Long-term impacts of coastal floods in Europe : a probabilistic analysis.
Date: 2023-01-23
Institutions: Joint Research Centre
Subjects: climate change, coastal region, economic consequence, environmental risk prevention, flood, natural disaster, natural hazard, research report

ISSN 1831-9424 

Long-term impacts  
of coastal floods in Europe: 
a probabilistic analysis 

Mongelli I., Vousdoukas M., Feyen L., Soria A., 
Ciscar JC 

2023 

EUR 31386 EN 

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Contact information  
Name: Ignazio Mongelli 
Address: c/Inca Garcilaso s/n, E-41092 Seville (Spain) 
Email: ignazio.mongelli@ec.europa.eu 
Tel.: +34 95 44 88 425 

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

JRC 132103 

EUR 31386 EN 

PDF 

ISBN 978-92-76-61673-3 

ISSN 1831-9424 

doi:10.2760/292047  KJ-NA-31-386-EN-N 

Publications Office of the European Union, Luxembourg, 2023
© European Union, 2023

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How  to  cite  this  report:  Mongelli  I.,  Vousdoukas  M.,  Feyen  L.,  Soria  A.,  Ciscar  JC,  Long-term  impacts  of  coastal  floods  in  Europe:  a 
probabilistic analysis, Publications Office of the European Union, Luxembourg, 2023, doi:10.2760/292047, JRC132103 

Contents  

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

1 

Introduction..................................................................................................................................................................................................................................................... 2 

2  Economic model and analysis ...................................................................................................................................................................................................... 4 

3  Results ................................................................................................................................................................................................................................................................. 7 

4  Conclusions .................................................................................................................................................................................................................................................. 10 

References ............................................................................................................................................................................................................................................................. 11 

List of abbreviations and definitions ........................................................................................................................................................................................... 13 

List of figures ..................................................................................................................................................................................................................................................... 14 

List of tables ........................................................................................................................................................................................................................................................ 15 

Annexes .................................................................................................................................................................................................................................................................... 16 

Annex 1. Direct coastal impacts .............................................................................................................................................................................................. 16 

Annex 2. Economic model .............................................................................................................................................................................................................. 16 

Annex 3. Consumption, damage to residential properties and savings ............................................................................................. 16 

Annex 4. Effective capital and reconstruction of the destroyed capital stock ............................................................................ 17 

Annex 5. Productivity growth and build-back-better scenario .................................................................................................................... 18 

Annex 6. Data and calibration of the model ................................................................................................................................................................ 18 

Annex 7. Results at country level............................................................................................................................................................................................ 20 

i 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Abstract  

In this report, we  quantify the long-term economic impacts of coastal flooding in Europe, in  particular,  how 
the  direct  coastal  damages  generate  long-term  economic  losses  that  propagate  and  compound  throughout 
the  century.  We  integrate  a  set  of  probabilistic  projections  of  inundation-related  monetary  impacts  (to 
residential  buildings,  firms’  physical  assets  and  agriculture  production)  into  a  stochastic  dynamic  economic 
model.  The  uncertainty  related  to  the  economic  agents’  behaviour  and  other  relevant  macroeconomic 
assumptions, i.e. how would consumers finance the repairing of their homes, how long does it take for a firm 
to reconstruct, do firms decide to build-back-better after the inundation and possibly compensate the losses 
with a productivity gain, is explicitly considered. The results show that the long-term total impacts of coastal 
floods are larger than the direct damages generated by the inundations: by the end of the century EU27 plus 
UK  could  lose  every  year  between  0.25%  -  0.91%  of  GDP,  under  a  high  emission  scenario  (RCP8.5).  The 
results  present  a  strong  regional  variation  and  in  some  countries  the  losses  for  the  economy  could  reach 
worrying proportions. 

1 

 
 
1 

Introduction  

The  68000  km  of  European  coastline,  where  around  40%  of  the  population  lives  and  half  of  GDP  is 
generated,  makes  of  Europe  a  region  highly  exposed  to  storm  surges  and  coastal  inundations  (Copernicus; 
Kulp and Strauss 2019). On February 1 1953, a combination of high tides and storms pushed the North Sea 
water to rise up to five meters above the average level and the consequent inundations of the coasts of the 
Netherlands,  Belgium,  Germany  and  the  UK  caused  the  death  of  2100  people  and  huge  economic  losses. 
While  mortality  of  coastal  inundations  has  been  decreasing  in  the  last  few  decades  (Bouwer  and Jonkman, 
2017), a similar trend has not been observed for monetary losses (Bevere et al. 2021; Guha-Sapir 2015). On 
November  2019,  a  flood  estimated  to  be  a  1-in-50  years  event  hit  Venice,  in  Italy,  and  caused  losses  and 
damages  of  around  a  billion  euros  (Godlewski  et  al  2020).  River  floods  in  Europe  now  have  larger  socio-
economic  consequences,  but  recent  studies  indicate  that  by  the  end  of  this  century  the  scenario  could  be 
different,  with  large  coastal  inundations  occurring  at  increasingly  shorter  recurrence  intervals  (Alfieri  et  al., 
2018). 

Understanding  and  assessing  economic  risks  associated  to  coastal  inundations,  and  more  in  general  to  all 
different  kinds  of  natural  disasters,  has  been  the  subject  of  a  growing  number  of  studies  combining  a 
biophysical model for the assessment of the natural hazards (providing the direct economic damages) and a 
macroeconomic model for the quantification of the additional indirect economic costs in the economy (Botzen 
et al. 2019).  

There  are  several  studies  providing  economic  damage  estimates  of  coastal  flooding  in  Europe  following 
different  economic  methodologies.  Bosello  et  al.  (2012)  combine  the  DIVA  biophysical  model  with  a  GTAP-
based  Computable  General  Equilibrium  (CGE)  model  to  estimate  the  indirect  economy-wide  implications  of 
sea level rise direct economic and physical impacts, such as land loss, salinization, (forced) migration, asset 
loss etc. Their results show very small indirect impacts in Europe, with positive GDP effects in many countries, 
which results from the economies’ capacity of mitigating direct impacts via the flexibility features inherent to 
the CGE modelling framework, such as factor substitution and international trade. 

Nishiura et al. (2020) assess the economic impact of coastal inundation at the global level with a CGE model, 
combined with a set of general circulation models and socioeconomic assumptions, finding rather small GDP 
and welfare losses in 2100 for a RCP 8.5 scenario. Schinko et al. (2020) use a CGE model and two economic 
growth models, concluding that GDP impacts for EU28 can be much larger, in a 4 - 6% range by 2100.  

