economic  benefits  of  prevention 
and  risk-transfer  measures  and  their  relationships.  They  would  also 
coordinate  the  national  bodies  and  streamline  efforts  with  other 
relevant stakeholders. 

Sector 

Multi 

Justification 

Benefits of a working group in the Commission are:  
  A single entry point for Member States and external stakeholders to 
the  Commission  with  regard  to  insurance  and  weather-related 
disaster  risk  reduction.  This  could  ease  the  coordination  with  the 
national bodies/ focal points. 

  Disaster  risk  management  is  a  shared  responsibility  across  many 
DGs,  and  coordination  and  harnessing  synergies  of  experience  and 

88 

 
 
 
 
 
 
 
 
 
 
 
 
Recommendation 7 

expertise is not yet optimal.  

  The  creation  of  a  working  group  supports 

the  European 
Commission’s  strategic  priority  of  a  financial  instrument  to  finance 
public policy. 

  An 

increased 

focus  on 

increasing  resilience  through  various 

mechanisms, including insurance.  

  As  discussed  above,  promoted  insurance  mechanisms  or  other 
actions  within  insurance  needs  to  be  discussed  with  the  relevant 
stakeholders  in  order  to  gain  consensus.  If  the  Commission  can 
agree  on  a  communication  plan  and  what  to  promote  or  discuss,  it 
will be easier to reach the relevant stakeholders as the multiple DGs 
will access different platforms and forums. 

Risks 

There  could  be  difficulties  in  generating  consensus  on  responsibilities, 
goals  and  objectives  within  the  working  group,  which  is  a  requirement 
for the benefits described above to be realised. 

Conditions 

Document  and  communicate  responsibilities,  goals  and  objectives  that 
have been commonly agreed on in the group. 

Principal actors 
and responsibilities 

The suggested DGs to be involved are DG ECHO, DG FISMA, DG CLIMA 
and DG AGRI. It is, however, the responsibility of DG CLIMA to take the 
initiative, form the working group and chair it. 

Timing 

Short term 

Budget implication  Although  costs  for  forming  and  managing  the  working  group  are  likely 
to  occur  in  the  implementation  of  this  recommendation,  the  over-
arching assumption is that, overall, through the increased streamlining 
and  harnessing  of  synergies  between  DGs,  there  should  not  be  any 
significant budget implications.  
The man-days required  for  coordination with national  bodies and other 
external  stakeholders is  not  seen  as  a  cost  falling  under  the  formation 
of  the  working  group.  On  the  contrary,  the  formation  of  a  working 
group  and  the  streamlined  practices  could  lower  the  administration 
costs required for coordination with external stakeholders. 

7.5 

Increase the role of cities and regions 

Recommendation 8 

Recommendation 8  Fund projects with the aim of increasing the capacity of cities to 
insure 

insurance  as  a  risk  management  tool  and 

use 
infrastructure.  

Justification 

It is recommended that the European Commission will in general act as 
a  facilitator  and  coordinator  between  stakeholders,  which  requires 
national  and  local  authorities  to  plan  and  implement  policies  and 
projects.  This  will  require  a  focus  on  capacity  building,  which  can  be 
financed through, for example, the LIFE Programme. 

Sector 

Risks 

City 

None 

Conditions 

Municipalities buy in to these capacity-building projects.  

Principal actors 

European Commission, municipalities, consultants 

89 

 
 
 
 
 
 
 
 
 
 
 
 
Recommendation 8 

and responsibilities 

Timing 

Ongoing 

Budget implication  Substantial funds would be required for capacity building at the start of 
the  initiatives  but  this  is  expected  to  create  long-term  benefits  as  the 
actions suggested in this study are to be taken on a national or regional 
level. 

Recommendation 9 

Recommendation 9  Create 

a  dialogue  between 

industry, 
municipalities  and  national  bodies  on  how  community  rating 
systems and the pooling of city insurance can be developed. 

insurance 

the 

Sector 

City 

Justification 

By facilitating a discussion between different stakeholders and taking an 
active  role,  the  European  Commission  would  explore  whether  these 
mechanisms  would  be  possible  to  implement  at  a  national  or  pan-
European level.  

Risks 

Local legislation and conditions may hinder the initiatives.  

Conditions 

The relevant stakeholders would need to be included in the discussion.  

Principal actors 
and responsibilities 

European  Commission,  insurance  industry,  municipalities,  Member 
States 

Timing 

Long term 

Budget implication 

The budget implications would be limited as the Commission would only 
be  responsible  for  creating  a  discussion,  which  should  fall  under  the 
other recommendations in this study for increased consultation between 
the European Commission and other external stakeholders, such as the 
insurance industry and cities and regions. 

90 

 
 
 
 
 
 
 
 
 
 
 
 
APPENDIX 1 
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APPENDIX 2 
CASE STUDY NARRATIVES AND ATTRIBUTE TABLES 

105 

 
 
 
 
 
 
 
 
 
 
 
Austria 

The opportunities for risk transfer in Austria are divided into two elements: a public mechanism 
in  the  form  of  a  catastrophe  fund  and  private  insurance,  which  interact  with  one  another.  In 
Austria,  with  the  exception  of  storms  and  hail  which  are  part  of  standard  property  insurance 
(and  a  voluntary  extension  for  businesses),  many  extreme  weather  events  are  insured  on  a 
voluntary  basis,  that  many  households  in  Austria  have  as  a  matter  of  course.  However,  the 
Austrian  catastrophe  fund  provides  compensation  when  other  extreme  weather  events  occur. 
This creates a large problem with charity hazard as the catastrophe fund and private insurance 
compete for coverage of extreme weather events. This can be seen as for major floods ~5% of 
the loss is compensated via insurance. This creates a potential gap in coverage as noted below 
the average compensation provided by the fund amounts to only 20-30% of that suffered. 

The post-disaster catastrophe fund (Naturkatastrophenfonds) is funded by 1.1% of the federal 
government’s share of income, capital yield and corporate taxes (corresponding to ~7 EUR for 
private households and  ~30 EUR for  businesses).  The fund  can build up a reserve  of up to 29 
mil  EUR.  Additional  resources  can  be  allocated  if  special  laws  are  enacted.  The  budget  of  the 
catastrophe  fund  is  allocated  to  both  prevention  and  compensation.  The  catastrophe  fund 
spends  ~73%  of  its  budget  on  preventive  measures  against  flooding  and  avalanches,  water 
quality assessments, warning and alarm systems, grants to the agricultural sector, and costs of 
preventing  water  contamination.  The  reaming  (~27%)  is  used  to  support  the  financial 
compensation paid by the lander to those affected, which corresponds to an average indemnity 
of about 20%–30% of the damage suffered. Infrastructure can be compensated by up to 50% 
of the loss. However, there is not a legal right to compensation or a certain level of assistance. 
However, political motivates can play a role in determining the level of compensation. 

Private market insurance coverage against losses resulting from natural hazards is available in 
Austria  since  the  mid-1950s,  in  particular  with  respect  to  losses  occurring  due  to  windstorm 
(air- flow with velocities >60 km/h) and hail (among others), though by the late 1990s flooding 
and  other  large  scale  extreme  weather  events  were  being  excluded  from  general  coverage. 
Private insurance companies provide limited coverage for extreme weather events. For example, 
most insurance companies cover for damages to private buildings and households up to a sum 
between 3700 EUR and 15 000 EUR, with a few insurers compensating up to 50% of the insured 
sum (Gruber, 2008). Business entities are granted more flexible contract conditions than private 
households,  in  particular  since  they  have  access  to  combined  policies  (specifically  “all  risk 
policies”),  hence,  higher  coverage  against  losses  resulting  from  natural  hazards  is  available. 
When requested, cover can be extended to a fixed maximum sum amounting to 20 000 EUR or 
25–50% of the building value.  

106 

 
 
 
 
 
 
 
 
 
 
Table A2.1 Attribute table for Austria 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

N/a 

N/a 

N/a 

Hazard(s) covered 

Flooding; Storms; Hail 

Flooding; Storms; 
Hail 

Hail (all); Storm 
(some), Flood 
(some), Rain (some), 
Drought (some), 
Frost(some) 

Official trigger 
Yes /no 
Description? 

No 

No  

No  

Type of insurance, 
e.g. bundled, 
parametric triggers 

Private bundled indemnity 
insurance 

Private indemnity 
insurance, bundled 
for small and median 
enterprises. 

Single risk hail and 
multi-peril insurance 
is mutual indemnity 
insurance. 

Compulsory vs. 
voluntary 

Voluntary: voluntary windstorm 
insurance is automatically 
included in standard household 
policies, flooding is a voluntary 
and limited extension. 

Voluntary: Coverage 
is a voluntary 
extension.  

Voluntary 

Damage covered 

Direct damage to buildings and 
contents. 

Direct damage to 
buildings and 
contents. 

Damage to crops and 
production losses. 

Limit of 
compensation 

For floods: Maximum 
indemnification applied to 
buildings a fixed amount, 
generally ranging from €5,000 
to €15,000. Indemnification 
limits for contents are similar to 
those for buildings. May be 
further limited in particularly 
high risk areas. 

Compensation is 
limited by the chosen 
sum insured per 
field. 

For floods: Maximum 
indemnification 
applied to buildings 
can be a percentage 
(up to 50%) of the 
capital insured, or a 
fixed amount, 
generally ranging 
from €5000 to 
€15,000. 
Indemnification limits 
for contents are 
similar to those for 
buildings, tough 
there is slightly more 
flexibility than 
compared to 
household insurance 
(e.g. can be 
extended to a max 
coverage of 
€20,000). 

May be further 
limited in particularly 
high risk areas. 

Individual policy 
deductibles 

Risk Reduction 
Incentive based on: 
Premiums 

Deductibles; risk exclusions in 
terms and conditions 

Deductibles; risk 
exclusions in terms 
and conditions 

Bonus-malus system, 
deductibles 

107 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Arable Farming: 60% 
for multi-peril 
insurance (hail 
reaches about 80%) 

Premiums are risk 
based. 50% of the 
premium is 
subsidized (split 
between the federal 
and regional 
governments). 

Private Reinsurance 
mixture of stop loss 
contracts and 
proportional 
contracts. 

Depends on chosen 
product:  
Single risk hail 
insurance: 3,0% of 
total sum insured. 

Multi-peril insurance: 
3.9% of total sum 
insured. 

Multi-peril insurance: 
Total average 
premium of about 
€57 per hectare. (ca) 

If a disaster is 
officially declared to 
be a catastrophe the 
farmer may get 
compensation if the 
damage surpasses 
1.000 € per farm, 
but there is no legal 
title to get 
compensation. 

Deductibles 
Other 

Market penetration 
rate 

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Flooding: <10% sum insured, 
but >85% of risks insured 
Storms/Hail: >85% sum 
insured and risks insured 

Penetration rate 
could be around 65% 

Storm insurance tends to not be 
risk dependent.  

Risk based 

Premiums are not dependent on 
risk. The use of risk zoning for 
pricing purpose is under 
consideration. 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

Private reinsurance, 
No reinsurance for the 
Naturkatastrophenfonds 

Private reinsurance 

Average annual 
premium 

Average property premium per 
capita in 2015 = ~320 EUR 

Access to public 
disaster response 
fund 

Yes, catastrophe fund 
compensates 20-30% 
of losses suffered. 
However, no formal 
rules to guide the 
decision. 

Yes, catastrophe fund 
compensates 20-30% of losses 
suffered. However, no formal 
rules to guide the decision. 

In principle the responsibility for 
coping with the damage caused 
by NatCat is attributed to the 
Länder, but in 1966 the Disaster 
Fund was settled. The main 
aims of the Fund are: 
- Finance preventive measures 
against avalanches and floods. 
- Support the Länder in 
covering incurred losses. 

Fund’s interventions 
Private properties. Private 
households and companies are 
usually granted 20-30% 
indemnity for the incurred loss 
by the Länder. The Disaster 
Fund then reimburses 60% of 
the financial aids spent by the 
Länder. 

Public properties. The Fund 
compensates 50% of the 

108 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

damage to Länder and 
municipal property. 

In case of extreme situations, 
the Austrian Parliament easily 
grants additional resources via 
public funds and / or tax relief, 
thus depending on pure political 
decision 

The post-disaster 
Naturkatastrophenfonds (as far 
as it covers private property) is 
major obstacle for the 
development of a pre-disaster 
NatCat insurance compensation 
scheme 

Pay-out speed 

Additional comments 

Payment within 4 
days after claims 
settlement. 

Farmers have to 
insure all of their 
arable land. 

References: Mahul and Stutley (2008); Diaz-Caneja et al. (2009); Maccaferri et al., (2012); Lamond and Penning-Rowsell 
(2014); Wharton Risk Management and Decision Processes Center (2016); Porrini and Schwarze (2012) 

109 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Bulgaria 

Bulgaria  has  an  insurance  sector  that  is  slightly  less  developed  than  other  European  countries 
(the  participation  rate  is 2.1%  of  per  capita income vs.  7.6%  on  average  across  the  EU).  The 
Insurance Act of 1997 was revoked due to the adoption of a new Insurance Code, which came 
into  effect  on  January  1,  2006.  Under  this  code  insurance  companies  can  approach  extreme 
weather events by: setting higher solvency requirements; developing equalization reserves; the 
use  of  reinsurance;  and  directly  excluding  extreme  weather  events  from  coverage.  There  is 
strong  international  presence  in  the  Bulgarian  insurance  market  on  the  whole  as  foreign 
investors  have  acquired  shares  in  most  of  the  Bulgarian  insurance  companies  or  provide 
reinsurance.  

Bulgaria  has  examples  of  compulsory  insurance  however  these  tend  to  be  for  automobiles  or 
work  related  accidents,  but  not  for  other  property  related  losses.  Furthermore,  each 
governmental  ministry  also  has  set  provisions  for  a  minimum  level  of  insurance  coverage  for 
their  employees  in  terms  of  their  safely,  e.g.  volunteers  during  natural  disasters.  There  are 
some  compulsory  insurance  products  in  terms  of  protecting  farmers,  i.e.  leases,  oil  storages, 
local dams maintained by agricultural associations. In some cases there is a dialogue regarding 
new  insurance  products  (e.g.  a  potential  income  insurance  for  farmers).  Fire  and  natural 
disaster insurance comprise approximately 12 percent of gross written premiums, corresponding 
to  at  most  ~10%  of  the eligible  population  (though  quite  possibly  lower  in  higher  risk  areas). 
There are very few products that specifically cover losses from flooding events and droughts as 
it is difficult to disentangle what a particular policy covers. In the case of agricultural insurance, 
the majority of coverage is against hail losses. 

However, it must be noted that due to the republican fund that provides compensation charity 
hazard  can  have  a  large  impact  on  the  number  of  household’s  wishing  to  buy  insurance.  For 
example, between 2007 and 2013 the Bulgarian government spent ~32mil EUR on state aid as 
compensation  for  agricultural  losses.  This  creates  interplay  between  ex-post  and  ex-ante 
measures. 

110 

 
 
 
 
 
 
 
 
 
 
Table A2.2 Attribute table for Bulgaria 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

N/a 

N/a 

N/a 

Hazard(s) covered 

Flooding; Storms 

Flooding; Storms 

Hail (most common); 
Storm; Torrential 
rain; Frost 

Official trigger 

No 

No 

No 

Yes /no 

Description? 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Single risk indemnity insurance 

Single risk 
indemnity 
insurance 

Single risk indemnity 
insurance 

Compulsory vs. 
voluntary 

Voluntary extension of property 
insurance 

Voluntary 
extension of 
property insurance 

Combined risk 
indemnity insurance 

Voluntary (required 
for access to 
government 
subsidies and history 
of compulsory 
insurance) 

Damage covered 

Direct damage to buildings and 
contents 

Direct damage to 
buildings and 
contents 

Damage to crops 

Limit of 
compensation 

In theory no limits on 
compensation. However, coverage 
tends to be a max of 85% of the 
potentially insured value. 

Individual policy 
deductibles 

Up to 2% of insured value. Many 
insurance companies may not apply 
any deductible. 

Up to insured value 

5% of insured value 

Risk Reduction 
Incentive based on: 

Deductibles 

Deductibles 

Deductibles 

Premiums 

Deductibles 

Other 

Market penetration 
rate 

~10% for flood insurance (at least 
less than 20%) 

111 

Bonus-malus system 
(i.e. no claims 
bonus) 

~27% of farms 

~50% of total 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

~10% for storm insurance (at least 
less than 20%) 

cultivated area 

Premium Setting 
rule, e.g. risk 
based, flat, fixed 
percentage of 
insured value? 

Commonly a flat rate as a 
percentage of the insured value.  

No subsidy for arable 
crops. 

There are limited examples of 
where flood insurance premiums 
increase based on proximity to 
rivers. 

Subsidies of up to 
80% on insurance for 
vegetables and fruit. 

Hail insurance tends 
to be priced across 
risk zones. 

Reinsurance access: 

Private reinsurance access 

Private reinsurance 
access 

Private reinsurance 
access 

Private reinsurance 

Public reinsurance 

Average annual 
premium 

~25-50 EUR, additional extensions 
for flood and storms at €20 (each) 

~€20 per extension  ~4.8% of insured 

value (~€5per 
hectare) 

Access to public 
disaster response 
fund 

There is the possibility of receiving 
ad-hoc compensation from the 
Republican fund. In 2008 this fund 
was valued at $31M, but is funded 
by government appropriations.  

Pay-out speed 

Within 15 days of claim received 

Within 15 days of 
claim received 

There is the 
possibility of 
receiving ad-hoc 
compensation from 
the Republican fund. 
In 2008 this fund 
was valued at $31M, 
but is funded by 
government 
appropriations.  

Aims to be within 15 
days after 
harvest/claim 
received. 

References: Mahul and Stutley (2008); Diaz-Caneja et al. (2009); EC (2014); Lefebvre et al., (2014); Maccaferri et al., 
(2012); DG Interrnal (2016) 

112 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Denmark 

Denmark  with  only  5,5  million  inhabitants  in  recent  years  experienced  high  costs  on  weather 
related  damages  amounting  to  at  least  35  billion  DKK  (~  7,7  billion  EUR)  since  1999.  In 
particular,  one  cloudburst  in  Copenhagen  on  2  July  2011  made  insurance  companies  pay  for 
90.000 damages at the total cost of 6,2 billion DKK (~ 832 million EUR). The increase in events 
and  damages  has  urged  for  an  advance  risk  management  system  combining  innovative 
mechanisms,  the  participation  of  both  public  and  private  actors  and  their  enhanced 
collaboration.  

Aligned with this, Denmark employs a mixture of compulsory and voluntary insurance. There is 
a tendency to move towards micro rating insurance. After heavy floods in 2010 and 2011 many 
companies  introduced  restrictions  on  coverage  in  basements  and,  in  certain  cases,  technical 
demands, e.g. backflow valves on the sewage system. Further, the industry foresees restrictions 
on  coverage  for  certain  future  events,  unless  the  necessary  climate  adaptation  measures  are 
taken.  

