DRAFT 23.11.09 Concept for a World Risk and Vulnerability ...luedeke/Allerlei/FINAL DRAFT World Risk Rep… · DRAFT: Concept for a World Risk and Vulnerability Report 9 The key goals

DRAFT – 23.11.09

Concept for a

World Risk and Vulnerability Report

Risk , Vulnerability and Adaptation Index at National and Local Scale

CONCEPT

Authors:

Dr.-Ing. Jörn Birkmann / UNU-EHS Dr. Torsten Welle / UNU-EHS

Mr. Jan Wolfertz / UNU-EHS Mrs. Dunja Krause / UNU-EHS

Mr. Nishara Fernando / UNU-EHS

External Reviewers during development: Prof. Omar D. Cardona / University of Manizales, Colombia

Dr. Stefan Schneiderbauer / EURAC, Italy

Project Team: Mr. Peter Mucke

Mrs. Katrin Radke

DRAFT: Concept for a World Risk and Vulnerability Report

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Acknowledgement (to be complemented) Experts who answered our questionnaire:

Stefan Schneiderbauer

Omar D. Cardona

Pascale Peduzzi

Andreas Siebert / MunichRe

Thomas Loster / MunichRe Foundation

xxx


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Content Acknowledgement (to be complemented) ............................................................................................. 2

Summary for Policy Makers..................................................................................................................... 4

Preface ..................................................................................................................................................... 5

I Rationale and Goals: World Risk , Vulnerability & Adaptation Index.................................................... 6

II State-of-the-Art .................................................................................................................................. 11

III Concept.............................................................................................................................................. 17

IV Indicators Selected and Methodology .............................................................................................. 25

IV-A Indicators Global/National Assessment........................................................................................ 29

IV-B Indicators Local Assessment .......................................................................................................... 58

IV-C Overview of Other Reviewed Indicators that were not Included in the Concept ......................... 63

V Presentation of Selected Results........................................................................................................ 64

VI Challenges and Barriers ..................................................................................................................... 69

VII Recommendations and Outlook ...................................................................................................... 70

Literature (DRAFT list, not yet exhaustive) ........................................................................................... 72

Annex 1: Expert Questionnaire ............................................................................................................. 76

Annex 2: Draft Figures on “Human Costs” Indicators ........................................................................... 72

Annex 3: Draft Figures illustrating expert judgement at national scale ................................................ 85


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Summary for Policy Makers

Has still to be written


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Preface By the Bündnis Entwicklung Hilft


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I Rationale and Goals: World Risk , Vulnerability & Adaptation Index Strategies for climate change adaptation and disaster risk reduction to extreme

events, sudden-onset and creeping hazards – such as sea level rise, floods,

droughts or typhoons – have to be based on reliable information. Lobbying and

awareness raising for disaster risk reduction and adaptation to ―inexperienced‖

hazards, as well as ―low-frequency‖ hazards, is extremely difficult. Already the

promotion and lobbying towards preparedness to frequently experienced hazards

of natural origin is a challenge in many countries. Most countries and donor

agencies still release major funding, when a disaster has already occurred.

However, it is important to increase the awareness regarding the necessity for

disaster risk reduction and climate change adaptation to extreme events, before

disaster or irreversible changes occur and cause major harm and losses.

Promoting a paradigm shift

Every disaster is context-specific and therefore difficult to predict precisely. The

research of the last 20 years, particularly in the field of natural hazards and

development cooperation, clearly points out that it is not solely the natural

hazard that leads to a disaster. Rather, most of the major risks and disasters are

triggered by vulnerable conditions of societies or elements exposed. Additionally,

the lack of coping and adaptation capacities are two other factors that often lead

to context conditions in which societies or social-ecological systems are not able

to deal with changing environmental conditions and natural hazards effectively.

