WorldRiskReport
Analysis and prospects 2017

WorldRiskReport
Analysis and prospects 20171.	Risk analysis and disaster preparedness – The contribution of the WorldRiskReports .  .  .  .  .  .  .  .  .  . 7Peter Mucke

1.1

Risk in a state of flux . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 8

1.2Risk analysis . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 8

1.3Disaster preparedness .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 10

1.4

Development-political relevance . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 14

2. WorldRiskIndex: A five-year perspective – Risk analysis 2012-2016 .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 19
Prof. Dr. Katrin Radtke, Stephan Luther, Lotte Kirch, Ruben Prütz

2.1	The concept .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 20
2.2 Analyses using a five-year perspective .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 21
2.3 Conclusions and recommendations for action .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 26
3.	In a Labyrinth of Indicators – What is special about the WorldRiskReport andhow can it be improved . .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 29
Christopher Schrader

Annex .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 38

WorldRiskReport ] 5

6 [ WorldRiskReport1 	Risk analysis and disaster
preparedness
The contribution of the WorldRiskReports

WorldRiskReport ] 7

1.1 Risk in a state of flux
Peter Mucke is
the CEO of Bündnis
Entwicklung Hilft.

Until recently, humans were rarely the directcause of extreme natural events. But asa result of their interference in the natural
world, they have increased the potential
risk massively. The destruction of mangrove
forests and coral reefs along the Southeast
Asian coastline, for example, has reduced
levels of protection against tidal waves andflooding. The clearing of mountain forests
intensifies the rate of soil erosion and, consequently, the scale of flooding, as has been
witnessed in Pakistan. Climate change and theincreasingly frequent occurrence of “climate
extremes” exacerbate this threat on an ongoing basis and increase the vulnerability ofsocieties (IPCC 2014).
The WorldRiskReport’s concept of “risk” is not
solely based on the probability of occurrenceof natural hazards and their severity, rather
it also considers human living conditions andthe development status of society (Bündnis
Entwicklung Hilft 2011). Prevention and theability to react and help quickly determine

whether extreme natural events become disasters. The WorldRiskIndex, as a componentof the WorldRiskReports, is created on thebasis of a nuanced understanding of disaster,and calculates the risk posed to 171 countries
worldwide by means of a multiplication of risk
and vulnerability. This allows for the parameters of the risk assessment to be expanded.
The present report for 2017 is a five-year
analysis of the reports from 2012 to 2016 that
prepares the groundwork for the further development of the WorldRiskIndex.
As a general rule, the following applies tothe risk level of all countries: A nation thatpossesses sufficient financial resources andfunctioning national and civil-societal structures, that confronts recurring natural eventswith an adaptive strategy and that is prepared
to invest in measures to adapt to changing
conditions such as weather and climate
extremes, will be less adversely impacted bynatural events.

1.2 Risk analysis
The WorldRiskIndexT

he WorldRiskIndex calculates the risk for
171 countries worldwide on the basis of thefollowing four components (see figure 1):++ Exposure to natural hazards such as earthquakes, hurricanes, flooding, drought andsea-level rise++ Vulnerability as dependent on infrastructure, nutrition, living conditions andeconomic circumstances++ Coping capacities as dependent on governance, preparedness and early warning
measures, access to healthcare, social andmaterial security++ Adapting capacities with respect toimpending natural events, climate change
and other challenges.

8 [ WorldRiskReport

The concept of the WorldRiskIndex, including
its modular structure, has been developedby both practical experts on the ground, andscientific experts located further afield. The
calculation of the index, which was performedby the Institute for Environment and Human
Security of the United Nations University(UNU-EHS) in the years 2011 to 2016 andcommissioned by Bündnis Entwicklung Hilft,
relies on datasets that are available worldwide.
The world’s nation states form the reference
parameter for the index (see Chapter 2).
The WorldRiskIndex serves to provide
answers to the following questions:++ How probable is an extreme natural event
and will it impact human beings?

++ How vulnerable is the population of acountry to natural hazards?

Sphere of natural hazards

Sphere of society

Exposure

Vulnerability

Exposure to natural hazards

Sum of the threecomponents

Sea-level rise, flooding,earthquakes, drought, hurricanes

Copingto reduce negative impacts inthe event of disaster

Susceptibility
Probability of sufferingdamage in the event ofdisasterWorldRiskIndex

Product of exposure and vulnerability

Adaptation
for long-term strategies tochange a society

Figure 1: The WorldRiskIndex and its components++ To what extent can societies cope withacute disasters?
++ Is a society taking disaster preparedness
measures against natural hazards that are
expected in the future?
The representation produced using the index
and its four components provides answers
to these questions and brings into focus
both the problems and the resulting fields ofaction.
Limits and weightings
Nevertheless, it is still important to keep thelimits of this representation in mind: As isthe case with any index, the WorldRiskIndex
can only take into account indicators forwhich comprehensible, quantifiable data isavailable. For example, direct neighborhood
assistance is highly important in the event of
a disaster but not measurable and thus notincluded in the calculation. Deviations indata quality for the individual countries also

arise, for example, if data is only collected bynational authorities and not by an independent international institution.
Social factors such as self-help capacities
and municipal and spatial structures playa lesser role in the WorldRiskIndex thantechnical and economic factors, since it ismore difficult to operationalize social factors- added to that fact that the corresponding
data base has so far proved insufficient. This
imbalanced weighting of simple, measurable
technical or economic factors, which is afeature of many global analyses, should beovercome in the WorldRiskIndex in future.
The modular structure of the index, which
can be supplemented and expanded, allows
for this issue to be overcome (Bündnis
Entwicklung Hilft 2011).

WorldRiskReport ] 9

Local and regional analyses
The risk within one country can vary according to region. Therefore, it is important thatthe calculation model of the WorldRiskIndex
can also be used for small-scale analyses. It
is often the case that different or additional
data relevant to a risk assessment are to befound at a regional or local level. Thanks to
its modular structure, the WorldRiskIndex
can be used to develop an analogous local orregional risk index. This was demonstrated

by the UNU-EHS institute in the 2011
WorldRiskReport, which drew on the example of several administrative units in Indonesia (Birkmann et al. 2011).
The following applies to all levels, whether
local, regional or national: The WorldRiskIndex is not a crystal ball – it does not claim to
be able to predict natural events. However, it
does provide comprehensive information on
the action fields that arise at the stated levelsof society.

1.3 Disaster preparednessEarthquakes, as with other extreme natural
events, cannot be prevented. Countries can,however, develop and implement strategies
and measures so as to protect themselvesfrom the impacts of extreme natural eventsand thereby limit the extent of damage. A
well-founded risk analysis – and the assessments that such an analysis yields – are ofcentral importance when it comes to disaster
preparedness. By way of example: In Italy in
2016, sufficient numbers of emergency rescue
services were on hand in Amatrice and thesurroundings areas and were able to support

The concept of the WorldRiskReport
The foundational idea of the WorldRiskReport has remained unchanged since 2011 – the year the first edition was released:“Whether it be an earthquake or a tsunami, a cyclone or floods, therisk of a natural event turning into a disaster always depends only
partly on the force of the natural event itself. The living conditions of
the people in the regions affected and the options available to respond quickly and to provide assistance are just as significant. Those
who are prepared, who know what to do in the event of an extreme
natural event, have a greater chance of survival. Countries that see
natural hazards coming, that are preparing for the consequences ofclimate change and are providing the financial means required will
be better prepared for the future. The WorldRiskReport contributes toan overall approach of looking at these links on a global level anddrawing forward-looking conclusions regarding assistance measures,
policies and reporting.” (Bündnis Entwicklung Hilft 2011)

10 [ WorldRiskReportor lead relief efforts for the affected population. However, the design of newer building
structures and the lack of safeguards against
earthquakes in older buildings were heavily
criticized. Many countries in the global South,
however, have neither sufficient coping capacities nor sufficient preparedness measures withrespect to potential disasters.
By interlinking exposure and vulnerability analyses, the risk assessment of theWorldRiskReports offers forward-looking
conclusions for politicians and practitioners
alike. With a view to the response that needs
to be taken as a result, a strengthening of theentire scope of disaster management is indispensable, beginning with disaster prevention(see figure 2). This includes preparedness
measures for risk minimization and compliance with building regulations, in addition to
the safeguarding of infrastructure and longterm structural and institutional adaptation
to the impact of extreme natural events andclimate change – from a local level, all the way
up to the national level.
From the perspective of Bündnis Entwicklung
Hilft, a forward-looking response also implies
a move away from a short-term appraisal ofdisasters towards a development-political
approach. Emergency relief and development
cooperation must constitute a consolidated
effort, all the way from the planning stage
to final implementation. The international

community’s current practical approach ofprimarily lending support once a disaster has
already struck, is not beneficial in the context
of impacted countries assuming “self-responsibility”. In order to achieve sustainable
results, long-term and forward-thinking
engagement and binding outlooks arenecessary. The WorldRiskReports provide
action-oriented suggestions for development-political measures – with a different
topic of focus every year: Logistics and Infrastructure (2016), Food Security (2015),
The City as a risk Area (2014), Health and
Healthcare (2013), Environmental Degradation and Disasters (2012), Governance andCivil Society (2011).
Logistics and infrastructure
Dilapidated transport routes, unsafe power
grids, buildings in a state of disrepair: During
extreme natural events, fragile infrastructure represents a direct threat for the local
population while also delaying the effective
potential for those affected to help themselves
and impeding humanitarian relief. In Nepal,
after the heavy earthquake of April 2015,
many areas had no more access to cleanwater. Streets were blocked off, telephonelines were destroyed and electricity supplies
came to a standstill. The country’s airport was
overworked because at the same time thousands were seeking to leave the country andhundreds wanted to enter to provide help.
Susceptible infrastructure and poor logisticalconditions often contribute to extreme natural
events also becoming humanitarian disasters.
The WorldRiskReport 2016 identifies a very
high need for action for the improvementof logistics and infrastructure in the highlyor very highly exposed countries of Benin,
Burundi, Haiti, Cambodia, Cameroon, Madagascar, Myanmar, Zimbabwe and Chad. The
starting framework conditions of the individual
countries can only be improved on a step-bystep basis. Safe building methods on theirown are not sufficient. Central hubs such as
airports, ports and depots, as well as electricity
and water supplies, must be assessed all theway down to the local level. In exposed areas,
logistics experts can assess and improve thecapacities, strengths and weaknesses of local
infrastructure in order to boost responsivenesslevels in the event of disaster.
Critical infrastructure plays a decisive role whendisaster strikes, since an outage or impairment
leads to supply shortages (BBK 2016). The
considerable interdependencies that exist
among these types of infrastructure can resultin a domino effect in the case of sector failure:
An outage in one sector can cause outages inother sectors, triggering a cascade of outages
and damage. The majority of this critical infrastructure is essential for functioning logistics
and, consequently, humanitarian logistics, andmust therefore be granted particular attention
in the context of disaster preparedness.
Rapid developments in fields such as information technology can offer promising opportunities for disaster management. For example,
methods of analyzing the situations in disasterareas have progressed greatly in the last few
years through progress in information technologies. Satellite images allow for a very precise
understanding of the scale of damage and social
media channels immediately provide an initial
information flow from the disaster region. In
addition, information technology can be used
to monitor transports and local storage warehouses, for example to safeguard cold chainsfor medicines.
Food security
Food security is the result of the interaction
between various different factors, ranging
from food production and storage and accessto clean water, all the way to social andpolitical dimensions such as the right to food,healthcare, power and property relations, aswell as access to resources. Extreme natural
events, like drought or floods and the impactsof climate change, can massively disturb
the entire food supply chain: Production,
processing and procuring of food may beaffected just as much as the warehousing andpreparation of food.

WorldRiskReport ] 11

Improving disaster management

Improving disaster management+ Strategic approach to disaster
preparedness, beginning at the
local level, all the way to theinternational level

sis
Disaster
preparedness+ Continued updates of forecasts
and warnings+ Use of new technology for
prognoses and early warning
systems

Extremenatural event/acute crisis

Coping withdisaster

st

ru

re

on

li e

f

Re c

+ Organize infrastructure,logistics and additional
fields more efficiently andsecurely (“build backbetter”)

Ea
rl

ng
rni
wa

Risk
an
aly

Preventiony+ Perform analyses of processes,
develop target concepts+ Risk assessment on a local, regionaland national level

c ti

on

E

ge
m er

nc

y

+ Coordinated action ofinternational
stakeholders+ Strengthening of local
self-relief

Figure 2: Disaster management cycle

Those suffering from hunger are neither ina position to stockpile supplies for a disaster, nor can they take long-term measures
to adapt to disaster risks. Environmental
protection and a changeover to resilient
cultivation methods thus tend fall by thewayside. To reduce the disaster risk in rural
areas, which are where most of those people
suffering from hunger reside, a targetedstrengthening of socio-economic structures isnecessary, for example through the creationof alternative means of income to agriculture,
the protection of land-ownership rights andthe expansion of microinsurance.
The common causes of food insecurity andextreme natural events can be redressed by,for example, avoiding cultures that overstrain
the soil, and by using seed that is suitable forthe respective climate zone.
The city as a risk area
In rapidly growing cities in particular,
municipalities face the great challenge ofinitiating city planning measures that reduce
vulnerability. In the future, thanks to people
being within reach in a relatively small area,
providing for them and reducing vulnerabilities could be better achievable.