Denmark  has  an  independent  council,  The  Danish  Storm  Council,  established  in  pursuance  of 
the Danish act relating to storm surges and windfall. This intergovernmental body decides based 
on scientific evidence  from  technical experts,  whether  a storm event may be considered as an 
event involving public compensation for damage costs. If a storm is considered a 20-year event 
with a probability of happening every 20 years, the Storm Council may liberate funds based on 
individual request from private estate owners.  

The Storm Council handles cases involving compensation following flooding from waterways and 
lakes  as  well  as  subsidies  for  reforestation  after  windfall.  It  also  supervises  and  considers 
complaints about insurance companies’ processing of storm surge cases. It is appointed by the 
Danish Minister for  Business and  Growth  and consists of an independent chair and eight other 
members.  Its  members  represent  insurance  companies,  citizens,  municipalities  and  ministries. 
The  Storm  Council  covers  damages  through  the  public  storm  surge  scheme.  The  scheme  is 
financed  by  an  annual  taxof  7  EUR  (30+20  DKK  per  year  per  policy)  included  in  the  insured’s 
fire  insurance  policy  (which  is  mandatory  for  property  owners).  In  regards  to  compensation 
payments,  insurance  companies  manage  them  on  behalf  of  the  Storm  Council.  Should  there 
however  be  a  disagreement  between  the  insurance  holder  and  the  insurance  company,  the 
Storm Council may interfere. 

With  regard  to  reforestation  in  relation  to  windfall  in  forests,  criteria  stipulate  that  you  must 
have  taken  out  insurance  against  storm  damage  on  trees,  and  the  insurance  must  cover  the 
entire  area  in  which  your  trees  have  been  overturned.  Your  forest  must  also  be  a  protected 
forest, and the area overturned by the storm must be greater than 1/60 of the total forest area. 

113 

 
 
 
 
 
 
 
 
 
 
Table A2.3 Attribute table for Denmark 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

Private insurers 
Danish Storm Council  

Private insurers 
Danish Storm Council  

N/a 

Hazard(s) covered 

Storm council: Storms (flooding 
from rivers and seas) 

Storm council: 
Storms (flooding 
from water ways and 
seas), windstorms 
affecting forests 

Private insurance: 
water risks; Hail 
(most common) 

Official trigger 
Yes /no 
Description? 

The Danish Storm Council: Yes, 
if the storm is declared to be a 
1/20 year event. 

The Danish Storm 
Council: Yes, if the 
storm is declared to 
be a 1/20 year event. 

No 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Indemnity insurance 
The Danish Storm Council: 
bundled indemnity insurance 

Compulsory vs. 
voluntary 

Private market: voluntary 
The Danish Storm Council: Tied 
with compulsory fire insurance. 

Damage covered 

Direct damage to buildings and 
contents 

Indemnity insurance 
The Danish Storm 
Council: bundled 
indemnity insurance 

Single risk 

Voluntary 

Voluntary 

Direct damage to 
buildings and 
contents 
Ref 

Damage to crops 

Limit of compensation 

Individual policy 
deductibles 

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 
Other 

Market penetration 
rate 

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

The deductible for flooding is 
the minimum of 5% of the loss 
or ~€ 700 (as of 2010) for 
single or two-family homes and 
for personal effects, doubling 
for all the other properties. 

Minimum of 10% of 
the loss or ~€1,400 
for property and 
contents damage. 

Deductibles 

Deductibles 

Deductibles 

>90% of households, for 
storms. 50%-75% for Flood 
insurance.  

80%-85% of 
cultivated area. 

Limited insurance schemes are 
risk based 

Limited insurance 
schemes are risk 
based 

The compensation for coastal 
flooding is protected by a 
limited state grantee (~€27M). 
The remaining risks is covered 
by private reinsurance. 

Access to private 
reinsurance 

Access to private 
reinsurance 

Average annual 
premium 

Average property premium per 
capita in 2015 = ~290 EUR 

114 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Coastal flooding has a cost of 
~€2.70 (a surcharge of ~€1.35 
if the state grantee is used). 

Indirect access for river flooding 
if the household is not insured 
and the flood was sufficiently 
rate.  

Access to public 
disaster response 
fund 

Pay-out speed 

Additional comments 

If the farm was 
insured there can be 
access to subsidies 
for replacement or 
cleaning costs. 

Pay-out is aimed to 
be completed by 
harvest at the latest. 

No real multi-peril 
agricultural 
insurance. 

References: Diaz-Caneja et al. (2009); Lamond and Penning-Rowsell, (2014); Danish Storm Council (2017); Maccaferri et 
al., (2012); Svensk Forsakring (2013); 

115 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
France 

Overall Description 

France  is  characterized  by  a  widely  successful  public-private  sector  partnership  in  providing 
affordable insurance  and  high  penetration  rates  (~100%  for  property  insurance  and  ~50-75% 
for  business  interruption),  due  to  its  focus  on  national  solidarity  and  strong  public  sector 
influence.  Since  198219,  all  households  and  business  who  enter  into  a  contract  of  insurance 
covering  damage  to  property  or  business  are  also  covered  against  the  effects  of  most  major 
disasters (floods, droughts, earthquakes, etc.).  

Weather related disasters are covered though three main insurance schemes: 

(1)  The  CatNat  scheme includes  in  its  scope  the  coverage  of the  majority of extreme  weather 
events (except storm, snow, and hail) for households, businesses and agricultural enterprises. It 
covers  buildings,  as  well  as  their  contents,  vehicles  and  business  interruption,  when  insured 
against  fire.  A  compulsory  extension  to  property  insurance,  CatNat  is  provided  by  private 
insurance  companies  and  funded  through  a  flat  rate  surcharge  (i.e.  12  %  on  building  and 
contents  insurance)  over  existent  policies  against  property  damages  (respectively  business 
interruption).  Insurers  are  free  to  choose  a  reinsurance  scheme  but  the  Caisse  Centrale  de 
Reassurance  (CCR)  benefits  from  the  state  guarantee  which  provides  with  a  monopolistic 
advantage.  CatNat  offers  low-priced  (re)insurance  and  channels  part  of  the  resources  into  a 
state-managed fund for natural risk prevention (see also below). In this scheme, compensations 
for natural disasters have to satisfy two conditions: a natural disaster must be recognized by an 
inter-ministerial  decree  and  the  property  affected  must  be  covered  by  a  “property  damage” 
insurance policy. 

(2)  The  Regime  des  Calamites  Agricoles  (Agricultural  Calamity  scheme)  offers  specific 
compensation for non-insurable bio-assetsof farmers (crops and livestock) through the National 
Fund  for  the  Management  of  Agricultural  Risks.  In  this  scheme,  compensations  for  natural 
disasters  have  to  satisfy  two  conditions:  an  agricultural  calamity  must  be  recognized  by  a 
decree from the ministry of agriculture and the farm business affected must be covered by an 
insurance policy (e.g. fire, crop or livestock insurance). 

(3) The Garantie tempete, grele, neige (TGN) covers risks related to storm, weight of snow on 
roofs  and  hail.  While  the  market  is  free,  insurers  are  under  the  obligation  to  offer  it  as  an 
extension  of  the  property  insurance  (it  is  integrated  in  the  multi-risks  part  of  the  insurance 
cover). 

In  average  since  1989  the  insurance  and  reinsurance  sector  paid  for  1,9  billion  EUR  damages 
per year. In 2010, the storm Xyntia generated 2,7 billion EUR payments, including 50% through 
TGN and 50% CatNat (CCR, 2015). Since the creation of CatNat on 1982, the insurance sector 
has been able  to cover all losses,  except once  in  1999, when the State guarantee provided to 
CCR was used. 

In  order  to  limit  adverse  selection  and  mitigate  for  the  moral  hazard  created  by  flat  rate 
premiums  and  state  guarantee  and  maintain  this  high  level  of  insurance,  the  national 
government,  territories  and  economic  players  put  a  high  emphasis  on  prevention  efforts,  at 
their distinct level of responsibility. 

Strategies for Risk Reduction 

Since  the  creation  of  the  NatCat  compensation  scheme  in  1982,  France  has  applied  a  flat 
premium  for  fire  risks  or  multi-peril  policies  and  compulsory  cover.  Insurers  are  free  to 
determine the policy premium. The flat surcharge is 12% and 6% for motor insurance with own 
damage  cover  (fire  and/or  theft).  The  problem  of  moral  hazard  is  exacerbated  by  the 
compulsory extension of cover which is provided at a flat premium not linked to the level of loss 
prevention  at  the  insured  risk.  In  order  to  avoid  the  problem  of  moral  hazard:  (1)  The 

19 Loi n° 82-600 du 13 juillet 1982 relative à l'indemnisation des victimes de catastrophes naturelles 

116 

 
 
 
 
 
 
 
 
                                                
reinsurance  programmes  with  the  CCR  (guaranteed  by  the  State)  have  evolved  towards  a 
greater sharing of fortunes between Cedant and Reinsurer, (2) The benefit of a direct insurance 
extension against natural disasters (CatNat) is provided subject to a  compulsory deductible for 
an amount set by the State, with no buy-back option (FFSA, 2013)  

Deductibles  can  be  adjusted  according  to  risk  profile;  this  possibility  offered  to  insurance 
companies  is  however  restricted  and  subject  to  centralised  procedures,  through  the  Bureau 
Central  de  Tarification  (BCT)  which  can  set  higher  deductibles.  Nevertheless,  in  reality  this 
prerogative  is  hardly  applied.  In  addition,  Insurers  can  invoke  BCT  in  case  of  non-compliance 
with the PPR(N) (“plan de prevention des risques  (naturels)”) prepared by the  French national 
administration At prefectural level (département) The PPR identifies areas at risk and should be 
followed  up  by  preventive  actions  by  policy  holders  or  competent  local  authorities.  In  case  a 
PPRN has been prescribed by the prefect but is not yet adopted (by prefectoral decree), due to 
instruction  delays),  deductibles  are  significantly  increased  for  relevant  areas  (factor  2  to  4)  at 
the  administrative  recognition  of  a  new  NatCat  event,  according  to  the  record  of  previous 
NatCat events at the same municipalities.  

A  small  part  of  the  CatNat  guarantee  extension  (12%  of  it)  contributes  to  the  Fonds  de 
Prévention des Risques Naturels Prévisibles (FPRNM, so called “Barnier fund”). The fund finances 
preventive  actions  taken  including  PAPI  (Programme  d’Action  de  Prévention  des  Inondations). 
PAPI imply a voluntary collaboration between various territorial actors to implement a complete 
series of structural and non-structural flood risk reduction actions at river basin or coastal level . 
The Barnier fund was initially criticised for not really financing hard prevention measures (such 
as  resilient  infrastructure  investments)  and  for  its  lack  of  transparency,  as  it  was  initially 
designed to finance expropriation only, but it is currently being eligible for many other purposes, 
with a current emphasis on measures at territorial level, rather than at individual level, as long 
as insureds do not perceive a priority in the increase of risk resilience for their assets.  

Another tool for risk reduction is the creation of The Observatoire National des Risques Naturels 
(ONRN) created in 2012 as a partnership between the Ministry of Environment, the CCR and the 
insurance  industry  (via  Mission  Risques  Naturels),  to  monitor  the  exposure  of  assets  and 
territories,  the  loss  records  at  various  scales  including  the  event  one  and  to  evaluate  public 
policies. It aims to contribute to the general increase of risk awareness and culture through the 
generation and dissemination of knowledge and data on natural hazards. 

Overall  the  involvement  of  French  insurance  industry  to  NatCat  risk  management  is  fairly 
integrated,  addressing  together  risk  transfer  (high  penetration),  Disaster  risk  reduction 
financing  resources  collection  and  allocation,  and  disaster  risk  reduction  financing  knowledge 
and data sharing for a better governance. In floods, the risk reduction measures tend more and 
more to compensate for the moral hazard created by flat rate premiums.  

Approaches for increased resilience 

One  way  forward  to  address  moral  hazard  would  be  to  completely  disconnect  the  affordability 
objective  (through  subsidies)  from  the  risk  prevention  objective,  by  the  means  of  avariable 
premium  dependant  to  both  hazard  exposure  and  asset  vulnerability,.  However  this  would 
require a deep reform of the French system, which is not foreseen, at least for the mass risks, 
considering  the  very  low  average  level  of  premium  paid  for  Nat  Cat  (of  an  average  annual 
premium of 200 to 300 €, for property damage multirisk line). One major obstacle to this is for 
course public acceptance for a significant increase of annual premium in exposed areas. Work is 
ongoing in terms of data and data sharing. The Observatoire National des Risques Naturels now 
publishes  insured  loss  and  prevention  data  (at  municipality  grid),  provides  support  to  risk 
reduction  decision  making  and  participative  governance.  Both  CCR  and  FFA  recently  produced 
their modelling-based studies of costs forecast in 2040, respectively 2050 for the main weather 
perils likely to be affected by climate change (CCR, 2015 ; FFA, 2015). Increasing competencies 
are  transferred  from  state  authorities  to  the  local  level.  Increasing  financial  support  has  been 
provided by the so-called Barnier fund to subsidize the State contribution to the  PPR and PAPI 
policies. Moreover, a law passed in January 2014, on the modernization of public action creating 

117 

 
 
 
 
 
 
 
 
a  targeted  and  compulsory  competence  relating  to  the  management  of  aquatic  environments 
and  flood  prevention  assigning  it  to  municipalities  and  their  associations  and  allowing  local 
authorities to raise taxes, and this may ultimately have a further impact on the prioritization of 
risk management investments between territories and ultimately on risk transfer, if the target of 
reducing the average annual cost of losses tends to be hit. 

In France, the involvement of the insurance industry representatives in every step of the public 
disaster risk reduction policy remains a key approach for increasing resilience through enhanced 
collaboration between the public and the private sector.  

118 

 
 
 
 
 
 
 
 
 
 
Table A2.4 Attribute table for France 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

Catastrophes Naturelles 
(CatNat), 1982 Garantie 
TGN (Tempete, Grele, 
Neige) 

Catastrophes Naturelles 
(CatNat), 1982 

Catastrophes Naturelles 
(CatNat), 1982 Garantie 
TGN (Tempete, Grele, 
Neige) 

Hazard(s) covered 

CatNat: All non-insurable 
risks (majority of 
extreme weather events) 
TGN: Storms, hail, 
weight of snow on roofs 

CatNat: All non-
insurable risks (majority 
of extreme weather 
events) 
TGN: Storms, hail, 
weight of snow on roofs 

FNGRA: Farm losses 
(crop damage) by what 
can be considered non-
insurable (all the studied 
disasters except for 
storms (wind and hail)). 

Official trigger 
Yes /no 
Description? 

CatNat: Yes, must be an 
officially declared 
disaster 
TGN: Contractual 
triggering clause 

CatNat: Yes, must be an 
officially declared 
disaster  
TGN: Contractual 
triggering clause 

FNGRA: Yes 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Bundled cover as a 
compulsory extension to 
property damage 
insurance.  

 Bundled cover as a 
compulsory extension to 
property damage and 
business interruption 
insurance.  

Crop yield / livestock 
insurance 

Compulsory vs. 
voluntary 

Compulsory extended 
coverage with property 
insurance.  

Compulsory extended 
coverage with property 
insurance and / or 
business interruption.  

Crop insurance is 
voluntary. However, all 
plots of the same crop 
must be insured. 

Damage covered 

Damage to 
household/buildings 
property and contents. 

Production losses due to 
extreme weather events. 

Damage to property 
buildings and contents, 
separate policy for 
business interruption. 
Applicable to agricultural 
sector. 

Limit of 
compensation 

Policy limit: total insured 
value 

Policy limit: total insured 
value 

Individual policy 
deductibles 

€380 for private 
households. 
Specific higher deductible 
for subsidence (1040 €) 

10 % of property 
damage with a minimum 
of €1,140. 

3 days with a minimum 
of €1,140 for business 
interruption. 

119 

Policy limit: total insured 
value can be set by the 
farmer but the 
indemnification is a fixed 
percentage ranging from 
12% to 35%, though it 
can go higher if several 
disaster events are 
declared in a short 
period. However, the 
more extreme the loss 
the greater the 
indemnification. 

Crop insurance policies 
have a 
deductible/franchise that 
requires the yield loss to 
be at least 30% or larger 
before payments begin. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 
Other 

Deductibles 

Increased deductible in 
case of several recent 
declaration of event in 
the same category of 
hazard at a municipality 
not covered by a PPRN 
prescribed. Stronger 
incentives for risk 
management at the 
community level. 

Increased deductible in 
case of several recent 
declaration of event in 
the same category of 
hazard at a municipality 
not covered by a PPRN 
prescribed Stronger 
incentives for risk 
management at the 
community level. 

Market penetration 
rate 

~100% for property 
insurance.  

~90% for property 
insurance.  
~50-75% for business 
interruption. 

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Flat Premium rate (12% 
surcharge of property 
insurance premium). 

Flat Premium rate (12% 
surcharge of property 
damage and/or Business 
interruption insurance 
premium). 

Between 25%-55% of 
cultivated area for per 
crops, for a total of 
about 30% of cultivated 
area. Field crops are 
most commonly insured, 
while fruits are the least 
insured. 

Crop premium rates 
differ per crop and 
location. However, the 
premiums are subsidized 
up to 65%. The premium 
could be an average of 
€8 EUR per hectare 
across crops 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

Average annual 
premium 

Yes (either separately 
from the CCR scheme or 
as a complement to the 
CCR scheme). 
Reinsurance by the CCR  
with unlimited state 
guarantee. 

Yes (either separately 
from the CCR scheme or 
as a complement to the 
CCR scheme). 
Reinsurance by the CCR 
with unlimited state 
guarantee. 

Yes (either separately 
from the CCR scheme or 
as a complement to the 
CCR scheme). 
Reinsurance by the CCR 
with unlimited state 
guarantee. 

Insurable climate risks 
on the private 
reinsurance market. 

Insurable climate risks 
on the private 
reinsurance market. 

Insurable climate risks 
on the private 
reinsurance market. 

About 30 EUR p.a. but e 
extreme dispersion in 
sizes of insured assets 

In 2013 field crops paid 
an average of €42 per 
hectare, while vegetables 
paid €1,406 per hectare. 

About 20 to 50 EUR in 
case of a private 
individual house(and 
contents) for the CatNat 
extension 

Average property 
premium per capita in 
2015 = ~250 EUR 
(baseline property 
premium = ~220 EUR) 

Access to public 
disaster response 
fund 

No 

No 

If a farm is already 
covered by the FNGRA 
then a very severe 
disaster can be asked to 
be considered as such 
and additional 
compensation or 
additional recovery costs 
may be compensated. 

Pay-out speed 

Less than 3 month after 
claim received or disaster 
declaration.  

Less than 3 month after 
claim received or 
disaster declaration, 
longer delays in specific 

Agricultural insurers 
intend to pay claims 
within 1 month after 
harvest. 

120 

 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

cases of BI claims. 

