Empirical evidence of declining global vulnerability to climate-related hazards

Abstract

Death tolls and economic losses from natural hazards continue to rise in many parts of the world. With the aim to reduce future impacts from natural disasters it is crucial to understand the variability in space and time of the vulnerability of people and economic assets. In this paper we quantified the temporal dynamics of socio-economic vulnerability, expressed as fatalities over exposed population and losses over exposed GDP, to climate-related hazards between 1980 and 2016. Using a global, spatially explicit framework that integrates population and economic dynamics with one of the most complete natural disaster loss databases we quantified mortality and loss rates across income levels and analyzed their relationship with wealth. Results show a clear decreasing trend in both human and economic vulnerability, with global average mortality and economic loss rates that have dropped by 6.5 and nearly 5 times, respectively, from 1980-1989 to 2007-2016. We further show a clear negative relation between vulnerability and wealth, which is strongest at the lowest income levels. This has led to a convergence in vulnerability between higher and lower income countries. Yet, there is still a considerable climate hazard vulnerability gap between poorer and richer countries.

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Global Environmental Change
journal homepage: www.elsevier.com/locate/gloenvcha
Empirical evidence of declining global vulnerability to climate-related
hazards
Giuseppe Formetta
a
, Luc Feyen
b,⁎
a
Fincons Group, Vimercate, Via Torri Bianche 10, Pal. Betulla, 20871, Vimercate (MB), Italy
b
European Commission, Joint European Research Centre (JRC), Ispra, Italy
ARTICLE INFO
Keywords:
Multi-hazard vulnerability
climate related hazard vulnerability
ABSTRACT
Death tolls and economic losses from natural hazards continue to rise in many parts of the world. With the aim to
reduce future impacts from natural disasters it is crucial to understand the variability in space and time of the
vulnerability of people and economic assets. In this paper we quantified the temporal dynamics of socio-eco-
nomic vulnerability, expressed as fatalities over exposed population and losses over exposed GDP, to climate-
related hazards between 1980 and 2016. Using a global, spatially explicit framework that integrates population
and economic dynamics with one of the most complete natural disaster loss databases we quantified mortality
and loss rates across income levels and analyzed their relationship with wealth. Results show a clear decreasing
trend in both human and economic vulnerability, with global average mortality and economic loss rates that
have dropped by 6.5 and nearly 5 times, respectively, from 1980–1989 to 2007–2016. We further show a clear
negative relation between vulnerability and wealth, which is strongest at the lowest income levels. This has led
to a convergence in vulnerability between higher and lower income countries. Yet, there is still a considerable
climate hazard vulnerability gap between poorer and richer countries.
1. Introduction
Natural hazards continue to cause increasing damage and loss of
life. Natural disaster costs globally reached US$314 billion dollars in
2017, more than double the yearly average cost over 2007–2016
(CRED, 2018). Key drivers behind rising losses are exposure changes in
terms of rising population and capital at risk (Bouwer, 2011;Visser
et al., 2014), as well as better reporting (Paprotny et al., 2018), whereas
evidence is growing that anthropogenic climate change is modifying
weather and climate extremes (e.g. Donat et al., 2016;Spinoni et al.,
2017). Recent independent studies project a further increase of climate
hazard impacts in the future connected to anthropogenic warming and
socio-economic drivers (e.g. Bouwer, 2013;Winsemius et al., 2016;
Dottori et al., 2018;Forzieri et al., 2018;Vousdoukas et al., 2018a,
2018b).
Concurrently, with the Sendai Framework for Disaster Risk
Reduction 2015–2030 (UNGA, 2015), the Sustainable Development
Goals (UNISDR, 2015) and the Paris Agreement on Climate Change
(UNFCCC, 2015), international agreements on disaster loss reduction,
development and climate action were recently signed. Disaster reduc-
tion, sustainable development and climate change are closely inter-
connected. Repeatedly, disasters have undermined or made void
decade-long poverty reduction efforts, especially in non-industrialized
countries (Mysiak et al., 2016), while the poorest countries will likely
be affected strongest by rising climate-related disaster risk in a warmer
world (Harrington et al., 2018). The Sendai Framework therefore ad-
vocates coherence between and mutual reinforcement of policy deci-
sions, monitoring mechanisms and implementation arrangements
aimed at reducing disaster risks.