Thus, instead of defining disasters primarily as physical occurrences, requiring

largely technological solutions, they are better viewed as a result of the complex

interactions between potentially damaging physical events (hazards, such as

floods, droughts, sea level rise – including salinisation, etc.) and the vulnerability

of a society, its infrastructure, economy and environment, which are determined

by human behaviour (see Birkmann 2006:10). Promoting disaster-resilient

societies requires, therefore, a paradigm shift away from the primary focus on

natural events or natural hazards towards the identification, assessment and

ranking of various vulnerabilities of societies and coupled social-ecological

systems.


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Information a key source for resilience and adaptation

In this context, the international community has formulated important goals

within the Hyogo Framework of Action 2005-2015, which was adapted as the

final document of the World Conference on Disaster Reduction in 2005. The

declaration points out that:

“the starting point for reducing disaster risk and for promoting a culture of

disaster resilience lies in the knowledge of the hazards and the physical, social,

economic and environmental vulnerabilities to disasters that most societies face,

and the way in which hazards and vulnerabilities are changing in the short and

long term, followed by action taken on the basis of that knowledge”

(UN/ISDR 2005:7).

Additionally, the Hyogo Framework underlines that a key activity within this

process is the:

“development of a system of indicators for disaster risk and vulnerability at

national and sub-national scales that will enable decision makers to assess the

impact of disasters on social, economic, and environmental conditions and

disseminate the results to decision makers, the public and populations at risk”

(UN/ISDR 2005:7).

Although the ability to measure, assess and evaluate vulnerability, coping and

adaptive capacities, as well as natural hazards, is increasingly being seen as a

key step towards effective risk reduction and climate change adaptation (see

IPCC 2009), the current development of respective indicators and index systems

is still a challenging and difficult task. Most indicator concepts developed within

the field of disaster risk- and climate change-related hazards predominantly

focus on the natural hazard side. Therefore, a key contribution also from the

perspective of development agencies, such as the Bündnis Entwicklung Hilft, has

to be the stronger emphasis on the societal component of disaster risk and

climate change adaptation. In this context, the development of indicators to

illustrate and visualise selected aspects of vulnerability, coping and adaptation is

key, in order to translate the abstract concepts into practical and measurable

variables. These indicators and indices are not intended to allow for the

measurement of the whole complexity of disasters, risk, vulnerability, coping and

adaptation. however, like other indicators, such as the ―unemployment rate‖ as


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an indicator for socio-economic development problems (and a lack of inner

societal integration), the indicators selected and developed in this study

represent specific characteristics of natural hazard exposure, vulnerability,

coping and adaptive capacity, thereby illustrating what is meant by these

abstract terms. Additionally, they should bridge the detailed knowledge and the

complexity of disasters risk or societal responses such as coping and adaptation

on the one hand, and the simplified communication of these issues and problems

to policy makers and the general public, on the other.

Goals for developing the indicators and indices

In this regard, the proposed concept for a ―World Risk, Vulnerability & Adaptation

Index‖ developed by the UNITED NATIONS UNIVERSITY Institute for

Environment and Human Security (UNU-EHS) within a commissioned study by

the Bündnis Entwicklung Hilft – aims to explore the feasibility and the

possibilities of the systematic development of such an index and indicator system

that accounts for both the natural hazard side and the societal vulnerability and

response capacity – including issues of long-term adaptation. The concept builds

on important work done in other reports and regions (UN/ISDR 2009, Cardona

2005a/IDEA 2005, UNDP 2004, Dilley et al. 2005). It integrates new subjects like

sea level rise and also aims to link global and local monitoring and indicator-

based assessment.

Beside the exploration of available data for these indicator systems, an additional

challenge is linked to the question on how to structure and operationalise factors

such as susceptibility, coping and adaptation. Although it is evident that societies

and communities are not solely susceptible to natural hazards, but also have

means that enable these groups to cope or adapt to these environmental

changes, the translation of these complex processes into measurable indicators

or criteria is still very difficult. Most current indicator concepts focus mainly on

the quantification of the natural hazards and the direct human and economic

losses (see Dilley et al. 2005 and Peduzzi et al. 2009).