Additional comments  Deductible can vary : 

- on a case by case 
basis, if multiple claims 
or if measures prescribed 
by PPRN are not put in 
practice by the insured 
and/or the municipality 
- At community level 
depending to the status 
of PPRN 
NB. Building codes are 
tailored according to risk 
zones according to the 
relevant PPRN  

References: UN (2008); Diaz-Caneja et al. (2009); Enjolras and Sentis (2011); Maccaferri et al., (2012); EC (2014); 
Wharton Risk Management and Decision Processes Center (2016); World Bank (2012); insurance Europe consultation; DG 
Internal Policies (2016) 

121 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Germany 

Before the reunification of Germany there was a sharp divide between East and West Germany. 
In  East  Germany  there  was  a  state  run  monopoly  with  fixed  flat  rate  premiums  and  the 
government  as  re-insurer  of  the  last  resort,  the  purpose  of  which  was  the  socialized  losses  in 
order to provide widespread extreme weather insurance for both property and contents. Under 
this  arrangement  prevention  measures  were  irrelevant.  While  in  West  Germany,  there  was  a 
combination  of  statutory  public  bodies,  which  acted  as  monopoly  fire  insurers,  and  private 
insurers.  The  premium  were  permitted  to  be  risk  based  by  the  regulators,  but  both  premium 
calculations  and  the terms  and  conditions  of  such  policies  where  strictly  supervised.  The  strict 
supervision  limited  extreme  weather  insurance  to  only  wind  and  hailstorms.  The  reasoning 
behind the strict supervision was fear of negative selection, preventing certain customers from 
being  able  to  access  insurance  coverage.  However,  there  was  an  exception  in  Baden-
Wurttemberg  that  required  mandatory  extreme  weather  insurance.  Additionally,  unlike  in  East 
Germany the employment of prevention measures was the responsibility of both individuals and 
governments. 

After the German reunification in 1990, the fears of negative selection was no longer viable due 
to  the  large  number  of  polices  enforce  in  East  Germany.  The  presence  of  many  East  German 
policies  allowed  for  the  creation  of  a  sufficient  risk  pool,  and  general  nationwide  extreme 
weather  insurance  was  allowed  in  1991.  In  1994  the  public  monopolies  were  disbanded  and 
transferred to private insurance companies. From 1991 to 2001 the German insurance industry 
was  in  the  initial  stages  of  increasing  their  experience  of  extreme  weather  outside  of  hail  and 
windstorms.  The  industry  and  regulators  agreed  on  the  potential  wording  of  extreme  weather 
insurance policies (“Erweiterte Elementarschadenversicherung”) allowing the market to provide 
insurance for other extreme weather events. 

Currently,  German  insurers  continue  to  provide  windstorm  and  hail  insurance  as  a  part  of  a 
standard  private  building  insurance  policy  (i.e.  that  covering  fire,  escape  of  water  etc). 
However,  coverage  against  more  extreme  weather  events  such  as  floods  is  insured  as  a 
voluntary extension  of  the policy.  The  extension tends to be risk based, with  the possibility of 
higher  excesses,  so  that  the  premium  remains  attractive  for  the  policyholder.  However,  the 
penetration of this extension is moderate because of a mixture of low risk perceptions and ad-
hoc  government  support after  large  scale  disaster events  that  introduce  an  element  of  charity 
hazard into the market. After the major flooding in 2002 the estimated penetration rate of flood 
insurance (for example) was ~15% (of private households) however exposure to several other 
large scale events, combined with attempts to increase risk awareness has led to a penetration 
rate of ~40% of households (~43% including sole covers against torrential rain) in the end of 
2016 – steadily rising. 

122 

 
 
 
 
 
 
 
 
 
 
Table A2.5 Attribute table for Germany 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

Nationwide NatCat Insurance 
from 1991 on (storm an hail for 
many decades) 

Nationwide NatCat 
Insurance from 1991 
on (storm an hail for 
many decades) 

Crop insurance 
against hailstorm is 
the most developed 

Hazard(s) covered 

Flooding (including heavy rains 
and backwater) 
Earthquakes, subsidence, 
landslides 
Snow pressure, avalanches 
Volcanic eruptions, storm, hail 

Mostly Hail, but 
multi-peril crop 
insurance also 
available 

Flooding (including 
heavy rains and 
backwater) 
Earthquakes, 
subsidence, 
landslides 
Snow pressure, 
avalanches 
Volcanic eruptions, 
storm, hail 

Official trigger 
Yes /no 
Description? 

No (solely based on insurance 
terms and conditions) 

No (solely based on 
insurance terms and 
conditions) 

No (solely based on 
insurance terms and 
conditions) 

Private bundled (and single risk) 
indemnity insurance 

Private indemnity 
insurance 

Single risk yield 
insurance 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Compulsory vs. 
voluntary 

Storm and hail damage tend to 
be automatically included in 
standard property insurance. 

Flood insurance and all the 
other natcat perils are a 
voluntary extension. 

Damage covered 

Direct damage to buildings and 
contents 

Limit of compensation  The total insured value 

Voluntary 

Production losses due 
to crop damage. 

Storm and hail 
damage tend to be 
automatically 
included in standard 
property insurance. 

Flood insurance and 
all the other natcat 
perils are a voluntary 
extension. 

Direct damage to 
buildings and 
contents; Business 
interruption 

The total insured 
value 

Individual policy 
deductibles 

Floods: on average 10% of total 
damage. Ranged between €500 
and €5,000 regarding floods. 
Windstorms and hail tends to 
not have a large deductible. 

Windstorms and hail 
tends to not have a 
deductible, while 
flooding related risks 
tends to have 
deductibles. 

8% of total insured 
value for cereal 
crops. Vegetables 
and fruits range 
between 10-25% of 
insured value. 

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 
Other 

Deductibles 
Premium discounts (less 
common than in commercial 
sector) 
Individual preventions measures 

Deductibles 
Premium discounts 
Individual 
preventions 
measures 

Bonus-malus 
deductibles 

Market penetration 
rate 

Coverage for storm and hail is 
over 95%, while for flooding it 
is ~40% of households (for 
building insurance) Regarding 
flood insurance the penetration 
rates are traditionally higher in 
former East Germany as 
compared to West Germany. 

123 

Coverage for storm 
and hail is over 95%, 
while for flooding it is 
~40% of households 
(for building 
insurance). 

In 2005 hail 
insurance 
represented about 
60% of farm land.  

 
 
 
 
 
 
 
 
 
 
 
 
Private reinsurance 
on a quota-share or 
stop-loss basis. 
There are no 
constraints for access 
to the international 
private reinsurance 
market. 

On average 1.2% of 
insured value, about 
€18 per hectare.  

ATTRIBUTE 

SECTOR 

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

Household/Buildings 

Commercial 

Agriculture (crop) 

Risk based (risk zoning is 
employed) 

Risk based 

Risk based 

Private reinsurance  

Private reinsurance 

Average annual 
premium 

Average property premium per 
capita in 2015 = ~225 EUR 

< €100 for approx. 90% of all 
buildings (lowest risk zones), 
further 8% approx. €150-200, 
remaining 2 % highest risk 
zones up to € 700 p.a. on 
average 

Access to public 
disaster response 
fund 

Yes, the German government 
can provide ad-hoc disaster 
compensation after an event. 

Pay-out speed 

By law, insurance industry has 
to pay out significant part of the 
compensation after one month, 
nearly all claims settled after 
one year. 

Additional comments 

General collaboration between 
the German insurance 
association and several other 
bodies in order to develop 
mechanisms that raise risk 
awareness and understanding. 
Therefore, are also attempts to 
expand the understanding the 
policymakers have in regards to 
their own policy.  

Yes, the German 
government can 
provide ad-hoc 
disaster 
compensation after 
an event. 

Yes, the German 
government can 
provide ad-hoc 
disaster 
compensation after 
an event. 

By law, insurance 
industry has to pay 
out significant part of 
the compensation 
after one month, 
nearly all claims 
settled after one 
year. 

Pay-out aims to be 
within 2-3 weeks 
after claim is 
processed, but by 2-
3 weeks after 
harvest at the latest. 

Only limited market 
competition in terms 
of the number of 
companies involved. 

Mainly provided 
through cooperatives 
and farmers 
associations. 

References: Mahul and Stutley (2008); Diaz-Caneja et al. (2009); Schaffnit-Chatterjee (2010); Maccaferri et al., (2012); 
Keskitalo et al. (2014); Wharton Risk Management and Decision Processes Center (2016); Hudson et al. (2017); Insurance 
Europe consultation 

124 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Hungary 

In 2009 the overall size of the premiums collected by the Hungarian insurance market as a ratio 
to GDP was 3.3% which was in line with other central and eastern European countries, though 
smaller than the EU as a whole (which stood at 8.9%). While the percentage of premiums as a 
proportion of GDP is rather low, in terms of coverage against extreme weather events ~75% of 
households with general household insurance have coverage against flooding, storms, hail as a 
standard  element.  In  principle,  the  purchase  of  general  household  insurance  is  voluntary. 
Though,  the  increase  use  of  mortgages  requiring  complete  coverage  has  been  increasing  the 
penetration  rate  over  time.  The  premiums  tend  to  be  independent  of  the  risk  faced  by  a 
particular policyholder as coverage is extended as a flat rate percentage of the general property 
premium, due to competition concerns as well  as the cross-subsidization of  high  and low risks 
that  is  achieved  through  the  widespread  coverage.  There  are  many  insurance  companies  who 
are  unwilling  to  provide  insurance  against  flooding  in  highly  flood  problem  areas,  such  as 
bulidings located in floodway’s (though other extreme weather events are insured against). To 
prevent these households from not gaining insurance the Hungarian government has created a 
compensation fund  (Wesselenyi Miklos Ar-es Belvizvedelmi  Alap) for these  high risk residential 
properties  who  must  pay  into  the  fund  in  order  to  gain  access,  though  the  participation  rate 
remained  small  (only  a  few  hundred  contacts).  However,  since  01/01/2017  no  new  contracts 
will be provided, only those taken out before 01/01/2017 will continue to be inforce until these 
policies expire. 

Crop  insurance  has  been  avaiable  since  1830  in  various  forms.  However,  the  National  subsidy 
for  agricultural  insurances  in  Hungary  was  first  introduced  in  1996,  but  this  form  of 
subsidisation was ended in 2003. A new system was built by 2007/8 with the name of National 
Agriculture damage Compensation (NAR) that covered crop losses from droughts, inland waters 
and  frost  via  compulsory  participation  but  limited  compensation.  In  2012  the  system  was 
reformed into the Complex Agricultural Risk Management System (MKR), which is a partnership 
between  the  NAR  and  private  insurance  companies.  There  are  two  pillars  to  the  MKR:  state 
provided  compensation;  private  insurance  compensation.  Farmers  are  eligible  to  receive 
compensation  from  the  first  pillar  if  they  suffer  a  yield  loss  of  at  least  30%  due  to  extreme 
weather events at the crop level, and if the sum of crop losses were at least 15% compared to 
the 3 previous years at the farm level. Farmers can claim for losses from droughts, floods, hail, 
storms  and  frost.  The  state  provided  coverage  has  a  penetration  rate  of  about  80%  of  total 
arable land. The second pillar of the scheme is private insurance coverage, which is a voluntary 
choice of individual farmers and subsidised by the state. The basic loss threshold is 30% for hail 
and  storms,  while  floods  and  droughts  stand  at  50%by  this  type  of  insurance.  The  voluntary 
extension  can  provide  coverage  for  a  greater  range  of  crops  and  the  specific  perils  that  the 
farmer wishes to insure. The penetration rate of this private but subsidised insurance is smaller 
at ~50% of total arable land. The penetration of non-subsidised insurances is decreasing, but it 
reaches  20%  of  total  arable  land  nowadays.  However,  it  must  be  noted  that  under  the  MKR 
system  the  total  compensation  for  a  loss  is  split  between  the  NAR  and  private  insurers.  The 
farmer receives first from insurance a payment, than the remained loss is compensated by the 
NAR. However, a farmer that does not buy private insurance can only be compensated for 50% 
of the loss suffered. So the farmers are motivated by the state to buy insurances. 

Table A2.6 Attribute table for Hungary 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

N/a 

N/a 

Agricultural Risk 
Management System : 
Pillar 1 (government 
calamity fund); Pillar 2 
(private insurance 
coverage) 

125 

 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Hazard(s) covered 

Cloudburst; Floods; Windstorms, 
Hail 

Floods; Storms; 
Other natural 
disasters 

Hail; Frost; Storm; 
Drought; Flood 

Official trigger 
Yes /no 
Description? 

No 

No 

No 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Bundled (and single risk) indemnity 
insurance combined with property 
insurance 

Bundled with all-
risk property 
policies 

Single risk yield 
insurance (pillar 1) 

Compulsory vs. 
voluntary 

Voluntary (required for mortgages)  Voluntary 

(required for 
mortgages) 

Combined risk yield 
insurance (pillar 1 and 
pillar 2)  

Private insurance 
coverage is voluntary.  

However, the Pillar 1 is 
compulsory if the farm 
is over 10 hectares. 

Damage covered 

Direct damage to buildings and 
contents based on replacement 
cost or repair values. 

Direct damage 
to buildings and 
contents. 

Damage to crops 

Limit of 
compensation 

Total insured value based on 
property prices (in 1990). Limit for 
the sum insured tends to be around 
~€96,000 

Individual policy 
deductibles 

Often there are no deductibles as it 
forms on optional part of the 
policy. The average deductible 
ranges from between 60 EUR to 
160 EUR per claim. 

Total insured 
value. Limit 
tends to be 
around 
~€380,000 

None that can 
be directly 
observed. 

Pillar 1: 50%-80% of 
loss 
Pillar 2: the remainder  

There are different 
damage thresholds per 
extreme weather event 
and crop type - 
average about 10%. 

Deductibles 

Deductibles 

Deductibles  

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 
Other 

Market penetration 
rate 

70%-75% of households 

Premium Setting 
rule, e.g. risk based, 
flat, fixed percentage 
of insured value? 

Rates more driven by competitive 
pressures rather than technical 
requirements. 

 About 50% of insured 
arable land in the 
private insurance pillar, 
 ~80% of arable land 
for the governmental 
pillar 

The private insurance 
element has free 
pricing with some 
government subsidies. 

 All risk premiums are 
set at a regional level, 
while risks are set at 
the national level in 
Pillar 1. (currently 
undergoing changes) 

Reinsurance access: 

Private reinsurance, generally stop-

Access to private  Private reinsurance 

126 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Private reinsurance 
Public reinsurance 

loss reinsurance for events with a 
return period of between 1/200-
1/250 or smaller. 

reinsurance.  

that is quite varied 
across insurers. 

Average annual 
premium 

 Average property premium per 
capita in 2015 was ~50 EUR per 
capita for buildings, ~100 EUR per 
year for combined 
building/contents insurance. 

Average premium rate (for 
buildings insurance) for an all-
perils-policy is 1.06 per mile. 

Access to public 
disaster response 
fund 

Pay-out speed 

The Wesselenyi Miklos 
compensation fund for floods and 
in-land waters protection. This fund 
provides government insurance in 
very high risk areas where 
insurance is not provided. They 
must pay a contribution to access 
this fund. Compensation is based 
on indemnity. Compensation limit 
is ~€83,000. However, since 
01/01/2017 the fund does not 
provide new contracts. 

Additional comments  Rapid expansion in recent years 

has been due to the expansion of 
mortgage backed buildings. 

Households in high risk areas might 
be excluded from coverage. Truly 
uninsurable households have 
recourse to a state disaster fund 
(Wesselenyi Miklos Ar-es 
Belvizvedelmi).  

Average of 1.1% 
of insured value 

(median value) is 4.3% 
of insured value in 
2007  

Private insurance 
coverage was €2,200 
on average between 
2012 and 2015. 

Access to government 
calamity fund on 
average, between 2012 
and 2015, was about 
€180. It means 3,2 
€/ha for arable land 
and 9,7€/ha for 
plantations and 
vegetables. 

The government 
calamity fund is in 
place that acts as the 
first pillar of the 
scheme. 

Pay-out aims to be 
within 30 days after an 
event or claim in Pillar 
2. Compensations are 
paid within next year’s 
March in Pillar 1. 

Limited competition in 
terms of the number of 
insurance companies 
offering insurance.  

References: Mahul and Stutley (2008); Diaz-Caneja et al. (2009); Pollner (2012); OECD (2015); Kemeny et al., (2013); 
Kemeny et al. (2014); Wharton Risk Management and Decision Processes Center (2016); Maccaferri et al., (2012); 

127 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Italy 

In  Italy  the  general  property  insurance  market  is  dominated  by  four  firms  (Generali,  Unipol 
group, Allianz group and Reale Mutua) that between them cover 70% of the market. Though as 
natural  disaster  coverage  is  a  voluntary  extension  of  homeowners  insurance,  when  combined 
with low risk perception and ad-hoc compensation, the overall penetration rate is quite low. The 
majority of policies cover losses such as storms and hail, while flood insurance policies are less 
common.  However,  for  a  flood  in  2014  in  Liguria  and  Emilia  Romagna  caused  320mil  EUR  in 
damage,  of  which  140mil  EUR  was  compensated.  Geographically,  2016  insurance  exposure  to 
natural  disaster  risk  both  for  businesses  and  for  dwellings  is  concentrated  (some  65%  of  the 
total)  in  the  North  of  Italy.  There  is  significant  exposure  in  the  regions  of  the  Center  as  well, 
however  –  about  25%  for  residential  housing.  Therefore,  while  the  literature  considers  the 
insurance  penetration  rate  low  overall,  there  are  occasions  when  there  is  a  relatively  high 
degree  of  compensation.  A  second  example  is  that  on  08/07/2015  the  town  of  Dolo  in  the 
Venice  metropolitan  area  was  struck  by  a  tornado,  causing  25mil  EUR  in  damage,  of  which 
10mil EUR was covered by private insurance. However, on the whole the offer of specific policies 
for flood risk is held back by a series of problems – for instance the risk of adverse selection and 
difficulty  in  setting  premiums.  In  Italy  agricultural  risk  management  has  been  coordinated  by 
the National Solidarity Fund (managed by the Ministry of Agriculture)  since 1970. The purpose 
of  this  body  is  to  support  agriculture  financial  loss  prevention.  The  National  Solidarity  Fund 
(NSF) has both ex post and ex ante  roles.  The  ex  post role is to provide direct compensation, 
while  its  ex  ante  role  is  to  support  insurance  coverage  or  provide  support  for  risk  reduction 
(after  2004  this  subsidy  has  become  more  decentralised,  an  example  is  the  use  of  anti-hail 
nets). The NSF can contribute up to 80% of the estimated insurance premium as a subsidy. The 
NSF  draws  a  distinction  between  insurable  risks  and  non-insurable  risks,  those  which  are 
insurable  cannot  receive  ex-post  compensation.  Subsidies  can  be  provided  for  single  risk, 
combined risk, or multiple peril crop insurances, though ~88% of the insured value is through 
combined risk policy with the remaining 12% as multiple perils (in 2015). A farmer must insure 
all  of  their  production  within  a  municipality  for  at  least  a  30%  production  loss.  The  market  is 
free  to  determine  its  own  behaviour,  though  the  ministry  of  agriculture  decides  what  can  be 
considered  insurable  as  well  as  provide  price  guides  on  certain  risk  reduction  measures  (e.g. 
hail  nets).  In  2007 the  Italian  co-reinsurance  pool was  created  to  support  the  development  of 
new  insurance  products  in  Italy.  The  pool  divides  the  risks  from  these  new  policies  between 
members, as well as a single entity that can be reinsured on the international market as well as 
comparing outcomes to similar products both domestic and abroad. The pool has a capacity of 
~190mil  EUR  consisting  of  both  insurance  and  reinsurance  companies.  The  pool  avoids 
competing  with  pre-existing  insurance  by  only  focusing  on  new  products.  Less  than  20%  of 
farmers are insured against extreme weather events (mostly located in the North of Italy), while 
~80%  of  farmers  are  active  on  credit  markets.  The  premiums  collected  in  the  agricultural 
market as a percentage of the insured value is ~5.4%. 