The Sendai Framework further calls for a multi-sectoral, multi-dis-
ciplinary and preventive disaster risk reduction strategy, which goes
beyond the traditional single hazard, response focused approach. It sets
as first priority for action the understanding of disaster risk in all its
dimensions. Disaster risk is the combination of three crucial compo-
nents: i) hazard: natural processes that may causes loss of life, health
impacts, property damages and environmental degradation; ii) ex-
posure: human, economic, or environmental assets located in hazard
prone areas; and iii) vulnerability: the susceptibility of people, eco-
nomic/environmental assets to the impacts of hazards (UNISDR, 2009).
Modelling the hazard component is an advanced research activity,
with ever improved process understanding, model conceptualizations
and parameterizations, spatial coverage and detail. Many studies have
analyzed historical trends in hazards based on observations (e.g.
Douglas et al., 2000;Hannaford and Marsh, 2006), reanalysis data (e.g.
https://doi.org/10.1016/j.gloenvcha.2019.05.004
Received 9 January 2019; Received in revised form 7 May 2019; Accepted 14 May 2019
⁎
Corresponding author.
E-mail addresses: Giuseppe.FORMETTA@ext.ec.europa.eu (G. Formetta), Luc.FEYEN@ec.europa.eu (L. Feyen).
Global Environmental Change 57 (2019) 101920
Available online 25 May 2019
0959-3780/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
T

Zolina et al., 2004;Jolly et al., 2015;Schemm et al., 2017) or statistics
at national, regional or global scale (e.g., Kundzewicz et al., 2013;
Turco et al., 2016).
Recently, much effort has been devoted to create spatially explicit
datasets for the dynamic quantification of exposure, such as population,
gross domestic product and land-use, from country (e.g. Jongman et al.,
2014) to continental (Paprotny et al., 2018) and global scale (Geiger
et al., 2018a,2018b;EC, 2015a;Kummu et al., 2018). The maps, al-
though often limited in temporal resolution (most of them available
every 5–10 years) and sometimes in spatial resolution (usually between
1 and 50 km), are spatially explicit and provide an added value for
understanding trends in natural disaster risk. In the last decade, the
explosion of earth observation (EO) data from space is providing more
detailed information for quantifying communities’ exposure to natural
hazards (Geiß and Taubenböck, 2017). Methods for data collection,
merging and processing algorithms have advanced and high resolution
satellite images are used to provide global maps of population density
(EC, 2015a), urban areas (Esch et al., 2013), and the built environment
(Gong et al., 2013). Recent advances in modeling exposure to natural
hazards include the use of the new technological paradigm of Big Data
(e.g. Yu et al., 2018) and volunteered geographic information systems
(e.g. Haworth and Bruce, 2015). The former includes user-generated
geo-localized quasi real-time information from micro-blogs (e.g.
Twitter, Facebook, Flickr, Instagram), whereas the latter is based on
sharing information through crowd-sourcing (e.g. Horita et al., 2015;
Cinnamon et al., 2016). Yet, no sufficiently long time series are avail-
able from these novel techniques for trend analysis.
Disasters occur when the hazard component interacts with
vulnerable exposed population, infrastructure, ecosystems and eco-
nomic activities. Vulnerability can be defined as the predisposition to
incur losses, hence it is the component that has the potential to trans-
form a natural hazard in a disaster. In this sense it is often referred to as
the “missing link” (Mechler and Bouwer, 2015;de Brito et al., 2018) for
understanding and eventually project climate risks in the future. Vul-
nerability, including all the actions aimed to reduce the impacts of
natural hazards, is dynamic in space and time, is hazard-specific, and
depends on environmental, economic, and social factors.
Being a key uncertainty in the disaster risk equation, there is
growing interest in understanding and quantifying vulnerability and its
dynamics. To date, few studies have analyzed trends in vulnerability at
continental to global scale. Jongman et al. (2015) and Tanoue et al.
(2016) assessed global river flood vulnerability dynamics by combining
high resolution modeling of flood hazard and exposure and demon-
strated a general decreasing trend in time of vulnerability. Bouwer and
Jonkman (2018) report decreasing mortality rates caused by storm
surges at global scale, and also human vulnerability to heat waves in
developed countries shows a declining trend (Sheridan and Allen,
2018). Whereas an increasing number of studies attempt to understand
present human and economic vulnerability to other hazards (see e.g.