Climate change impacts other than coastal inundations have also been the subject of similar analyses. Dottori 
et al. (2018) apply a multi model dynamic framework to estimate direct and indirect economic consequences 
of river floods in Europe at different warming levels and under different socio-economic scenarios. Tanoue et 
al. (2020) uses a CGE model to explicitly account for indirect consequences due to business interruption of a 
flood  occurred  in  Thailand  in  2011;  according  to  the  authors,  the  total  indirect  effects  on  GDP  would  be 
around three times the direct losses and last for more than two decades.  

While most of the existing literature focuses primarily on trade, intersectoral and factor substitution effects, 
they  often  neglect  or  offer  a  simplified  view  on  long-term  growth  dynamics.  In  the  present  study,  we 
therefore  try  to  complement  the  scope  of  the  existing  literature  and  explore  more  in  detail  how  different 
growth dynamics assumptions and scenarios might either amplify or mitigate the consequences on GDP and 
Welfare of those initial climate impacts. 

Severe and  more frequent inundations will  most likely generate large losses of  physical assets that in turn 
will cause both a temporary fall in the productive capacity and changes in household expenditure behaviour, 
which  will  trigger  a  series  of  cascade  effects  on  investment,  income,  consumption  and  productivity.  The 
magnitude and direction of these effects will largely depend on: i) how fast firms reconstruct and return to 
the  pre-disaster  level  of  productive  capacity,  ii)  how  households  finance  the  repairing  of  the  residential 
properties,  iii)  the  existence  or  not  of  spillovers  from  investments  to  productivity,  iv)  and  the  technology 
embodied  in  the  assets  replaced  after  the  disaster.  In  particular,  households  can  repair  the  residential 
damages either via a reduction in consumption or in savings, a choice that can have different macroeconomic 
implications as the reduction in savings will reduce investment, and therefore future growth, while a reduction 
of consumption will affect their overall welfare level. Similarly, firms can reconstruct the damaged physical 
assets  in  various  ways,  which  will  also  affect  the  long-term  growth  prospects  as  the  capital  stock  of  the 
economy  will  be  adjusted  in  different  manners.  Those  features  relate  to  the  duration  of  the  reconstruction 
process,  whether there are spillovers from investments to productivity, and the technology embodied in the 
assets  replaced  after  the  disaster.  All  those  possible  responses  by  households  and  firms  are  uncertain  and 
cannot  be  easily  characterized.  Therefore,  we  have  implemented  a  probabilistic  scheme  to  consider the  full 

2 

 
spectrum  of  possible  alternatives.  To  our  knowledge,  this  approach  has  not  been  applied  so  far  in  the 
literature.  

In  the  present  analysis,  direct  coastal  damages projections  are  taken  from Vousdoukas et  al  2017  and are 
derived  with  a  risk  analysis  based  on  the  model  LISCOAST  (Large-scale  Integrated  Sea-level  and  Coastal 
ASsessment Tool) that assesses coastal flood impacts under present and future climates for a high emissions 
scenario (RCP8.5). In LISCOAST, state-of-the-art large-scale modelling tools and datasets are used to quantify 
hazard, exposure  and  vulnerability  and quantify  consequent  risks  in  monetary  terms.  The  extent,  depth  and 
frequency of coastal flooding during the century is the result of the evolution of the climate and the extreme 
sea  levels,  with  the  latter  driven  by  extreme  wind,  atmospheric  pressure  and  tidal  levels.  The  framework 
combines  all  the  above  factors  as  well  as  the  major  sources  of  uncertainty  in  a  Monte  Carlo  approach, 
considering also the spatial dependence of extreme weather events utilizing Copulas (see Vousdoukas et al. 
2018 and SI for further information). In a second step, the direct impacts are used as an input to an economic 
growth stochastic model à la Solow that allows to consider different responses by households and firms to 
the  climate  shocks  in  a  probabilistic  way.  A  similar  model  has  been  used  in  other  climate  impacts  analysis 
(Tsigaris and Wood 2016, Dottori et al. 2018 and Schinko et al. 2020). Moreover the papers by Fankhauser 
and  Tol  (2005),  Dietz  and  Stern  (2015)  and  Bosello  and  De  Cian  (2014)  and  more  recently  Piontek  et  al. 
(2019) provide with interesting overviews of both methodological and numerical issues related to the use of 
long-term  growth  models  for  climate  impacts  analysis.  Our  growth  model  is  aligned  to  the  official 
demographic and economic projections for the EU Member States until 2100 (DG ECFIN 2021). Full details of 
the model are provided in the Supplementary Information.  

The report is organized as follows. Section 2 describes the modelling framework used to evaluate the long-
term economic impacts of coastal inundations in the EU, which is the treated and described in details in the 
Supplementary  Information.  Section  3  presents  and  discusses  the  results  of  the  analysis.  Finally,  Section  4 
presents our conclusions. 

3 

 
2  Economic model and analysis  

For the economic analysis we use a Solow-Swan model, which represents a milestone of the economic growth 
literature and still today provides a good representation of the long-term economic dynamics (Solow 1956).  

We calculate economic losses from coastal flooding and their propagation in time. In this context, the saving 
rates  are  very  relevant,  because  they  determine,  ceteris  paribus,  how  fast  the  economy  recovers  after  a 
disaster.  Moreover,  as  pointed  out  in  Verschuur  et  al.  2020,  Walsh  and  Hallegatte  2020  and  Feyen  et  al., 
2020,  losses  of  welfare  provide  a  broader  view  of  the  impacts  that  better  reflects  how  physical  impacts 
affect people and households. Therefore, in addition to the usual measure expressed as percentage of GDP, 
we  provide  also  estimates  of  welfare  losses  measured  in  terms  of  consumption  per  capita  above  the 
autonomous level. 

In  our  analysis,  coastal  flooding  generates  three  different  types  of  direct  physical  impacts,  which  are: 
damages  to  residential  buildings,  damages  to  firms’  physical  assets  (the  capital  stock)  and  foregone 
agricultural output. These direct damages are derived from LISCOAST and further details are provided in the 
Annexes.  

In order to  have a representation of the  uncertain economic agents’, i.e.  firms and consumers, reaction and 
behaviour with respect to the coastal impacts, we implement a stochastic version of the Solow-Swan model 
where  some  of  the  crucial  parameters  of  the  analysis  are  assumed  to  be  uniformly  distributed  within 
plausible ranges. The assumption of uniform distributions is somehow arbitrary, but justified by the absence 
of better information. Previous examples of this type of modelling framework can be found in Kesten 1971, 
Schenk-Hoppé and Schmalfuss 2001 and Feicht and Stummer 2010. 