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The key goals of this study can be defined as followed:

Development and testing of the feasibility of developing indicators and an

index to measure risk, vulnerability and adaptation capacities to natural

hazards and climate change-related threats at national and local scales;

Translation of abstract terms into measurable indicators and criteria that

allow the visualisation of selected characteristics and features of these

complex concepts and processes;

Development of scientific methodologies to combine and merge very

different aspects of disaster risk, vulnerability, coping and adaptation by

means of indicators and statistical methodologies;

Development of an indicator and index system that is modular in its

structure and therefore can be modified, if needed, in the future;

Development of a system that is mainly based on data that is publicly

available and updated annually;

Furthermore, the indicator system and index should enable practitioners

and experts to communicate the necessity for preventive measures

towards risk reduction and climate change adaptation;

Finally, the index and indicator system should be one additional

information source for the general public and decision makers.

The World Risk, Vulnerability and Adaptation Index is not intended to capture the

whole complexity of hazards and their generation or the various and context-

specific features of vulnerability. Rather, it should give a first overview and, in

this context, should stimulate further discussions on how to improve coping

capacities and adaptation strategies towards extreme events and natural hazards

– with a special emphasis on societal vulnerability. Furthermore, it is important

to note that also adaptation strategies – that are expected to get more attention

in the post-Copenhagen process - have to be based on sound information and

knowledge. Adaptation strategies should be accompanied by appropriate

monitoring and evaluation instruments, in order to show whether real

vulnerability reduction is achieved or whether, to the contrary, mal adaptation

takes place.


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Target Audience

The target audience for the indicator and index system are both stakeholders

working in the field of disaster risk reduction, climate change adaptation and

development cooperation as well as politicians and decision makers who define

priorities and guidelines for adaptation and risk reduction strategies to extreme

events and natural hazards. Moreover, the Index should also inform the scientific

community about further research needs and the necessity to improve data, in

order to be able to monitor the progress and the failure towards building

disaster-resilient communities. That means the gaps of data identified within this

study for example regarding the insufficient data on educational expenditure

should be taken as an advise to further improve the data quality in this area.


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II State-of-the-Art

The target audience for the indicator and index system are both stakeholders

working in the field of disaster risk reduction and climate change adaptation as

well as politicians and decision makers. The following section gives a brief

overview of other approaches in the field, focusing on disaster risk at the global

or regional level. The overview of selected current approaches also deals with

some of their comparative advantages and shortcomings. The review of existing

approaches might also help to understand the new elements of the World Risk

Vulnerability and Adaptation Index.

Disaster Risk Index (UNDP and UNEP GRID) 2004

In 2004, the United Nations Development Programme (UNDP) published the

study ―Reducing Disaster Risk. A Challenge for Development‖, introducing the

Disaster Risk Index (DRI). The DRI distinguishes several classes of natural risks

at the national level (Fig. 1), based on a model developed by UNEP GRID (United

Nations Environment Programme, Global Resource Information Database). The

UNEP GRID model assesses different physical and socio-economic parameters

that influence the impacts of natural hazards. Terminologically, the study is in

line with the United Nations International Strategy for Disaster Reduction

(UN/ISDR), which means that the underlying comprehension and definition of

risk as a function of natural hazard and vulnerability equals the approach of the

presented concept.

Fig. 1: Spatial distribution of DRI classes (0=no mortality risk to 7=highes mortality

risk), source: Peduzzi et al. 2009:1157.


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As a primary goal and intention, the DRI seeks to explain the correlation of a

country’s development status and its vulnerability to external (natural) hazards.

It thus combines the physical exposure to hazards (annual average population

exposed per spatial unit) and the vulnerability (expressed through socio-

economic variables) to calculate the mortality risk for a certain hazard type

(Peduzzi et al. 2009). Adverse effects of natural hazard impacts other than

mortality, for example the population affected, are not included in the

calculation.