12 [ WorldRiskReport

The decisive factor here is the way in whichurbanization evolves: whether houses andresidential developments are located in
exposed zones, whether urban growth iswell coordinated and whether that growthis accompanied by investment in sanitation,
educational institutions and infrastructure. In areas where – opposed, or at most­tolerated, by the government – only slums
or informal settlements arise, urbanization
becomes a severe driver of risk. However
in areas where urban life and work leadto h­ igher incomes, and where the city’s
facilities, such as welfare centers, hospitals,
emergency rescue services or early warning
systems are made readily available, urbanization can act as a mitigating factor for risk.
It is in urban areas that good coordination ofearly warning mechanisms and emergency
relief is particularly important. These conditions must be established early on in thecontext of disaster preparedness measures,and must, where necessary, be tested andaccompanied by corresponding training.
It is also important to reach those people
who, due to disability, illness or age forexample, require special protection and extra
assistance.

Health and healthcare
A society with a high standard of health, andhealthcare that is available and affordable foreveryone, is less vulnerable to extreme natural events. At the same time, health must be
viewed as a multidimensional construct that
affects the vulnerability of a society in wide
variety of ways. Healthcare is more than justthe number of doctors and hospital beds at asociety’s disposal.
For example, it is often the easily avoidable
infectious diseases that have fatal consequences for a society. Pneumonia and diarrhea are together responsible for 21 percent
of deaths among children under five yearsof age (UN IGME 2017). And the illnesses
themselves, even if they don’t prove fatal, arestill a massive barrier to development bothfor the child itself and for society as a whole.
A decisive driver of infectious disease is the
proportion of people in a country who do nothave access to closed feces disposal systems.
This proportion, in percentage terms, has
fallen in the last few years, but there are still
892 million people without the necessary
access (WHO/UNICEF 2017). Here, disaster
preparedness needs to be strengthened
through the creation of secure water supplies
and sanitation.
Extreme shortfalls in supply tend to occur
above all in developing countries as a resultof a lack of financing of healthcare andhealth services. The poorest people are leftby the wayside: the world’s most underdeveloped countries and populations that do noteven have access to a subsistence-level wageof 1.25 dollars per day. Coping capacities inthe event of disaster pose immense challenges for these countries in particular.
Environmental degradation and disasters
Forests and riparian wetlands or coastal
ecosystems such as mangroves, coral reefs
and sea-grass reduce exposure to extreme
natural events. These, of course, act as natural

buffers and protective barriers that reducethe impacts of extreme natural events such as
landslides or storm tides. By providing food,medicines and building materials, they reducethe vulnerability of societies. Ecosystems canalso enhance coping capacities in the eventof a disaster. For example, if supply lines aresevered, food and fresh water can be obtained
from the immediate environment – as longas that environment is intact. Intact ecosystems also have a direct influence on adapting
capacities: If the environment is in goodcondition, there is then a greater diversity of
future planning options. For example, in Haiti
and other deforested areas, the opportunities
for diversified strategies are greatly reduced,
even though, with a view to the future, diversified opportunities for adaptation would be
extremely helpful (Bündnis Entwicklung Hilft2012).
Since the UN Conference on Sustainable
Development (“Rio+20”) in June 2012, itis clear that the issues of environmental
degradation, poverty and disaster risk areincreasingly being considered as interrelated
phenomena. Now consequences need to be
drawn from these debates to create sustainable disaster preparedness. The COP 23 UN
climate change conference that is to take placein November 2017 offers the next opportunity
in this direction.
Governance and civil society
Governmental and non-governmental
development cooperation must always work
towards making itself redundant. This applies
also to the field of disaster risk reduction.
The responsibility of national governments,
particularly in high-risk countries, must
therefore be strengthened. In the context of
weak governance in particular, it is essential
to support national governments, even before
the occurrence of a disaster, in order to develop their capacities for disaster prevention
and relief, and to help them in fulfilling their
responsibility to protect.

WorldRiskReport ] 13

It is often the case when disasters occur
that, as a result of a misunderstanding of theefficiency criteria, measures are implemented
solely by external stakeholders. National
governments and local civil society are
removed from the equation or replaced. The
result is a further loss of legitimacy and alack of coping and adapting capacities going
forward. In order to break through this mechanism, and in accordance with the principle ofsubsidiarity, local government structures mustbe facilitated in particular, with the additional
involvement of local civil society.

Disaster risk reduction should be legally
grounded in the form of an international
convention that ensures that governments ofrisk-affected countries assume the responsibility of protecting their own populations. This
type of convention would provide a basis for
inserting concrete legal claims of this nature
into the national legislation of the countries
that sign and ratify the convention. This would
provide civil society with an important instrument for making governments accountablewith respect to their responsibility to protect.

1.4 Development-political relevanceT

gives rise to income opportunities, for example
with respect to local manual trade work.

Worldwide challenges

In cases where necessary goods such as food,drinking water and building materials are notavailable locally, for example due to the widespread degradation or destruction caused by
the disaster, humanitarian relief from abroadthen remains essential. In this context, relief
organizations attribute an even greater levelof importance to coordination. This is implemented in the clusters established as part of
the UN’s coordination work. Care has to betaken in this context that the internationalrelief organizations do not dominate proceedings. Local organizations must continue tohold the reins when it comes to coordinationcommittees.

he future will also bring with it extreme
natural events and other causes of disasters
and acute crises. It is not enough to constantly
improve disaster relief. Considerable efforts
are also required in the context of governmental framework conditions - particularly
in countries that are frequently impacted
by disasters - and with respect to development-based cooperation.

Wherever possible, supply channels to thoseaffected should be self-organized while utilizing local resources, rather than “flying in”
relief from abroad. International organizations
should operate less often as active stakeholders themselves, and instead place their focus
on the strengthening of local stakeholders.
This approach is becoming more and morewidespread. As a result, measures such as the
allocation of vouchers or cash are becoming
increasingly important in the context ofinternal relief. Using vouchers and cash, it
is possible for local stakeholders to shop in
local markets and commission manual trade
work on location, thereby supporting the
local economy (Bündnis Entwicklung Hilft/
United Nations University 2016). Food-only
relief is increasingly losing importance, as
regional markets are strengthened in its place.
In addition, the reconstruction work that is
performed is frequently remunerated, which

14 [ WorldRiskReport

Financing requirements
It would cost an additional 160 US dollars a
year per person – provided across the period
2015 to 2030 – to completely eliminate hunger
for all persons living in extreme poverty (FAO/
IFAD/WFP 2015a), thereby making a decisive
contribution to a reduction in vulnerability.
To achieve this, the international community
would have to invest a total of 265 billion
dollars per year to ensure long-term fulfillment of this target. This is an achievable goal,
since that figure only represents 0.3 percentof the worldwide gross domestic product.

According to the UN’s “Achieving Zero
Hunger” report (FAO/IFAD/WFP 2015b)
this money is required for social protection(67 billion US dollars per year), investment inrural development and agriculture (140 billion
US dollars) and investment in urban development (58 billion US dollars).
The supply channels to refugees and internallydisplaced persons are particularly precarious.
In 2016, extreme natural events displaced
3.5 times more people internally than violence
and war (IDMC 2017). The human rightto food is in many cases not guaranteed bydomestic governments. Neighboring states arethen overburdened and the United Nations
High Commissioner for Refugees (UNHCR)
and the World Food Programme (WFP)
often do not have sufficient finances to deal
with the problem. The UNHCR, for example,
estimated its financial requirements for 2017at 7.31 billion dollars. Full financing up tothat amount by the end of the year is very
unlikely, however. In the past, there has beena large discrepancy between financial requirements and delivered financing. For example,
the UNHCR’s donations appeal for 2016
only raised 55 percent of the target amount(UNHCR 2016).
Future modeling of climate change and its
impacts show that the frequency of extreme
natural events will increase along with theregeneration period for both the societies thatare impacted and the affected ecosystems(Bündnis ­Entwicklung Hilft/United Nations­University 2015). Conflict and crisis situations
increase vulnerability and thus intensify
negative impacts when extreme natural eventsoccur. In this sense, disaster preparedness
requires the curtailment of climate change,
the development of adaptive strategies against
weather-related and other disasters, andpolitical solutions for the millions of refugees
and internally displaced persons worldwide.
Extensive political efforts and financial assistance are needed from international financeinstitutions and donor countries – specificallyfor the purposes of disaster preparedness.

Governmental responsibility
The interaction between governmental
responsibility and the methods for influencing
civil society is complex. “Weak statehood”
represents a considerable problem in thecontext of disasters and increases the need forinternational relief. The starting point must be
to strengthen the parameters of civil society
and to demand, support and also augment
governmental action. The point of intersection
between reducing disaster risk and promoting
good governance is a central development-political field of action. In this context, thegrowth of urban populations is only one of thechallenges facing countries and their cities.
Effective urban planning is necessary to counteract increasing urban poverty and the spreadof slums and informal settlements, along withthe associated protection of the general populace in the case of extreme natural events.
Paradigm shift
The WorldRiskReport serves to replace thegenerally widespread short-term approach toassessing disasters with a development-political approach: Aspects such as preparedness,
protection of particularly vulnerable groups
and risk management must be foregroundedin the context of both the analyses that areundertaken and future measures.
Unsolved problems such as resource limitations and weak governance intensify thevulnerability of societies to natural hazards,
but also their deficiencies in relation tocoping and adapting capacities. At the sametime, these social parameters are negatively
impacted by extreme natural events. In short,disasters prevent developmental progress, anda lack of developmental progress increases
disaster risk (see figure 3). In order to breakthis vicious circle, strategies for disasterrisk reduction must in future be an integral
component of comprehensive strategies forsustainable development.

WorldRiskReport ] 15

Deficits in disaster management and their consequences
Vulnerable household located in fragile,disaster-prone neighborhood. A flood,
drought, earthquake or heavy storm
threatens to strike the area.

preparedn
k of
ess
Lac

Lack of early w
arn
in
g

No early-warning system in place or
early warning does not reach people,
who then remain in the exposed area.

Extremenatural event/acute crisis

Im

The vulnerability of households
has risen again. Recovery andreconstruction after the disasterare hindered.

media

te i m p a c tconsequences
erm
gt
n
o

L

Death or loss of livelihood, destruction of living space, land, livestock,
harvests and the supply of essential foodstuffs.

C ris

is c a u s e d by d amageExtremenatural event/acute crisis

Financial resources for reconstruction
are lacking, people suffer from foodinsecurity, tools and livestock are soldoff, children no longer attend school.

Figure 3: Downward spiral caused by insufficient disaster management

Alongside acute disasters in the wake ofextreme natural events, relief organizations
and the international community are also
called on to respond to long-lasting disasters
and crises that are generally the result ofpolitical factors, for example in Syria, Iraq,
Sudan, Yemen and Afghanistan. These present
humanitarian logistics with tasks that are
different from those associated with acutedisasters. It is not the speed of relief efforts
that is decisive, rather the long-term supply ofaid, a lack of access routes, safety issues andshifting political or military responsibilities
represent the greatest challenges.

16 [ WorldRiskReport

The concept of Bündnis Entwicklung Hilft is
to integrate emergency relief and development-based cooperation into one approach,and to combine these factors to greater effectin their practical application. Risk assessment,
prevention and coping and adapting strategies
are components of this concept.

Strong need for action at a glance
The 15 countries with the highest
exposure worldwide

The 15 countries with the highest
vulnerability worldwide

The 15 countries with the highest risk
worldwideCountryExp. (%)

Country

Vuln. (%)

Country

Risk (%)

Vanuatu
Tonga
Philippines
Japan
Costa Rica
Brunei Darussalam
Mauritius
Guatemala
El Salvador
Bangladesh
Chile
Netherlands
Solomon Islands
Fiji
Cambodia

63.66
55.27
52.46
45.91
42.61
41.10
37.35
36.30
32.60
31.70
30.95
30.57
29.98
27.71
27.65

Chad
Eritrea
Afghanistan
Haiti
Niger
Central African Republic
Liberia
Sierra Leone
Mozambique
GuineaMadagascar
BurundiMali
Guinea-Bissau
Nigeria

74.36
73.98
73.61
73.11
72.63
72.50
71.52
71.28
70.11
70.01
69.30
69.30
69.14
68.70
67.92

Vanuatu
Tonga
Philippines
GuatemalaBangladesh
Solomon Islands
Costa Rica
Cambodia
El Salvador
Timor-Leste
Papua New Guinea
Brunei Darussalam
Mauritius
Nicaragua
Fiji

36.45
28.57
27.69
20.46
19.57
18.77
17.16
16.92
16.74
16.37
16.34
16.22
15.11
14.88
13.50

The 15 countries with the highest
susceptibility worldwide

The 15 countries with the greatest lackof coping capacities worldwide

The 15 countries with the greatest lackof adaptive capacities worldwide

CountrySus. (%)

CountryCountry

Madagascar
Mozambique
Liberia
BurundiChadUnited Rep. of Tanzania
Eritrea
ZambiaCentral African Republic
Haiti
Niger
Sierra Leone
Comoros
Malawi
Zimbabwe

65.58
65.19
63.51
62.96
62.94
62.92
62.71
62.31
62.25
62.01
60.78
59.18
58.61
57.68
57.42

Afghanistan
Sudan
Chad
Haiti
Yemen
Central African Republic
GuineaGuinea-Bissau
Iraq
Zimbabwe
Eritrea
BurundiMyanmarNigeria
UgandaL. of. Cop. (%)

92.82
92.57
91.50
90.62
90.24
89.63
89.58
89.14
89.09
88.71
88.31
88.13
88.06
87.86
87.67

L. of Ad. (%)

Afghanistan
Eritrea
Niger
Mali
Chad
Sierra Leone
Haiti
Liberia
GuineaCentral African Republic
Benin
Pakistan
Cote d'Ivoire
Guinea-Bissau
Mauritania

71.81
70.92
70.11
68.87
68.63
68.38
66.71
66.24
66.17
65.61
64.68
63.88
63.70
63.35
62.93

Data base: WorldRiskIndex, mean value calculation 2012 – 2016

WorldRiskReport ] 17

What does the WorCountries with comparable exposure levels can face different
degrees of disaster risk.
The WorldRiskIndex uses a danger-exposure value to show
the percentage of a nation’s population, as an annual average,that could potentially be subjected to earthquakes, hurricanes,
droughts and flooding and/or be affected by sea level rise of one
meter (WorldRiskReport 2011). But this does not necessarily meanthat countries that are highly exposed automatically exhibit a high

Rank
21.
119.