Tánago et al. (2016) for an overview on drought vulnerability), typi-
cally they are carried out at subnational level (i.e. region, state, or river
basin) and dynamics in vulnerability are not well addressed
(Jurgilevich et al., 2017).
In this paper we assess the temporal dynamics in the last three
decades of human and economic vulnerability to weather-related dis-
asters in a global, multi-hazard, spatially explicit framework. In
Fig. 1. Evolution in time of the reported events, fatalities, and damages occurred between 1980 and 2016. In red is reported the trend line.
G. Formetta and L. Feyen Global Environmental Change 57 (2019) 101920
2

agreement with other studies that have analyzed natural disaster losses
(e.g., Neumayer and Barthel, 2011;Bouwer, 2011;Jongman et al.,
2015;Tanoue et al., 2016;Bouwer and Jonkman, 2018; Su et al., 2018),
we express vulnerability by mortality rates (reported fatalities as a
percentage of exposed population) and loss rates (reported losses as a
percentage of exposed GDP). We further investigate the relationship
between vulnerability and wealth. Trends in impacts are based on re-
cords from Munich RE’s NatCatSERVICE (Munich RE, 2018), one of the
most complete natural disaster databases available. Dynamics in ex-
posure are derived from the most recent spatially explicit time-variant
population and GDP global maps (EC, 2015a;Geiger et al., 2018b). We
quantify the exposed population and GDP based on a neighborhood of
the geo-referenced reported event location and perform a sensitivity
analysis on the parameter to define this area.
2. Materials and methods
Vulnerability (V) describes the relationship between the exposure to
a weather-related hazard and the impact. It is analyzed in this study in
terms of effects on population (people killed by the weather-related
hazard) and economy (monetary losses caused by the hazard). The
vulnerability of population is quantified as “mortality rate” (Jongman
et al., 2015;Peduzzi et al., 2012), i.e. the ratio between the people
killed (R
fat
) by a climate disaster and the people exposed to the hazard
(R
p-exp
). Similarly, for economic losses the “loss rate” is used (Jongman
et al., 2015), which is the ratio between the economic loss (R
loss
, con-
verted in US$-PPP at the time of the event) caused by the climate dis-
aster and the Gross Domestic Product (GDP, converted in US$-PPP at
the time of the event) exposed to the hazard (R
gdp-exp
). We note that
GDP may not fully correspond to the wealth stock exposed to disasters.
However, due to the absence of good measures of wealth we use GDP as
a proxy for wealth, similar to other studies (e.g., Neumayer and Barthel,
2011;Jongman et al., 2015;Tanoue et al., 2016). Assuming mortality
and economic loss rates as an indicator of vulnerability is based upon
the hypothesis that the rates are higher in more vulnerable regions than
in less vulnerable regions.
For the period 1980–2016 we have analyzed the seven weather-
related hazards listed in Appendix A, Table A.1: general floods, flash
floods, coastal floods, cold related hazard, heatwaves, droughts, and
wind related hazards. Data on reported fatalities and direct losses
caused by natural disasters in the analyzed period were obtained from
Munich RE’s NatCatSERVICE database. This includes the date, the im-
pact (fatalities and official reported economic losses), the type/subtype
of the natural disaster, the geo-reference (latitude and longitude) of the
center of impact and a description of the event. The section on ‘Disaster
database and hazard classification’ in Appendix A provides more in-
formation on the dataset and on how we have assigned the events and
their impacts to the seven hazard classes analyzed.
The affected area of a given event is not reported in NatCatSERVICE
and it is very difficult to delineate for each disaster. Neumayer and
Barthel (2011) defined the affected area as a square with size of
100 km × 100 km around the reported georeferenced centroid. We
apply a similar method using a circle around the center of impact. In
order to assess the influence of the size of the estimated area exposed
we perform a sensitivity analysis using four different values for the
radius (50, 100, 200, and 400 km). Low values are typically more sui-
table for localized hazards such as flash floods and wind storms whereas
a higher radius better reflects spatially more extensive hazards such as
droughts and heatwaves. In the absence of detailed information on the
actual affected area, the simplification of using a circle with arbitrary
radius may introduce bias in the estimated affected area, and conse-
quently in the absolute mortality and loss rates. However, as this error
is likely to be random, with no systematic relatively more under- or
overestimating of the true affected area in earlier or later periods
(Neumayer and Barthel, 2011), it should not have a significant impact
on the trend in vulnerability.