128 

 
 
 
 
 
 
 
 
 
 
Table A2.7 Attribute table for Italy 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

N/a 

N/a 

Hazard(s) covered 

Flooding; Storms 

Flooding; Storms 

National 
Solidarity Fund 
(thereafter: FSN)  

Private insurance 

Co-insurance pool 
(ISMEA) 

Hail; Storms; Frost; 
Floods; Excess Rain; 
Drought 

The NSF only covers 
what the government 
has published to be 
non-insurable risks, 
the remaining risks 
are insured on the 
private market.  

New insurance 
products can be 
supported via the co-
insurance pool. 

No 

No 

No 

Official trigger 
Yes /no 
Description? 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Compulsory vs. 
voluntary 

Private indemnity insurance 

Private indemnity 
insurance 

Voluntary 

Voluntary 

Single or combined 
risk indemnity 
insurance. Limited 
yield insurance.  

Voluntary (must 
insure whole farm 
cropland) 

Damage to crops 

Damage covered 

Direct damage to buildings and 
contents 

Direct damage to 
buildings and 
contents 

Limit of compensation  As for the indemnity limits, less 

Total insured value 

than the amount of the 
rebuilding value is declared in 
the fire-coverage section of the 
policy. These limits, fixed by the 
companies or chosen by the 
clients with a premium that 
changes proportionally, 
fluctuate between a minimum of 
50% and a maximum of 70% in 
case of housings. . 

Individual policy 
deductibles 

The last point that deserves to 
be emphasized is the application 
of deductibles. They range from 
5% to 20% of the value insured  

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 

Systematic presence of 
deductibles. 

Systematic presence 
of deductibles. 

Systematic presence 
of deductibles. 

129 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Low (higher in the 
north as compared to 
the south). Few 
farms carry 
insurance for more 
than two consecutive 
years. 

Theshare of insured 
value on total crop 
production has not 
exceeded 20% (on 
average 
14% of value and 
9% of surface area in 
total production for 
the period 2001–
2012, 
see Capitanio et al., 
2011, 2013) 

less than 20% of 
farmers seem to 
have insurance.  

Premiums are 
subsidized (as long 
as the insurance 
covers losses in 
excess of 30% of 
historical annual 
production) by up to 
80% (for combined 
perils). Average 
subsidy is 40% 

Access to private 
reinsurance for the 
insurance industry 
The FSN is backed by 
the ministry of 
agriculture. 
ISMEA has a capacity 
of ~€190M. 

Average premiums 
range from between 
6.1% of insured 
value to 11.4% of 
insured value. The 
premium rate differs 
across crops. 

Other 

Market penetration 
rate 

<10%. About 50%%of overall 
national fire premiums come 
from personal lines and of this 
only 2% includes the natural 
catastrophe extension, t 

Coverage against 
NatCat can be an 
extension of fire 
policies. The 
extension is quite 
common in 
commercial policies 

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Very few policies are linked to 
the building typology 
(earthquake-proof, traditional, 
masonry), while the others 
don’t regard the building 
structural performance limiting 
the degree to which premiums 
are risk based 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

Access to private reinsurance 

Access to private 
reinsurance 

Average annual 
premium 

Average property premium per 
capita in 2015 = ~80 EUR 

Pay-out speed 

The time accident statement is 
required by the homeowners 
within a time span ranging 
between three and ten days. 
The damages can be assessed 
by private contracts or experts 
appointed by the parties, and 
the payments are scheduled to 
be liquidated within 30 days 
from the agreement between 
the parties or the administrative 
check of the coverage. 

130 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Additional comments 

However, the householders can 
obtain an advance payment on 
damage claim amounting to a 
maximum of 50% of the 
expected indemnity. 

SIGRA - Flood insurance and 
risk man scheme; supported by 
ad-hoc governmental 
Assistance. 

Insurance companies are 
obliged to set aside reserves for 
NatCat, Such reserves are tax-
deductibles. 
The FSN can provide ex-post 
payments for risks deemed 
uninsurable (must be 
recognized as a disaster by 
central government). 

Ex-post interventions lead to 
underestimation of the risks 
potentially faced. 

Insurance companies 
are obliged to set 
aside reserves for 
NatCat, Such 
reserves are tax-
deductibles. 

Insurance companies 
are obliged to set 
aside reserves for 
NatCat, Such 
reserves are tax-
deductibles. 

The FSN can provide 
ex-post payments for 
risks deemed 
uninsurable, crop 
risks are deemed 
insurable. 

References: Diaz-Caneja et al. (2009); Maccaferri et al., (2012); Di Falco et al., (2014); Landini, (2015); Menapace et al. 
(2016); Santeramo et al., (2016); 

131 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Poland 

Due to the growing use of mortgages for new buildings the insurance penetration rate reaches 
~63%  (with  policies  generally  covering  a  bundle  of  extreme  weather  events),  even  if  the 
purchase of disaster insurance is based upon the idea of voluntary purchase (though the size of 
coverage per policy may be limited). 

Compulsory  insurance  is,  however,  the  case  in  agricultural  farms  whereby  a  comprehensive 
package of insurance (against a range of extreme weather events) for farm buildings in addition 
to  crops  of  the  farmer  wishes  access  to  other  avenues  of  governmental  support.  This  crop 
insurance  covers  risks  to  a  wide  range  of  crop  types  to  damage  that  was  caused  by  flooding, 
hail, droughts and frosts. However, despite the legal obligation to buy insurance only ~10 % of 
farms are insured (~24% of insurable area) 

Despite the growing insurance coverage the state can provide ad-hoc compensation to eliminate 
the effects of extreme weather events in terms of repairing damaged infrastructure and homes 
as a type of social assistance to satisfy the basic needs of living. For example, in 2010 a lump 
sum of 6000 PLN (~1500 EUR) was paid to  those affected by a major flood. In Poland all who 
are affected by a natural disaster event have the possibility of compensation regardless of their 
insurance coverage situation. However, the current Polish risk management is characterized by 
a high degree of uncertainty as victims cannot rely on sufficient governmental compensation or 
insurance indemnities to finance a full recovery. 

132 

 
 
 
 
 
 
 
 
 
 
 
Table A2.8 Attribute table for Poland 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

N/a 

N/a 

N/a 

Hazard(s) covered 

Windstorms; Floods; Hail 

Windstorms; Floods; 
Hail 

Hail; Flood; Droughts 

No 

No 

No 

Official trigger 
Yes /no 
Description? 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Bundled indemnity insurance  

Indemnity insurance 

Compulsory vs. 
voluntary 

Voluntary (required for 
mortgages) 

Voluntary 

Single risk crop 
insurance 

Combined risk crop 
insurance 

Compulsory for 
farmers, insuring at 
least 50% of crops is 
required to gain 
access to subsidies.  

Damage covered 

Damage to buildings and 
contents 

Damage to buildings 
and contents 

Damage to crops 

Limit of compensation  The upper limit for building 

contents is ~€3,500, while for 
building insurance the limit is 
~€30,000.  

Max 2% of insured value. 

Deductibles 

Individual policy 
deductibles 

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 
Other 

On average 10% of 
the sum insured 

Deductibles 

Market penetration 
rate 

Around 40-60% of households 
for floods. 

Around 90% for windstorms. 

Very low (foreign 
companies better 
insured than 
domestic) 

About 24% to 30% 
of arable land (~10% 
of farms) 

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Not risk based. Premiums tend 
to be driven by competition 
rather than technical 
requirements.  

Premiums are 
subsidized by up to 
65%. 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

Access to private reinsurance, 
generally for events that have 
return periods of at least 1/200-
1/250.  

133 

Private reinsurance 
of both stop-loss and 
quota. 

Single risk (i.e. hail) 
are found to be 
easily reinsured. 
Multi-peril insurance 
faces moderate 
constraints. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Average annual 
premium 

Access to public 
disaster response 
fund 

Pay-out speed 

The average insurance premium 
per capital is ~23-30 EUR which 
contains the natural disaster 
extension.  

 Historically, flood coverage has 
been 20% of the basic rate for 
fire insurance. 

Ad-hoc informal compensation 
is considered part of social 
assistance. In 2010 those 
affected by a flood received 
~€1,500. 

Government provides 
reinsurance 
regarding drought 
risks. 

Average premium of 
about €820 per year 
(6% of insured 
value) across all 
policies.  

Ad-hoc informal 
compensation is 
considered part of 
social assistance. 

Ad-hoc informal 
compensation is 
considered part of 
social assistance. 

Pay-out aims to be 
within 30 days after 
loss has been 
reported. 

Farmers can insure 
part of their field, but 
at least 50% must be 
insured for at least 
one of the extreme 
weather events 
mentioned. 

Additional comments 

High coverage rate due to link 
with mortgages. 

Government bodies are 
considering the introduction of a 
law to make natural disaster 
insurance obligatory. 

Monitoring is poor 
and thus insurance 
penetration rates are 
relatively low. 

References: Mahul and Stutley (2008); Diaz-Caneja et al. (2009); Lamon and Penning-Rowsell, (2014); Pollner, (2012); 
Soliwoda, (2016); Wharton Risk Management and Decision Processes Center (2016); PIU (2017); Maccaferri et al., (2012) 

134 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Romania 

Romania  is  troubled  by  several  natural  perils,  including  earthquake,  flood  and  landslides.  To 
provide  cover  against  these  to  owners  of  dwellings,  the  government  has  set  up  a  compulsory 
insurance  scheme  in  2007.  The  scheme  called  ”PRAC”  after  its  Romanian  initials  (Romanian 
Program  for  Catastrophe  insurance)  is  built  on  principles  similar  to  several  other  national 
catastrophe insurance schemes, but with special features of its own including an important role 
for insurance industry and local authorities strongly involved in ensuring compliance.  

On  4th  November  2008  the  Parliament  of  Romania  voted  the  law  260,  on  compulsory  home 
insurance  against earthquakes, landslides and floods.  This law regulates the conditions for  the 
compulsory  insurance  owned  by  natural  or  legal  persons,  the  relations  between  the  insured 
person  and  the  insurer  and  the  rights  and  obligations  of  each  party  to  the  compulsory  home 
insurance  contract, the  establishment, tasks, organization and  operation of the Insurance  Pool 
against  Natural  Disasters  (PAID)  and  the  provisions  of  PAD  (Insurance  policy  against  natural 
disasters). 

All the stakeholders interviewed  agreed that the most relevant criteria of using insurance  as a 
tool  for  managing  extreme  weather  risks  is  the  ability  of  the  insurance  sector  to  absorb  large 
losses. Thus a solid insurance system should provide the necessary measures in order to cover 
the most unfavourable situations which may occur at a time. 

Secondly the ability to deliver fast and secure payments after a natural disaster is determined 
by  the  time  of  action  and  material  resources  available  of  the  insurance  companies,  which  are 
key factors in the process of restoring the previous situation. 

An  important  attribute  in  risk  management  insurances  is  the  insurance  penetration  rate. 
Therefore,  the  higher  the  number  of  insured  persons,  the  greater  is  the  capacity  of  the 
communities  to  restore  the  initial  conditions.  According  to  official  data  received  from  PAID 
Romania, the penetration rate at 31.12.2016 was approximately 20%. 

This low penetration rate can pe explained through: 

 

 

 

From  2009  (year  of  PAID  establishment)  up  to  2015,  PAD  was  compulsory  unless 
homeowners has a facultative insurance.  
in Romania, is a problem that is as much cultural model and the revenue model of the 
consumer 
public  awareness  system  is  not  active  enoughthere  are  not  provided  penalties  for 
owners who do not have insurance policy PAD 

The  best  known  insurance  scheme  in  Romania  is  PAD,  the  policy  of  insurance  against  natural 
disasters  (a  compulsory  insurance  policy  which  covers  damage  from  floods,  earthquakes  or 
landslides  on  dwellings)  and  it  was  introduced  in  2008  by  the  law  260.  Regarding  the 
affordability and availability of the insurance policy, PAID considers that PAD scheme has a low 
performance,  because  the  policy  is  mandatory  and  the  affordability  may  be  a  problem  for  the 
poor families.  The ability to provide quick  and  certain compensation payments after a disaster 
event is subject to possible difficulties in providing property documents.  

Romania is troubled by several extreme weather events such as droughts, fluvial flooding, hail, 
heatwaves, pluvial flooding and also windstorms. However, PAD insurance only covers damages 
caused by natural disaster (defined by the law 260/2008: earthquakes, landslide and flood, as 
natural phenomena) on dwellings.  

As for the agriculture affected especially by droughts, starting 2016 the insurance company Die 
Österreichische  Hagelversicherung  România  brought  to  Romania  an  insurance  scheme  for  the 
farmers.  For  now  the  penetration  rate  is  very  low  considering  the  entrance  on  Romanian 
insurance market. 

135 

 
 
 
 
 
 
 
 
Table A2.9 Attribute table for Romania 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Household/Buildings 

Commercial (e.g. 
transport, energy) 

Agriculture 

Name, year of 
establishment 

PAID, 2009 

Hazard(s) covered 

Floods are insured by PAID, and 
storms (wind and hail) can be 
insured separately from private 
insurers. 

Official trigger 
Yes /no 
Description? 

No 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Bundled indemnity insurance 

Compulsory vs. 
voluntary 

Damage covered 

Compulsory for homeowners to 
have a PAID policy, insurance 
companies will not provide 
voluntary extensions if the 
household does not have this 
coverage). 

PAID: Direct damage to buildings 
and contents. Neither the 
adjoining parts, appurtenances, 
facilities nor the utilities which are 
not structurally attached to the 
building where the dwelling is 
located or the goods inside the 
dwelling, shall be covered by this 
policy insurance. 

Storms: direct damage to 
buildings 

N/a 

Spring frost; Heavy 
rains; Storms; Hail; 
Droughts 

No 

Single risk crop 
insurance 

Combined risk crop 
insurance 

Voluntary 

Damage to crops 
(e.g. wheat, corn, 
barley, sunflowers, 
grape vines) 

Limit of compensation  There are two potential limits: 

The insured value 

€20,000 for dwellings with 
reinforced concrete frames, 
metal, or with outside walls made 
of burnt brick, or wood 
€10,000 for dwellings with 
outside walls made of unburnt 
bricks or other forms of adobe. 

Individual policy 
deductibles 

None that can be identified  

None that can be identified  

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 
Other 

136 

An average of 10% 
insured value (15% 
for fruit trees) 

A bonus-malus 
system, deductibles 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Market penetration 
rate 

PAID: 20% (high regional 
variance, between 9%-31%) 

Storms: given that insurers will 
not insure households who do not 
hold a policy issued by PAID, the 
penetration rate is at most 20% 
(following the same regional 
pattern as PAID) 

PAID: Flat fixed rates 

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

PAID: Risk is placed within a 
national pool (of 12 stakeholders, 
each with a share less than 15%) 
that can be reinsured on private 
markets (excess of loss). The 
state acts as a lender of last 
resort to the program in the case 
of overwhelming losses. In 2014-
2015, total insurance capacity 
was valued at €450M , with 
retention of €1M matching with 
annual aggregate deductible. 

Privately insured risks: private 
reinsurance 

Average annual 
premium 

Average property premium per 
capita = ~10 EUR on 2015 

Access to public 
disaster response 
fund 

Pay-out speed 

Additional comments 

PAID: There are two potential 
policies: 
€20 per year for €20,000 
coverage 
€10 per year for €10,000 
coverage 

None due to the presence of the 
governmental pool. 

Pay-out aims to be within 60 days 
after receiving the claim. 

Additionally, the pool covers 
earthquake risk. Private insurers 
act as the distribution channel for 
PAID, they have a commission 
rate of 20%.  

137 

Varies across crops. 
On average in 2005 
it was ~11% of 
cultivated area in 
2005. By 2009 it had 
increased to ~34% 
of cultivated land. 

A degree of risk 
based pricing as 
rates differed 
dependent on 
location and crop 
type. However, other 
companies insure at 
a flat rate per 
hectare.  

However, in 2005 
there was a 50% 
subsidy for crop 
insurance premiums. 

Access to private 
reinsurance (quota-
share treaties).  

An average rate of 
2% of the insured 
value (averaged 
across crops). About 
€18 per hectare 

Pay-out aims to be 
within 15-30 days 
after a claim. 

Parts of a field can 
be insured. 

Droughts can be 
insured with an 
additional surcharge 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

References: Mahul and Stutley (2008); Badea (2008); CCS (2008); Diaz-Caneja et al. (2009); Radu-Neacsu (2014); 
Dragos and Mare (2014); Wharton Risk Management and Decision Processes Center (2016); Maccaferri et al., (2012); 

since 2016 due to an 
Austria insurance 
company entering 
the market. 

138 

 
 
 
 
 
 
 
 
 
 
 
 
Spain 

Insurance  cover to weather  and  climate-related  risks is provided in Spain making  a distinction 
between  residential,  commercial,  industrial  properties,  equipment  and  infrastructures,  and 
personal  injury,  which  are  covered  by  the  so-called  Extraordinary  Risks  Scheme,  and 
agricultural productions, covered by the Combined Agricultural Insurance. 

The Extraordinary Risks Scheme relies on the principle of compulsory extension in most policies 
for property damages, personal injury and business interruption. At the moment of underwriting 
the  policy  this  extension  has  to  be  included  and,  unless  stated  that  any  or  all  of  the 
extraordinary  risks  (hydrometeorological,  geophysical  and  man-made)  are  covered  by  the 
private  insurer,  automatically  the  Consorcio  de  Compensación  de  Seguros  (CCS)  takes  the 
coverage  and  indemnifies  directly  the  policyholders  in  case  of  a  claim  caused  by  any  of  the 
extraordinary  risks.  For  that,  there  is  a  surcharge  calculated  after  the  amount  insured, 
depending  on  the  line  of  insurance  and  the  kind  of  exposure  that  applies  at  the  moment  of 
underwriting  and  is  included  in  the  total  premium.  Private  insurers  transfer  this  surcharge  to 
CCS,  retaining  a  5%  commission  for  themselves.  The  cornerstones  of  the  Extraordinary  Risk 
Scheme  are  the  principle  of  compensation,  being  all  hazards  covered  across  Spain  without 
geographical distinction and the principle of private-public collaboration. 