The  uncertainty  in  the  response  of  firms  and  households  to  the  coastal  shocks  is  structured  in  five 
mechanisms.  The  first  mechanism  relates  to  the  repairing  of  residential  buildings  by  the  consumer.  In 
particular,  the  repairing  of  the  damages  to  private  properties  is  considered  as  part  of  the  autonomous 
consumption, which is the subsistence or minimum expenditure that must be made, even without any income. 
Households  are  assumed  to  finance  the  repairing  by  either  dissaving  (or  asking  a  bank  loan),  and  increase 
their overall expenditure level, or by reallocating their consumption and giving up spending on more welfare-
enhancing consumption categories. In the first case, households preserve their living standard but make less 
financial resources available for investments in the future at national level, which in turn slows down both the 
renovation and reconstruction of the firms’ productive capital stock. In the second case their overall welfare 
level is negatively affected in the short-term, but the economy does not suffer any drop in total investments. 
The choice between dissaving or consumption reallocation might depend on several factors, as for instance a 
more or less precautionary behaviour, the level of income or of indebtedness as well as the functioning of the 
credit  system,  among  others.  The  full  spectrum  of  reaction  possibilities  is  captured  in  this  analysis  by 
specifying  this  choice  as  a  uniform  distribution  between  a  minimum  of  zero,  i.e.  dissaving  by  the  entire 
amount  of  the  damage  and  no  change  in  welfare,  and  a  maximum  of  1,  i.e.  complete  reallocation  of 
consumption with the entire damage affecting the households’ welfare level. 

The second mechanism concerns the available resources for the reconstruction of the capital stock. Damages 
to  the  firms’  physical  assets  are  modelled  as  a  loss  of  the  productive  capital  stock,  as  a  lower/damaged 
capital stock produces a lower output. As suggested by  Hallegatte and Dumas (2009), the model also takes 
into account the frictions and inefficiency inherent to the process of rebuilding that follows the destruction of 
physical  assets  after  a  coastal  flood  event  (Hallegatte  and  Dumas  2009).  We  assume,  in  fact,  that  the 
amount  of  resources  that  can  be  used  for  the  rebuilding  activities  are  not  unlimited.  Instead,  due  to 
institutional rigidities and logistic difficulties, only a small fraction of the overall national investments can be 
diverted  from  the  renovation  and  growth  of  the  existing  capital  stock  to  the  reconstruction  activities.  This 
parameter  is  assumed  to  be  uniformly  distributed  between  0.01%  of  total  investments,  a  very  pessimistic 
scenario that generate accumulation of the damages in  time, and 1%,  which is a very optimistic value that 
allow asset damages to be repaired almost entirely in one time period, which in our model corresponds to one 
year. 

Destructive  events  like  coastal  flooding  might  be  in  theory  beneficial  for  the  economic  system  if  the 
destroyed productive assets are replaced with more efficient, modern productive technologies. This aspect of 
post-disaster economic implications has been analysed and discussed theoretically and empirically in various 
works  (Skidmore  and  Toya  2002;  Albala-Bertrand,  J.  M.,  1993;  Crespo  et  al  2008;  Hallegatte  and  Dumas, 
2009; Benson and Clay 2004, Zhou and Zhang 2021; Panwar and Sen 2019; Cavallo et al 2013; Hornbeck and 
Keniston  2017),  but  to  our  knowledge  remains  unexplored  for  EU  countries  in  relation  to  specific  coastal 
flooding scenarios. The third mechanism, therefore, relates to the degree of capital upgrade, as explained in 

4 

 
what follow. In order to account for the potential positive effects of coastal flooding on growth, we partially 
reformulate the equation for the growth of technology/productivity level, to take in to account the fact that 
the  money  spent  on  reconstruction  upgrades  the  technological  level  of  the  economy  and  increases  its 
productivity. We use an approach similar to the one explained by Hallegatte and Dumas (2009) and assume 
that after a disaster, firms have the possibility of replacing the damaged assets with newer ones that embody 
the  technological  level  of  the  European  frontier,  which  in  this  case  we  assume  to  be  the  one  of  Denmark. 
Assuming that firms will replace all damaged assets with frontier technology, is an optimistic hypothesis and 
there are several reasons that this may not be the case. It is likely that firms might not have access to the 
frontier  technology,  or  may  luck  the  financial  resources;  so  they  may  gravitate  to  repairing  at  lower  costs, 
physical assets partially damaged by the inundation. Another reason that they may not upgrade with the most 
modern  equipment  is  time  constraints,  as  they  may  need  to  restart  their  activities  as  soon  as  possible,  in 
order to limit the losses or to ensure basic services (Hallegatte and Dumas 2009). To model this uncertainty 
we  assume  that  the  share  of  damaged  assets  replaced  with  frontier  technology  is  a  random  parameter 
uniformly distributed between 0 and 1.  

A  fourth  mechanism  affects  the  trade-off  between  quality  and  duration  of  the  reconstruction  process.  As 
suggested  in  Hallegatte  and  Dumas  (2009),  there  might  be  a  trade-off  between  the  quality  of  the 
reconstruction  and  its  duration,  i.e.  with  large  embodiment  of  new  technologies in  reconstructed  assets the 
reconstruction process takes longer. Hallegatte and Dumas (2009) suggest that the quality duration trade off 
occur for a variety of reasons, i.e. the new technology is not immediately available or its functioning depends 
on workers training that takes time, and propose a simple relationship where the larger the embodiment of 
new  technologies  the  longer  the  duration  of  the  reconstruction.  We  assume  that  this  trade-off  between 
quality and duration is a random parameter uniformly distributed between 1, i.e. no trade off, and 10 a very 
pessimistic assumption where the installation of frontier technology takes 10 times longer.  

The  fifth  mechanism  is  related  to  the  source  of  productivity  growth.  With  the  exception  of  the  part  of  the 
overall productivity that depends on the amount of assets replaced after the disaster, the overall level of the 
economy’s productivity can be assumed to be either exogenous, i.e. manna from heaven, or endogenous, i.e. 
growing in  part as a  function of the level of the investments per worker (Arrow  1952, Romer 1986,  Kumar 
and Russell 2002, Dietz and Stern 2015). We assume that both exogenous and endogenous productivity have 
equal chances. 

Impacts  from  agricultural  output  losses  are  subtracted  from  the  overall  output  of  the  economy.  A  lower 
output of the economy translates, in turn, into lower resources for investments and a slower accumulation of 
physical assets compared to the baseline, no-climate impacts scenario. 

Figure  1.  Uncertainty  dimensions  analysed.  Each  of  the  10000  inundation  projections  is  analysed  under  different 
economy configurations (100). The resulting ensemble comprises both physical and economic uncertainty. 

Source: JRC, 2022. 

5 

 
 
 
 
The  present  analysis  has  two  uncertainty  dimensions,  as  represented  in  Figure  1.  First  dimension  is  the 
uncertainty of the biophysical phenomena that is represented with the 10000 realisations of coastal flooding 
resulting from the Monte Carlo simulation. The second dimension is the economic uncertainty that depends on 
the different model specifications, i.e. exogenous technology vs. productivity spillovers from investments, and 
on the firms and households behavioural choices reflected in the parameter uncertainty. Each of the 10000 
coastal flooding realisation is analysed  with  100 different model and agents’ choice specification randomly 
sampled from the specified distributions. The 100 different specifications give enough representation of the 
parameter range while keeping the computing time within reasonable limits.  