Exposed population and GDP at the time of the event have been
derived from the Global Human Settlement Layer (GHSL, EC, 2015a;
Pesaresi et al., 2013) population maps and the world-wide spatially
explicit GDP maps presented in Geiger (2018b). GHSL provides spa-
tially detailed estimates of the population at 1 km resolution for the
target years 1975, 1990, 2000 and 2015. The GDP maps were originally
available in 10-years increments between 1850 and 2100 at 5 arcmin
resolution. We filled the gaps in time for the analyzed period
(1980–2016) by linearly interpolating the population and GDP maps
between target years, assuming a constant population and GDP growth
rate in between.
For each reported event in the NatCatSERVICE database we overlaid
the circle centered in the georeferenced centroid and a fixed radius (in
turn 50, 100, 200 and 400 km) with the population and the GDP maps
for the year in which the disaster occurred. We then aggregated the grid
values within the circle to obtain the location-specific R
p-exp
and R
gdp-
exp
. For general and coastal floods we further masked the population
and GDP exposure maps with the 100 year return period respective
global scale flood inundated maps (Dottori et al. (2016),https://data.
jrc.ec.europa.eu/collection/id-0054; and Vousdoukas et al. (2018a,
2018b), respectively). In this way, within the circle of interest, only
people and economic assets within river and coastal flood plains are
considered.
The resulting mortality and loss rates are presented by income
groups based on the present day World Bank classification (https://
datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-
does-the-world-bank-classify-countries). We defined two income
groups: i) low/middle low (which includes the World Bank low and
lower middle income categories) and ii) high/middle high (which in-
clude the upper middle and high income categories). This choice
Table 1
Summary of the global trend analysis for reported number of events, damages
and fatalities. The table reports the variable G(reported events, damages and
fatalities), the regression coefficient for the year (b), its t and p-value of the
regression model
= + +G a b year
.
Hazards Variable b, year coeff. t-value p-value
Reported events 17 events/year 10.3 ***
All Reported
damages
2.6 billion US$2016/year 3.9 ***
Reported fatalities 365 fatalities/year 0.71
Reported events 5 events/year 10.3 ***
Flood Reported
damages
0.7 billion US$2016/year 3.4 **
Reported fatalities 12 fatalities/year 0.28
Reported events 5 events/year 7.2 ***
Flash flood Reported
damages
0.1 billion US$2016/year 3.9 ***
Reported fatalities 6 fatalities/year 0.7
Reported events 0.7 events/year 9.2 ***
Coastal flood Reported
damages
0.35 billion US$2016/
year
1.8 *
Reported fatalities 24 fatalities/year 0.18
Reported events 1.5 events/year 6.3 ***
Cold related Reported damages 0.12 billion US$2016/year 2.2 *
Reported fatalities 19 fatalities/year 2.4 *
Reported events 0.2 events/year 3.1 **
Heatwave Reported
damages
– – –
Reported fatalities 270 fatalities/year 1.1
Reported events 0.8 events/year 5.3 ***
Drought Reported
damages
0.2 billion US$2016/year 2.8 *
Reported fatalities – – –
Reported events 4 events/year 9.4 ***
Wind Reported
damages
0.9 billion US$2016/year 2.2 *
Reported fatalities 44 fatalities/year 0.1
Significance p-value: *** < 0.001; ** [0.01-0.001]; * [0.1-0.01]. Variables in
italic do not show a statistically significant trend.
G. Formetta and L. Feyen Global Environmental Change 57 (2019) 101920
3

Fig. 2. Mortality rates for the analyzed hazards (expressed as number of fatalities per 10 000 people exposed). Results for each hazard represent 10-year moving
average of the median (for each year per income class) mortality rates for two income levels (low/middle-low income in green and high/middle-high income in blue)
and all countries (average of low/middle-low and high/middle-high income classes). Multi-hazard mortality rates are the sum of single hazard median values.
Fig. 3. Loss rates for the analyzed hazards. Results for each hazard represent 10-year moving average of the median (for each year per income class) loss rates for two
income levels (low/middle-low income in green and high/middle-high income in blue) and all countries (average of low/middle-low and high/middle-high income
classes). Multi-hazard loss rates are the sum of single hazard median values.