CCS  is  a  state-owned  enterprise  established  permanently  in  1954  (it  was  created  as  a 
provisional  tool  in  1941)  that  performs  many  functions  at  the  service  of  the  insurance  sector. 
CCS has, nevertheless, its own legal nature and assets and receives no funding from the public 
budget,  being  surcharges  and  investment  returns,  after  taxes,  its  only  income.  CCS  Board  of 
Administration  is  drawn  equally  from  government  officials  and  private  insurers.  Under  the 
Extraordinary Risks Scheme are covered floods (riverine, pluvial and coastal) and strong winds 
exceeding  120  km/h.  The  average  surcharge  per  policy  issued  in  2016  is  ~14  EUR,  which  a 
mean  insured  value  of  ~210,000  EUR.  CCS  has  had  an  average  combined  ratio20  of  56%  in 
1981-2016.  The  surpluses  are  used  to  create  an  equalization  reserve  specifically  for  this 
purpose  that  currently  has  a  capacity  of  ~EUR8bn.  All  other  hydrometeorological  risks  (hail, 
direct damage from rain or snowfall, landslides, winds <120 km/h) are assumed directly by the 
private insurance companies. 

The  Combined  Agricultural  Insurance  was  created  in  1980  and  also  involves  a  public-private 
partnership.  Agroseguro  is  the  manager  of  a  coinsurance  pool  of  23  companies  (in  2017) 
including CCS. Private companies can choose to integrate into the pool or not, as long as they 
are  qualified  for  that  purpose  by  the  Spanish  Insurance  Supervisor  Authority.  Additionally, 
public  bodies  are  also  involved  in  the  Administration  Board  of  Agroseguro.  The  Combined 
Agricultural  Insurance  provides  coverage  against  damage  to  crops,  livestock,  aquaculture  and 
forests. Unlike with the Extraordinary Risk Scheme managed by CCS, crop insurance is, in most 
cases,  voluntary.  The  Government  participates  actively  in  this  branch  of  insurance  by 
supporting, monitoring, and promoting the scheme, which, since its inception, has served as an 
instrument  for  managing  the  agricultural  sector  and  stabilizing  farm  income.  The  scheme  is 
subsidized by the Central State Administration under the National Agricultural Insurance Agency 
a subsidiary body of the Ministry of Agriculture and Fishing, Food, and the Environment and by 
some regional governments. 

20 Combined ratio=(operating expenses + claims paid)/Written premiums 

139 

 
 
 
 
 
 
 
 
 
 
                                                
Table A2.10 Attribute table for Spain 

ATTRIBUTE 

SECTOR 

Name, year of 
establishment 

Hazard(s) covered 

Household/Buildings 

Commercial 

Agriculture (crop) 

CCS (Consorcio de 
Compensación de 
Seguros), 1954 

All extreme weather 
events that can be 
considered 
extraordinary: flood 
(pluvial, fluvial and 
coastal) and extreme 
winds (>120kph) 

CCS, 1954 

Agroseguro, 1980 

All extreme weather 
events that can be 
considered 
extraordinary: flood 
(pluvial, fluvial and 
coastal) and extreme 
winds (>120kph). 

Hail, flood, windstorm, 
drought, heatwave and 
other weather or 
climate-related risks: 
some pests and diseases 

Non extraordinary 
weather events are: 
hail, snow weight, land 
mass movements and 
non-extreme winds 
(<120kph) 

Non extraordinary 
weather events are: 
hail, snow weight, land 
mass movements, and 
non-extreme winds 
(<120kph) 

No 

No 

No 

Official trigger 
Yes /no 
Description? 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Bundled combined 
indemnity insurance.  
For wind, trigger is wind 
speed >120 kph 

Bundled combined 
indemnity insurance. 
For wind, trigger is 
wind speed >120 kph 

Yield insurance, 
combined risk. 
Index insurances (for 
pasture and apiculture) 

Compulsory vs. 
voluntary 

The extraordinary risk 
coverage is compulsorily 
attached to insurance 
policies covering a 
household or building, 
although it is not legally 
compulsory to insure 
residential properties 
(unless other 
requirements, such as 
those from mortgage 
banks) 

The extraordinary risk 
coverage is 
compulsorily attached 
to insurance policies 
covering commercial 
property or when 
contracting business 
interruption insurance, 
although it is not 
legally compulsory to 
underwrite such 
insurance policies. 

All agricultural insurance 
is Voluntary (except for 
banana and tomato 
produced in Canary 
Islands) 

Damage covered 

Direct damage to 
buildings and contents. 
Some extra costs are 
also covered: 
demolition, mud 
removal, etc. 

Direct damage to 
buildings and contents. 
Some extra costs are 
also covered: 
demolition, mud 
removal, etc. 
Business interruption 

Crop damage 
Livestock 
Forestry 
Aquaculture 

Limit of 
compensation 

Those of the original 
policy underwritten with 
the private insurer. 

Those of the original 
policy underwritten 
with the private 
insurer. 

Sum insured 

Individual policy 
deductibles 

None 

7% of claim for 
commercial properties. 
For business 
interruption, the 
deductibles applied to 
the original policy 
apply. 

Differs per modality and 
insurance line, but 
averages between 10%-
30% of the total insured 
value.  

Risk Reduction 

Risk reduction is the 

Risk reduction is the 

There are certain 

140 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Incentive based on: 
Premiums 
Deductibles 
Other 

responsibility of the 
state. Nevertheless CCS 
invests on raising 
awareness. 

responsibility of the 
state. Nevertheless 
CCS invests on raising 
awareness. 

minimal requirements to 
be insured, established 
by the Ministry of 
Agriculture. There is also 
a bonus-malus system. 
Finally, there are 
discounts for preventive 
measures (hail, frost 
and others). 

Market penetration 
rate 

75%-100% 

Premium Setting 
rule, e.g. risk based, 
flat, fixed 
percentage of 
insured value? 

Flat rate premiums. 0.08 
per mille of the insured 
value 

75%-100% (for 
property and contents 
insurance) 

27% of Agricultural 
production was insured 
in 2015, varying greatly 
among crop types. 

Between 2000 and 2015 
premiums averaged 
4.9%of total insured 
value. 53% of the 
premium is subsidized. 

There was an element of 
risk zoning employed. 

Flat rate premiums of 
0.12 per mille of the 
insured value in offices 
and shops, 0.21 per 
mille for industrial 
risks. 

For business 
interruption for a 
surcharge of 0.25 per 
mille of the insured 
value is applied. 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

CCS does not currently 
transfer its risk to the 
private reinsurance 
market (although it 
could if deemed 
necessary) 

CCS does not currently 
transfer its risk to the 
private reinsurance 
market (although it 
could if deemed 
necessary). 

CCS has an equalization 
reserve for these 
purposes. 

CCS has an 
equalization reserve for 
these purposes. 

Average annual 
premium 

Pay-out speed 

Average property 
premium per capita in 
2015 = ~150 EUR 

Mean of 13.78€ 
surcharge for residential 
property (2015); mean 
insured value 172,363€. 

Mean of 34.46€ for 
commercial property 
(2015); mean insured 
value ~200,000€. 
Mean of 414€ for 
industries; mean 
insured value ~€2 
million. 

The target is to pay 
85%-90% of claims 
within 60 days after a 
catastrophic event. 

The target is to pay 
85%-90% of claims 
within 60 days after a 
catastrophic event. 

Additional comments  The Extraordinary Risk Scheme managed by CCS 

provides also coverage against personal injury. 
Therefore life and accident insurance policies are 
also included in the scheme and this coverage is 
valid even for extraordinary events (as defined) 
affecting Spanish residents and policyholders 

141 

A mixture of reinsurance 
channels. The CCS 
provides reinsurance 
distinguishing between 
experimental (i.e. 
systemic risks as 
drought and frost) and 
viable lines (i.e. hail). 
CCS is the only reinsurer 
for experimental lines 
with 75% of the share of 
commercial premiums. 
Private reinsurers tops 
up part of the 
reinsurance for viable 
lines . 

4.9% of total insured 
value (2000-2015) 

Within 60 days from the 
end of harvest (average 
of 40 days) 
Within 40 days for 
livestock. 

The farmer must insure 
all agricultural 
productions (crops, 
livestock, forest 
production, aquaculture 
production) of the same 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

outside Spain. 
Motor cars are also included in the Scheme. 

type. 

References: Diaz-Caneja et al. (2009); Swiss Re (2015); Wharton Risk Management and Decision Processes Center (2016); 
CCS (2016); Najera (2014) 

Sweden 

Sweden  relies  on  a  private  insurance  market  with  little  government  interference  to  provide 
extreme  weather  event insurance.  The  Swedish  insurance  industry  currently  offers  good  cover 
for  all  climate  related  claims.  For  instance,  cover  against  flooding  is  included  in  ordinary 
householder and home owner policies, which is relatively unusual. Further, the penetration rate 
is  high  with  one  of  the  reasons  for  this  being  that  it  is  compulsory  for  both  household  and 
commercial mortgages.  

Insurance  companies  in  Sweden  have  so  far  been  able  to  give  good  protection  for  all  climate 
related  events  through  the  basic  insurance  products.  However,  when  climate  related  events 
increase  in  its  frequency  and  intensity,  this  might  not  be  possible.  The  basic  criteria  for 
insurance  schemes,  that  the  event  is  sudden  and  unpredictable,  will  no  longer  be  met  and 
climate adaptation and risk reduction measures at all levels in society are therefore fundamental 
in order to ensure affordability and availability of insurance in Sweden.  

Table A2.11 Attribute table for Sweden 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

N/a 

N/a 

N/a 

Hazard(s) covered 

Floods; Storms 

Floods; Storms 

Hail; Droughts 

Official trigger 
Yes /no 
Description? 

Type of insurance, 
e.g. bundled, 
parametric triggers 

No 

No 

No 

Private bundled indemnity 
insurance 

Private bundled 
indemnity insurance 

Private indemnity 
insurance (hail and 
droughts may be 
combined) 

Compulsory vs. 
voluntary 

Voluntary (required for 
mortgages) 

Voluntary (required 
for mortgages) 

Voluntary 

Damage covered 

Damage to property and 
contents 

Damage to property 
and contents 

Limit of compensation 

In theory none: average 
coverage in 2010 was 
~€55,000. 

Hail: damage to 
crops 
Droughts: replanting 
costs 

Hail: The limit is 
based on the 
average value (based 
on average prices) 
Droughts: The limit 
is based on the 
average cost of 
replanting per 
hectare.  

142 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Individual policy 
deductibles 

10% of claim (bounded by 
~€320-€1,100) 

Deductibles are 
dependent on the 
crop and range from 
between 0%-15% of 
insured value. 

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 
Other 

Market penetration 
rate 

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

Deductibles 

Deductibles 

Deductibles 

>95% of households  

>75% of business 
property for flooding 

~60% of insurable 
crops (~ same 
percentage of farms) 

Limited risk differentiation  

Limited risk 
differentiation  

Np premium 
subsidies 

Private reinsurance 

Private reinsurance 

Private reinsurance 

Average annual 
premium 

Average premium ~€1,300 
(own calculations from data) 

Access to public 
disaster response 
fund 

No 

No 

No 

Pay-out speed 

Within one month 

Within one month 

Additional comments 

Limited competition 
in terms of the 
number of 
companies offering 
crop insurance. 

References: Diaz-Caneja et al. (2009); Brandstrand and Wester (2014); EC (2014); Svensk Forsakring (2016); UN (2007); 
Maccaferri et al., (2012); Svensk Forsakring (2013); 

143 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The United Kingdom 

The insurance market is free from government interference outside of general regulation (There 
have  only  been  very  rare  cases  of  limited  ad-hoc  government  compensation  after  a  disaster). 
Therefore,  the  insurance  market  is  predominantly  driven  by  competition  between  private 
insurance companies, whereby premiums are generally risked based. However, many banks or 
mortgage providers require that the property have full insurance coverage in order for the loan 
to  be  provided.  Therefore,  this  informal  mortgage  requirement  creates  an  informal  purchase 
requirement across both high and low risk policyholders that aids in the overall affordability of 
general  insurance  coverage.  There  are  many  companies  that  are  involved  in  the  general 
property insurance market.  

The  prominent  problem  facing  the  UK  insurance  industry  in  terms  of  extreme  weather  events 
has  been  flooding.  In  the  insurance  industry  and  the  UK  struck  a  gentleman’s  agreement  that 
insurance  coverage against flooding  was to  be  provided at affordable rates on the condition of 
continued  state  investment  in  flood  defences.  However,  in  the  year  2000  this  informal 
agreement  was  formalised  in  the  Statement  of  Principles,  which  stated  that  homeowners 
insurance would cover flooding if the property had an annual chance of being flood of 1.3% or 
smaller.  The  statement  was  originally  planned  to  end  in  2007  but  was  extended  while 
discussions  for  Flood  Re  were  underway,  which  formally  began  in  2016  (for  more  about  Flood 
Re, see below). 

Table A2.12 Attribute table for the United Kingdom 

ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Name, year of 
establishment 

Flood Re for high risk flood risk 
properties established in 2016 

N/a 

Hazard(s) covered 

Floods and Storms 

Floods; Storms 

No 

No 

N/a 

Hail 

No 

Official trigger 
Yes /no 
Description? 

Type of insurance, 
e.g. bundled, 
parametric triggers 

Compulsory vs. 
voluntary 

Private bundled indemnity 
insurance 

Private indemnity 
insurance 

Private indemnity 
insurance 

Voluntary (required for 
mortgages, hence bundled into 
property insurance) 

Voluntary (required 
for mortgages, hence 
bundled into property 
insurance) 

Voluntary 

Damage covered 

Direct damage to buildings and 
contents 

Limit of compensation 

In theory none other than the 
agreed upon insured value 

Damage to crops 

Direct damage to 
buildings and 
contents 

Business interruption 

In theory none other 
than the agreed upon 
insured value 

Individual policy 
deductibles 

Very policy dependent. In 
relatively low risk areas the 
excess ranges between 115 EUR 
and 290 EUR.  

Very policy 
dependent 

For most crops none, 
but for fruit there is 
a co-insurance 
payment of 10%. 

Flooding: in higher risk areas 
before Flood Re could be 
between ~3000 and 6000 EUR 
and ), but were only about £50 

144 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Household/Buildings 

Commercial 

Agriculture (crop) 

Risk Reduction 
Incentive based on: 
Premiums 
Deductibles 
Other 

(in low risk areas). However, a 
policy seceded to Flood Re has 
an excess of ~290 EUR. 

Deductibles; examples of 
minimal risk management 
measures in place in order to 
gain affordable insurance. Very 
few incentives for additional risk 
reduction past any minimal 
required standard 

Market penetration 
rate 

75%-95% of households  

Premium Setting rule, 
e.g. risk based, flat, 
fixed percentage of 
insured value? 

Risk based for the most part 
(especially in high risk areas), 
though there appears to be little 
variation for flood risk on the 
whole. Low flood risk 
households have a ~12 EUR 
surcharge on their premiums, 
while very high risk households 
have their flood risk premiums 
capped based on their 
property’s property tax value. 

Deductibles 

Deductibles; 
examples of minimal 
risk management 
measures in place in 
order to gain 
affordable insurance. 
Very few incentives 
for additional risk 
reduction past any 
minimal required 
standard 

Hail coverage 
corresponds to about 
7% of total cultivated 
area. 

Fixed percentage of 
insured values as 
there might not be 
sophisticated 
methods to 
determine rates. 
However, rates can 
differ across crops 
and in quite broad 
regional zones. 

Reinsurance access: 
Private reinsurance 
Public reinsurance 

Private reinsurance 

Private reinsurance 

Private reinsurance 

Flood Re is a pool for high flood 
risk households (that can be 
privately reinsured) with the 
possibility of placing a levee on 
insurers to make up for 
shortfalls. 

Most crops are at 
~1% of the total 
insured value. For 
fruit crops it is 
~5.5% of the insured 
value. 

An average premium 
of about €30 per 
hectare. 

Average annual 
premium 

Average property premium per 
capita in 2015 = ~275 EUR 

Combined building and contents 
insurance of about 420-450 EUR 
(in 2012)for general household 
insurance. A DEFRA study noted 
the difficulty in assessing 
premiums due the presence of 
deductibles. However, 
premiums overall may have 
fallen to ~333 EUR in 2013 

An average rate of about 3 EUR 
per ~1,200 EUR. 

Flood Re: premiums seceded to 
Flood Re range from ~243 EUR 
to ~1400 EUR based on council 
tax bounds (for combined 
property and contents 
insurance). The most common 
premium is expected to be 

145 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

SECTOR 

Access to public 
disaster response 
fund 

Pay-out speed 

Additional comments 

Household/Buildings 

Commercial 

Agriculture (crop) 

~320 EUR. 

No 

No 

No 

Aims to be paid after 
harvest. 

Whole fields must be 
insured; all crops of 
the same type must 
be insured. 

The ABI provides information 
ways that policyholders can 
limit damage in the case of 
extreme weather events.  

Flood Re will only provide 
insurance for buildings that 
were in place before 
01/01/2009. Moreover, Flood 
Re will be reviewed every 5 
years 

References: Lamond and Proverbs (2008); NIA (2008); Lamond et al., (2009a, 2009b); Diaz-Caneja et al. (2009); 
Penning-Rowsell and Pardoe (2012); Maccaferri et al., (2012); Surminski and Eldridge (2015); Wharton Risk Management 
and Decision Processes Center (2016) 

146 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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149 

 
 
 
 
 
 
 
 
 
 
 
 
APPENDIX 3 
MCA SCORING 

150 

 
 
 
 
 
 
 
 
 
 
Household MCA scoring 

All  these  criteria  are  considered  holistically  across  the  various  disasters.  While  it  is  true  that 
some  nuance  across  disasters  is  lost  due  to  the  fact  that  some  extreme  weather  events  are 
highly  localised  (such  as  fluvial  flooding)  for  the  most  part  across  Europe  we  have  insurance 
being  provided  as  a  bundle  of  risks  and  not  as  a  singular  risk,  where  I  think  that  looking  at 
separate disasters is more relevant. 

Criterion 1: Insurance penetration rate 

This  is  measured  as  the  percentage  of  households  that  have  coverage  against  the  set  of 
relevant  extreme  weather  events  (i.e.  flooding  (fluvial,  pluvial,  and  coastal),  hail,  and 
windstorm) for this sector. The three types of flooding were merged into a single indicator such 
that  coastal  countries,  e.g.  Bulgaria,  can  be  fairly  compared  to  landlocked  countries,  e.g. 
Austria. 

This should be judged as the average penetration rate across the extreme weather events. For 
example  Germany  has  a  penetration  rate  of  95%  for  hail,  95%  for  windstorm,  and  40%  for 
flooding; providing a rate of ~70%. The UK on the other hand is presented as a range between 
75-95% and therefore the mid-point should be used (i.e. 85%). 

We  focus  on  the  percentage  of  households  with  an  insurance  policy  because  the  amount  of 
coverage provided is not consistency across the various case studies. For the most part the only 
information  we  have  is  ‘up  to  total  insured  value’.  Therefore,  this  should  be  made  clear  that 
while important is not how we are looking at it. 

The  ‘holistic’  penetration  rate  is  then  compared  to  Table  A3.1  and  is  awarded  the  required 
points. 

 Table A3.1 Scoring metric for criterion one 

4 points 

3 points 

2 points 

1 point 

0 points 

[81,100] 

[61,80] 

[41,60] 

[21,40] 

[0,20] 

Criterion 2: Risk signalling ability 

This criterion measures the ‘holistic’ ability of insurance market structures to act as a signal of 
risk or risk reduction  potential.  Each element that  a  scheme displays can be awarded with the 
associated  points.  For  example  a  country  that  employs  large  deductibles  (2  points)  and  risk 
based  premiums  (2  points)  would  score  4  points,  while  a  market  with  small  deductibles  (1 
point) and flat rate premiums (0 points) would only score 1 point.  