The DRI is based on mortality data from the Emergency Events Database (EM-

DAT) of the Centre for Research on the Epidemiology of Disasters (CRED) that

contains approximately 15,000 large disaster events. Hazard events have to fulfil

at least one of the following criteria, in order to be included in the database:

• Ten (10) or more people reported killed

• One hundred (100) people reported affected

• Declaration of a state of emergency

• Call for international assistance

(http://www.emdat.be/criteria-and-definition)

In addition to the physical exposure and mortality data from CRED, 32 socio-

economic variables were selected as potential vulnerability indicators, but “only

five of them were finally retained by the multiple regression analysis (i.e. GDP

purchasing power parity per capita, modified percentage of arable land,

percentage of urban growth, percentage of country forest coverage, transformed

value of the percentage of the country dedicated to crop land)” (Peduzzi et al.

2009:1156).

Limitations of the DRI, intended to be reduced within the World Risk,

Vulnerability and Adaptation Index, result from the consideration of mortality risk

only, as well as from the selection of the above vulnerability criteria that are very

narrowly concentrating on the quality of the environment and composition of the

economy. Most indicators used within the DRI focus mainly on the direct

consequences of disasters, in terms of the identification of the variables that

directly correlate with observed fatalities and losses. The proposed World Risk,

Vulnerability and Adaptation Index, in contrast, puts more emphasis on the

context conditions and accounts for direct losses, as well as the broader

http://www.emdat.be/criteria-and-definition


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development and governance context of a country, through indicators like the

Corruption Perceptions Index (CPI).

Natural Disaster Hotspots (International Bank for Reconstruction and

Development/World Bank/Columbia University, 2005)

The hotspots study by Dilley et al. (2005) presents an alternative approach for

an index of the global risk to natural hazards. This includes economic losses, in

addition to mortality, and disposes a higher resolution (5km x 5km grid) than the

DRI. The primary goal of this study is to identify regional hotspots of risk. Like

the DRI, it uses the EM-DAT CRED database as basis for its calculation and is

thus limited to large events. Within the study, three separate indices are

calculated:

1) Index on mortality risk (based on gridded population of the world data),

2) Index on risk of absolute economic losses, and

3) Index on risk of economic losses as proportion of GDP per grid.

The societal vulnerability within this study is estimated based on mortality and

economic losses (for different levels of income) of past events. Areas with low

population density or without agricultural importance are not regarded in this

study, which might be problematic, as these areas show high relative mortality

to floods which can lead to the exclusion of particularly vulnerable areas

(Birkmann 2007). Risk is calculated based on natural events, elements exposed

and vulnerability. The fundamental drawback of this study results from the lack

of specific indicators of vulnerability. Vulnerability, in the understanding of this

concept, cannot simply be determined by past losses of life and economic values.

PhD-Thesis of Stefan Schneiderbauer (2007)

In his PhD-Thesis on “Risk and vulnerability to natural disasters – from broad

view to focused perspective. Theoretical background and applied methods for the

identification of the most endangered populations in two case studies at different

scales”, Schneiderbauer presents a very comprehensive vulnerability analysis,

which gave important methodological input to the proposed concept.

Based on the availability and actuality of data, Schneiderbauer selected 37

variables and performed a principal component analysis (PCA) to obtain a fewer

meaningful indicators. He then developed a composite indicator for hazard-

independent vulnerability at national scale (Fig. 2).


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In the process of concept development, it was considered to use a PCA for the

creation of the Index, in order to describe the variability of the different input

variables and to weight them accordingly. The idea was rejected, however, due

to the modular structure of the Index which allows the modification of weights

and input variables over time (please see chapter on methodology for details).

Fig. 2: Hazard Independent Vulnerability of Populations , an estimation at national level

(dark green = very low vulnerability to dark red=very high vulnerability), source:

Schneiderbauer 2007:63.

UN/ISDR – Global Assessment Report 2009

The ―Global Assessment Report on Disaster Risk Reduction: Risk and poverty in a

changing climate. Invest today in a safer tomorrow‖ has its focus on the relation

of poverty and risk in the face of climate change. The report shows disaster risks

and their causes on a global scale and introduces measures for disaster risk

reduction. The emphasis is thus put on a more applied access to disaster risk and

disaster risk reduction. The intention of the report is not only to picture risk

exposed population, but also to make the connection of the recent findings of the

climate change community and the expected increasing damages, thereby

putting in a claim of an improved risk reduction.