The  model  is  calibrated  using  publicly  available  data.  Projections  for  population,  GDP  and  productivity  are 
taken from the Ageing Report 2021 (DG Ecfin, 2021); while additional data are taken  from the Penn World 
Table 10, i.e. initial stock of capital and cross country depreciation rates, and World Development Indicator of 
the World Bank, i.e. country specific saving rates (Feenstra et al. 2015; Eurostat - European Commission 2021, 
The World Bank, 2021). 

6 

 
3  Results 

The  economic  consequences  for  Europe  (EU  countries  plus  UK)  of  future  coastal  flooding  under  a  high 
emissions scenario (RCP8.5) are presented in Figure 2 in terms of GDP and Welfare percentage losses w.r.t. to 
the counterfactual economic  scenario that  does not include any coastal flooding shocks into the model. For 
both GDP and Welfare, in Figure 2 we are comparing direct impacts, which are mainly represented by asset 
losses  either  in  the  form  of  productive  capital  for  firms  or  of  residential  property  for  consumers,  with  the 
associated income or welfare losses. We distinguish between direct impacts on production or firms and direct 
impact  on  Welfare  or  households.  The  first  corresponds  to  the  combined  value  of  the  lost  agricultural 
production,  the  value  of  productive  assets  damaged,  plus  the  part  of  the  residential  damages  repaired  via 
dissaving.  The  second,  only  the  residential  damages.  Being  a  comparison  between  a  stock  variable  and  the 
associated flow variable of the model, the comparison is not entirely meaningful in economic terms and must 
be taken only for the sake of comparing direct vs. indirect losses to the economy and answering the question 
of  how  large  are  income  losses  for  similar  levels  of  direct  losses,  which  provides  with  an  indication  of 
economic resilience.  

In particular, panel A in Figure 2 compares the distribution of indirect GDP losses, represented by the shaded 
areas and the purple line in the middle, with the distribution of the direct impacts on production represented 
by the red lines in the graph. For both direct and indirect impacts, the range of the results corresponds to the 
95%  of  the  overall  distribution,  i.e.  the  very  likely  range  in  IPCC  terms  (Mastrandrea  et  al.  2010).  The  thick 
purple  line  in  the  middle  corresponds  to  the  mean  value.  The  majority  of  projections,  i.e.  our  most  likely 
scenario that corresponds to the dark grey part in the figure (Figure 2, panel A), cumulate around a value of 
0.57%  of  GDP  by  2100.  The  indirect  losses  in  2100  for  EU27  +  UK  vary  within  a  wide  range  comprised 
between 0.25% - 0.91% of GDP relative to the baseline. 

In panel B of Figure 2 the results for both GDP and Welfare are shown for the five macro-regions and taken 
as the average of the last thirty years of the projection period 2071-2100. The most affected region for GDP 
is  South,  which  comprises  Bulgaria,  Greece,  Italy,  Malta,  Portugal  and  Spain,  with  losses  varying  within  the 
very  likely  range  of  0.18% to  0.78%  (mean  0.46%).  UK  and Ireland is the  second  most  affected  region  for 
GDP with losses comprised in the very likely range of 0.19%-0.72% (mean 0.44%). Center-South, comprising 
France, Romania and Slovenia, and North, comprising Denmark, Estonia, Finland, Latvia, Lithuania and Sweden 
have comparable GDP losses at the end of the century comprised in the very likely range of 0.16% – 0.74% 
(mean  value  of  0.43%)  and  0.16%  –  0.75%  (mean  value  of  0.43%),  respectively.  The  Center-north  region 
(Belgium,  Germany,  Nederland  and  Poland)  GDP  impacts  are  projected  to  be  the  lowest  in  Europe  and 
comprised in a range of  0.03%  – 0.30% (median 0.13%). The large range of variation for the GDP impacts 
depends  on  the  range  of  the  direct  damages  and  on  the  stochastic  assumptions  underlying  the  economic 
analysis. Clearly, a longer reconstruction process, a reconstruction without any capital upgrading, large share 
of  residential  damages  repaired  via  dissaving  (and  not  consumption  reallocation)  and  large  spillovers  from 
capital loss to productivity will generate larger GDP losses, while the contrary  is true for a variation of those 
parameters in the opposite direction.  

The  impacts  on  Welfare  are  transmitted  via  two  main  channels:  directly  by  the  amount  of  repairing  that  is 
financed  via  an  increase  of  the  non-welfare  enhancing  minimum  consumption  and  indirectly  by  a  gradual 
reduction of the income due to a general impoverishment of the economy, i.e. lower level of GDP, when the 
residential damages are repaired via dissaving. In both cases, the households have to bear the entire impacts 
with limited possibilities of reducing it and in fact, the range of welfare impacts corresponds to the range of 
the residential damages. Most affected regions in terms of Welfare are North and Centre-South, with impacts 
in  the  range  of  0.61%-1.04%  (mean  0.81%)  and  0.57%-0.86%  (mean  0.71%).  Slightly  smaller  losses  are 
projected for South and UK & Ireland, where Welfare is affected in a range of 0.52%-0.85% (0.67% mean) 
and 0.45%-0.78% (0.60% mean), respectively. Also for Welfare the region with the lowest impact is Centre-
North, with impacts comprised in the range of 0.078%-0.53% (0.24% mean). 

For  both  GDP  and  Welfare,  the  long-term  losses  are  larger  than  the  direct  physical  impacts  hitting  the 
economy’s production or the households’ welfare. The ratio between direct and indirect losses is indicative of 
different degrees of resilience. Our projections show that UK and Ireland, South and to some extent Centre-
South are the regions with low levels of long-term resilience with a unit of direct loss hitting the production 
side  of  the  economy  generating  2.58,  1.91  and  1.59  units  of  indirect  losses.  Low  levels  of  resilience  are 
reflected also in an increase of the uncertainty range between direct and indirect losses. 

For Welfare losses, indirect impacts are generated as second order effect of the model. Households can only 
temporarily  avoid  impacts  on  their  welfare;  should  they  decide  to  dissave  for  the  entire  amount  of  the 

7 

 
damages and preserve their welfare, those impacts would anyway affect their income in subsequent periods. 
Larger ratios of indirect losses over direct losses are, also for Welfare, recorded for those regions with lower 
level of resilience. UK & Ireland, South and Centre-South are the regions with the largest ratios between direct 
and indirect losses also for Welfare: 2, 1.76 and 1.37, respectively. 