The  only  binary  elements  are  small  or  large  deductibles.  In  order  to  judge  the  size  of 
deductibles  the  mid-point  of  the  range  of  deductibles  should  used  (i.e.  a  deductible  for 
windstorms of 500 EUR and 5000 EUR for floods would have a mid-point of 2750 EUR scoring 2 
points). 

151 

 
 
 
 
 
 
 
 
 
 
 
 
Table A3.2 Scoring metric for criterion two 

2 points  

1 point 

0 points 

  Large deductibles (i.e. larger 

than 2000 EUR) 
  Premium discounts 
  Required vulnerability 

standards 

  Risk based premiums 

  Small deductibles (i.e. smaller 
than 2000 EUR but larger 
than 0 EUR ) 

  Awareness campaign 

  No risk signalling 

Criterion 3: Ability to absorb large losses 

This criterion judges the ability of the sector to absorb large losses holistically across the various 
extreme weather events. While it these factors provide insurers with the ability to diversity their 
sources of capital such that they can better perform in the case of large scale events. However, 
this must be tempered with the knowledge that the Solvency II regulations require firms to be 
able  to  survive  a  1/200yr  loss  event.  Moreover,  there  may  be  concern  that  the  presence  of 
public  mechanism  creates  a  type  of  charity  hazard  whereby  private  agents  become  more  lax 
with  regards  to  when  they  receive  government  aid.  However,  while  important  consistent 
information across the countries could not be collected. Moreover, we do not make a judgement 
about a government’s ability to provide such a grantee as this can be quite time dependent (i.e. 
in the middle of a recession vs. an economic boom). 

The elements in Table 3 are often mutually exclusive of one another in the 2 point and 1 point 
columns. Therefore, a country either has access to private reinsurance for all risks (2 points) or 
it does not (1 point). 

For example, Denmark has access to private sector reinsurance for most risks (1 point), with a 
disaster  wide  pool  for  coastal  flooding  (2  points)  with  a  limited  state  grantee  (2  points)  for  a 
total of 5 points. France on the other hand has access to private reinsurance for some risks (1 
point) while it has access to public insurance for others (1 point) with an unlimited state grantee 
(3 points) for a total of 5 points). 

Table A3.3 scoring metric for criterion three 

3 points 

2 points 

1 point 

  Unlimited State grantee 

  Access to private reinsurance 

  Access to private reinsurance 

for NatCat losses  

for all risks 

for some risks 

  Access to sector/disaster wide 

  Access to sector/disaster wide 

pool for all risks 

  Access to public sector 

 

Reinsurance for all risks 
limited State grantee for NatCat 
losses  

pool for some risks 
  Access to public sector 

Reinsurance for some risks 

152 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Criterion 4: Availability and affordability 

Unlike  the  other  indicators  provided  above  there  are  two  sub-elements  for  this  criterion:  4a  – 
affordability  and  4b  availability.  The  final  score  for  a  market  on  criterion  4  is  the  sum  of  the 
score for 4a and 4b. 

4a Affordability 

This  indicator  is  judged  by  the  percentage  of  the  population  that  would  find  the  presented 
insurance premium ‘unaffordable’ as defined in Hudson et al. (2016). This is a  residual income 
definition,  whereby  a  household  finds  insurance  unaffordable  if  the  premium  is  larger  than 
(income-poverty  line).  The  poverty  line  is  defined  as  60%  of  national  median  income.  This 
focuses on the low-income households and the burden that insurance premiums place on them. 

One of the common features of the insurance markets across Europe is that it is hard to 
distinguish which part of the premium comes from what particular extreme weather event. 
Therefore, we should not distinguish the separate premiums. It must also be acknowledged that 
some premiums for localised events grow quite rapidly in higher risk areas (i.e. storm premiums 
might be fairly constant across a country, while flood risk premiums could strongly vary by 
neighbourhood). 

Therefore, the premium to be used is the average property premiums per capita in 2015. This 
forms the minimum percentage of the population that would find the premiums unaffordable. 
This is with the caveat that in more hazardous areas or less well protected places the premiums 
may be higher and more unaffordable, but these is likely to occur in very specific areas which is 
not the correct level of study for this application.  

For example, the average property premium per capita in Hungary in 2015 was ~100 EUR. 
Therefore, at least 16% of the population would find such premiums unaffordable scoring 
Hungary 1 point on this sub-criterion 

Table A3.4 scoring metric for criterion four a 

Indicator 

4 points 

3 points 

2 points 

1 point 

0 points 

[0,5] 

[6,10] 

[11,15] 

[16,20] 

[>=21] 

The percentage of the 
population finding 
insurance unaffordable 

4b Availability 

This is judged as the number of extreme weather events that can be insured against out of the 
following events: 

1)  Flooding (at least one of the following: pluvial, fluvial or coastal) – there is no real 

distinction made between the different types of flooding as landlocked countries cannot 
suffer from coastal flooding and as such the comparison would be unfair. Once again, 
the impression of these events is holistically viewed 

2)  Hail 
3)  Windstorm 

Care  must  be  taken  where  it  is  known  that  exclusions  are  enforced.  For  example,  in  Hungary 
one can buy a bundle that covers windstorm, hail, cloudburst and fluvial flooding. However, in 
high  flood  risk  areas  there  are  cases  of  exclusions  (since  the  removal  of  the  governmental 
insurance scheme) and so universal coverage is not possible. Hence, in this case Hungary would 
score 2 points (1 for windstorms and 1 for hail). 

153 

 
 
 
 
 
 
 
 
 
Table A3.5 scoring metric for criterion four b 

Indicator 

1 point 

The number of extreme weather 
events that can be insured against 

1 point per extreme weather 
disaster 

Overall on criterion 4, Hungary has scored 1 point on criterion 4a and 2 points on criterion 4b. 
Therefore, the total score for Hungary on criterion 4 would be 3 points. 

Criterion 5: Quick and certain compensation payments 

Of  the  two elements of  this  criterion,  for  the  household  sector information  of  the  quickness  of 
compensation  payments  was  inconsistently  available  for  all  markets  as  it  tended  to  be  very 
company specific and dependent on when policyholders started the indemnity process. 

Therefore, for the household sector the focus is on the certainty of receiving compensation. 

Table A3.6 scoring metric for criterion five 

Indicator 

2 points 

1 point 

0 points 

Certainty 

  No ad-hoc government 

compensation is 
possible. There are 
only formal 
mechanisms for risk 
transfer with clear 
rules 

  Limited private sector 
insurance coverage 
and ad-hoc 
government 
compensation 
  Specialist loss 

adjusters are used to 
assess and process 
claims. 

  Complete reliance on 
ad-hoc government 
compensation 

The  scoring  metric  presented  in  Table  6  focus  on  the  clarity  at  which  policyholders,  or  those 
affected,  more  generally  can  expect  regarding  their  compensation.  It  can  be  that  a  household 
knows  that  once  they  have  a  value  for  their  loss  the  insurer  will  provide  a  pre-agreed  upon 
amount providing a great deal of certainty, while another area maybe reliant on the will of the 
government at the time to provide compensation.  

For example, Romania has no scheme in place for ad-hoc government compensation as this role 
has been transferred to the PAD pool (2 points) with specialist loss adjusters (1 point) for a total 
score of 3 points. While Bulgaria on the other hand has limited insurance coverage and ad-hoc 
compensation (1 point). 

154 

 
 
 
 
 
 
 
 
 
 
 
 
Agriculture extreme weather insurance MCA scoring 

All  these  criteria  are  considered  holistically  across  the  various  disasters.  While  it  is  true  that 
some  nuance  across  disasters  is  lost  due  to  the  fact  that  some  extreme  weather  events  are 
highly  localised  (such  as  fluvial  flooding)  for  the  most  part  across  Europe  we  have  insurance 
being  provided  as  a  bundle  of  risks  and  not  as  a  singular  risk,  where  I  think  that  looking  at 
separate disasters is more relevant. In this we focus on crop insurance as the most consistent 
information was provided. 

Criterion 1: Insurance penetration rate 

This  is  measured  as  the  percentage  of  farms  that  have  coverage  against  the  set  of  relevant 
extreme weather events (i.e. flooding (fluvial, pluvial, and coastal), hail, windstorm, droughts) 
for  this  sector.  This  should  be  judged  as  the  average  penetration  rate  across  the  extreme 
weather events in  terms  of  the  percentage of arable land that  is insured. However,  it  must be 
noted  that  not  all  crops  are  insured  equally  and  where  a  distinction  is  made  the  mid-point 
should be used. 

 For example Germany has a penetration rate of 60% of farm land for hail, and little for  other 
perils. However, as hail is the most commonly insured event across Europe we do not make the 
distinction between the extreme weather events. Therefore in this case Germany would score 2 
points, while Italy with 9% would score 0. Moreover, as was mentioned during the focus group 
crops  are  very  sensitive  at  differing  times  of  the  year  so  most  farmers  are  willing  to  take  a 
gamble when it comes to indemnity/yield losses.  

We focus on the percentage of farms with an insurance policy because the amount of  coverage 
provided  is  not  consistency  across  the  various  case  studies.  For  the  most  part  the  only 
information  we  have  is  ‘up  to  total  insured  value’.  Therefore,  this  should  be  made  clear  that 
while important is not how we are looking at it. 

The ‘holistic’ penetration rate is then compared to Table 1 and is awarded the required points. 

 Table A3.7 Scoring metric for criterion one 

4 points 

3 points 

2 points 

1 point 

0 points 

[40+] 

[31,40] 

[21,30] 

[11,20] 

[0,10] 

Criterion 2: Risk signalling ability 

This criterion measures the ‘holistic’ ability of insurance market structures to act as a signal of 
risk or risk reduction  potential.  Each element that  a  scheme displays can be awarded with the 
associated  points.  For  example  a  country  that  employs  large  deductibles  (2  points)  and  risk 
based  premiums  (2  points)  would  score  4  points,  while  a  market  with  small  deductibles  (1 
point) and flat rate premiums (0 points) would only score 1 point.  

The only binary elements are small or large deductibles. In order to judge the size of 
deductibles the mid-point of the range of deductibles should used (i.e. a deductible for 
windstorms of 30% and 10% EUR for floods would have a mid-point of 20% scoring 1 points). 

A  note  to  be  concerned  with  is  that  in  the  agricultural  insurance  market  premiums  are  often 
subsided  by  the  state.  The  presence  of  such  subsidies  limits  or  prevents  the  ability  of  the 
insurance  market  to  provide  a  full  signal  of  risk  as  the  price  signal  becomes muddled.  To  this 

155 

 
 
 
 
 
 
 
 
 
end  the  presence  of  premium  subsidies  prevents  the  market  from  displaying  risk  based 
premiums. 

Table A3.8 scoring metric for criterion two 

2 points  

1 point 

0 points 

  Large deductibles (i.e. a loss 

  Small deductibles (i.e. a loss 

  No risk signalling 

of 30% or more) 
  Premium discounts 
  Required vulnerability 

standards 

  Risk based premiums 
  Bonus-malus 

less than 30% ) 
  Awareness campaign 

Criterion 3: Ability to absorb large losses 

This criterion judges the ability of the sector to absorb large losses holistically across the various 
extreme weather events. While it these factors provide insurers with the ability to diversity their 
sources of capital such that they can better perform in the case of large scale events. However, 
this must be tempered with the knowledge that the Solvency II regulations require firms to be 
able  to  survive  a  1/200yr  loss  event.  Moreover,  there  may  be  concern  that  the  presence  of 
public  mechanism  creates  a  type  of  charity  hazard  whereby  private  agents  become  more  lax 
with  regards  to  when  they  receive  government  aid.  However,  while  important  consistent 
information across the countries could not be collected. Moreover, we do not make a judgement 
about a government’s ability to provide such a grantee as this can be quite time dependent (i.e. 
in the middle of a recession vs. an economic boom). 

The elements in Table 3 are often mutually exclusive of one another in the 2 point and 1 point 
columns. Therefore, a country either has access to private reinsurance for all risks (2 points) or 
it does not (1 point). 

Table A3.9 scoring metric for criterion three 

3 points 

2 points 

1 point 

  Unlimited State grantee for 

  Access to private reinsurance 

  Access to private reinsurance 

NatCat losses 

for all risks 

for some risks 

  Access to sector/disaster wide 

  Access to sector/disaster wide 

pool for all risks 

  Access to public sector 

 

Reinsurance for all risks 
limited State grantee for 
NatCat losses  

pool for some risks 
  Access to public sector 

Reinsurance for some risks 

Criterion 4: Availability and affordability 

Unlike  the  other  indicators  provided  above  there  are  two  sub-elements  for  this  criterion:  4a  – 
affordability  and  4b  availability.  The  final  score  for  a  market  on  criterion  4  is  the  sum  of  the 
score for 4a and 4b. 

156 

 
 
 
 
 
 
 
 
 
 
 
 
 
4a Affordability 

This indicator is simply judged as the premium as a percentage of the insured value. This is a 
different concept to that employed regarding household insurance as crop insurance is closer to 
a commercial protection policy.  

The premium as a percentage of the insured value should be treated as inclusive of any subsidy 
that is provided. For example, in the UK the premium was ~1% of the insurable value with no 
subsidy while in Spain it was ~5% with a ~50% subsidy or a net premium of 2.5% of insurable 
value. In this case the UK would score 4 points while Spain would score 2 points. 

Where  multiple  premiums  are  presented  for  varying  events  the  mid-point  of  the  estimates 
should be used. 

Table A3.10 scoring metric for criterion four a 

Indicator 

4 points 

3 points 

2 points 

1 point 

0 points 

[0,1] 

(1,2] 

(2,4] 

(4,6] 

>=6 

The premium 
as a 
percentage of 
the total 
insured value 

4b Availability 

This is judged as the number of extreme weather events that can be insured against out of the 
following events: 

1)  Flooding (at least one of the following: pluvial, fluvial or coastal) – there is no real 

distinction made between the different types of flooding as landlocked countries cannot 
suffer from coastal flooding and as such the comparison would be unfair. Once again, 
the impression of these events is holistically viewed 

2)  Hail 
3)  Windstorm 
4)  Droughts/heatwaves 

Not  all  extreme  weather  events  are  equally  pressing  across  Europe,  however,  we  treat  all 
events  equally  in  that  climate  change  or  random  chance  can  change  the  risk  profile  facing  a 
farmer. 

Table A3.11 scoring metric for criterion four b 

Indicator 

1 point 

The number of extreme weather 
events that can be insured against 

1 point per extreme weather 
disaster 

For  example  in  the  UK  only  hail  crop  insurance  was  found  and  would  therefore  score  1  point 
while in Spain coverage is provided against flooding, hail windstorms, droughts and heatwaves 
and would therefore score 4 points. 

157 

 
 
 
 
 
 
 
 
 
 
The total score on the  criterion  is the  sum  of  the separate scores for  4a and 4b.  Therefore, in 
the  above  examples  the  UK  would  score  a  total  of  5  points  (4  from  4a  and  1  from  4b)  while 
Spain would score 6 points (2 from 4a and 4 from 4b) 

Criterion 5: Quick and certain compensation payments 

5a Certainty of compensation 

This criterion follows the same logic as with the household sector. 

Table A3.12 scoring metric for criterion five a 

Indicator 

2 points 

1 point 

0 points 

Certainty 

  No ad-hoc 

  Limited private 

government 
compensation is 
possible. There are 
only formal 
mechanisms for risk 
transfer with clear 
rules 

sector insurance 
coverage and ad-hoc 
government 
compensation 
  Specialist loss 

adjusters are used to 
assess and process 
claims. 

  Complete reliance on 
ad-hoc government 
compensation 

5b Quickness of compensation 

The faster and clearer a compensation payment is provided the points a market will score. 

Table A3.13 scoring metric for criterion five b 

Indicator 

4 points 

3 points 

2 points 

1 point 

0 points 

Speed 

Up to 15 days 

Up to 30 days 

Up to 60 days 

Up to 90 days 

Only viable 
information was 
that the claim 
was to be 
settled after 
harvest 

Standardisation process 

Both  the  household  and  agricultural  sector  MCA  studies  use  the  same  process  to  standardise 
scores before they are aggregated. The first one based on a continuous ranking (Equation (3)) 
and the second on a relative ranking (Equation (4)). 

MCA𝑐,,𝑖 =

𝑟𝑎𝑤 𝑠𝑐𝑜𝑟𝑒

(MCA𝑐,𝑖)

𝑀𝑎𝑥 𝑝𝑜𝑖𝑛𝑡𝑠 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑓𝑜𝑟 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑜𝑛 𝑐

MCA𝑐,,𝑖 =

(MCA𝑐,𝑖)
(MCA𝑐,𝑚𝑎𝑥)

𝑟𝑎𝑤 𝑠𝑐𝑜𝑟𝑒

𝑟𝑎𝑤 𝑠𝑐𝑜𝑟𝑒

− (MCA𝑐,𝑚𝑖𝑛)

𝑟𝑎𝑤 𝑠𝑐𝑜𝑟𝑒

− (MCA𝑐,𝑚𝑖𝑛)

𝑟𝑎𝑤 𝑠𝑐𝑜𝑟𝑒 

158 

(3) 

(4) 

 
 
 
 
 
 
 
 
 
 
 
 
   
 
 
 
 
The continuous ranking system highlights how well a case study performs on a criterion relative 
to  the  best  possible  outcome,  while  the  relative  ranking  system  ranks  case  studies  based  on 
how  well  they  perform  compared  to  one  another.  So  while  the  relative  ranking  system  may 
identify  ‘best  practice’  insurance  schemes  relative  to  other  countries’  schemes,  the  continuous 
ranking  system  shows  how  close  (or  far  off)  this  best  practice  scheme  is  from  the  ‘ideal’ 
scheme.  Taken  together  the  two  systems  can  more  robustly  highlight  which  cases  score  most 
highly. 