G. Formetta and L. Feyen Global Environmental Change 57 (2019) 101920
4

allowed us to have representative samples of reported events for all
hazards. Especially in the case of droughts and heatwaves, fewer events
per year are reported compared to for example flood or wind related
hazards.
Finally, we analyzed the relationships between mortality (and loss)
rates and wealth for single and multi-hazards. For each reported event
we linked the mortality (and loss) rates and the GDP per capita in PPP
of the country in the year in which the event occurred. We then binned
the GDP per capita in 15 equally-sized classes according its 15 quantiles
and reported the average values of GDP per capita and mortality (loss)
rates for each class.
3. Results
For the 7 climate-related hazards considered, the number of events
recorded in NatCatSERVICE over the analyzed period (1980–2016) is
16,412. The total reported fatalities amount to 815,293 and total da-
mages to 2,562 billion US$2016 (see Appendix A, Table A.2). The most
events are reported for floods (5275) and wind (4570), while for
heatwaves only 231 events are recorded. Wind-related disasters are the
most lethal and account for nearly 40% of the total fatalities, while
wind-related and general flood hazard each represent about one third of
the total economic losses. In NatCatSERVICE there are no drought
events for which fatalities are reported, whereas the number of heat
waves with reported economic losses is less than 30 (Fig. 1). We
therefore only look at human vulnerability for heat waves and at eco-
nomic vulnerability for drought.
The number of reported events and economic impacts (deflated but
not normalized with respect to exposed wealth of the year of the event)
show a statistically significant increasing trend in the analyzed period
both for the 7 hazards together and for each individual hazard (see
Table 1 and Fig. 1).
The trend in reported fatalities is also increasing but it is not sta-
tistically significant. We find a multi-hazard trend in reported (deflated
but not normalized) damage of 2.6 billion US$/year (bvalue in
Table 1). This is in line with the economic loss growth rate of 3.4 billion
US$/year presented in Neumayer and Barthel (2011). The difference is
due to the use of a longer time window (1980–2016 vs 1980–2009) and
a slightly lower coverage of hazards, as Neumayer and Barthel (2011)
include all natural hazards apart from geophysical ones (total sample of
19,360 events) and here only the most relevant climate-related hazards
with a sufficiently large sample size to perform a hazard-specific vul-
nerability analysis are considered (total sample of 16,412). The stron-
gest growth in reported events is observed for flash floods, general
floods and wind related hazards, with the latter two also showing the
strongest rise in economic losses (growth rate of 0.7 and 0.9 billion US
$2016/year, respectively). The smallest rise in the number of reported
events is for heatwaves and drought, hence for hazards that occur more
sporadically in time.
Over the analyzed period and based on the 7 most common climate-
related hazards considered herein, global multi-hazard human (Fig. 2)
and economic (Fig. 3) vulnerability show a declining trend across all
the radii. From 1980–1989 to 2007–2016, the 10-year moving average
mortality rate, averaged over all hazards, radii and both income groups,
Fig. 4. Mortality rates as function of the wealth for multi and single hazards. Mortality rates are expressed as number of fatalities per 10 000 people exposed. Wealth
is approximated by the GDP per capita (in US$-PPP) at the time of the event.
G. Formetta and L. Feyen Global Environmental Change 57 (2019) 101920
5

has reduced more than 6-fold, while the economic loss rate dropped by
nearly five times (Table B.1 and B.2 in Appendix). The reduction in
vulnerability is stronger earlier in the analyzed period and levels off
with time. Further, vulnerability converges between lower and higher
income countries due to the stronger vulnerability reduction in less
developed countries.
These general trends can also be observed for the individual ha-
zards, and this for the different radii of influence considered. There are,
however, a few exceptions. The most notable one is that human vul-
nerability to heatwaves seems higher in high/middle high income
countries. This could be related to several issues with reporting heat
mortality, particularly in low income countries, such as non-uniform
reporting conventions, not accurate reporting of some causes of deaths,
or incomplete information on death certificates (Gall et al., 2009;
Mathers et al., 2005;Sehdev and Hutchins, 2001;Azhar et al., 2014).