Country
Haiti
New Zealanddisaster risk. By way of example: New Zealand and Haiti are both
highly exposed, but the WorldRiskIndex indicates a low disasterrisk for New Zealand and, however, a very high disaster risk for
Haiti. The reason for this is the second factor assessed by theWorldRiskIndex: the vulnerability of a country.

Exposure: highVulnerability: very highExposure: highVulnerability: very low

Examples:Benin
Burkina Faso
BurundiChad
Haiti
Madagascar
Niger
Sierra Leone
Zimbabwe
Togo

Examples:Australia
Ireland
New ZealandSouth Korea

WorldRiskIndex
11.89 %
4.42 %

Exposure
16.26 %
15.44 %

Vulnerability
73.11 %
28.66 %

Data: see appendix

Vulnerability is a man-made driver of disaster risk.
Whether those persons exposed to hazards ultimately become
victims of disaster in the wake of extreme natural events depends
greatly on their level of vulnerability. The vulnerability of acountry is determined by the societal factors that influence what
a population can or cannot do in response to a natural disaster.
If natural disasters strike vulnerable societies then the disasterrisk is considerably greater. In the WorldRiskIndex, vulnerability is

High vulnerability increases disaster risk
Exposure

Vulnerability

Low vulnerability reduces disaster risk
Risk

Susceptibility

calculated using three components: susceptibility, lack of coping
capacities and lack of adaptive capacities (see page 22/23).
These components are themselves derived from a series ofbasic indicators, such as the percentage of a population that isundernourished and the literacy rate – factors that are not a fact ofnature but rather in part the result of political activity.

Exposure

Vulnerability

Risk

Susceptibility
Coping capacities

Adaptive capacities

Coping capacities

Adaptive capacities

rldRiskReport tell us?
Sound preparedness can reduce the disaster risk for highly exposed countries
but cannot eliminate it completely.
Countries with low or very low vulnerability are well protectedagainst extreme natural events. The driving factors behind thisare, among others, robust infrastructure (WorldRiskReport 2016), a
steady nutritional basis (WorldRiskReport 2015) and a functioning
health system (WorldRiskReport 2013). However, if a country is asheavily exposed to extreme natural hazards as say Japan, theneven the very best preventative measures can never completely

avert disaster. This was made clear as recently as 2011 when theearthquake that shook the country caused the Fukushima nuclear
disaster, along with a host of other devastating effects. The WorldRiskIndex assigns exposure a sufficiently large role by giving it thesame weighting as vulnerability in the calculation of disaster risk.

Japan

Data: see appendix

Rank
17.

Exposure
45.91 %

Vulnerability
29.33 %

WorldRiskIndex
13.47 %The poor are disproportionately affected by disasters – and are more likely to die asa result.
For persons living in structurally unsound dwellings or earning
low incomes, the odds of overcoming a natural disaster are much
lower. In developing countries, natural disasters often result in
extreme, life-threatening conditions and the destruction of aperson’s means of survival. In highly developed countries on theother hand, economic losses are more pronounced, with damage
to infrastructure a major factor. This is illustrated clearly by acomparison between Chile and Nepal: Between 1996 and 2016,
Poverty increases the danger of loss of life

Chile was hit by seven earthquakes, resulting in the deaths of 600
persons and damage of 30.3 billion US dollars. Nepal was struckby three earthquakes in the same period, with a loss of 9,000
human lives but economic losses of only 5.1 billion US dollars.

Where wealth is greater, economic losses are higher$¥€

18 [ WorldRiskReport2 	WorldRiskIndex:
A five-year perspective
Risk analysis 2012 – 2016

WorldRiskReport ] 19

Prof. Dr. Katrin Radtke
is senior research
associate at IFHV of
the Ruhr University
Bochum, Stephan
Luther is a freelance
geographer, and Lotte
Kirch and Ruben Prütz
are employees of
Bündnis Entwicklung
Hilft.

Drought in East Africa, landslides in Sierra
Leone, flooding in South Asia, hurricanes
in the Caribbean and the United States andearthquakes in Mexico – the 2017 list of
extreme natural events with drastic effects isalready a long one. In East Africa, millions
of people are suffering from hunger in thewake of drought, having lost livestock andtheir basic agricultural livelihoods. In SouthAsia, thousands of persons were forced to fleefrom flooding after the destruction of theirhomes. In the US, Hurricane Harvey caused

estimated damages of over 70 billion dollars.
These disasters have demonstrated once
more just how diverse the effects of extreme
natural events can be for human populations,the agricultural sector and the infrastructure.
This is because the severity of an extreme
natural event is not the only factor that
determines the scope of human suffering andeconomic damage – societal, political andeconomic structures influence the nature andscale of the damage in equal measure.

2.1 The conceptT

he WorldRiskIndex shows the disaster riskfor 171 countries worldwide. It is calculated
on a country-by-country basis through themultiplication of exposure (to natural hazards)
and vulnerability (societal). Vulnerability iscomprised of three components: susceptibility,
lack of coping capacities and lack of adaptive
capacities (Bündnis Entwicklung Hilft 2011).
For the determination of the exposure level,
the following five natural hazards are taken
into account: earthquakes, hurricanes, flooding, drought and sea-level rise. Overall, thevalues (in percentage terms) for exposure andvulnerability in the period from 2012 to 2016
were calculated according to 28 indicators thatprovide information on potentially endangered areas, in addition to social, economicand ecological conditions in societies. Of those
28 indicators, five fall under the category of
exposure and 23 under vulnerability. The
modular composition of the four components exposure, susceptibility, lack of coping
capacities and lack of adaptive capacities
contained in the WorldRiskIndex, including
the weighting of the individual indicators, isillustrated in Figure 4. This is a series of index
values between 0 and 1 that can be represented as percentage values between 0 and 100
per cent. Every component is divided into five“classes” using quantiles, ranging from “verylow” to “very high”. This enables comparisons
between the 171 countries with respect totheir individual components and risk value(Birkmann et al. 2011). The results are shown

20 [ WorldRiskReportin map form with the aid of geographic information systems (GIS). The description of theindividual indicators and the method of calculation, in addition to the data sources used, aredocumented at www.WorldRiskReport.org.
For 2017, the WorldRiskIndex has not beenrecalculated, rather a data analysis of the years
2012 to 2016 has been undertaken. The aim ofthe data analysis is to deduce what developments have occurred over this five-year period.
At the same time, the collated analyses also
serve as a basis for the planned further development of the WorldRiskIndex (see Chapter 1
and Chapter 3).
The present data analysis has been calculatedusing the respective average values (arithmeticmean) for each of the 171 countries – as relates
to risk, exposure, vulnerability, susceptibility,
lack of coping capacities and lack of adaptive
capacities – for the years 2012 to 2016. The
index values from 2011 have not been taken
into account in this calculation, since the2011 index was partly based on data sources
different from those used in subsequent years.
Also not included are the countries São Toméand Princípe and Samoa, since the risk levelsof both countries have not been calculated
since 2014 due to a lack of data. A calculation
has also been undertaken to determine thechange, expressed in percentage points, which
the individual countries experienced from2012 to 2016 as relates to vulnerability andits various components. An averages-based
analysis of exposure to natural hazards is notnecessary, since the employed data sets from2012 to 2016 were not updated in this period
due to unavailability of data and the actual
speed of change of climactic and geophysicalconditions.
In addition to the calculations of the averagevalues of the individual countries, the medianvalues of various groups of nations were
calculated and compared in the 2017 analysis.
As part of this process, the 171 countries ofthe WorldRiskIndex were classified in accordance with their geographical location, grossnational income per capita, and their exposureand vulnerability class. Wherever averagevalues of the individual countries are referenced in the following text, this is a reference
to the arithmetic mean. On the other hand,the stated mean values of country groups

are median values. The median, in contrast
to the arithmetic mean, is robust in the faceof extreme outliers of a group and therefore
better suited for the calculation of the groupvalue. If the arithmetic mean were used insuch cases, the extreme values of individual
countries could heavily influence the groupvalues and distort the results. The aim of thecomparison of country groups is to derive
conclusions on the relationships between thevarious groups themselves, but also on therelationship between individual countries andthe groups they belong to. The country groups
have been compared based on the following
four descriptive statistics:++ Median risk++ Median exposure++ Median vulnerability++ Median change in vulnerability inpercentage points

2.2 Five-year analyses
WorldRiskIndex 2012 – 2016

T

he WorldRiskIndex for the period 2012
– 2016 shows that the global hotspots forhigh disaster risk are located in Central America, West and Central Africa, Southeast Asiaand Oceania (see graphic “Global Hotspots”).
With a risk value of 36.45 percent, Vanuatu isthe country with the highest risk, followed byTonga with 28.57 percent and the Philippines
with 27.69 percent. By contrast, Saudi Arabia(1.21 %), Malta (0.61 %) and Qatar (0.09 %)register the lowest disaster risk. Overall,highly developed countries demonstrating a
low exposure level come out best in the risk
assessment (see table “WorldRiskIndex 20122016” in the overview, p. 40-42). If one looks
at the countries assessed in the 2012-2016
WorldRiskIndex as a totality, the averagevulnerability level is 47.78 percent. This value
dropped by 2.35 percentage points between
2012 and 2016. It is notable that the valuesof the three vulnerability components have
improved to very different degrees: Susceptibility has dropped by 0.78 percentage points,

lack of coping capacities by 0.8 percentage
points, and lack of adaptive capacities by 4.73
percentage points.
Country groups
The results of the country groups areillustrated in figure 5. The country group
analyses focus on groups in the areas of“Geographical location” and “Income”.
These and other results of the analyses areavailable at www.WorldRiskReport.org.
When comparing the country groups according to exposure class, it is noteworthy that thecountry group “Very high exposure” demonstrates the highest average vulnerability value
at 51.53 percent (medium). Countries withvery low exposure, on the other hand, are theleast vulnerable at 41.03 percent (low). Thereis, however, no linear correlation across all
the groups. For example, the “High exposure”
country group is on average less vulnerable
than the “Medium exposure” country group. A
glance at the vulnerability classes makes clear

WorldRiskReport ] 21

Exposure

Susceptibility
Public infrastructure

Population
exposed to:AS hare of the population without
access to improved sanitationB	Share of the population without
access to an improved water
sourceA Earthquakes
B Storms
Insufficient global
data available

C Floods

Housing conditions
	Share of the population living inslums; proportion of semi-solidand fragile dwellings

D Droughts
E Sea-level rise

Nutrition
C	Share of population
undernourished

Number of people in a country who areexposed to the natural hazards
earthquakes (A), cyclones (B) and/or
flooding (C)Poverty anddependenciesD	
Dependency ratio (share of under15- and over 65-year-olds in relation to the

Number of people in this country who arethreatened by drought (D) and/or
sea level rise (E)working population)E	Extreme poverty population
living with USD 1.25 per day orless (purchasing power parity)(each weighted half owing to the uncertainty of the data base)

Economic capacity andincome distribution

Number of total population in country

F	Gross domestic product percapita (purchasing power parity)
G	Gini index

Exposure

Exposure

WorldRiskIndex
Figure 4: Calculation of the WorldRiskIndex (from WorldRiskReport 2016)that the countries with very high vulnerability
are not the most exposed to natural hazards.
Focus geographic location: In an intercontinental comparison, the African countries
demonstrate the highest vulnerability levelat 65.53, while European countries havethe lowest. Europe is also the continent inwhich vulnerability has decreased the mostin the period from 2012 to 2016, showing a-2.81 percent change. The worst performer
with respect to change in vulnerability is
Oceania. Consisting primarily of island
nations, Oceania is the sole geographic region

22 [ WorldRiskReportto experience an increase in vulnerability(+0.44 percentage points). Furthermore,
Oceania is the region with the highest exposure. As a result, Oceania has the highest
disaster risk according to the WorldRiskIndex.
In an intercontinental comparison, Europe,
at 3.60 percent, exhibits not only the lowest
vulnerability level but also the lowest risk.
On the whole, Europe registers the narrowest
range of index values in the context of exposure, vulnerability and risk values, as well as
for change in vulnerability, and can thus beconsidered the most homogeneous region
with respect to disaster risk.