The most affected countries are Cyprus, Greece, Croatia and Denmark with relative GDP losses comprised in a 
very likely range of 7.5% – 2.99% (mean 5.12%), 1% – 4.68% (mean 2.69%), 0.55% – 2.48% (mean 1.5%), 
0.29% – 1.84% (mean 1.84%) and Welfare losses equal to 5.86% – 7.9% (mean 6.76%), 2.6% – 4.9% (mean 
3.57%),  1.42%  –  2.77%  (mean  2.20%),  1.38%  –  2.75%  (mean  2%),  respectively.  The  full  list  of  results  for 
countries is included in the Annex. 

In none of the countries where impacts for both GDP and Welfare are large, the upgrading of the capital stock 
generates  gains  or  has  a  significant  mitigation  effect.  With  few  exceptions,  the  technological  level  for 
European  economies is  not  low  and  the  marginal  improvement  from a  build-back-better policy  is therefore 
limited. There are nonetheless countries where according to these projections there might be some GDP and 
Welfare gains and these are: Bulgaria with 30% chances of GDP gains larger than 0.05% and 20% chances of 
Welfare gains larger than 0.03%; Estonia with 5% chances of GDP gains larger than 0.007%; Slovenia with 
5% chances of GDP gains larger than 0.01%. The countries benefitting from a build-back-better scenario are 
those with larger gaps with respect to the technological frontier. 

The  impacts  on  Welfare  expressed  in  euro  per  worker  (real  terms in  2015  prices)  are  plotted  in  panel  C  of 
Figure  2  for  the  values  at  the  end  of  the  century.  The  error  bars  report  again  the  5%  and  95%  quintiles. 
Around 5% with average income losses larger than 3000 euros, which indicate an increase of poverty rates in 
the  more exposed  regions.  Around  30% of EU workers will be exposed  by  2100 to an average income loss 
above 1000 euro per year. These figures are expressed as country averages that might hide sub-country-level 
disparities, which in turn could exacerbate the overall impact profile. 

8 

 
 
 
Figure 2. Panel A) distribution of impacts for EU+UK on GDP, the grey to black colour scale represents the concentration 
of the GDP impacts around the most likely scenarios. The red lines show the mean (continuous) and the very likely range 
of the direct impacts (dashed); panel B) shows both the GDP and Welfare impacts for the five considered macro regions 
and by the end of the century (average of the last 30 years from 2071 to 2100) expressed as relative change w.r.t the 
reference  scenario  (%  of  GDP  and  %  of  Per  capita  consumption;  annuitized).  Panel  C)  indicates  the  welfare  losses  per 
country and measured in thousands euros; the plot compares the impacts with the population shares of each country over 
the overall population. The error bars (in panel B and C) reflect the very likely range for the impacts, while the different 
colours refer either to direct (red) or total (purple) impacts. 

Source: JRC, 2022. 

9 

 
 
 
 
 
4  Conclusions 

This work integrates a large ensemble of probabilistic direct coastal damage projections in a macroeconomic 
framework in order to comprehend how and to what extent physical impacts would interfere with the process 
of  economic  growth  and  generate  long-term  economic  losses  in  EU.  Direct  physical  impacts  of  coastal 
flooding projected up to 2100 under a high emissions scenario (RCP8.5) have been used in combination with 
an extended/stochastic Solow model to assess the macroeconomic implications in terms of annuitized relative 
GDP and Welfare losses. In our analysis, we complement the uncertainty range assessed for the biophysical 
phenomena with the uncertainty related to the behaviour of the households in a post inundation scenario and 
to the firms’ response with respect to the duration and quality of the reconstruction process. The majority of 
the direct coastal flood impacts come from damages to residential properties, whose repairing is expected to 
affect  the  consumption  vs.  saving  decision.  Households  will  dissave,  increase  their  overall  expenditure  and 
repair the damages without any immediate welfare loss, or in alternative reshuffle their consumption, give up 
some  other  expenditure  along  with  part  of  their  welfare,  and  repair.  Large  coastal  flood  impacts  are  also 
associated to the loss of productive physical assets and we model the reconstruction process with different 
duration  and  different  levels  of  upgrades  of  the  replaced  assets.  Instead  of  analysing  extreme  cases  we 
model  these  scenarios  in  terms  of  parameters  distribution  and  analyse  a  larger  spectrum  of  plausible 
scenarios.  

The results show that in addition to the direct losses produced by the inundations, the EU economy might lose 
between 0.25% - 0.91% (median 0.57%) of the annual GDP and Welfare by the end of the century. The most 
affected region for GDP is the South where losses might be in the range of 0.18% to 0.78% (mean 0.46%). 
While  most  affected  regions  for  Welfare  are  North  and  Centre-South,  with  losses  in  the  range  of  0.61%-
1.04% (mean 0.81%) and 0.57%-0.86% (mean 0.71%). Impacts on GDP and Welfare for some countries like 
Greece,  Cyprus  and  Croatia  could  reach  levels  that  might  represent  a  serious  problem  for  their  long-term 
prospects but also a menace of poverty for part of the population. 

We  also  analysed  the  economic  effect  of  replacing  damaged  assets  with  new  installed  assets  that  have  a 
higher productivity. The results indicate a mitigation of the impacts only for economies with large technology 
gaps, where the upgrade of the capital stock might generate significant productivity gains. However, on an EU 
basis we can conclude that it is very unlikely that the benefits of a build-back-better policy could be sufficient 
to  compensate  or  even  having  a  significant  mitigation  of  the  direct  physical  losses  for  the  case  of  coastal 
floods. 

In our analysis, the savings rates determines the level of resilience of the economy: a low saving rate makes 
the transition back to pre-inundation levels slower and therefore amplifies the indirect impacts on GDP and 
Welfare. Our results underline the relevance of saving rates and of  promoting  risk management  policies as 
mitigation measures of long-term economic impacts of coastal flooding.  

A limitation of the present analysis is the assumption of uniform distribution for the agents’ reaction to the 
disaster. A major extension of the present study would be to provide an empirical foundation to the choice of 
different distributions for different agents in different countries or regions, which would allow cross-country 
heterogeneity to be reflected in the analysis more accurately. 