Similarly, poor reporting in developing countries of drought damages
likely explains the low economic loss rates, especially prior to 1995-
2000. The decreasing trends in hazard vulnerability confirms previous
findings at global and regional scales for river floods (Huang, 2013;
Jongman et al., 2015;Tanoue et al., 2016), storm surges (Bouwer and
Jonkman, 2018), heat waves (Sheridan and Allen, 2018), and winds
(Paul et al., 2018).
Notwithstanding the convergence in time of the vulnerability be-
tween lower and higher income countries for the analyzed hazards, the
present (10-year average over 2007–2016) multi-hazard mortality rate
is still 4.4 times larger in low/middle low income countries (see Table
B.2, Appendix B). Hence, over the last four decades the difference in
multi-hazard human vulnerability between poorer and richer countries
reduced by almost 2.5 times (see Table B.1 in Appendix B). The present
gap in human vulnerability varies strongly between hazards. For low/
middle-low income countries, hazard vulnerabilities are higher by a
factor ranging from 1.5 for general floods up to 9 for coastal floods and
wind related hazards. For heatwaves, reported fatalities suggest higher
human vulnerability in high/middle-high income countries. Yet, as
previously stated, this can likely be attributed to under-reporting of
heat mortality, especially in low income countries.
Economic loss rates show similar behavior in time, with patterns
also consistent across the different radii analyzed. The 10-year average
2007–2016 multi-hazard economic loss rate is almost four times higher
in lower income countries compared to higher income countries (see
Table B.3, Appendix B). This is about halve compared to the period
1980–1989 (see Table B.4, Appendix B). The present gap in economic
vulnerability for single hazards ranges between 1.4 for cold related
hazards to around 10 for wind related hazards. Coastal floods, flash
floods, droughts, and general floods show a factor difference of 3.2, 2.3,
2.3, and 2.2 respectively (see Table B.3, Appendix B). Jongman et al.
(2015) found a factor difference between high and low income coun-
tries of 17 for mortality and 3 for losses caused by floods at the end of
the 2000s. These values are higher compared to 1.5 for mortality and
2.2 for losses that we find for general floods. This is in part because the
values of Jongman et al. (2015) reflect the difference between low- and
high-income countries from the four-class World Bank classification
(low-, lower middle, upper middle-, and high-income categories). Dif-
ferently from Jongman et al. (2015) our classification is based two
Fig. 5. Loss rates (in US$-PPP at the time of the event) as function of the wealth for multi and single hazards. Wealth is approximated by the GDP per capita (in US
$-PPP) at the time of the event.
G. Formetta and L. Feyen Global Environmental Change 57 (2019) 101920
6

income groups: low/middle low (which includes the low- and lower
middle categories) and high/middle high (which includes the upper
middle- and high-income categories). Moreover, the fact that we ana-
lyze a different time period partly explains the discrepancy with results
of Jongman et al. (2015).
There is a clear negative relationship between mortality and loss
rates and wealth, here approximated by the GDP per capita in PPP of
the year of the event (Figs. 4 and 5, respectively). The latter have been
derived from figures C.1 and C.3 in Appendix C, through the binning
procedure described in Section 2. For better visualization of the trend
we fitted a power law function through the data with non-linear re-
gression. In all cases the parameters of the functions showed a statis-
tically significant p-value (all p-values < 0.1).
The decline in vulnerability with increasing wealth is consistent
across the radii analyzed. It is strongest for the lowest ranges of GDP per
capita and weakens as income levels become higher. This holds both for
human and for economic vulnerability. The reduction in mortality and
loss rates with increasing wealth is evident both for the multi-hazard
analysis as well as for the single hazards, apart from heatwave mor-
tality. This confirms previous findings for multiple hazards (e.g. Toya
and Skidmore, 2007;Kahn, 2005) and for specific hazards (e.g.
Jongman et al., 2015 and Tanoue et al., 2016 for floods). The patterns
described for Figs. 4 and 5 can also be observed in the raw data (Figs.
C1-C2 in Appendix C). These further show the high variability in
mortality and loss rates across GDP per capita for the different hazards,
indicating that there is large uncertainty around the smoothed curves
obtained after binning.
For floods, the fitted monotonically decreasing trend line does not
align well with the data for the lowest income ranges. It can be argued
that the nonlinear relationship between mortality (loss) rate and wealth
shows an initial increase before showing a monotonic decrease, as
suggested by Kellenberg and Mobarak (2008) and Zhou et al. (2014).