Insufficient global
data available

Lack of Coping capacities

Lack of Adaptive
capacities

Government and authorities

Education and researchA	Corruption Perceptions Index
B	Good governance (Failed States Index)

A	Adult literacy rate
B	Combined gross school
enrollment

Disaster preparedness and early
warning

Gender equity

	National disaster risk
management policy according
to report to the United Nations

C	Gender parity in education
D	Share of female
representatives in the
National Parliament

Medical services

Environmental status /Ecosystem protectionC	Number of physicians per
10,000 inhabitantsD	Number of hospital beds per
10,000 inhabitants
Insufficient global
data available

E	
Water resourcesF	Biodiversity and habitat
protectionG	
Forest management
H	
Agricultural management

Social networks
	Neighbors, family andself-help

Insufficient global
data available

Material coverage
E	Insurance (life insurances excluded)

Adaptation strategies
	Projects and strategies toadapt to natural hazards andclimate change
Investment
I Public health expenditureJ	Life expectancy at birth
K	Private health expenditure

33 %
33 %

33 %

+

Vulnerability

Focus income distribution: When subdivided
on the basis of gross national income (GNI)
per capita, and in accordance with the WorldBank categories “High income”, “Higher
middle income”, “Lower middle income” and“Low income”, countries with high income
levels show the lowest level of exposure.
Conversely, the “Lower middle income” group
has the greatest level of exposure to natural
hazards. With respect to vulnerability, the“High income” group is the least vulnerable.
Meanwhile, the group with the lowest GNI
per capita registers the highest vulnerability
value at 68.70 percent. This all results in

an increased risk for the groups with low
income levels. The change in vulnerability
assessed along the lines of income distribution does not follow a clear pattern. The“Higher middle income” group has registered the greatest change in vulnerability(-2.55 percentage points), while the group withthe lowest per-capita GNI (“Low income”)
has experienced the lowest degree of change(-1.77 percentage points).

WorldRiskReport ] 23

Country group
Total
Geographic
location

IncomeExposure

Vulnerability

Worldwide
AfricaThe Americas
Asia
EuropeOceania
High income
Higher middle income
Lower middle income
Low income
Very high exposure
High exposure
Medium exposure
Low exposure
Very low exposure
Very high vulnerability
High vulnerability
Medium vulnerability
Low vulnerability
Very low vulnerability

Risk x̃
6.57
7.96
6.91
6.41
3.60
14.92
3.33
6.28
7.84
9.62
13.15
7.71
6.70
4.81
2.56
8.55
8.24
6.70
4.22
3.33

Exposure x̃

Vulnerability x̃

12.45
12.45
16.15
12.61
10.93
26.32
10.23
13.51
13.67
13.17
25.77
15.00
12.46
10.56
7.50
12.51
14.65
14.12
11.11
10.82

47.78
65.53
46.12
50.06
34.22
54.48
32.50
46.43
57.26
68.70
51.53
49.40
50.86
44.94
41.03
67.29
58.15
47.90
40.37
30.91

Change invulnerability x̃
-2.35
-1.67
-1.94
-2.45
-2.81
0.44
-1.94
-2.55
-2.46
-1.77
-1.83
-2.48
-2.86
-2.31
-1.74
-1.66
-2.60
-2.42
-2.48
-2.35

Figure 5: Comparison by country group (data based on WorldRiskIndex 2012 – 2016)

Countries compared to country groups
What does this picture look like when
individual countries are compared to theirrespective country groups according to income
and geographic location? To determine thispicture, four countries from different global
regions were selected to form an illustrative
example. In terms of selection criteria, thenations chosen are situated in different world
regions, have varying income levels, haveregistered significant changes in vulnerability values or are particularly relevant to
the work of the member organizations of
Bündnis Entwicklung Hilft. The countries inquestions were then subjected to an individual
assessment.
Ethiopia compared to the “Africa” and “Lowincome” country groups: In landlocked Ethiopia, exposure to natural hazards is low at
11.12 percent, and thus slightly below the African average (x̃ exposure 12.45 % – medium)and the average of the “Low income” countrygroup (x̃ exposure 13.17 % – medium). With
regard to vulnerability, the Ethiopian value isvery high at 67.01 percent. Compared to the“Africa” country group (x̃ vulnerability 65.53 %– very high), Ethiopia’s vulnerability is slightly
above the average value for countries in the

24 [ WorldRiskReport

region. Compared to the “Low income”(x̃ vulnerability 68.70 % – very high) countrygroup, Ethiopia’s vulnerability is slightly
below the average for the countries in thisincome class. From 2012 to 2016, Ethiopia’svulnerability decreased by 6.88 percent. When
compared to Africa as a continent (x̃ change
in vulnerability of -1.67 percentage points),
Ethiopia’s vulnerability experienced a strong,
above-average decrease between 2012 and2016. The same applies to a comparison withthe countries in the “Low income” group(x̃ change in vulnerability of -1.77 percentage
points). This change in Ethiopia’s vulnerability, and with that its risk level, is the result ofa clear improvement in all three vulnerability
components. More specifically, the country
has been able to improve its lack of adaptive
capacities (-7.28 percentage points) andcoping capacities (-8.37 percentage points) inrecent years. Although not quite as strong an
improvement, Ethiopia’s susceptibility level
still fell by 4.99 percentage points between
2012 and 2016.
Bolivia compared to the “the Americas” and“Lower middle income” country groups: Witha value of 8.98 percent, Bolivia’s exposurelevel is very low and well below the average for
the Americas (x̃ exposure 16.15 % – high). Thesame applies to a comparison between Bolivia
and the “Lower middle income” country group(x̃ exposure 13.67 % – medium). Conversely,
Bolivia registers a vulnerability level of
54.92 percent, which is considered high. In
comparison to the Americas (x̃ vulnerability
46.12 % – medium), Bolivia’s vulnerability isthus slightly above the average for countries inthis region. On the other hand, if the vulnerability value of Bolivia is compared with thevalue of the “Lower middle income” countries(x̃ vulnerability 57.26 % – high), it is slightly
below the group average. Between 2012 and2016, Bolivia’s vulnerability was reduced by
6.09 percent. Compared to “the Americas”
country group (x̃ change in vulnerability of-1.94 percentage points), Bolivia’s vulnerability value has thus experienced an above-average decrease. Bolivia’s comparatively strongchange in vulnerability is also apparent in
its relationship to the value registered by the“Lower middle income” group (x̃ change invulnerability of -2.46 percentage points). In
Bolivia’s case, the decrease in vulnerability
and the associated reduction in disaster risk
are attributable to the progress made in termsof susceptibility (-7.83 percentage points) andlack of adaptive capacities (-9.77 percentage
points). With respect to coping capacities,
there has only been a comparatively slight
improvement in the Andean country(-0.66 percentage points).
Qatar compared to the “Asia” and “High
income” country groups: As is the case withmost of the other states of the Arabian Peninsula, Qatar registers very low exposure with avalue of 0.28 percent. Furthermore, the smallsized emirate has the lowest average exposure
value of all high-income states (High incomex̃ exposure: 10.23 % – low). The same is trueof a comparison between Qatar and Asia(x̃ exposure 12.61 % – medium). The vulnerability of Qatar is very low at 32.25 percent,similar to the vulnerability value of the “High
income” countries (x̃ vulnerability 32.50 %– very low). In a comparison with the “Asia”
country group, (vulnerability 50.06% –medium), Qatar’s vulnerability is significantly
below the average for the countries in thisregion. From 2012 to 2016, Qatar’s vulnerability decreased by 8.00 percentage points,
which indicates a greater-than-average change
measured against the comparison value for
Asia (change in vulnerability of -2.45 %). The
improvement registered by Qatar becomeseven clearer in a comparison with the “High
income” group (x̃ change in vulnerabilityof -1.94 percentage points). Qatar’s strong
decrease in vulnerability is due to its significant improvement in coping capacities andadaptive capacities (-11.45 and -12.61 percentage points respectively). By contrast, Qatar’s
susceptibility has barely changed in the same
period (+0.07 percentage points).
Tonga compared to “Oceania” and “Higher
middle income” country groups: Tonga –the small island nation in the South Pacific –demonstrates very high exposure to natural
hazards with a value of 55.27 percent. Tonga’svalue is considerably higher than of the Oceania average (x̃ exposure 26.32 % – very high).
The high exposure value of Tonga is also
considerably greater than that of the “Higher
middle income” group (x̃ exposure 13.51 % –medium). Tonga’s average vulnerability value
is 51.70 percent (medium), similar to the average value of the “Higher middle income” group(46.43 %). In a comparison with the “Oceania”
group (x̃ vulnerability 54.84 % – high) Tonga’s
vulnerability is below the average for the countries in that region. From 2012 to 2016, Tonga’s
vulnerability increased by 1.30 percentage
points, which represents a greater-than-averageincrease when compared to the “Oceania” group(x̃ change in vulnerability of +0.44 percentage
points). Tonga’s development becomes evenclearer when compared to the “Higher middle
income” group. The vulnerability level of thisgroup experienced a -2.55 percent decreaseduring the assessment period. Despite Tonga’s
comparatively high income and high developmental level, there was still no improvement inany of the three vulnerability components. In
fact, all vulnerability components experienced
an increase between 2012 and 2016: Susceptibility rose by 0.75 percent, lack of coping
capacities by 0.49 percent and lack of adapting
capacities by 2.65 percent.

WorldRiskReport ] 25

2.3 Conclusions and recommendations for actionT

he results of the collated data analysis fromthe 2012 – 2016 WorldRiskIndex yield anumber of central conclusions, which in turnallows for the formulation of recommendations for action as regards the developmental
policy of industrial nations and disaster
preparedness in high-risk countries.
+ Risk is unevenly distributed and therefore
cannot be tackled in a haphazard way.
Since 2011, the WorldRiskIndex has shownthat disaster risk affects some countries andregions particularly severely. In this context,
it is hardly surprising that the “High income”
group of nations faces the lowest level of risk.
However, it is interesting to note that thislow risk level is not only attributable to low
vulnerability in these countries, but also low
exposure. In the case of the disaster hotspotsof Central America, West and Central Africa,
Southeast Asia and Oceania it is notablethat high exposure is mostly the result of a
combination of several natural events, suchas earthquakes, hurricanes, flooding anddrought. Weather-related natural events suchas flooding and hurricanes already cause over
70 percent of natural disasters today and will
most likely be intensified in future by climatechange (CRED/ UNISDR 2015). A close coordination between disaster-risk management
and climate change adaptation is therefore
indispensable, and must be consolidated inthese regions in particular. At this point wemust also ask why small developing island
nations such as Tonga, of all places, which
demonstrate a very high average exposure
value and have been supported over thelast few years in the focused creation of an
integrated strategy for interlinking disastermanagement, climactic adjustment andsustainable development, have not only been
unable to reduce their vulnerability, but haveeven experienced an increase in vulnerability.

26 [ WorldRiskReport++ Progress is being made in disaster risk
reduction but a greater focus on coping
capacities is required.
The data analysis shows that in the last five
years, progress has been made in the field of
adaptive capacities, in particular. Here, theinternational community is on the right path,thanks to the Hyogo 2005 and Sendai 2015
action plans for reducing disasters caused bynatural hazards. This development will mostly
likely also have a positive, long-term effect on
coping capacities. The international community and NGOs should stick to their current
policy of strengthening adaptive capacities,
while investing in long-term measures inrisk hotspots, such as Oceania, in particular.
Examples include investments in consolidating education and research, equal societal
participation, the protection of ecosystems
and the expansion of healthcare systems. At
the same time, governments in those samehigh-risk countries must, starting now, placea greater emphasis on coping capacities tobetter counteract the effects of extreme natural
events in the short term. Important factors inthis context are, among others, anti-corruption
measures and good governance, in addition to
the continued expansion of national disaster
preparedness platforms and early-warning
systems. The necessary initiative must growfrom within the affected countries themselves.
At the same time, they should receive sufficient international support in overcoming
their challenges.

++ Disaster risk reduction is dependent on
income levels and is therefore not sustainable without coherent economic and development policies.
The results of the analysis of the WorldRiskIndex 2012 – 2016 show that there is a correlation between development and a reduction invulnerability. However, the assumption that“the highest income equates to the highest
reduction in vulnerability” does not add up
because the scale of exposure and vulnerability also plays a decisive role. In the caseof low exposure and vulnerability, wealthy
countries only invest small sums in reducing
their vulnerability. But it is interesting to notethat there is obviously an income threshold
above which vulnerability can be successfully
reduced. As a result, it was not the most
exposed countries with medium incomes,
or the most vulnerable countries with low
incomes which could bring about a particularly sizeable reduction in vulnerability – rather
the countries with high middle incomes wereable to achieve this. The result of this analysis
makes clear once more that sustainable development and disaster risk reduction are closelyinterlinked. Combating poverty and generally
boosting economic strength – along with anassociated reduction in susceptibility – aretherefore among the most important measuresfor disaster risk reduction.