10 

 
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List of abbreviations and definitions  

GDP 

Gross Domestic Product 

RCP8.5 

Representative Concentration Pathways 8.5 

13 

 
 
List of figures  

Figure 1. Uncertainty dimensions analysed. Each of the 10000 inundation projections is analysed under 
different economy configurations (100). The resulting ensemble comprises both physical and economic 
uncertainty...............................................................................................................................................................................................................................5 

Figure 2. Panel A) distribution of impacts for EU+UK on GDP, the grey to black colour scale represents the 
concentration of the GDP impacts around the most likely scenarios. The red lines show the mean (continuous) 
and the very likely range of the direct impacts (dashed); panel B) shows both the GDP and Welfare impacts 
for the five considered macro regions and by the end of the century (average of the last 30 years from 2071 
to 2100) expressed as relative change w.r.t the reference scenario (% of GDP and % of Per capita 
consumption; annuitized). Panel C) indicates the welfare losses per country and measured in thousands euros; 
the plot compares the impacts with the population shares of each country over the overall population. The 
error bars (in panel B and C) reflect the very likely range for the impacts, while the different colours refer 
either to direct (red) or total (purple) impacts. .................................................................................................................................................9 

Figure 3. Comparison of the GDP projection of the AR2021 and the GDP projected by the calibrated model 19 

14 

 
 
List of tables  

Table 1. Calibrated parameters for the European countries analysed in the study: d (depreciation rate), s 
(saving rates), b and c (parameters for the endogenous growth of productivity). ................................................................ 20 

Table 2. Results by country ..................................................................................................................................................................................... 21 

15 

 
 
Annexes  

Annex 1. Direct coastal impacts 

The direct physical impacts associated to inundations are estimated in four steps. First, estimate present and 
future  extreme  sea  levels  along  Europe’s  coastlines  based  on  state-of-the-art  projections  of  sea  level  rise, 
waves,  storm  surges  and  tides  for  a  high  emissions  (RCP8.5)  pathway.  Second,  delineate  the  land  areas 
inundated  when  extreme  sea  levels  overtop  current  coastal  protection  and  derive  the  corresponding  flood 
inundation depth using 2-D hydraulic modelling; third, overlay the inundation maps with exposure information 
on population and land use; fourth, translate this into direct flood losses using functions that relate the depth 
of inundation with economic damage to the assets inundated, and into the number of people flooded, taking 
into account gridded socio-economic projections. 

The coastal flood risk analysis is based on the model LISCOAST (large-scale integrated sea-level and coastal 
assessment tool). The modular framework has been developed to assess weather-related impacts in coastal 
areas in present and future climates. It combines state-of-the-art large-scale modelling tools and datasets to 
quantify  hazard,  exposure  and  vulnerability  and  compute  consequent  risks.  In  order  to  reflect  the  large 
uncertainty  that  underlies the  extent, frequency  and  timing  of  the  sea  level  rise  related  inundations;  in  this 
study we  use a set of Monte Carlo projections of the extreme sea level rise events (ESL) obtained with the 
combination  of  a  GCM  ensemble,  with  probabilistic  distribution  of  extreme  wind,  atmospheric  pressure  and 
maximum tidal level (see Vousdoukas et al 2017 for further information). 

Annex 2. Economic model 

The model considers three main categories or channels of SLR damages, similar to the damage mechanisms 
considered in Fankhauser and Tol (2005): agriculture output losses (Agr), damage to capital stock (Cap) and 
damage to residential buildings (Res). Those direct damage estimates are provided by the sectoral, bottom-up 
biophysical models. 

The dynamic adjustment of the economic system is assumed to be the following: the agriculture damages are 
fully absorbed during the year of the climate shock, the damages to residential buildings are all repaired by 
the household in the year of the climate shock by increasing minimum consumption and reducing savings and, 
finally, the  damages  to  capital  stock  are  repaired  in  the  year of  the  shock  up  to  a  certain  threshold,  which 
makes these damages to accumulate and generate compounding negative effects over time.  

In our modelling framework, EU countries UK are represented as a collection of closed economies where firms 
have access to the same constant returns to scale production technology, which is based on the combination 
of two inputs, i.e. capital and labour, and a technology/productivity multiplier. The closed economy assumption 
is  an  important  model  characteristic,  in  fact  countries  would  likely  re-build  their  capital  stock  also  by 
importing  capital  goods,  increasing  the  trade  deficit  and  paying  with  trade  surplus  later,  which  would 
smoothen the shock over time. Capital mobility could be a possible extension of the present analysis. 

The  economy  produces  one  single  good,  which  is  used  for  both  consumption  and  investments;  the  latter 
contribute  to  the  accumulation  of  the  firms’  stock  of  physical  asset,  while  the  former  contributes  to  the 
households’ welfare.  

The production technology uses capital and labour to produce a homogenous final good. 

𝑌(𝑡) = (𝐴(𝑡) ∙ 𝐿(𝑡))

1−𝛼

𝛼
∙ (𝐾(𝑡) − 𝐷𝑎𝑚(𝑡))

− 𝑨𝒈𝒓(𝑡) 

Eq. 1 

Eq. 1 is a standard Cobb-Douglas production function where Y is the output of the economy, A is a scalar for 
the  productivity  of  labour  input,  L  is  labour,  K  is  the  capital  stock,  Dam  is  the  amount  of  physical  assets 
destroyed by the inundations and Agr is the agricultural loss. L is assumed to grow according to 𝐿(𝑡) = 𝐿0 ∗
 𝑒𝜔𝑡, where L0 is labour at time 0 and ω is the constant exogenous rate from the Ageing Report 

Annex 3. Consumption, damage to residential properties and savings 

Eq. 2 is the equation for consumption 𝑪, where 𝒔 is the exogenous saving rate.  

16 

 
 
 
 
 
𝐶(𝑡) = (1 − 𝑠) ∗ 𝑌(𝑡) 

Eq. 2 

In Eq. 2 𝑹𝒆𝒔 stands for the overall damaged residential property. The damages to residential properties are 
assumed to increase consumption. Therefore, the overall saving  𝑺 of the economy (country) is calculated as 
the  difference  between  total  output  (GDP)  and  consumption  minus  the  part  of  the  residential  damages 
repaired via dissaving, as shown in Eq. 3. 

𝑆(𝑡) = 𝑌(𝑡) − 𝐶(𝑡) − 𝑹𝒆𝒔(𝑡) ∗  𝜑 

Eq. 3 

We  assume  that  𝜑~U(0, 1).  Therefore,  when  𝜑 = 0  the  households  are  assumed  to  reshuffle  consumption 
and repair the  damages to their property (i.e. no dissaving) and by doing so they reduce their welfare  by a 
proportional amount, while when 𝜑 = 1 households dissave and increase their expenditure level to cover the 
repairing  cost.  Being  a  model  for  closed  economies  the  usual  assumption  that  domestic  investment  equals 
domestic savings (𝑆 = 𝐼) applies. 

Welfare 𝑊 is calculated as: 𝑊(𝑡) =

𝐶(𝑡) − (1−𝜑)∗𝑹𝒆𝒔(𝑡)

𝐿(𝑡)

Annex 4. Effective capital and reconstruction of the destroyed capital stock 

Following Hallegatte et al. 2007, the overall investment of the economy 𝐼 is composed of two different types 
of  investments,  i.e.  investments  that  increase  the  productive  capital  and  compensate  for  the  natural 
depreciation of the assets (𝐼𝑛), and those that are used to reconstruct the capital assets of the firms after a 
climatic event has occurred, (𝐼𝑟).  