159 

 
 
 
 
 
 
 
 
APPENDIX 4 
STAKEHOLDER CONSULTATION 

160 

 
 
 
 
 
 
 
 
 
 
Table A4.1: Categories of relevant stakeholders 

Table A4.2: Stakeholders consulted throughout the process 

 COUNTRY 

ORGANISATION 

Austria 

Austria 

Bulgaria 

Denmark 

Denmark 

Denmark 

Denmark 

Denmark 

EU Wide 

France 

France 

France 

France 

France 

France 

France 

Germany 

Germany 

Germany 

Germany 

Germany 

Italy 

Italy 

Italy 

Italy 

Multi 

Multi 

Multi 

GRAWE  

Verband der Versicherungsunternehmen Österreichs 

Bulgarian Insurance Association 

Danish Technical University 

Forsikring og Pension Danmark 

Danish Coastal Authority 

Danish Storm Council 

City of Copenhagen 

Insurance Europe 

Mission Risques Naturels 

Ecole Polytechnique 

French Federation of Insurance Companies (FSSA) 

Scor 

Caisse Central de Reassurance 

CEPRI    

Autorité de Contrôle Prudentiel 

B2B Protect GmbH 

German Insurance Association (GDV) 

Hannover Re 

Allianz 

Munich Re 

Italian National Association of Insurance Companies (ANIA) 

Confagri 

Coldiretti 

FEEM 

ClimateWise 

FM Global 

 ICLEI 

161 

 
 
 
 
 
 
 
 
 
 COUNTRY 

ORGANISATION 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

Multi 

ICMIF 

Insurance Development Forum (IDF) 

CCRE-CEMR 

Afore Consulting 

ARCTIK 

Milieu Ltd  

Climate Kic 

OECD 

UNEP 

ACT Alliance EU 

C40 

CEMR/PLATFORMA 

Eurocities 

FUEDI/Crawford 

Copa-Cogeca (European Farmers European Agri-Cooperatives) 

Geneva Association 

Netherlands 

Dutch Association of Insurers 

Netherlands 

Achmea 

Netherlands 

Maastricht University  

Norway 

Norway 

Romania 

Romania 

Finance Norway 

Norwegian Environment Agency 

Pool of Insurance Companies Against Disasters (PAID) 

National Association of Insurance and Reinsurance Companies in 
Romania (UNSAR) 

Romania 

General Inspectorate for Emergency Situations (IGSU) 

Spain 

Spain 

Spain 

Spain 

Sweden 

Sweden 

Sweden 

Sweden 

Sweden 

Sweden 

Sweden 

State Entity for Agricultural Insurance (ENESA) 

Spanish Insurance Consortium 

Association of Spanish Insurers (UNESPA) 

Agroseguros 

AIG Europe 

Trygg Hansa 

Länsförsäkringar 

If Skadeforsakring 

Insurance Sweden 

SMHI 

Stockholm Environment Institute  

Switzerland 

Zurich Insurance 

Switzerland 

Swiss Re 

United Kingdom  Marsh&McLennan Companies 

United Kingdom  Guy Carpenter 

United Kingdom 

Flood Re 

United Kingdom 

London School of Economics  

United Kingdom  Guy Carpenter & Company Ltd  

162 

 
 
 
 
 
 
 
 
 
 
Table A4.3: Stakeholder responses to questionnaire 

 COUNTRY 

POSITIVE RESPONSE 

Bulgaria 

1 private sector insurer (DZI) 

Romania 

1 insurer (PAID) 
1 academic (INHGA) 

Hungary 

1 governmental agency (AKI) 

Poland 

Sweden 

Denmark 

France 

Insurance Sweden 
4 private insurers (Länsförsäkringar AB, If P&C insurance Ltd, AIG 
Europe Limited, Trygg Hansa) 

Danish Insurance Association 
Danish Storm Council 

2 academic (University of Montpellier 3 Paul Valéry, Ecole 
Polytechnique) 
1 public insurer (CCR) 
1 public body (MRN) 

The UK 

1 Charity (national flood forum) 

Germany 

German Insurance Association 

Austria 

Italy 

Spain 

EU Wide 

Austrian Insurance Association 
Austrian Hail Insurance Company 

Italian insurance association 

1 public insurer (CCS) 
1 academic (FEEM) 

OECD  
Researcher (wanted anonymity) 
Insurance Europe 
FERMA 

In all countries at least three organisations have been contacted, and usually many more. Gaps 
in  certain  member  states  and/or  type  of  stakeholder  reflect  a  lack  of  responsiveness  to  our 
solicitations. 

Table A4.4: Stakeholders who contributed to the focus groups and the final event 

 COUNTRY 

ORGANISATION 

Austria 

GRAWE / VVO 

Denmark  

City of Copenhagen 

Denmark  

Forsikring og Pension 

EU Wide 

EU Wide 

EU Wide 

France 

Germany 

Germany 

Germany 

Italy 

Norway 

Spain  

Insurance Europe 

ICLEI - Local Government for Sustainability 

OECD 

Mission Risques Naturels 

Allianz 

Munich RE 

MCII 

Coldiretti 

Finance Norway 

ENESA 

Switzerland 

Swiss Re 

Switzerland 

Zurich Insurance 

UK 

UK  

London School of Economics 

ClimateWise 

163 

 
 
 
 
 
 
 
 
 
 
 
Table A4.5 Stakeholder assessment and rational for the importance of the studied extreme weather events 

ATTRIBUTE 

Spain 

Sweden 

Germany 

EU wide 

Austria 

The United Kingdom  Hungary 

(agriculture) 

Not an important 
factor in Germany. 
Communities invest in 
climate proofing the 
dikes, not insurance 

In temperate 
climates, more 
frequent nuisance 
coastal flooding 
linked to sea-level 
rise will challenge 
the insurability of 
coastal areas. 

With sea level rise 
damages will 
increase during the 
century along the 
south coast of 
Sweden 

Austria is a 
landlocked country 

Projections re that 
coastal risk will 
increase 

Coastal flooding 
/ storm surges 

Droughts 

Large cities away from 
the coast. 
Infrastructures suffice to 
cope with this peril.  
Only the Spanish 
coastline prevents more 
damages by coastal 
flooding combined with 
large cities away from 
the ghost. Sea level 
could increase this risk 
in the medium-term. 

Drought damages in 
Spain are important 
(and expected to be 
even more important in 
the future), but quite 
limited to agriculture. 
Incremental losses in 
irrigated agriculture.  

Not directly insured, 
but indirect insurance 
(business interruption) 
possible. 

Fluvial flooding 

Associated with flash 
floods, is important, 
particularly in Northern 
Spain. 

Upgraded prevention 
measures will 
safeguard those along 
rivers. 

The eastern parts 
of Sweden is 
already hit by 
drought, which 
effects the drinking 
water. Not so large 
effect on insurance 
as it for those 
living there. 

Periods of long rain 
will causes lakes 
and rivers to flood. 
These periods will 
increase. 

Slight increase 
expected 

Better safeguards 
and risk-prevention 
work 

Major flood defence 
schemes will address 
much of this, even if 
the risks increase. 
Risk could be reduce 
significantly through 
appropriate planning. 

The impact of 
climate change on 
vary across regions. 
As a result, there is 
not the same level 
of overall risk 
increase found for 
pluvial and coastal 
flooding. unlikely to 
be a significant 
increase in the area 
susceptible to fluvial 
flooding in the 
context of climate 
change. 

Hail 

1-2 most relevant 

The more energy there 

Sweden hasn´t 

Increase in number 

164 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

Spain 

Sweden 

Germany 

EU wide 

Austria 

The United Kingdom  Hungary 

(agriculture) 

impact in agriculture 

Heatwaves 

Heat wave damages for 
in livestock.  

Heatwaves are expected 
to increase in the future. 

It might be possible to 
consider including 
personal damages from 
heat waves in the future 
(currently excluded). 

Flood losses (pluvial, 
fluvial and coastal 
together) add up to 75% 
of all natural disaster 
compensations paid by 
CCS. 

Pluvial flooding 

is in the atmosphere 
(due to rise of the 
mean global 
temperature), the 
more likely torrential 
rain and hail 
phenomena are. 

Not insured in property 
insurance (-> life 
insurance). 

Most underestimated 
risk. 

and intensity of 
events 

seen the heavy 
hails that has hit 
Europe yet, but the 
climate change will 
probably change 
that. 

Not a large 
insurance problem. 
Mainly for health 
care. But forest fire 
can occur when it 
is a heat wave 

Increase of flash 
floods 

Heavy rains 
increases due to 
climate change and 
can hit all over 
Sweden. We have 
seen an increase in 
events and costs. 

Pluvial flooding is 
probably rather under 
recorded and will 
increase, particularly 
through intense 
rainfall events. Major 
defence schemes will 
not be available for 
many areas and often 
small numbers of 
properties will be 
affected in each area. 
Lots of small scale 
interventions are 
required alongside 
rigorous planning 
policies. The result is 
that real risk may well 
increase. 

One of the most 
significant 
challenges for flood 
schemes as the 
expected increase in 
intensity of pluvial 
rain will expand the 
area that is 
susceptible to 
flooding to areas 
with no history of 
flooding. 
That said, the 
broader 
susceptibility to 
flood damage 
should reduce the 
adverse selection 
problems inherent 
in flood insurance 
(i.e. where only 
high-risk 
policyholders 

165 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

Spain 

Sweden 

Germany 

EU wide 

Austria 

The United Kingdom  Hungary 

(agriculture) 

Windstorms 

Occasional losses of life 
could make it rank 
higher.  

Good building codes, 
well prepared property 
in Germany. 

Although with less 
frequency than in 
other parts of Europe, 
windstorms cause 
significant losses in 
Spain, particularly in 
its Northernmost third 
and in mountainous 
areas. Wind damages 
add up to 18% of all 
natural disaster 
compensations paid by 
CCS under ERIS. 

The general concerns 
is that there has been 
a moderate increase in 
extreme weather 
related losses. 

The stakeholders 
expect that heavy rain 
and sea level rise will 
be the main problem in 
the near term. Though 
there is a large 
variability in between 
years. 

Trends in risk 
between 1990 
and 2015 

Losses in agriculture 
have increased 
noticeably, especially 
those related to 
droughts and 
heatwaves. The former 
are related to 
governance and 
exposure as compared 
to climate change. It is 
expected that this will 
worsen.  
The loses paid by CCS 
have doubled between 
1990 and 2015, but the 
number of policies 
covered has also 
increased 3.3 times in 
the same period. 
A steady increase in 
weather-related losses, 
but this is more due to 

purchase 
insurance). 

the evidence on the 
impact of climate 
change on 
windstorms is very 
mixed (i.e. only 
some studies expect 
a likely increase in 
frequency and/or 
intensity).  

Storms do have 
most effects on 
property and 
forest. However, 
storms is not 
Climate driven in 
Sweden 

Climate warming 
causes a general 
increase in storm 
events 

About 5%-7% 
increase in losses 
annually. An 
approximately 10% 
increase annually is 
expected 

Accurate values 
are not known. 
The variability of 
yield fluctuations 
may have approx. 
doubled. It is 
expected to 
increase. 

As people buy more 
goods, costs will 
increase. Of course, 
the financial costs to 
people are only one 
element of the impact 
of flooding. Research 
on health, for 
example, is showing 
that flooding incidents 
have a far greater 
impact than 
previously thought. 

Most sources 
suggest that losses 
have increased 
over time (by a 
multiple of 3x to 
10x for flooding, 
for example, since 
1970) although the 
relative 
contribution of 
asset accumulation 
compared to 
changes in the 
frequency or 
intensity of 
disasters is 
debatable. 

Very difficult to 
answer. The 
amount of losses is 
mostly affected by 
higher sums insured 
and inflation at the 
same time as 
improved protection 
strategies. Agrees 
with the overall 
scientific consensus. 
In order to ensure 
controllable 
increase of losses in 
those countries, 
which upgrade their 
prevention 
measures. 
Furthermore, adapt 
the building codes 
where necessary 

166 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ATTRIBUTE 

Spain 

Sweden 

Germany 

EU wide 

Austria 

The United Kingdom  Hungary 

(agriculture) 

increased coverage and 
the increased insured 
values of the properties 
rather than to an 
increase of the levels of 
hazard, at least in a 
short-medium term. 

Premiums have altered 
little in terms of 
agricultural insurance. 
The general CCS 
insurance has been 
mostly unchanged. 
There is no major 
change expected. 

Trends in 
Premiums 
between 1990-
2015 

Due to extreme 
weather insurance 
being covered by a 
general insurance 
policy, premiums have 
remained quite stable. 
Keeping insurance as a 
general product is 
expected to keep 
premiums low. A lack 
of adaption may lead 
to a withdrawal of 
insurance or a more 
rapid increase in 
premiums. 

and invest in public 
risk awareness 
campaigns. 

Nationwide extreme 
weather insurance 
is available since 
1994. Products and 
actuarial analysis 
took a few years. 
Products came to 
market widely at 
the end of the 
1990s. The 
premiums since that 
time have remained 
very stable. We 
have seen an 
increase in overall 
building insurance 
premiums over the 
last 3-4 years but 
not in the wake of 
extreme weather 
events.  
If prevention 
measures are 
constantly upgraded 
and there is no 
“tipping point” of 
sudden extreme 
climate change, we 
expect an average 
annual increase in 
extreme weather 
insurance premiums 
along the “ideal” 
rate of inflation (~ 
2 %). 

167 

General storm 
insurance 
premiums have 
increased by about 
3% annually, with 
a further increase 
of about 1% 
annually 

They have increased 
very significantly. 
With Flood Re in place 
premiums and 
excesses for those 
most at high risk will 
be constrained, but 
will be released again 
once the scheme 
comes to an end in 
2039. Overall they will 
increase. 

Accurate values 
many not known. 
Since 1990, the 
total was insured, 
the high fees 
associated with 
low penetration. 
increased until 
2012 the charges, 
since the 
introduction of 
solid MKR 
premium reduction 
can be observed. 
It is expected to 
grow, stagnate or 
in addition to 
increasing absolute 
contribution. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A4.6 Score of self-assessment on the success of an insurance market 

Spain 

Sweden 

Germany 

EU wide 

The insurance 
penetration 
rate (i.e. the 
percentage of 
potential 
policyholders 
with an 
insurance 
policy). 

Insurance is 
provided as a 
compulsory 
extension of 
most 
insurance. This 
creates a high 
penetration 
rate 

All household 
insurances and 
insurances for 
SMEs have 
cover. To keep 
the 
penetration 
the 
municipalities 
and state have 
to adapt risks 
from climate 
change 

Most public 
schemes high-
levels of 
penetration 
mainly due to 
requirements.  

Very good 
sales figures 
with new 
policies, 
difficult sale to 
business in 
force. There is 
a lack of risk 
awareness and 
loss 
compensation 
by the 
government. 
Government 
has to stop ad 
hoc loss 
compensation 
and 
communicate 
this to the 
public. Risk 
awareness 
should be 
further 
strengthened. 

The United 
Kingdom 

Flood Re was 
introduced in 
April 2016, 
53,000 
properties 
were reported 
as being in the 
scheme by 
September 
2016, with a 
possibility of 
about 350,000 

The ability of 
insurance to 
act as a signal 
of risk or to 
promote risk 
management 
and 
preparedness. 

The ability of 
the insurance 
sector to be 
able to absorb 
large losses. 

The surcharges 
are not linked 
to the risk and 
don’t act as 
signals of 
particular risk 
levels. 
CCS devotes 
part of its 
resources raise 
awareness and 
promote DRR. 

Well 
established 
PPP. Insurance 
Compensation 
Consortium 
covers 
extraordinary 
losses, and is 
financed 
through a flat 
rate, with the 
potential of 
government 
support. 

We try to 
promote risks, 
but it is not 
always well 
received from 
the 
municipalities 
and state since 
adaptation 
costs  

Risk based 
pricing is 
already in 
place in 
Germany. Very 
good location 
based 
information 
about the risk 
available.  

Most (if not all) 
public schemes 
(weather-
related) have 
limited risk 
variation in 
premiums 
charged. 

It has the 
potential, but 
is not 
delivering this 
yet. 

This is one of 
the things that 
the scheme is 
designed to do 

The Solvency 
II regime at EU 
level does all 
the necessary 
regulation. The 
market is able 
to cope with 
large scale 
events. 

A critical 
function of 
public schemes 
is to support 
the insurance 
sector's ability 
to absorb large 
losses 
although there 
have been 
very few (if 
any) examples 
of 
governments 
needing to 
provide 
support to the 
insurance 
sector to cover 
losses (most 
government 
funding after 
disasters has 
been to cover 
uninsured 
losses). 

168 

 
 
 
 
 
 
 
 
 
 
 
The United 
Kingdom 

It is up to each 
member 
insurance 
company to do 
this. However, 
there may an 
opportunity to 
compare 
practices 
between 
insurance 
companies 
once there is 
sufficient data 

Calls to our 
advice line 
reflect that 
many people 
are now able 
to access more 
affordable 
insurance than 
in the past. 

Spain 

Sweden 

Germany 

EU wide 

The ability to 
provide quick 
and certain 
compensation 
payments after 
a disaster 
event 

There is legal 
certainty about 
the 
compensation 
payments.  
CCS controls 
the whole loss 
adjustment 
and claim 
process is 
quick and 
straightforward
. 

It is a matter 
of definition of 
quick. The 
damage have 
to be 
assessed, 
which can take 
some while. 
Meanwhile, 
compensation 
is payed, but 
not to the full 
amount. 

Expertise and 
workforce for 
loss 
adjustment are 
in place or 
available on 
short call. 

It is unclear 
whether there 
is any 
advantage in 
this regard in 
having a public 
insurance 
scheme. 

The 
affordability 
and availability 
of insurance 
policies / 
premiums. 

The binding 
nature of the 
extension of 
the coverage 
and the wide 
base of 
policyholders 
allow for very 
affordable 
premiums. In 
effect 
premiums are 
highly 
subsidized. 

The cost is low 
due to large 
penetration 

Approx. 95 % 
of all private 
buildings can 
be insured 
against for 
under 200,- 
EUR p.a. with 
full 
replacement 
value 
compensation 
under all 
future building 
codes. 

This is where 
public 
insurance 
schemes have 
been most 
successful in 
ensuring that 
insurance is 
available and 
affordable. 
Public 
insurance 
schemes that 
achieve broad 
coverage 
would normally 
be able to 
provide 
insurance 
coverage for 
cheaper based 
on their ability 
to pool a large 
number of 
risks. 

Self-assessed 
score (1-3) 

2,8 

2,4 

2,8 

1,1 

2,3 

169 

 
 
 
 
 
 
 
 
 
 
 
 
Table A4.7 Aggregated national monetary losses (‘000 USD) from the extreme weather events studied. 

Bulgaria  Demark  Germany  Spain 

France 

Italy 

Hungary  Austria 

Poland 

Romania  Sweden  United Kingdom 

1990 

1991 

1992 

1993 

1994 

1995 

1996 

1997 

1998 

1999 

190000 

4440000 

4500000 

1910000 

20000 

250000 

50000 

10000 

4790000 

5000 

772000 

110000 

50000 

900000 

751700 

30100 

400000 

697300 

384000 

1010000 

1150000 

625000 

1000 

1438700 

100000 

9300000 

2000 

1000 

3000 

529600 

824300 

700000 

500 

576600 

6010 

32000 

200 

5000 

3400 

187000 

236000 

650000 

300000 

360000 

5000 

10000 

800000 

10000 

175000 

3500000 

110000 

248400 

150000 

1000 

150000 

150000 

1865400 

2604939 

2180000 

3300000 

12500000 

293400 

32000 

10000 

60000 

160000 

133680 

2000 

50 

75000 

8050000 

55000 

20000 

600500 

7418150 

2001 

300000 

72000 

132350 

275000 

5000 

700000 

135000 

2002 

1000 

13650000 

87000 

1191000 

646000 

30000 

2405000 

150 

290 

400050 

2003 

2004 

1950000 

880000 

5900050 

5205000 

100000 

280000 

130000 

14285 

96000 

170 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Bulgaria  Demark  Germany  Spain 

France 

Italy 

Hungary  Austria 

Poland 

Romania  Sweden  United Kingdom 

2005 

457000 

1300000 

790000 

48000 

700000 

1313000 

2800000 

700000 

2006 

2007 

2008 

2009 

2010 

2011 

100000 

5500000 

400000 

250000 

400000 

100000 

9648000 

10000 

2700000 

80000 

278000 

500000 

50000 

70000 

1900000 

3200000 

20000 

700000 

100000 

50000 

484000 

1000000 

340000 

5730000 

872000 

440000 

3080000 

1111428 

500 

545000 

1000000 

2012 

4400 

395000 

1337601 

5900 

2946000 

2013 

17700000 

655000 

780000 

1000000 

11000 

1500000 

2014 

932000 

400000 

485000 

1087000 

2015 

5800 

924000 

1143000 

Notes: Data from EM-DAT presented in market prices at the time of impact

724000 

2400000 

171 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
APPENDIX 5 
REPORT ON FINAL EVENT (30.06.2017) 

172 

 
 
 
 
 
 
 
 
Introduction 

Towards the end of this study, Ramboll and IVM organised a stakeholder consultation in the form of 
a conference. The main purpose behind this event was for Ramboll and IVM to present the findings 
of  this  report  to  relevant  stakeholder  groups,  particularly  stakeholders  of  the  insurance  industry, 
public authorities, and agriclture. This was envisaged to achieve two things: first, it would generate 
feedback on the findings of this report from relevant stakeholders, which could then be integrated 
in its finalisation; and second, it would disseminate the findings of this study.  