WorldRiskReport ] 27

WorldRiskIndex
Serbia7.23

Netherlands
8.41

Germany
3.09
USA
3.90

Dominican Rep.
11.34
Mexico
6.25

Haiti
11.89
Guatemala
20.46

Niger
11.53
Rwanda7.35

Bolivia
4.93

Botswana
5.32

Chile
11.74

WorldRiskIndex
Components of the WorldRiskIndex at the global and local level
Exposure
Exposure tonatural hazards

Natural hazard sphere

Susceptibility
Likelihood of
suffering harm

CopingCapacities to reduce
negative
consequences

Adaptation
Capacities forlong-term strategies
for societal change

Vulnerability – Societal sphere

WorldRiskIndex (WRI) in %Exposure in %Vulnerability in %very low

0.09 – 3.52

very low

0.28 – 9.25

very low

26.19 – 35.56

low

3.53 – 5.62

low

9.26 – 11.53

low

35.57 – 45.11

medium

5.63 – 7.35

medium

11.54 – 13.85

medium45.12 – 51.70

high

7.36 – 10.48

high

13.86 – 17.45

high

51.71 – 62.62

very high10.49 – 36.45

very high17.46 – 63.66

very high62.63 – 74.36

no data available

no data available

no data available

Data: Source IREUS, based on the PREVIEW Global Risk Data Platform, CReSIS, CIESIN and global databases; detailed information at www.WorldRiskReport.org; Max. = 100 %,Syria
5.64

Russian Mongolia
Federation 3.08
3.78

Qatar
0.09

China
6.81

Japan
13.47

Philippines
27.69

Bangladesh
19.57

Yemen
6.01
India
7.00

Ethiopia
7.45

Vanuatu
36.45

Myanmar
9.06

Tonga
28.57

Timor-Leste
16.37
Madagascar
11.11

New Zealand
4.42

Values resulting from mean value calculation 2012 – 2016

Country

WRI

Bangladesh
Bolivia
Botswana
Chile
China
Dominican Rep.
Ethiopia
Germany
GuatemalaHaiti

19.57 %
4.93 %
5.32 %
11.74 %
6.81 %
11.34 %
7.45 %
3.09 %
20.46 %
11.89 %Country
31.70 %
8.98 %
10.55 %
30.95 %
14.43 %
23.14 %
11.12 %
11.41 %
36.30 %
16.26 %Classification according to the quantile method.

61.72 %
54.92 %
50.40 %
37.93 %
47.18 %
49.01 %
67.01 %
27.13 %
56.36 %
73.11 %= Exposure,

India
Japan
Madagascar
MexicoMongolia
Myanmar
Netherlands
New Zealand
Niger
Philippines= Vulnerability

WRI
7.00 %
13.47 %
11.11 %
6.25 %
3.08 %
9.06 %
8.41 %
4.42 %
11.53 %
27.69 %Country
11.94 %
45.91 %
16.03 %
13.84 %
6.52 %
14.87 %
30.57 %
15.44 %
15.87 %
52.46 %

58.62 %
29.33 %
69.30 %
45.14 %
47.17 %
60.95 %
27.49 %
28.66 %
72.63 %
52.78 %

Qatar
Russian Federation
RwandaSerbia
Syria
Timor-Leste
Tonga
USA
Vanuatu
Yemen

WRI
0.09 %
3.78 %
7.35 %
7.23 %
5.64 %
16.37 %
28.57 %
3.90 %
36.45 %
6.01 %

0.28 %
9.38 %
11.98 %
18.05 %
10.56 %
25.73 %
55.27 %
12.25 %
63.66 %
9.04 %

32.25 %
40.28 %
61.32 %
40.04 %
53.44 %
63.61 %
51.70 %
31.81 %
57.26 %
66.46 %

Global Hotspots
The world’s regions with the highest risk: Central America, Central and West
Africa, Southeast Asia and Oceania
Global hotspots for disaster risk are found in Oceania, Southeast
Asia, Central America and Central and West Africa. These areas havevery high danger-exposure levels due to the threat of natural disasters such as earthquakes, hurricanes, flooding, drought and/or sea
level rise, alongside frequently high levels of vulnerability. Global
warming and the resulting climate change increase the intensityand frequency of weather-influenced natural disasters, for example

torrential rain and hurricanes. As a result, countries such as Vanuatu,
the Philippines and Guatemala face the combined threat of earthquakes, hurricanes and flooding, but only have limited protective
measures available in the event of natural disasters. They have insufficient capacities to effectively boost disaster preparedness andthus counteract serious exposure to danger.

Central America
Southeast Asia

Oceania

Central andWest Africa

Classification according to appendix

Disproportionate number of island/coastal nations among thecountries with the highest exposure
Among the 15 countries with the highest exposure levels worldwide, eight are island nations and the remaining seven are locatedby the sea. These countries are particularly exposed to hurricanes, flooding and rising sea levels. At the same time, coastal regions are often densely populated: Today, around 40 percent of
the world’s overall population lives in regions that are less than
100 km away from the coast (United Nations 2017). To make matters worse, devastating human interference in the environment
increases the potential damage of extreme natural events considerably. The destruction of ecosystems such as coral reefs andmangrove forests deprive these territories of their natural protective mechanisms, for example in the case of storm flooding(WorldRiskReport 2012). Disaster protection is thus indispensable
in these regions.

Classification according to appendix

Accumulation of very high vulnerability in Africa
In total, 13 of the 15 countries with the highest vulnerability
ratings are found on the African continent. Outside Africa
only Haiti and Afghanistan have similar degrees of disaster
vulnerability compared to the likes of Chad, Eritrea, Niger andthe Central African Republic. Nations such as Liberia and Zambia
are admittedly only minimally exposed to natural disasters, but
due to their political, economic and social circumstances, they arehighly vulnerable in the event of a natural disaster. Susceptibility
levels are very high in these countries while coping capacities andlong-term capacities for adapting to natural hazards and climatechange are inadequate. The percentage of persons who live
below the poverty line and/or receive insufficient nourishment iscomparatively high in these “hotspots”, while medical care is poorand governmental programs, for example in the environmental
protection sector, are inadequate.

Classification according to appendix

28 [ WorldRiskReport3 	 In a Labyrinth of Indicators
What is special about the WorldRiskReport andhow can it be improved

WorldRiskReport ] 29

Christopher Schraderis a freelance sciencejournalist, working e. g.
for Süddeutsche Zeitungand Die Zeit.

L

et’s return once more to the topic of
Vanuatu and Kiribati. Both countries arelocated in the South Pacific. Each appears
to be the stuff of dreams for any explorer
looking to leave behind the drudgery of lifein Germany, particularly when it comes tothe weather. But before making that dream a
reality, it is helpful to take a look at the facts,
beginning with the 2016 WorldRiskReport.
Here we see that Vanuatu remains, after aperiod of several years, at the top of the list of
countries whose populations are threatenedby natural disasters, and whose governmentis incapable of protecting, or barely evenhelping, its citizens. Kiribati, conversely, mostrecently occupied position 164 of 171 on thelist – just behind Sweden and way behind
Germany! It should be crystal clear whichcountry is the safer option.
This lead in is not entirely new – I’ve used it
before, on September 5, 2013, when I reported
for the Süddeutsche Zeitung on that year’sWorldRiskReport. And now the publishersof that very report have asked me to write aretrospective and prospective assessment of
the WorldRiskReport.* It became apparent
that much of what I have to say can be understood with a view to the examples of Kiribati
and Vanuatu, hence the recycling of my­opening lines.
There are two key methodological factorsthat differentiate the WorldRiskReport andWorldRiskIndex (WRI), on which the former
is based, from many other risk calculations.
Firstly, the main indicators used in thesereports are not monetary figures relating
to shattered infrastructure and impaired
economic performance, but rather the humanactors that could be exposed to life-threatening dangers and, more specifically, thenumber of those persons as a percentage ofthe country’s total population. In 2011, thismethod represented a new departure, but it
has since been adopted by the United Nations

which, in March 2015 in the Japanese city of
Sendai, resolved to implement a new strategy:“The two foremost goals of the Sendai Framework for Disaster Risk Reduction are geared
toward the number of fatalities and affected
persons per 100,000 inhabitants”, says
Rhea Katsanakis from the UNISDR (United
Nations International Strategy for Disaster
Reduction), a Geneva-based office of the UN
Secretariat. “This is why the WorldRiskIndex,
which has for years used the proportion of the
population exposed to risk as its basis, is sointeresting.”
Secondly, the authors of the WorldRisk­Report
do not define the respective risk in the waythat an actuary or environmental researcher
would – as the product of damage andprobability of occurrence – but rather as the
product of two different factors: “exposure”
and “vulnerability”. The first pertains to
extreme natural events that could affect a
subsection of the population – in this caseearthquakes, storms, flooding, drought anda rise in sea levels. The second factor relates
to the shortage of all resources needed by thestate and its citizens to help those people whoare in immediate danger, rectify damage in thelong term, and decide upon and fund preventative measures against the next occurrence.
“Disasters are socially constructed”, explains
Terry Cannon from the Institute of Development Studies at the University of Sussex
who, as co-author of the book “At Risk:
Natural Hazards, People’s Vulnerability
and Disasters” (Routledge, 1994 and 2004),
has pioneered this approach to defining
risk. “Natural hazards happen of course
independently of humanity, but whether or
not they lead to a disaster is determined bysociety. Power and resource allocation affects
how much and who suffers from a hazard.”
This approach is frequently employed today,with the focus shifting toward the reduction ofsocietal influences that drive vulnerability.

* Full disclosure: I have been paid a customary standard rate for writing this article, selected my own interviewees and questions, and thistext has been linguistically redacted, but not edited as regards to content. Bearing in mind that I am not a scientist, but rather a journalist, I
am not in a position to offer an expert opinion. My conclusions are thus in each case subjective and potentially incomplete.

30 [ WorldRiskReportThis can be clearly seen in the WorldRisk­
Report. Vanuatu has been at the top of thelist for years, primarily because almost
64 percent of the country’s population could
potentially become victims of natural disasters; nowhere on earth is that percentage
greater. The island republic northeast ofAustralia, with a population of 270,000,
is located along the tropical cyclone tracks
and lies on the Pacific Ring of Fire, a chainof volcanoes along a series of tectonic plateboundaries that encircle the Pacific Ocean.
Severe earthquakes occur here frequently
and these can also cause tsunamis (volcanic
eruptions are not classified as a risk by theWorldRiskReport). In Kiribati, on the other
hand, only three percent of the country’s
roughly 115,000 people are under threat
because the country straddles the equatorand international dateline in the centerof the Pacific plate and is thus generally
unaffected by storms in either hemisphere.
Geographical misfortune weighs heavier
than misgovernment
The exposure levels of these two Pacificnations are thus considerably different, and
it is this factor that determines their placesin the rankings, as opposed to their vulnerability levels, which are very similar. On thevulnerability scale they are both in the top
tercile – in 48th and 50th place in 2016.
When it comes to governmental shortcomings, African countries sit at the very topof the list: The Central African Republic,
followed by, among others, Niger, Sierra
Leone and Zimbabwe. Out of the top 20
countries in this category, 18 are in Africa.
But only one of these is in the higher rankings when applying the overall risk indicator:
Guinea-Bissau. In contrast, 18 of the 20
most “exposed” countries feature in the top
20 countries most at risk overall. There are
two countries that manage to avoid the top
positions in that list, one of which is the
Netherlands. While it faces a considerable
threat from natural hazards, the country canstill mobilize state resources on a completely

different scale when compared to the poor
nations of the global South. Japan, however,is not one of the two exceptions. It is caught
in a situation analogous to that of Vanuatu,
in that it faces the double threat posed byearthquakes/tsunamis and storms. It thus
cannot escape the list of the 20 most dangerous countries, despite its wealth, given that46 percent of its population is exposed tonatural hazards.
Overall, exposure has a considerably
greater effect on risk than vulnerability:
Geographical misfortune weighs moreheavily on a nation that misgovernment
in this index. Assessed mathematically, it
quickly becomes apparent why this is thecase. The former varies more, by a factor of
212 between 0.3 and 63.7 percent, while thelatter varies by a factor of three between 24.8
and 74.8 percent. This leads me to my firstsuggestion: Those who calculate this index in
the future should attempt to create a numerical scale of vulnerability between 0 and 100
in order to further accentuate the differences. Countries such as Syria, Libya or Sudan,
whose national infrastructure has been
reduced to rubble, in addition to potentiallyimminent candidates Venezuela and Yemen,would then make dramatic movements on
the list – or at least experience a shift greaterthan the, at most, seven or eight positions
indicated by the lists published between
2012 and 2016.
Moreover, since the data on exposure havenot changed since 2012 (and for drought, thedata have not changed since the beginning),
this explains the overall absence of change inthe list. This does not only apply to the eternal frontrunner Vanuatu. Since 2012 a totalof 92 countries have scarcely moved (i.e. atmost five places in five years) or not moved
at all in the rankings. Out of the top 20
risk-affected nations in 2016, 19 have occupied the top rankings of the list since 2012.
The bottom end of the scale provides a similar picture. A handful of the usual suspects
have always been there: Switzerland, Luxemburg, the Scandinavian countries, but also

WorldRiskReport ] 31

the states of the Arabian Peninsula (extreme
heat is not one of the natural hazards taken
into consideration), followed by Israel andEgypt (political crisis is not a metric either),Grenada and Barbados in the Caribbean, andof course Kiribati.
But to be clear: The point is not to optimize
this list to create a new frontrunner every
year and somehow generate more interest
among journalists. But the lack of change
should also not be an artefact of the calculation method.
Greater focus on island nations
Bündnis Entwicklung Hilft, the body thatcommissions the report, is not the only
institution that compiles these types oflists. The approaches taken, however, andthe points of focus applied, vary from institution to institution. In its World Disaster
Reports, the International Federationof Red Cross and Red Crescent Societies(IFRC) primarily registers deaths andnumbers of affected persons, albeit in absolute numbers, and they are not expressed asa proportion of the overall population. The
UNISDR, on the other hand, has for many
years used monetary losses – as measured
against economic performance, a nation’s
capital stock or its level of social expenditure– as its main barometer. The institution’s
most recent document of this kind was the2015 “Global Assessment Report” (GAR).
These two international organizations share acommon data source: the Centre for Researchon the Epidemiology of Disasters (CRED) at
the Catholic University of Louvain in Belgium(not used by the WorldRiskIndex). Anothernoteworthy example is Germanwatch, which
publishes its annual Climate Risk Index each
Fall, at the same time as the UN Climate
Summit. This report is based on data provided by the Munich Re Insurance Company andcompiles only the number of fatalities and theeconomic losses caused by past meteorological disasters (storms, floods, heatwaves).