𝐼(𝑡) = 𝐼𝑛(𝑡) + 𝐼𝑟(𝑡) 

Eq. 4 

As  in  Hallegatte  et  al  2007,  we  assume  that  there  are  short-term  constraints:  (i)  insurance  companies  or 
public institutions need time to redirect high amounts of money to reconstruction activities or (ii) limited skills 
or  organization  capacity  of  the  reconstruction/building  sector  of  the  economy,  which  make  impossible  the 
immediate  mobilization  of  all  the  financial  resources  needed  for  the  reconstruction  process,  even  if  those 
investments have higher returns compared to normal investments. 

Eq. 5 refers to the investment used for reconstruction of the capital assets of the firms, the variable  𝐼𝑟. The 
short-term  constraints  are  reflected  in  the  parameter  𝑓𝑚𝑎𝑥,  which  is  the  fraction  of  𝐼  that  can  be 
immediately redirected or mobilized for reconstruction investments.  

𝐼𝑟(𝑡) = min (𝐷𝑎𝑚(𝑡),

𝐼(𝑡) ∗ 𝑓𝑚𝑎𝑥) 

Eq. 5 

According  to  Eq.  5,  the  investment  for  reconstruction  𝐼𝑟  are  equal  to  𝐷𝑎𝑚(𝑡)  if  the  fraction  of  destroyed 
capital  stock  is  lower  than  the  fraction  of  investments  available  for  reconstruction.  However,  when  the 
variable 𝐷𝑎𝑚(𝑡) is larger than the threshold the reconstruction process takes longer as the constraints to the 
reconstruction  activity  becomes  operational  in  the  model.  In  fact,  if  the  damaged  capital  is  lower  than  the 
fraction  of  the  total  investments  𝐼  that  can  be  readily  mobilized  and  redirected,  the  damaged  capital  is 
repaired in one-period time. However, the reconstruction process might take longer than one period, and this 
depends on whether the stock of damaged capital is larger than the amount of financial resources that can 
be redirected from business as usual investment activities. 

Eq. 6 represents the rule for the accumulation of potential. The parameter  𝛿 is the depreciation rate. We use 
the country specific depreciation rates as reported in the Penn World Table  10 (Feenstra, Inklaar & Timmer, 
2016), which reflects that different countries may have a different composition of their capital stock. 

𝐾̇ (𝑡) =   −𝛿 ∙ 𝐾(𝑡) + 𝐼𝑛(𝑡) 

Eq. 6 

Eq.  7  refers  to  the  growth  of  the  fraction  of  the  destroyed  capital  𝐷𝑎𝑚,  which  is  equal  to  the  difference 
between the capital lost at time t, i.e. 𝐶𝑎𝑝, and the investment for reconstruction 𝐼𝑟.  

17 

 
 
 
 
 
 
Annex 5. Productivity growth and build-back-better scenario 

𝐷𝑎𝑚̇

(𝑡) = 𝑪𝒂𝒑(𝑡) − 𝐼𝑟(𝑡) 

Eq. 7 

Productivity  growth  equation  has  three  components.  Productivity  grows  in  time  in  part  exogenously,  i.e.  the 
part  of  the  equation  𝐴 ∙ g, in  part  endogenously  as  an  implication  of  the  investments  in  physical  assets  𝑏 ∙
𝐼𝑟∗𝜃
𝐾0

  and  also  as  a  result  of  the  upgrade  of  the  capital  stock  in  the  aftermath  of  a  disaster,  i.e. 

 (

𝐼𝑛

)

𝐿

∙

𝑐

(𝐴𝑓𝑟𝑜𝑛𝑡 − 𝐴).  In  particular,  we  consider  that  the  physical  assets  installed  with  the  reconstruction,  i.e.  the 
term  𝐼𝑟 ∗ 𝜃,  embodies  a  level  of  technology  that  corresponds  to  the  European  technological  frontier  and 
therefore  increases  the  level  of  overall  productivity  of  the  economy.  We  assume  that  the  parameter 
𝜃~U(0, 1). 

𝐴̇ =  𝐴 ∙ g +  b ∙ (

𝐼𝑛
𝐿

𝑐
)

+  

𝐼𝑟 ∗ 𝜃
𝐾0

∙ (𝐴𝑓𝑟𝑜𝑛𝑡 − 𝐴) 

Eq. 8 

In Eq. 8 the variable 𝐴𝑓𝑟𝑜𝑛𝑡 is used as a proxy for the European technological frontier and corresponds to the 
productivity  growth  of  Denmark.  In  order  to  capture  the  trade-off  between  quality  and  duration  of 
reconstruction,  we  make  the  parameter  𝑓𝑚𝑎𝑥  dependant  on  the  size  of  𝜃.  In  particular  we  assume  that: 
𝑓𝑚𝑎𝑥 =   𝑓𝑚𝑎𝑥0 ∗

 , where 𝑓𝑚𝑎𝑥0~U(0.001, 0.01) and 𝛾~U(1, 10). 

1
1−(𝛾−1)∗𝜃

Annex 6. Data and calibration of the model 

Three main data sources have been used to calibrate the model: the ECFIN Ageing Report 2021 (AR), the Penn 
World  Table  10.0  (PWT)  the  World  Development  Indicator  of  the  World  Bank  (The  World  Bank,  2021).  The 
model’s  parameters, i.e.  the  saving  rate,  the  depreciation  rate  etc.,  have  been  calibrated  at  country  level  in 
order to reflect cross country heterogeneity as much as possible, considering the stylized nature of the model 
used.  Country  specific  saving  rates  and  depreciation  rates  reflect,  to  some  extent,  the  different  level  of 
economic structural resilience. For instance, a country with a high saving rate  is characterized with a higher 
prudent attitude, which makes that country more resilient and with a better recovery capacity compared to a 
country with a lower saving rate, i.e. where current consumption is preferred to future one.  

Furthermore, as the depreciation rate reflects the composition and type of capital assets, economies with a 
higher  depreciation  rate  invest  in  short-lived  assets  (computers  and  software  are  more  prone  to  obsolesce 
and physical deterioration) that require larger volume of investments, compared to an economy with a lower 
depreciation rate, whose functioning is more based on long-lived assets (infrastructure and building)1. 