The mortality rate for heatwaves does not show a clear relation with
wealth. This could be due to under-reporting in lower income countries
combined with recent extreme heatwaves events that occurred in high
income countries, such as the July-September 2010 Russian heatwave
with a total of 56,000 fatalities and the July-August 2003 European
heat wave that caused a total of 68,312 fatalities.
4. Discussion and conclusions
Understanding vulnerability of our societies to hazards remains a
critical hurdle in accurate disaster risk assessments. In this work we
presented, to our best knowledge, the first global scale, spatially vari-
able multi-hazard analysis of dynamics in human and economic vul-
nerability to the most impacting climate hazards. Expressing natural
hazard impacts as a share of the exposed population/GDP rather than in
absolute terms helps in understanding the greater burden for poorer
countries. Although high income countries may suffer higher absolute
losses, in lower income countries people and their belonging are less
protected and more vulnerable to natural hazards (UNISDR, 2018). Our
findings have important implications. Improved protection against ha-
zards has counter-balanced the effects of increasing exposure on dis-
aster risk, with the global average 2007–2016 multi-hazard human
mortality and loss rates dropping of about 6.5 and nearly 5 times as
compared to the period 1980–1989, respectively. The more a country is
developed the higher are the investments in protection measures to
natural hazards, early warning systems, and disaster risk management
strategies. These actions facilitate not only the response but also the
recovering phase that follow a natural disaster (e.g. Cavallo and Noy,
2010). This is confirmed by the clear negative relation between vul-
nerability and wealth for all the analyzed hazard except heatwaves.
This effect is strongest at lower income levels and diminishes with in-
creasing wealth, which has resulted in a reduction of the vulnerability
gap between higher and lower income countries because lower income
countries have adapted relatively faster compared to higher income
countries. Nevertheless, a considerable vulnerability gap between low
and high income countries is still evident for specific hazards such as
coastal floods and wind related mortality rates (factor of 9) and for
wind related loss rate (factor of 10). This suggests that poorer countries
remain particularly vulnerable to these hazards and that huge invest-
ments or changes in these societies may be needed to further reduce
their vulnerability to them. For example, implementing and main-
taining coastal protection measures can be very costly and may only be
achievable when a certain level of wealth is attained. In many lower
income tropical and subtropical countries with coasts, mangroves have
also declined rapidly as they are cleared for coastal development and
aquaculture and logged for timber and fuel production (Polidoro et al.,
2010), counteracting efforts to reduce coastal flood risk. Wind-related
hazards are less confined in space compared to for example river and
flash floods, and reducing their impacts requires changes in building
and infrastructure standards over extended domains.
Carrying out the vulnerability analysis by grouping countries in two
income classes (namely high and low income countries) averages loss
and mortality rate differences between countries classified as high and
medium-high income, and low and medium low income, respectively.
For floods these differences have been found marked (e.g., Jongman
et al., 2015). The subdivision of countries in two broad income groups
was adopted to include all hazard types in a common vulnerability
analysis framework. For hazards such as heatwaves and droughts the
sample of events with reported impacts were not sufficiently large to
build vulnerability functions using a country classification based on
four income categories.
Understanding vulnerability is hampered by the availability of
harmonized and reliable data of human, environmental and economic
losses. It is widely acknowledged that NatCatSERVICE is one of the
most comprehensive global disaster loss databases available. Like most
of the global/regional publicly available or proprietary databases (e.g.
EM-DAT, DesInventar, Swiss Re’s Sigma) it suffers weaknesses such as
under/over/miss reporting of the impacts, gaps in historical records,
and bias by high impact events (e.g. Gall et al., 2009;Gall, 2015).
Events having limited time-space context, so called invisible or ne-
glected events (e.g. Zaidi, 2018;Wisner and Gaillard, 2009;Khan and
Kelman, 2012) remain largely unobserved and unreported and con-
stitute an additional source of underestimation of the impacts.