Idyllic beaches at risk
VanuatuTonga
Solomon Islands
Timor-Leste
In September 2017 Hurricane “Irma” swept
across the Caribbean island of Barbuda, theBritish Virgin Islands and Cuba, among others.
In doing so, it left a trail of devastation. It was
clear once more just how exposed island nations
are to natural disasters. And they are not only
threatened by sudden hurricanes: the creeping
progress of rising sea levels is also a menace.
Without effective disaster preparedness and along-term strategy to address climate change,
the livelihoods of island residents are in extreme
danger.

Figure 6: Selected island states among countries with very high risk in the WorldRiskIndex 2012 – 2016

32 [ WorldRiskReport

Mauritius
Fiji
Jamaica

Haiti
Dominican RepublicCape Verde

The first thing that becomes apparent in thesereports from other organizations is the greaterrole played by island nations. Many of these
are absent from the WRI for two reasons.
Firstly, the data base is often clearly insufficient for the complex calculations of the WRI.
This does not only apply to crisis-ridden countries such as Somalia, South Sudan and North
Korea, or micronations such as Monaco orAndorra, but also to islands and island groups
such as Trinidad and Tobago, St. Lucia,
Dominica, the Maldives and Samoa. Secondly,islands and regions such as Puerto Rico,Montserrat, Martinique, French Guiana, theCanary Islands, the Azores and Niue are notassessed individually, rather they are grouped
together with the countries they belong to(the USA, UK, France, Spain, Portugal, New
Zealand).
In both cases, the WRI is incomplete, sinceother indicators show that some of theseregions are both very susceptible and alreadyburdened with great losses. According to
CRED, between 1994 and 2013, 37 percent of
the population of Montserrat was affected bynatural disasters, in St. Lucia the figure was
39 percent and in French Guiana 49 percent.
Dominica comes 5th in the UNISDR report,
Puerto Rico 9th – both suffered severe
economic damage. In the Germanwatchorganization’s most recent climate risk
index, Dominica ultimately took top placefor 2015. Assessments of all these countries
or territories would be advisable for future
WorldRiskReports. This is theoreticallypossible, according to its creators, as themethods used in creating the WRI allow not
only for calculations for nation states but also
for smaller territories, all the way down to theregional level. This was demonstrated in thefirst edition of the report in 2011, which dealt
with regions of Indonesia.
Which ranking is correct?
For the countries that occur on every list there
should, in spite of different methodologies, beat least some similarities that can be observed
between the various rankings. According to

the WRI, countries that are exposed to manyhazards and whose state organs provide barely
any protection can be assumed to have been
affected more often and suffered more severe
losses in the past. And indeed Vanuatu, Tonga
and the Philippines, the top three of the WRI,
are also found in the upper rankings of the
UNISDR, which compares average annual
damage caused by disasters with a country’ssocial expenditure. And in a ranking based on
IFRC data, these countries are also way out infront if the absolute numbers are expressed asa percentage of the population affected using
the World Bank’s population data.
But there are also contradictions. The
UNISDR list, for example, ranks the Bahamas
number one for damage caused by disasters(WRI ranking 122) – the country loses on
average three times as much money annuallyfrom natural disasters than it can invest insocial services. The top 25 places also include
Grenada and the United Arab Emirates, whichhave comfortable positions in the 160-to-170
bracket in the WRI. There are considerable
differences when it comes to Central America
as well: Costa Rica, El Salvador and Nicaragua
are in the WorldRiskReport top 20, with
Honduras a significant distance below them in
30th place. At the UN Office there is a reverse
dynamic, with Honduras in 10th place aheadof its neighbors. This may be the result of themassive damage caused by Hurricane Mitch,which killed approximately 7,000 Hondurans
in 1998. The GAR only takes into accountpast events, while the WRI also looks forward
to the future. Nevertheless, the deviation is
astounding.
The CRED list of affected populations also
holds a few surprises when compared to the
WRI. The Belgian organization puts Eritrea
and Mongolia at the forefront – nations thatare way down the list of the WRI. Lesotho,
Kenya and Moldova also assume very different
positions. And at the very top of the CRED
rankings for the period 1994 to 2013 is­Kiribati. 52 percent – not three percent – ofthe population were reported to be affected bynatural disasters.

WorldRiskReport ] 33

It is not wise here to dismiss these types ofdifferences by pointing to variances in methodology. Severe and regular financial losses
resulting from natural disasters are ultimately
to the detriment of any country. They are theburden undertaken by a society trying to pickitself up after a major disaster. “These types oflosses undermine all efforts toward economic
development”, says Rhea Katsanakis fromthe UNISDR. “When we talk to governments
about the statistics, it mostly only starts to
get interesting when figures are on the table:
damage in relation to GDP.” Taking thesefactors into account in the WorldRiskReport
would not require a complete re-evaluation of
the core decisions involved in the risk assessment. The aforementioned damage relating tolosses could be listed under “Vulnerability”.
What burdens countries and keeps them from
fulfilling their duties
To further accentuate the scope of factors anddistinguishing effects inherent to vulnerability, it would also be possible to incorporate thevariables used by a team at the University ofNotre Dame in creating the ND Gain Index.
This index has been compiled annually since1995 and measures a country’s vulnerability to
climate change, the impact of which, in termsof increasing future meteorological risks, isinsufficiently represented in the WRI from theoutset. The US approach involves 3 categories:“exposure”, “sensitivity” and “capacity”.
The first category comprises, among otherfactors, the expected changes in crop production, ground water resources or bacterial
pathogens, as well electricity generation
trends in hydropower plants. The second category deals with figures on the independence offood imports, numbers of persons dwelling inslums and the percentage of people that live
up to five meters above sea level. The third
category essentially contains data on, among
other things, disaster preparedness, surfaced
roads and ecological reservations. The
creators of the WRI could certainly make useof these same data sources and perhaps also
the various constituent parts of the ND Gain

34 [ WorldRiskReport

Index itself. Here, a number of the indicators,
including food, water and infrastructure, spanthe entire spectrum from 0 to 100 percent,which would be of benefit, mathematicallyspeaking, to the vulnerability metric of the
WRI.
The UNISDR also recently began calculating
its risk data using socio-economic data. A
so-called “aggravating coefficient” in the 2016
GAR Atlas is intended to highlight the “likely
impact of disaster risk in contexts with highsocial, economic and environmental fragility”,
according to the report. It is ultimately thesame approach that the WRI has pursued
from the beginning, although somewhat
different in terms of its mathematical designand content. 14 different indicators are incorporated into the aggravating coefficient. Thereis an overlap with several indicators of theWorldRiskReport but it contains, in addition,a broad set of economic factors, namely inflation, unemployment, savings, welfare spending and spending on infrastructure (such as
paved roads and internet access). Based on
this calculation, the Bahamas returns to the
top of the list. Dominica, Grenada and Puerto
Rico are also found towards the top, as is
Vanuatu. Here Kiribati is also located at thebottom of the ranking.
Some more suggestions for vulnerability:
Among all the circumstances that prevent
countries from preparing for, and reacting
effectively to, extreme natural events, anumber of political and socio-economic
factors seem to be absent: Violence, for example, civil war, large-scale criminality – often
as the delayed effect of armed conflicts. Ethnic
grievance and religious strife are other notable factors. Migration also plays a role: Theflight of persons within one country or acrossnational borders. Another totally subjective
inclusion is freedom of the press. Suchindicators have only factored into the WRI
to a limited degree. The Fragile States Index,
which is used as an indicator for good governance, also measures migration, the fragmentation of ruling elites, and the so-called“security apparatus”. However, at the end of

the day, all of these factors combined add up
to only eight percent of the WRI’s vulnerability score.
Could it be much simpler?
Of course, it is important to avoid facets ofsociety being counted twice in different indicators. And if new indicators were to co-variate
with older ones, not much would be gained.
Mathematically speaking: There should not be
any large-scale correlations between the individual indicators, insofar as this is possible.
This needs to be addressed by the researchers
undertaking the calculation. But the existing
set of indicators should also be subjected tothis check: Are these types of correlations not
likely to occur in the numbers of undernourished persons and the extremely poor, who
are located side by side in the subcategoryof “susceptibility”? Or the index values for
doctors and hospital beds per 10,000 citizens
on the one hand, and public health expenditure on the other?
One final point on the broader topic of thevulnerability calculation: It would of course
become increasingly complex if researchers
added, say, another a dozen or dozen-and-ahalf indicators to the existing 23. “Reality is
too complicated to be illustrated by a global
index”, as was remarked already in the very
first WorldRiskReport. Before this type ofcomplexity becomes crippling, a radical
simplification may help. Whoever reads thelist of indicators of the Human Development
Index of the UNDP, for example, will recognize a certain similarity to the vulnerability
indicators of the WRI. The countries thatare worst off in both lists include the Central
African Republic, Chad, Eritrea and Niger, in
addition to Afghanistan, while Vanuatu andKiribati are in the top third of each ranking –in one list two places apart, in the other three.
How big is the impact?
How were the reports received and what
impact did they have? The German press,
for one, gave a lot of coverage but none of it

was critical. Media outlets in the Philippines,
India, Australia, Papua New Guinea, ­Nigeria,
Guatemala, Malta and the Netherlands
published reports with likeminded sentiments. Peter Mucke, the CEO of Bündnis
Entwicklung Hilft, states that the report has
been cited in the parliaments in Manila andJakarta. Furthermore, the German Federal
Foreign Office and Federal Ministry for
Economic Cooperation and Development
have both attested to references to the reportin proposals they have received from affected
countries. Those nations drew attention to thethreat they face by pointing to their ranking
in the index. Occasionally the reports evenreceive endorsement from unwanted sources:
An author from the “Eike” association, a groupof German climate-change deniers, cited theWorldRiskIndex as evidence for his thesisthat the Maldives are in no way threatened byclimate change – after all, the island state is
not mentioned in the WRI.
But while praise from conspiracy theorists
need not be taken seriously, the criticisms ofexperts should be. Take Rhea Katsanakis, of
UNISDR, who questions how governments
are supposed to put the information in theWorldRiskReport to use: “They must prioritize investment in the reduction of risks, andto do that they need information”. In the WRI,
however, all the individual details are merged
into one figure. “This is not very helpful
for planning at the national level”, says the
Geneva-based UN employee. That said, thepublishers of the WorldRiskReport do offer
detailed numerical information upon request.
Terry Cannon, from the Institute of Development Studies in Brighton, levels far more
drastic criticism. He claims the ranking makes
a mockery of the term “risk”, a concept he,among others, helped define. “The index iscompletely useless. Who can get any benefit
from the ranking of states? It helps neither
governments nor NGOs to make rational useof their money.” According to the Briton, if
nation states and aid organizations were togear their efforts toward the results of this list
– with the sole aim of improving a country’s

WorldRiskReport ] 35

Figure 7: Global needs for action in the field of food security (world map from WorldRiskReport 2015)

ranking – this would likely not yield the best
possible result in terms of aid. “Often you
can achieve much by improving water accessand sanitation. But in the index that would
only translate into a small progress because
it changes only a partial indicator.” What’smore, these types of interventions only affectvulnerability, which has a much smaller practical effect on a ranking than exposure.
The WRI’s contracting body has also had
similar deliberations. From the very outset,
its reports have included a focus on a central
issue that stands side-by-side with the index
itself. Since 2013, that central issue has
gravitated closer to the individual indicatorsin the vulnerability index (and these issues
are now placed ahead of the updated country
list in the printed report). Indeed, in the last
two years, this focus issue has offered a much
deeper insight into the WRI: The most at-risk
36 [ WorldRiskReportnations were studied in relation to the issuesof Food Security (2015) and Infrastructureand Logistics (2016) and divided into groups,
based on the need for action. These types ofassessments appear better suited for steering
remedial measures and aid relief – and todampen Cannon’s outright criticism of the
WRI’s calculation method.

A summary of the recommendations:++ The exposure metric needs a more up-to-

date calculation method and must place
greater focus on the changing hazards
associated with climate change, with thepossible inclusion of heat and forest fire in
the data.

++ When calculating vulnerability, the numerical range from 0 to 100 should be fully
utilized, to give the vulnerability factor thesame effective risk-determining weight as
the exposure factor.

++ The list of countries examined should

include several currently omitted island
nations and territories that belong to
distant, and often wealthy, countries.

++ Several indicators that affect how a country
deals with natural hazards seem to beabsent. These include not only the burden
associated with the resulting damage, butalso political and economic variables, suchas civil war, violence and migration.

++ A more detailed account of the individual

factors that bestow a significant risk to a
country can yield priorities for developmental policy. For this reason, the central
issues of the WorldRiskReport should
continue to be included and possibly evenexpanded upon.