The  baseline  replicates  the  GDP  projections  of  the  AR  2021  and  assumes  the  growth  rates  for  labour  as 
reported in the AR 2021. The basic  features of the  model are very similar to the one used to generate the 
baseline GDP projections of the Ecfin Ageing Report, which represents an advantage for the calibration of the 
parameters (DG ECFIN 2021). In particular, while Labour input is projected to fall for most countries, both for 
the on-going demographic shift and also for a reduction of the average number of hours worked, productivity 
is expected to be the main if not the only source of growth for EU countries. The AR2021 assumes that labour 
productivity will grow during the next decades in part exogenously, for an increase of total factor productivity, 
and  in  part  because  of  capital  deepening,  i.e.  increase  of  the  amount  of  physical  assets  per  worker.  We 
therefore calibrate our model in such a way that these particular assumptions are reflected in the parameters. 
We calibrate our model and choose the parameters such that the sum of squared errors between the model 
solution  and  the  projected  data  of  the  AR2021  is  minimised.  The  calibration  is  based  on  a  nonlinear 
optimisation  procedure  and  for  this  reason  is  quite  dependant  on  the  initial  guess  for  the  estimated 
parameters. We therefore implement a random selection for the initial guess of the parameters, which allows 
a sufficient exploration of the parameter space. All model’s parameters, except for the output elasticity, are 
allowed to  vary.  However, we preserve the information about the saving and the  depreciation rates, i.e. the 

1 While for EU countries, which have relatively small structural differences among them, this type of cross-country heterogeneity could be 
a reasonable proxy for economic resilience, it could be a less reliable one for developing economies. In that case, a low aggregate 
depreciation  rate  would  reflect  that  certain  type  of  assets  are  more  expensive  and  also  that  there  is  a  shortage  of  the 
complementary human capital, which makes those economies even less resilient. 

18 

 
 
 
 
                                                        
 
parameters s and d, which we have from the official statistics, by using a smaller range for the initial guesses 
for  these  parameters  compared  to  the  others.  The  calibration  mainly  focus  on  the  estimation  of  the 
parameters b and c of Eq. 8, where for the exogenous growth rate g we keep it exogenous and corresponding 
to the growth rate of the AR2021.  

Figure 3. Comparison of the GDP projection of the AR2021 and the GDP projected by the calibrated model 

Source: JRC, 2022. 

Figure 3 offers a visual comparison of the projections of GDP from the AR2021 and the GDP projected by the 
calibrated model. Table 1 lists the calibrated parameters and the corresponding sum of squared error (SSE). 

19 

 
 
 
 
 
Table 1. Calibrated parameters for the European countries analysed in the study: d (depreciation rate), s (saving rates), b 
and c (parameters for the endogenous growth of productivity). 

d 

s 

b 

c 

sse 

0.044 

0.231 

0.003 

0.643 

0.005 

0.059 

0.206 

0.000 

0.251 

0.000 

0.034 

0.145 

0.001 

0.286 

0.002 

0.037 

0.251 

0.002 

0.595 

1.589 

0.049 

0.283 

0.002 

0.595 

0.008 

0.049 

0.253 

0.000 

0.028 

0.000 

0.041 

0.191 

0.003 

0.598 

0.007 

0.043 

0.193 

0.004 

0.638 

0.001 

0.043 

0.204 

0.003 

0.604 

0.094 

0.037 

0.124 

0.004 

0.609 

0.118 

0.029 

0.104 

0.006 

0.624 

0.004 

0.050 

0.205 

0.002 

0.463 

0.000 

0.077 

0.265 

0.004 

0.597 

0.008 

0.038 

0.185 

0.004 

0.617 

0.031 

0.046 

0.173 

0.000 

0.002 

0.001 

0.033 

0.217 

0.000 

0.029 

0.001 

0.063 

0.231 

0.003 

0.573 

0.000 

0.051 

0.275 

0.002 

0.630 

0.045 

0.041 

0.332 

0.002 

0.614 

0.035 

0.055 

0.161 

0.002 

0.564 

0.037 

0.030 

0.161 

0.003 

0.602 

0.001 

0.066 

0.221 

0.000 

0.014 

0.005 

0.047 

0.267 

0.002 

0.649 

0.020 

0.046 

0.216 

0.002 

0.478 

0.001 

BE 

BG 

CY 

DE 

DK 

EE 

ES 

FI 

FR 

GB 

GR 

HR 

IE 

IT 

LT 

LV 

MT 

NL 

NO 

PL 

PT 

RO 

SE 

SI 

Source: JRC, 2022. 

Annex 7. Results at country level 

Results  are  presented  in  Table  2  as  the  average  of  the  period  2071  to  2100,  which  we  refer  to  as  end  of 
century. 

20 

 
  
 
 
 
Table 2. Results by country 

Welfare (% w.r.t. reference) 

GDP  (% w.r.t. reference) 

mean 

95% CI 

mean 

95% CI 

Cyprus 

Greece 

Croatia 

Denmark 

Ireland 

Nederland 

France 

Lithuania 

Great Britain 

Italy 

Sweden 

Belguim 

Romania 

Spain 

Slovenia 

Finland 

Portugal 

Latvia 

Germany 

Estonia 

Poland 

Malta 

Bulgaria 

6.76 

3.57 

2.20 

2.01 

0.97 

0.79 

0.74 

0.57 

0.55 

0.50 

0.38 

0.37 

0.37 

0.33 

0.23 

0.23 

0.22 

0.22 

0.16 

0.15 

0.14 

0.03 

0.02 

5.86 - 7.9 

 2.60 - 4.9 

 1.42 - 2.77 

 1.38 - 2.75 

 0.81 - 1.16 

 0.00 - 2.63 

 0.60 - 0.91 

 0.38 - 0.78 

 0.39 - 0.75 

 0.40 - 0.62 

 0.27 - 0.52 

 0.05 - 1.14 

 0.28 - 0.46 

 0.20 - 0.46 

 0.15 - 0.34 

 0.11 - 0.41 

 0.11 - 0.34 

 0.09 - 0.34 

 0.06 - 0.33 

 0.10 - 0.19 

 0.08 - 0.2 

 0.02 - 0.04 

5.12 

2.69 

1.52 

0.98 

0.56 

0.29 

0.46 

0.38 

0.42 

0.33 

0.23 

0.27 

0.18 

0.23 

0.13 

0.13 

0.15 

0.14 

0.10 

0.07 

0.08 

0.02 

 2.99 - 7.5 

 1.00 - 4.68 

 0.55 - 2.48 

 0.29 - 1.84 

 0.17 - 0.99 

 0.00 - 1.12 

 0.17 - 0.78 

 0.12 - 0.68 

 0.19 - 0.7 

 0.12 - 0.57 

 0.11 - 0.38 

 0.02 - 0.91 

 0.02 - 0.37 

 0.07 - 0.41 

-0.01 - 0.3 

 0.03 - 0.29 

 0.01 - 0.32 

 0.01 - 0.28 

 0.03 - 0.23 

-0.01 - 0.16 

-0.01 - 0.19 

 0.00 - 0.03 

-0.08 - 0.12 

-0.02 

-0.14 - 0.09 

Source: JRC, 2022. 

21 

 
  
  
 
 
 
 
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