NatCatSERVICE is a database owned by Munich RE, which primary
interest is to understand insured losses. In order to verify potential bias
in the data towards insured losses in richer countries, a comparison
with EM-DAT in terms of number of events, fatalities and losses is
presented in Figures D1-D6 in Appendix D. The events classification in
EM-DAT has been done following the methodology presented for
NatCatSERVICE (see Appendix A). We note that less than 5% of EM-
DAT events could not be classified because lack of information on the
event type. The comparison shows that the number of reported events is
in general larger in NatCatSERVICE, especially for higher income
countries. The total number of fatalities across all hazards is very si-
milar between databases for both income groups. Total losses seem in
general somewhat higher in NatCatSERVICE for the higher income
countries, whereas there is no consistent difference between the data-
bases across the time period for lower income countries. The most
notable differences for certain hazards (e.g., wind/coastal and floods/
flash floods) likely relate to a different categorization of some events in
these classes in the respective databases. Moreover, losses from loca-
lized events such as flash floods or winds often are insured and reported
by insurance companies but not necessarily appear in (international)
newspapers and thus in EM-DAT.
Most disaster databases only include estimates of direct losses that
are immediately visible after the occurrence of the event. Indirect losses
that may occur in the aftermath of the event, such as loss of jobs or
business interruption, as well as consequential losses visible months or
years after the disaster, such as reduced country GDP and lower cur-
rency exchange rate, are not typically documented (e.g. Wirtz et al.,
G. Formetta and L. Feyen Global Environmental Change 57 (2019) 101920
7

2014;Gall, 2015). These impacts can vary strongly and most estimates
of their magnitude are based on modeling rather than empirical ana-
lysis (Kousky, 2014). Further, apart from the fatalities, people can
suffer a wide range of impacts from disasters, often with delayed effects
(e.g., Schmitt et al., 2016). Hence, human and economic vulnerability
go beyond mortality and direct economic loss rate considered herein.
In order to achieve progress in reaching the disaster risk reduction
targets of the Sendai Framework and implementing the Sustainable
Development Goals, there is a need for a well-defined, accurate, stan-
dardized and systematic procedure to collect disaster impacts, espe-
cially at the local level. Evaluations of damage and risk mitigation costs
should be fed into national and international open-access databases to
improve the evidence basis for better understanding vulnerability and
decision making to reduce it (Kreibich et al., 2014). The UN Office for
Disaster Risk Reduction (UNISDR) has therefore stepped up efforts to
improve the collection of data on disaster losses. In March 2018 it
launched the Sendai Framework Monitor (SFM), an online tool de-
signed to capture data on achieving the Sendai targets. By October
2018, already 61 countries have started using the SFM and report
mainly on four targets for disaster losses: mortality, numbers of people
affected, economic losses and damage to critical infrastructure.
Another critical issue for understanding vulnerability is the exact
delineation of the area exposed to damaging intensities of the hazard. It
is unique for each hazard event and it may vary considerably among
disasters of the same hazard type. For example, wind related hazards
can act very local (e.g. tornado) or induce damages over extended do-
mains (e.g. tropical cyclone). We show that the trend in vulnerability vs
time and wealth is not strongly affected by the delineation of the area
exposed, as the shapes of the functions are consistently decreasing
across the analyzed radii. However, for risk assessments it is important
to accurately quantify vulnerability (i.e. mortality and loss rates) in
order to more reliably translate the exposed people, assets and wealth
into human and economic loss estimates. Hence, reporting of disaster
losses should also include a better delineation and mapping of the exact
area affected.
Finally, there is need for spatially explicit information on socio-
economic drivers of vulnerability and impacts. We used GDP as a proxy
for the wealth exposed and this may lead to biased estimates of the
actual stock exposed. Especially when economies become more service-
oriented (developed countries) this may overemphasize loss reductions
in these countries in recent years. This could be overcome by using for
example information on capital stock, yet this information is not
available globally at the relevant temporal and spatial resolution.
Further, we show a clear negative relation between GDP per capita and
vulnerability, yet the latter depends on several other factors, such as aid
dependency, inequality, education level, infrastructure, health status
and size of the financial sector (e.g., Toya and Skidmore, 2007). More
research is needed to understand and quantify the contribution of these
drivers of vulnerability.
Acknowledgements
The research that led to these results received funding from DG
CLIMA of the European Commission as part of the 'PESETA IV - Climate
Impacts and Adaptation in Europe' project (Administrative Agreement
JRC 34547-2017 / 340202/2017/763714/SER/CLIMATE.A.3). We
thank Munich RE for providing loss and mortality data from the
NatCatSERVICE database (https://www.munichre.com/en/
reinsurance/business/non-life/natcatservice/index.html).
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:10.1016/j.gloenvcha.2019.05.004.
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