WorldRiskReport ] 37

Susceptibility (mean values 2012 – 2016)
dependent on public infrastructure, nutrition, income and the general economic framework

very low

9.36 – 16.53

low

16.54 – 21.55

medium

21.56 – 30.35

high

30.36 – 47.64

very high46.65 – 65.58

no data available
Max. susceptibility = 100 %,Classification according to the quantile method

Karte B1
Karte B2
Lack of coping capacities (mean values 2012 – 2016)
dependent on governance, medical care and material security

very low

36.81 – 54.98

low

54.99 – 68.49

medium68.50 – 76.91

high

76.92 – 83.96

very high

83.97 – 92.82

no data available
Max. lack of coping capacities = 100 %,Classification according to the quantile method

Karte B3
Lack of adaptive capacities (mean values 2012 – 2016)
related to future natural events and climate changevery low

25.23 – 34.79

low

34.80 – 42.33

medium42.34 – 47.69

high47.70 – 56.55

very high56.56 – 71.81

no data available
Max. lack of adaptive capacities = 100 %,Classification according to the quantile method
Data: Source IREUS, based on the PREVIEW Global Risk Data Platform, CReSIS, CIESIN and global databases; detailed information at www.WorldRiskReport.org

Exposure (mean values 2012 – 2016)
Exposure of the population to the natural hazards earthquakes, storms, floods, droughts and sea level rise.

very low

0.28 – 9.25

low

9.26 – 11.53

medium

11.54 – 13.85

high

13.86 – 17.45

very high17.46 – 63.66

no data available
Max. exposure= 100 %,Classification according to the quantile method

Vulnerability (mean values 2012 – 2016)
Exposure of the population to the natural hazards earthquakes, storms, floods, droughts and sea level rise.

very low

26.19 – 35.56

low

35.57 – 45.11

medium45.12 – 51.70

high

51.71 – 62.62

very high62.63 – 74.36

no data available
Max. vulnerability = 100 %,Classification according to the quantile method

WorldRiskIndex (mean values 2012 – 2016)
WorldRiskIndex as the result of exposure and vulnerability

very low

0.09 – 3.52

low3.53 – 5.62

medium

5.63 – 7.35

high

7.36 – 10.48

very high10.49 – 36.45

no data available
Max. risk = 100 %,Classification according to the quantile method

WorldRiskIndex, countries in alphabetical order (mean values 2012 – 2016)

Country
Afghanistan
Albania
Algeria
AngolaArgentina
Armenia
Australia
Austria
Azerbaijan
BahamasBahrain
Bangladesh
Barbados
BelarusBelgium
BelizeBenin
BhutanBolivia
Bosnia and Herzegovina
Botswana
BrazilBrunei Darussalam
Bulgaria
Burkina Faso
BurundiCambodia
Cameroon
CanadaCape VerdeCentral African Republic
Chad
Chile
China
Colombia
Comoros
Congo
Costa Rica
Cote d'Ivoire
Croatia
Cuba
CyprusCzech Republic
DenmarkDjibouti
Dominican Republic
EcuadorEgypt
El Salvador
Equatorial Guinea
Eritrea
Estonia38 [ WorldRiskReport

WRI

Rank

Country

9.69 %
9.93 %
7.76 %
6.57 %
3.69 %
6.49 %
4.24 %
3.63 %
5.99 %
4.14 %
1.77 %
19.57 %
1.21 %
3.19 %
3.33 %
6.60 %
11.39 %
7.84 %
4.93 %
6.31 %
5.32 %
4.23 %
16.22 %
4.34 %
9.62 %
10.48 %
16.92 %
11.19 %
3.13 %
10.51 %
6.81 %
11.07 %
11.74 %
6.81 %
6.76 %
7.44 %
7.40 %
17.16 %
9.04 %
4.21 %
6.35 %
2.76 %
3.52 %
2.99 %
9.99 %
11.34 %
7.66 %
2.30 %
16.74 %
4.56 %
6.32 %
2.45 %

40.
39.
59.
86.
131.
87.
121.
133.
99.
124.
164.
5.
168.
143.
141.
85.
25.
57.
111.
93.
108.
122.
12.
120.
41.
35.
8.
27.
144.
32.
79.
29.
22.
80.
81.
66.
68.
7.
43.
123.
90.
150.
137.
149.
37.
26.
60.
159.
9.
117.
92.
156.

Ethiopia
Fiji
FinlandFranceGabon
GambiaGeorgia
Germany
Ghana
GreeceGrenadaGuatemalaGuineaGuinea-Bissau
GuyanaHaiti
Honduras
Hungary
Iceland
India
Indonesia
Iran (Islamic Republic of)
Iraq
IrelandIsraelItaly
JamaicaJapan
JordanKazakhstan
Kenya
Kiribati
Korea, Republic of
KuwaitKyrgyzstan
Lao People's Democratic Republic
LatviaLebanonLesothoLiberia
Libyan Arab Jamahiriya
Lithuania
Luxembourg
Madagascar
MalawiMalaysia
Mali
Malta
Mauritania
Mauritius
MexicoMongolia

WRI

Rank

7.45 %
13.50 %
2.25 %
2.73 %
6.14 %
11.99 %
6.64 %
3.09 %
8.69 %
7.11 %
1.44 %
20.46 %
8.42 %
13.50 %
11.65 %
11.89 %
10.82 %
5.55 %
1.54 %
7.00 %
10.49 %
4.87 %
4.77 %
4.56 %
2.40 %
4.63 %
12.08 %
13.47 %
4.76 %
3.74 %
6.94 %
1.76 %
4.80 %
3.46 %
8.27 %
5.69 %
3.43 %
5.02 %
7.03 %
7.84 %
3.89 %
3.06 %
2.55 %
11.11 %
8.13 %
6.47 %
8.68 %
0.61 %
8.19 %
15.11 %
6.25 %
3.08 %

65.
15.
161.
151.
95.
20.
84.
146.
45.
72.
167.
4.
49.
16.
23.
21.
31.
105.
166.
75.
33.
112.
114.
118.
157.
116.
19.
17.
115.
129.
76.
165.
113.
138.
51.
100.
139.
109.
74.
58.
127.
148.
154.
28.
54.
89.
46.
170.
52.
13.
94.
147.

Country
MoroccoMozambique
MyanmarNamibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
NorwayOman
Pakistan
PanamaPapua New Guinea
Paraguay
Peru
Philippines
PolandPortugal
Qatar
Republic of Moldova
Romania
RussiaRwandaSaudi Arabia
SenegalSerbiaSeychelles
Sierra Leone
Singapore
Slovakia
Slovenia
Solomon Islands
South Africa
Spain
Sri Lanka
Sudan
Suriname
Swaziland
SwedenSwitzerland
Syrian Arab Republic
Tajikistan
ThailandRepublic of Macedonia
Timor-Leste
Togo
Tonga
Trinidad and Tobago
Tunisia
TurkeyWRI

Rank

Country

6.87 %
8.93 %
9.06 %
5.59 %
5.37 %
8.41 %
4.42 %
14.88 %
11.53 %
8.19 %
2.29 %
2.72 %
7.11 %
7.43 %
16.34 %
3.71 %
6.91 %
27.69 %
3.35 %
3.65 %
0.09 %
4.98 %
6.47 %
3.78 %
7.35 %
1.21 %
10.86 %
7.23 %
2.56 %
10.45 %
2.36 %
3.56 %
3.62 %
18.77 %
5.62 %
3.23 %
7.52 %
7.97 %
8.48 %
7.64 %
2.19 %
2.50 %
5.64 %
7.16 %
6.35 %
6.12 %
16.37 %
10.48 %
28.57 %
7.56 %
5.62 %
5.42 %

78.
44.
42.
104.
107.
50.
119.
14.
24.
53.
160.
152.
73.
67.
11.
130.
77.
3.
140.
132.
171.
110.
88.
128.
69.
169.
30.
70.
153.
36.
158.
136.
134.
6.
103.
142.
63.
55.
48.
61.
162.
155.
101.
71.
91.
96.
10.
34.
2.
62.
102.
106.

Turkmenistan
UgandaUkraineUnited Arab Emirates
United Kingdom
United Republic of Tanzania
United States
Uruguay
Uzbekistan
Vanuatu
Venezuela
Viet Nam
Yemen
ZambiaZimbabwe

WRI

Rank

6.65 %
6.66 %
3.10 %
1.98 %
3.60 %
7.96 %
3.90 %
4.03 %
8.64 %
36.45 %
5.99 %
12.84 %
6.01 %
7.46 %
9.96 %

83.
82.
145.
163.
135.
56.
126.
125.
47.
1.
98.
18.
97.
64.
38.

Countries not listed in the WorldRiskIndex
AndorraAntigua and Barbuda
Democratic Republic of Congo
Dominica
Federated States of Micronesia
Liechtenstein
Maldives
Marshall Islands
MonacoMontenegro
Nauru
North Korea
Palau
Saint Lucia
Samoa
San Marino
São Tomé and Príncipe
SomaliaSouth Sudan
St. Kitts and Nevis
St. Vincent and the Grenadines
TuvaluWorldRiskReport ] 39

WorldRiskIndex overview (mean values 2012 – 2016)

Rank

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.

Country

Vanuatu
Tonga
Philippines
GuatemalaBangladesh
Solomon Islands
Costa Rica
Cambodia
El Salvador
Timor-Leste
Papua New Guinea
Brunei Darussalam
Mauritius
Nicaragua
Fiji
Guinea-Bissau
Japan
Viet Nam
Jamaica
GambiaHaiti
Chile
GuyanaNiger
Benin
Dominican Republic
Cameroon
Madagascar
Chad
SenegalHondurasCape Verde
Indonesia
Togo
BurundiSierra Leone
Djibouti
Zimbabwe
Albania
Afghanistan
Burkina Faso
MyanmarCote d'Ivoire
Mozambique
Ghana
Mali
Uzbekistan
Suriname
GuineaNetherlands
Kyrgyzstan
Mauritania
Nigeria
Malawi
Sudan
United Republic of Tanzania
BhutanLiberia
Algeria

40 [ WorldRiskReport

WorldRiskIndex

36.45 %
28.57 %
27.69 %
20.46 %
19.57 %
18.77 %
17.16 %
16.92 %
16.74 %
16.37 %
16.34 %
16.22 %
15.11 %
14.88 %
13.50 %
13.50 %
13.47 %
12.84 %
12.08 %
11.99 %
11.89 %
11.74 %
11.65 %
11.53 %
11.39 %
11.34 %
11.19 %
11.11 %
11.07 %
10.86 %
10.82 %
10.51 %
10.49 %
10.48 %
10.48 %
10.45 %
9.99 %
9.96 %
9.93 %
9.69 %
9.62 %
9.06 %
9.04 %
8.93 %
8.69 %
8.68 %
8.64 %
8.48 %
8.42 %
8.41 %
8.27 %
8.19 %
8.19 %
8.13 %
7.97 %
7.96 %
7.84 %
7.84 %
7.76 %

Exposure

63.66 %
55.27 %
52.46 %
36.30 %
31.70 %
29.98 %
42.61 %
27.65 %
32.60 %
25.73 %
24.94 %
41.10 %
37.35 %
27.23 %
27.71 %
19.65 %
45.91 %
25.35 %
25.82 %
19.29 %
16.26 %
30.95 %
22.90 %
15.87 %
17.06 %
23.14 %
18.19 %
16.03 %
14.89 %
17.57 %
20.01 %
20.26 %
19.36 %
15.56 %
15.13 %
14.65 %
16.34 %
14.96 %
21.25 %
13.17 %
14.32 %
14.87 %
13.67 %
12.73 %
14.48 %
12.55 %
16.18 %
18.12 %
12.03 %
30.57 %
16.63 %
12.47 %
12.06 %
12.34 %
11.86 %
12.01 %
14.81 %
10.96 %
15.82 %

Vulnerability

57.26 %
51.70 %
52.78 %
56.36 %
61.72 %
62.62 %
40.28 %
61.18 %
51.36 %
63.61 %
65.53 %
39.46 %
40.45 %
54.64 %
48.74 %
68.70 %
29.33 %
50.66 %
46.79 %
62.18 %
73.11 %
37.93 %
50.85 %
72.63 %
66.75 %
49.01 %
61.52 %
69.30 %
74.36 %
61.82 %
54.09 %
51.89 %
54.19 %
67.38 %
69.30 %
71.28 %
61.16 %
66.55 %
46.75 %
73.61 %
67.17 %
60.95 %
66.12 %
70.11 %
60.03 %
69.14 %
53.40 %
46.80 %
70.01 %
27.49 %
49.75 %
65.65 %
67.92 %
65.88 %
67.20 %
66.27 %
52.93 %
71.52 %
49.04 %

Susceptibility

35.16 %
28.45 %
32.97 %
36.82 %
40.39 %
44.37 %
21.81 %
41.69 %
29.64 %
52.09 %
53.28 %
16.44 %
18.63 %
36.80 %
25.34 %
53.60 %
17.27 %
27.22 %
26.50 %
44.52 %
62.01 %
20.25 %
28.62 %
60.78 %
53.26 %
28.86 %
43.02 %
65.58 %
62.94 %
46.71 %
35.08 %
34.07 %
32.36 %
55.67 %
62.96 %
59.18 %
38.28 %
57.42 %
20.56 %
56.21 %
54.76 %
36.23 %
47.64 %
65.19 %
45.58 %
54.91 %
30.79 %
28.49 %
54.27 %
14.66 %
27.01 %
48.01 %
54.38 %
57.68 %
51.77 %
62.92 %
31.60 %
63.51 %
23.06 %

Lack of coping
capacities

81.19 %
81.45 %
80.92 %
80.78 %
86.41 %
85.01 %
64.49 %
86.81 %
75.49 %
82.18 %
84.21 %
63.97 %
61.49 %
81.35 %
75.17 %
89.14 %
38.12 %
76.74 %
72.02 %
82.97 %
90.62 %
58.34 %
79.28 %
87.01 %
82.32 %
74.04 %
85.01 %
83.37 %
91.50 %
80.95 %
81.60 %
70.46 %
80.94 %
85.30 %
88.13 %
86.28 %
82.54 %
88.71 %
74.23 %
92.82 %
84.03 %
88.06 %
87.02 %
84.43 %
77.93 %
83.66 %
77.88 %
71.25 %
89.58 %
41.43 %
76.55 %
86.02 %
87.86 %
83.92 %
92.57 %
83.34 %
75.10 %
84.80 %
77.42 %

Lack of adaptive
capacities

55.42 %
45.18 %
44.45 %
51.48 %
58.37 %
58.47 %
34.55 %
55.04 %
48.96 %
56.55 %
59.10 %
37.96 %
41.23 %
45.76 %
45.72 %
63.35 %
32.61 %
48.01 %
41.86 %
59.04 %
66.71 %
35.20 %
44.65 %
70.11 %
64.68 %
44.12 %
56.53 %
58.95 %
68.63 %
57.80 %
45.59 %
51.15 %
49.27 %
61.17 %
56.81 %
68.38 %
62.67 %
53.54 %
45.46 %
71.81 %
62.71 %
58.55 %
63.70 %
60.71 %
56.57 %
68.87 %
51.54 %
40.66 %
66.17 %
26.40 %
45.70 %
62.93 %
61.53 %
56.05 %
57.25 %
52.53 %
52.10 %
66.24 %
46.65 %

Rank

Country

60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.