Ramboll  and  IVM  sought  to  achieve  a  balance  in  the  composition  of  stakeholders  attending  the 
event in accordance with their relevance to the findings of this report.  Among the 76 participants 
who had signed up to the event, 25 represented the insurance industry, 25 represented public sec-
tor  organisations21,  9  represented  research  organisations22,  6  represented  agricultural  interest 
groups,  2  represented  non-governmental  organisations,  and  the  remaining  9  represented  consul-
tancies23. Of the 76 invitees who had confirmed their attendance, XX were present at the event. 

The event was held between 10.00 and 17.00 on the 30 of June 2017 at the premises of the Euro-
pean Commission, at Rue Philippe Le Bon 3 in Brussels.  

Below is a summary of the proceedings of the event24. 

Welcome / Opening 

Dr. Kondrup (EU DG Clima) emphasized that the focus of climate change adaptation should be on 
providing strong support to cities and making infrastructure more resilient and linked this to the US 
where  there  exists  a  strong  support  from  cities  regarding  climate  change  adaptation.  Insurance 
needs to be in line with this and meet the needs of stakeholders. 

Ms. Boulehouat (EU DG Echo) encouraged to enforce Disaster Risk Management using, among oth-
er tools, insurance. She argues that the focus of the EU needs to be on pushing for strong collabo-
ration between the private and public sectors.  

Session 1 – Promoting risk awareness and reduction 

The issue of affordability should be addressed. Especially for low-income households, it is important 
to  alleviate  the  financial  risk  by  encouraging  taking  out  extreme  weather  insurance  coverage  by 
providing  insurance  vouchers  or  tax  credits.  Regarding  indirect  measures  of  DRR,  the  idea  of  re-
quirements to obtain insurance (e.g. building codes) should be strengthened. 

Tom Herbstein (ClimateWise) focused on ClimateWise’s vision of where the insurance industry can 
go in the future. He addressed the challenge of the protection gap and explained that the current 
market is  very challenging. He argued in favour of increasing regulatory attention, both in the UK 
and abroad. Communication through risk pricing is only one part; there should be done more, for 
example  a  focus  on:  cities  (i.e.,  multisector  partnerships  with  industry  and  other  stakeholders), 
investment (i.e., recognizing the huge potential in asset management and the need for benchmark-
ing tools), regulation (i.e., supporting regulators’ council). It is important for the industry to take a 
leadership role. 

Michael Szonyi (Zurich flood insurance alliance) argues that the goal should be to promote invest-
ments so others will join too. They aim to capacitate NGOs and public sectors, and support multi-
sector alliances. In providing examples of solutions, he mentions: the development of the first vali-
dated  flood  resilience  measurement  framework  which  is  in  line  with  Sendai  Framework,  the  Post 
Event Review Capability (PERC) and cross-cutting learning and sharing enabled by their floodresili-
ence.net. 

21 Among these, 12 from European institutions. 
22 Among these, four from IVM. 
23 Among these, five from Ramboll. 
24 The agenda for the event can be found in Appendix 6. 

173 

 
 
 
 
 
 
                                                
Inés Isabel La Moneda (ENESA) discusses the Spanish agriculture insurance system. She explains 
that  many  perils  are  covered,  participation  is  voluntary,  and  insurance  policies  subsidized.  They 
have an average penetration rate of 40% but it differs from crop to crop. She argues that the suc-
cess of the system is due to the collaboration between the actors from public and private sectors. 
Regarding  risk  awareness  and  risk  reduction  she  mentions  the  minimum  requirements  (admin, 
economical and technical), premium  discounts for  implementing  DRRs, and the use  of  the bonus-
malus system. She concludes that their system has advantages for the insurance market as well as 
the farmers. 

The following is a summary of the follow up questions and discussions with the audience: 

 

It would be interesting to analyse the surcharge compared to the general taxation? 

-  The study did not look at the specific differences between a surcharge and general 

taxation. 

  One  of  the  recommendations  is  to  support  low-income  households  through  subsidies, 
though  a  recent  conference  showed  affordability is not  an  issue.  When  affordability  is  not 
the problem, how can the issue of low penetration rates be solved?  

It depends how you define affordability. 

- 
-  Although politically challenging, don’t pay out premiums to those who did not take 

out insurance coverage.  

  Although a great idea in theory, there is debate about the practicality of implementation of 

a resilience-label. 

  Who will be regulating the funds if tax increases or a surcharge is enforced? 

-  Each  country  should  have  their  own  body  that  will  regulate  the  funds.  This  is  not 

recommended to do on a EU-wide level.  

Session 2 – Closing the protection gap  

The report’s findings show that it is recommended to promote the bundling of perils to create mul-
ti-peril crop insurance. Furthermore, banks should be urged to require full and comprehensive in-
surance and a focal point or authority should help to bring together the different stakeholders. 

Thomas Hlatky (GRAWE) chairs this session and discusses the issue of closing the protection gap. 
Risk transfer should be at the core of the insurance industry. He also discusses whether it should 
be called a protection gap or  a protection vacuum.  This leads  to the  addressing the following  im-
portant question: how do you build a bridge in a vacuum? 

Paola  Grossi  (Coldiretti)  responds  that  their  main  goal  is  to  increase  farmers’  awareness.  Perils 
cross country boundaries and therefore policies could have important role in enforcement. For ex-
ample, in Italy, farmers need to insure a minimum of 3 perils, but some insure up to 9. Therefore, 
the  share  has  increased  significantly.  She  does  acknowledge  that  mandatory  protection  is  very 
difficult. 

Wouter  Botzen  (IVM-VU)  poses  the  question  of  why  there  is  a  protection  gap.  Recent  academic 
studies  show  that  people  tend  to  purchase  insurance  after  a  disaster  hit.  So  awareness,  or  lack 
thereof,  is  one  part of  the  causes  but  the  situation  is  more  complex.  Some  governments  provide 
funds  after  a  disaster  also  to  those  without  insurance  coverage,  hence  there  is  less  incentive  for 
people to get coverage. Some solutions to this could be to develop awareness campaigns which can 
help, although the time span is limited. Furthermore, government compensation could be provided 
through loans or only to people who are then required to purchase insurance going forward. Sum-
marizing,  there  is  potential  for  advancing  the  markets  but  it  will  then  limit  consumers’  freedom 
which is a sensitive issue. 

Laura Schaefer (MCII) focuses in her work on developing countries and will therefore take the de-
bate  to  a  global  perspective.  She  explains  that  in  developing  countries  only  2%  of  the  losses  is 
covered by insurance. When comparing this to EU and the US, it appears that in developing coun-
tries people  can’t  rely  on insurance  and  coping  means  to  sell  assets  while they  actually  need  the 
insurance  safety  net.  Therefore,  they  need  a  regulatory  environment  that  incentivizes  people  to 
take out insurance and partnerships are necessary. The debate should not only be about quantity 
but also quality: insurance needs to be affordable, accessible and sustainable. Insurance solutions 
need to be imbedded in DRR management. 

174 

 
 
 
 
 
 
Eddy  Vanbeneden  (Guy  Carpernter)  argues  that  the  transferring  of  risk  to  the  private  market 
should be promoted. There is still room to improve the public-private partnership and there is  in-
creased recent willingness to cultivate the initiatives, especially after events. He also supports the 
bundling of perils in one offer and mentions that it is good to have the legal environment in a coun-
try for the social-environment. 

The following is a summary of the follow up questions and discussions with the audience: 

 

It is mentioned that the differentiation by actuarial scientists between different perils that 
can occur simultaneously, such as storms, hails etc., is not very workable. An example of 
this  are  last  year’s  problems  in  the  Netherlands.  Because  of  climate  change,  you  need 
changes in the wording of clauses so insurance buyers become more confident.  

  Compliment on the excellent timing of the conference. Linking to the previous point, infor-
mation sharing should be improved to build better trusts between the insurers and buyers. 
What would you think of creating an observatory at EU level that monitors climate risk and 
provide info and expertise on how to access insurance schemes? 

-  Mr Vanbeneden agrees and thinks this is in line with what Thomas mentioned. The 
existing models are sophisticated but only look at one peril.  They need a scenario 
approach. He also agrees that language needs to be improved.  

-  Mr Hlatky: talked to Max and we are taking the models too seriously. We are miss-

ing the point.  

 

Fire  insurance  in  Italy  is  inversely  related  to  outcome  risk  so  it  is  not  a  solution;  so  how 
could we solve the problem? 

-  Mr Hudson: same problem as mentioned beforehand. Things might work for the EU 
as a whole, but need to be studied in detail to identify country-level solutions. 
-  Mr  Botzen:  the  relevant  issue  is  with  the  bundling  of  perils.  For  a  consumer,  the 
differentiation between perils such as hail and storm is difficult to make. This cre-
ates a low demand of disaster insurance which could be address by bundling. This 
is very beneficial for consumers but maybe less for the insurers. However, for local 
situations it could be a solution.  

 

 

The idea of a protection gap in the EU is challenged: it might exist in developing countries 
but not here. An info data bank would need to be fed by many companies but the likelihood 
of  this  actually  happening  is  deemed  very  low.  Furthermore,  recommendations  should  be 
addressed not to the EC but to countries or sub-national level.  
Thomas  has  a  question  to  the  EC:  if  we  don’t  follow  these  recommendations,  we  will  be 
drive back to a pre-insurance age and that the gap is widening. Then it could be that socie-
ty would become more individual reliant.  

-  Max  Linsen  responds  that  none  of  the  recommendations  are  invented  by  us  but 
based  on  input  obtained  from  a  group  of  stakeholders.  The  main  idea  of  the  rec-
ommendations is to create an environment where insurance can have a role to im-
prove DRR. Today’s questions are also very much focused on insurance itself rather 
than its potential as a tool for DRR. 

Session 3 – The role of public and private entities in the risk cycle 

The  report  recommends  to  use  a  surcharge  on  private  property  insurance  premiums  to  directly 
finance and construct risk reduction infrastructure. For agriculture, it is recommended to develop a 
risk management association with a focus on protecting farmers against income variations due crop 
yields.  This  can  be  achieved  by  supporting  multi-risk  yield  insurance  or  the  employment  of  risk 
reducing measures. A  working  group in  the  European  Commission could be used to enable cross-
DG collaboration and coordination with national bodies. 

Peter  Schulze  (EC  DG  ECFIN)  moderated  this  session,  emphasising  the  complexity  of  the  EC  in 
certain PPP situations. Furthermore, he recognized the challenges for the private sector in terms of 
cooperating in a PPP setting. 

Leigh Wolfram (OECD) started her talk off with showing the importance of risk transfer instruments 
in the recovery after an economic disruption by a natural hazard. However, although the risk has 
been increasing over time, insurance coverage has not. For PPPs, she firstly recommends to ensure 
the  availability  of  necessary  data,  technology  and  expertise  for  quantifying  exposure  to  cata-
strophic  risk  by  collaborating  across  sectors.  She  also  highlights  the  need  to  support  insurability 
and affordability (e.g. raising risk awareness and risk reducing measures). 

175 

 
 
 
 
 
 
Swenja Surminski (London School of Economics) followed with her talk on supporting comprehen-
sive climate risk management through insurance and the roles for the public and the private sector. 
Her vision is that communities and businesses are well adapted so that losses are kept to a mini-
mum and do not lead to unnecessary public budget reallocations, but are shared through efficient 
private markets. In addition, the public sector can set a resilience-supportive framework and look 
after  those  who  are  most  vulnerable.  In  order  to  achieve  this  vision,  the  insurance  industry  can 
support  resilience  efforts  by  offering  risk  transfer,  risk  device  and  information  and  investments. 
The latter is mostly focused on prevention and adaptation efforts to avoid new risks and high loss-
es, though restricted as there is currently a lack of business case for resilience. 

The following is a summary of the follow up questions and discussions with the audience: 

  Creating a new working group is less desired. Rather, there is the suggestion of improving 

 

the existing bodies and make better use of existing knowledge and information.   
There is still huge potential to account for resilience. Currently we spend much more mon-
ey  on  repair  and  recovery  rather  than  preparedness.  One  of  the  reasons  for this  that  the 
private sector struggles to internalize risk. We need to create and foster the development 
of a positive narrative about resilience. 

  Risk landscape is changing so continuing innovation in required to deal with the challenges. 

Session 4 – The role of cities and regions  

From  the  report’s  results,  it  is  recommended  to  fund  projects  under  the  LIFE  Programme  for  the 
Environmental and Climate Change 2014-2020 to increase the capacity of cities to use insurance as 
a  risk  management  tool  and  insure  infrastructure.  In  addition,  a  dialogue  should  be  created  be-
tween the insurance industry, municipalities and national bodies on how community rating systems 
and city pooling can be developed.  

Moderator: Sandro Nieto Silleras (EC DG CLIMA) 

Alberto  Terenzi  (ICLEI)  started  this  session  of  by  asking  the  question:  insuring  whom,  insurance 
what?  He  emphasises  the  current  challenges  of  cities,  such  as  growing  urban  population,  infra-
structural demands, and climate change mitigation and adaptation. Cities are often seen as a single 
actor,  while  they  entail  a  broader  spectrum  of  actors  and  stakeholders.  Risk  reduction  has  to  be 
imbedded into a larger sustainability strategy. 

Mia Ebeltoft (Finance Norway) shows that the Norwegian insurance system is a solidarity scheme, 
with a flat premium and a 100% insurance penetration rate. However, urban flooding (e.g. sewage 
backup) is not a part of NatCat insurance, but is costlier than all the NatCat losses. Using insurance 
loss data,  she showed that a loss database  could contribute in finding previously unknown risks, 
improve  understanding  of  how  climate  change  affects  society,  and  helps  with better  collaboration 
within the municipalities. Her case study shows the importance of sharing insurance loss data in a 
save and controlled manner. 

Jan Rasmussen (City of Copenhagen) closed the session with a case study on the City of Copenha-
gen. A cloudburst over Copenhagen, with 150 mm in 2 hours, and about 1 billion euro in damages, 
this  was  a  game  changer  for  the  city  leading  to  the  development  of  the  Cloudburst  Management 
Plan. The Cloudburst Management Plan aimed to secure the city up to a 100 year cloud burst in a 
20 year time span. By partnering with the insurance industry on a city level, insurance premiums 
could be lowered, to compensate for increasing water taxes, maintaining a balance is costs for the 
citizens of Copenhagen. Other  benefits included the use  of insurance  data and direct communica-
tion with citizens. 

The following is a summary of the follow up questions and discussions with the audience: 

 
 

There is a need to create a dialogue between communities, industry and national bodies. 
It  is  suggested  to  incorporate  recommendations  regarding  the  region  level  as  they  are 
quite powerful. 

176 

 
 
 
 
 
 
 
 
Closing remarks 

Christian Nyerup Nielsen (Ramboll) 

  We are grateful to have received a lot of input. 
  Stakeholder engagement has been an important dimension of the project and we hope that 

discussions will continue 

  Business case for use of insurance against climate changes losses exist but it requires close 

cooperation between public and private sector at meso-level 

Max Linsen (European Commission) 

  Realistic expectations 
  Connecting dots 
  Create a framework/environment by governments and business cases by private sector. 
  Resilience as a system.  
 

Focus: 

-  Formulate common information needs. 

 

Future: 

-  Scrutinize the recommendations.  
-  What is the right level of facilitation?  

177 

 
 
 
 
 
 
 
 
APPENDIX 6 
DISTRIBUTED AGENDA FOR THE FINAL EVENT 

178 

 
 
 
 
 
 
 
 
 CONFERENCE AGENDA: INSURANCE AND CLIMATE RELATED DISASTERS 
Brussels, 30th of June 

9:30 - 10:00 

REGISTRATION 

WELCOME, PURPOSE AND BACKGROUND OF THE MEETING 

10:00 - 10:45 

Claus Kondrup– European Commission, DG CLIMA 
Naçira Boulehouat - European Commission, DG ECHO 
Xavier Le Den – Ramboll 

SESSION 1: PROMOTING RISK AWARENESS AND REDUCTION 

Moderator: Laura Schmidt- European Commission, DG ECHO 

Introduction of policy recommendations: Paul Hudson – IVM 

10:45 - 11:45 

Key notes: 

Tom Herbstein – ClimateWise 
Michael Szönyi – Zürich Insurance 
Inés Isabel La Moneda– ENESA (National Agency on Agricultural Insurance, Spain) 

Questions 

SESSION 2: CLOSING THE PROTECTION GAP (PANEL DISCUSSION) 

Moderator: Thomas Hlatky – GRAWE 

Introduction of policy recommendations: Paul Hudson – IVM 

11:45 - 12:45 

Panel: 

Paola Grossi - Coldiretti 
Eddy Vanbeneden  - Guy Carpenter 
Laura Schaefer – MCII 
Wouter Botzen – IVM 

Questions 

12:45 - 13:30 

LUNCH 

SESSION 3: THE ROLE OF PUBLIC AND PRIVATE ENTITIES IN THE RISK CYCLE 

Moderator: Peter Schulze - European Commission, DG ECFIN Intro-

duction of policy recommendations: Matilda Persson – Ramboll Key 

13:30 - 14:30 

notes: 

Leigh Wolfrom – OECD 
Swenja Surminski– London School of Economics 

Questions 

14:30 - 14:45 

COFFEE BREAK 

SESSION 4: THE ROLE OF CITIES AND REGIONS 

Moderator: Sandro Nieto Silleras- European Commission, DG CLIMA 

Introduction of policy recommendations: Matilda Persson – Ramboll 

14:45 - 15:45 

Key notes: 

Mia Ebeltoft – Finance Norway 
Jan Rasmussen – City of Copenhagen 
Alberto Terenzi – ICLEI 

Questions 

WRAP UP REMARKS 

15:45 - 16:00 

Christian Nyerup Nielsen - Ramboll 
Max Linsen - European Commission, DG CLIMA 

16:00 - 17:00 

NETWORKING DRINK 

179 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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