EcuadorSwaziland
Trinidad and Tobago
Sri Lanka
ZambiaEthiopia
Comoros
PanamaCongo
RwandaSerbiaTajikistan
GreecePakistan
Lesotho
India
Kenya
Peru
MoroccoCentral African Republic
China
Colombia
UgandaTurkmenistan
Georgia
BelizeAngolaArmenia
Romania
Malaysia
Cuba
ThailandEritreaBosnia and Herzegovina
MexicoGabon
Republic of Macedonia
Yemen
Venezuela
Azerbaijan
Lao People’s Democ. Rep.
Syrian Arab Republic
TunisiaSouth Africa
Namibia
Hungary
TurkeyNepal
Botswana
LebanonRepublic of Moldova
Bolivia
Iran (Islamic Republic of)
Korea, Republic of
Iraq
JordanItaly
Equatorial Guinea
IrelandWorldRiskIndex

7.66 %
7.64 %
7.56 %
7.52 %
7.46 %
7.45 %
7.44 %
7.43 %
7.40 %
7.35 %
7.23 %
7.16 %
7.11 %
7.11 %
7.03 %
7.00 %
6.94 %
6.91 %
6.87 %
6.81 %
6.81 %
6.76 %
6.66 %
6.65 %
6.64 %
6.60 %
6.57 %
6.49 %
6.47 %
6.47 %
6.35 %
6.35 %
6.32 %
6.31 %
6.25 %
6.14 %
6.12 %
6.01 %
5.99 %
5.99 %
5.69 %
5.64 %
5.62 %
5.62 %
5.59 %
5.55 %
5.42 %
5.37 %
5.32 %
5.02 %
4.98 %
4.93 %
4.87 %
4.80 %
4.77 %
4.76 %
4.63 %
4.56 %
4.56 %

Exposure

16.15 %
12.76 %
17.54 %
14.79 %
11.37 %
11.12 %
10.97 %
16.45 %
11.65 %
11.98 %
18.05 %
12.98 %
21.11 %
11.36 %
11.40 %
11.94 %
10.69 %
14.40 %
13.25 %
9.39 %
14.43 %
13.84 %
10.16 %
13.19 %
14.69 %
13.31 %
10.18 %
14.51 %
15.77 %
14.60 %
17.45 %
13.70 %
8.55 %
14.02 %
13.84 %
11.95 %
14.38 %
9.04 %
13.15 %
13.16 %
9.55 %
10.56 %
12.45 %
12.08 %
10.41 %
15.61 %
12.25 %
9.16 %
10.55 %
11.14 %
11.11 %
8.98 %
10.19 %
14.89 %
8.08 %
10.53 %
13.85 %
8.22 %
14.74 %

Vulnerability

47.45 %
59.91 %
43.12 %
50.83 %
65.67 %
67.01 %
67.78 %
45.15 %
63.56 %
61.32 %
40.04 %
55.15 %
33.71 %
62.54 %
61.69 %
58.62 %
64.95 %
48.02 %
51.86 %
72.50 %
47.18 %
48.84 %
65.53 %
50.37 %
45.21 %
49.57 %
64.55 %
44.75 %
41.06 %
44.27 %
36.37 %
46.34 %
73.98 %
44.99 %
45.14 %
51.36 %
42.53 %
66.46 %
45.56 %
45.49 %
59.60 %
53.44 %
45.19 %
46.52 %
53.75 %
35.56 %
44.26 %
58.67 %
50.40 %
45.11 %
44.77 %
54.92 %
47.78 %
32.25 %
59.05 %
45.17 %
33.40 %
55.52 %
30.91 %

Susceptibility

27.80 %
45.62 %
19.28 %
26.15 %
62.31 %
56.80 %
58.61 %
27.74 %
53.03 %
55.14 %
18.55 %
35.12 %
17.40 %
37.04 %
49.64 %
38.22 %
54.20 %
29.05 %
28.12 %
62.25 %
26.43 %
28.32 %
55.81 %
25.92 %
25.85 %
27.91 %
52.03 %
21.55 %
21.46 %
19.85 %
18.63 %
20.30 %
62.71 %
19.55 %
23.57 %
33.04 %
20.63 %
46.30 %
23.35 %
21.96 %
40.85 %
26.33 %
21.43 %
30.35 %
46.04 %
16.31 %
19.83 %
42.61 %
35.53 %
20.72 %
23.08 %
40.28 %
19.19 %
14.52 %
32.05 %
22.77 %
16.81 %
29.87 %
15.78 %

Lack of coping
capacities

74.66 %
80.72 %
69.04 %
78.80 %
80.34 %
81.73 %
83.96 %
67.52 %
85.96 %
79.58 %
66.78 %
76.41 %
51.31 %
86.71 %
79.55 %
80.47 %
85.82 %
73.84 %
75.84 %
89.63 %
70.41 %
75.95 %
87.67 %
75.86 %
64.21 %
74.16 %
85.52 %
71.04 %
61.54 %
68.17 %
57.20 %
75.62 %
88.31 %
70.65 %
71.86 %
75.73 %
64.65 %
90.24 %
74.79 %
69.84 %
84.89 %
83.39 %
72.54 %
69.33 %
71.05 %
53.56 %
68.57 %
81.27 %
67.72 %
69.91 %
68.09 %
79.71 %
80.35 %
46.52 %
89.09 %
68.18 %
54.98 %
85.08 %
45.51 %

Lack of adaptive
capacities

39.89 %
53.37 %
41.03 %
47.54 %
54.36 %
62.51 %
60.77 %
40.19 %
51.70 %
49.24 %
34.79 %
53.92 %
32.42 %
63.88 %
55.87 %
57.17 %
54.82 %
41.18 %
51.62 %
65.61 %
44.69 %
42.27 %
53.10 %
49.34 %
45.58 %
46.63 %
56.09 %
41.65 %
40.17 %
44.80 %
33.28 %
43.09 %
70.92 %
44.75 %
39.99 %
45.30 %
42.32 %
62.85 %
38.55 %
44.68 %
53.06 %
50.59 %
41.60 %
39.88 %
44.16 %
36.80 %
44.37 %
52.13 %
47.94 %
44.69 %
43.13 %
44.78 %
43.81 %
35.73 %
56.00 %
44.55 %
28.40 %
51.60 %
31.43 %

WorldRiskReport ] 41

RankCountry

119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145.
146.
147.
148.
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
165.
166.
167.
168.
169.
170.
171.

New Zealand
Bulgaria
Australia
BrazilCroatia
BahamasUruguay
United States
Libyan Arab Jamahiriya
RussiaKazakhstan
Paraguay
Argentina
Portugal
Austria
Slovenia
United Kingdom
Slovakia
Czech Republic
KuwaitLatviaPolandBelgium
Spain
BelarusCanadaUkraineGermany
Mongolia
Lithuania
Denmark
CyprusFranceOman
Seychelles
Luxembourg
Switzerland
Estonia
IsraelSingapore
Egypt
NorwayFinlandSwedenUnited Arab Emirates
Bahrain
Kiribati
Iceland
GrenadaBarbados
Saudi Arabia
Malta
Qatar

42 [ WorldRiskReport

WorldRiskIndex

4.42 %
4.34 %
4.24 %
4.23 %
4.21 %
4.14 %
4.03 %
3.90 %
3.89 %
3.78 %
3.74 %
3.71 %
3.69 %
3.65 %
3.63 %
3.62 %
3.60 %
3.56 %
3.52 %
3.46 %
3.43 %
3.35 %
3.33 %
3.23 %
3.19 %
3.13 %
3.10 %
3.09 %
3.08 %
3.06 %
2.99 %
2.76 %
2.73 %
2.72 %
2.56 %
2.55 %
2.50 %
2.45 %
2.40 %
2.36 %
2.30 %
2.29 %
2.25 %
2.19 %
1.98 %
1.77 %
1.76 %
1.54 %
1.44 %
1.21 %
1.21 %
0.61 %
0.09 %

Exposure

15.44 %
11.66 %
15.05 %
9.53 %
11.53 %
10.71 %
11.10 %
12.25 %
7.80 %
9.38 %
9.11 %
7.03 %
9.55 %
10.93 %
13.60 %
11.59 %
11.60 %
10.21 %
10.82 %
9.04 %
9.26 %
9.79 %
11.66 %
10.23 %
8.46 %
10.25 %
7.50 %
11.41 %
6.52 %
8.88 %
10.87 %
7.44 %
9.25 %
6.41 %
5.99 %
9.12 %
9.56 %
7.23 %
6.41 %
7.82 %
4.72 %
8.58 %
8.19 %
7.97 %
5.93 %
4.27 %
3.05 %
5.67 %
3.13 %
3.46 %
2.93 %
1.65 %
0.28 %

Vulnerability

28.66 %
37.19 %
28.19 %
44.43 %
36.52 %
38.67 %
36.33 %
31.81 %
49.93 %
40.28 %
41.03 %
52.79 %
38.59 %
33.37 %
26.65 %
31.25 %
31.08 %
34.85 %
32.50 %
38.24 %
37.10 %
34.22 %
28.54 %
31.53 %
37.68 %
30.55 %
41.34 %
27.13 %
47.17 %
34.49 %
27.54 %
37.04 %
29.47 %
42.37 %
42.77 %
27.95 %
26.19 %
33.83 %
37.42 %
30.14 %
48.75 %
26.64 %
27.43 %
27.47 %
33.37 %
41.41 %
57.69 %
27.19 %
46.12 %
34.99 %
41.14 %
37.10 %
32.25 %

Susceptibility

16.42 %
18.14 %
15.05 %
24.80 %
17.75 %
18.14 %
20.66 %
16.53 %
25.01 %
21.35 %
18.08 %
31.02 %
21.36 %
17.56 %
14.26 %
14.97 %
16.46 %
14.25 %
14.87 %
12.02 %
20.76 %
17.22 %
15.60 %
15.95 %
16.77 %
14.85 %
19.00 %
15.11 %
32.32 %
18.99 %
14.92 %
14.46 %
16.17 %
16.44 %
21.60 %
12.32 %
14.37 %
18.01 %
19.08 %
14.16 %
21.62 %
14.04 %
15.45 %
15.08 %
10.39 %
13.28 %
42.18 %
14.68 %
24.86 %
16.53 %
16.08 %
15.03 %
9.36 %

Lack of coping
capacities

43.33 %
57.02 %
42.04 %
67.17 %
56.69 %
53.99 %
50.78 %
48.56 %
75.81 %
59.07 %
62.69 %
78.87 %
59.72 %
48.10 %
36.81 %
50.87 %
46.82 %
55.25 %
50.32 %
64.93 %
55.43 %
53.72 %
40.40 %
50.00 %
60.92 %
45.91 %
61.99 %
37.69 %
65.48 %
50.36 %
39.47 %
57.74 %
43.22 %
63.53 %
63.23 %
41.17 %
37.60 %
51.38 %
58.65 %
48.83 %
76.91 %
39.08 %
38.99 %
40.36 %
56.29 %
65.68 %
83.19 %
41.67 %
69.20 %
49.96 %
68.49 %
57.10 %
49.03 %

Lack of adaptive
capacities

26.21 %
36.42 %
27.48 %
41.32 %
35.13 %
43.87 %
37.55 %
30.36 %
48.97 %
40.41 %
42.33 %
48.49 %
34.70 %
34.45 %
28.88 %
27.92 %
29.95 %
35.05 %
32.31 %
37.77 %
35.11 %
31.71 %
29.64 %
28.65 %
35.33 %
30.90 %
43.03 %
28.57 %
43.73 %
34.13 %
28.23 %
38.91 %
29.03 %
47.14 %
43.47 %
30.36 %
26.60 %
32.10 %
34.54 %
27.43 %
47.72 %
26.78 %
27.86 %
26.96 %
33.44 %
45.28 %
47.69 %
25.23 %
44.28 %
38.49 %
38.85 %
39.17 %
38.36 %

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WorldRiskReport ] 43

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