The report underlines the particularly high affectedness of the poor, who suffer

most from natural hazards (compare Fig. 3). One of the key messages of the

report is that the implementation of risk-reducing measures can simultaneously


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abate poverty and make a contribution to development as well as to climate

change adaptation.

Fig. 3: Mortality and economic losses from tropical cyclones

compared to the exposure of different income levels, source:

UN/ISDR 2009:32.

The analysis is undertaken on a local scale in twelve Asian and Latin American

states, taking into account a total of 126,620 survey reports, covering the period

from 1970 to 2007. Among the key findings of the report were the importance of

high frequent, smaller magnitude hazard events, that were indeed not

responsible for most of the fatalities, but accounted for the majority of other

risks, for example the damage or destruction of housing.

Another claim of the report is thus to put a stronger focus on small-scale events,

which are not yet accounted for in most global statistics, but crucial for the

vulnerability of people to natural hazards.

Indicators of Disaster Risk and Risk Management (Inter-American

Development Bank/ Universidad Nacional de Colombia, Manizales, IDEA)

Produced under the lead of Omar Dario Cardona, this study can be regarded as a

special case, compared to the approaches above, as it evaluates the national

levels of vulnerability and disaster risk management performance in relation to

an assumed maximum considered hazard event and does not analyse the

number of people killed and/or affected from past events. The study builds a


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very complex model for measuring vulnerability to natural disasters, based on

four independent indices (for detailed explanations see Cardona 2005a, 2005b):

1) Disaster Deficit Index (DDI)

The DDI measures the risk of financial and macro-economic impacts a

country may experience resulting from the exposure to a maximum considered

event occurring in a given timeframe (derived from hazard return periods of 50,

100 and 500 years) and factors attenuating the potential impacts (e.g. insurance

schemes, financial resources reserved for emergencies, potential external

assistance, etc.).

2) Local Disaster Index (LDI)

The LDI focuses on lower magnitude events at the local level, which via

their cumulative impact, determine chronically social and environmental risks.

These events are mostly not encountered in larger scale databases, although

they may have a very serious effect on societal vulnerability to natural hazards.

3) Prevalent Vulnerability Index (PVI)

The PVI captures the predominant vulnerability conditions, assessing the

exposure of prone areas, the socio-economic fragility and the lack of social

resilience. It thus takes into account the underlying structural conditions shaping

vulnerability (vulnerability as starting point for analysis in the sense of O’Brien et

al., 2004).

4) Risk Management Index (RMI)

The RMI refers to the risk management performance of a country and thus

to its hazard response capacities. The input variables portray the respective level

of identification of risk, risk reduction, disaster management, and governance

and financial protection.

Pooling these four indicators, the approach of Cardona and his team represents

probably the most complex model for vulnerability analysis available at the

international scale. It is, however, geographically limited to twelve countries in

Latin America and the Caribbean and not feasible to apply at global scale, mainly

due to the lack of data availability.


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III Concept

The concept of the indicator and index system is based on the core

understanding of risk within the natural hazards and disaster risk reduction

research school. Hence, the term risk is mainly understood as the outcome of the

interaction between a natural hazard event and the vulnerable conditions of the

exposed element or society (see UN/ISDR 2004, Wisner et al. 2004, Birkmann

2006, IDEA 2005). This definition stresses the fact that risk is not solely an

outcome of the probability and magnitude of the natural hazard event (flood,

storm, earthquake), but also determined by the vulnerable conditions of the

society or the coupled social-ecological system exposed.

In this context, the concept developed by the UNITED NATIONS UNIVERSITY

Institute for Environment and Human Security (UNU-EHS) puts emphasis

particularly on the societal pre-disposition to be affected and the capacities of

societies to respond to these hazards and creeping changes. In this regard, the

World Risk, Vulnerability and Adaptation Index aims to capture and measure four

major factors:

a) exposure to the natural hazard, including its frequency,

b) susceptibility of the exposed society or communities,

c) coping capacities and

d) adaptive capacities.

The following figure illustrates this meta-framework.


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Fig. 4: Structure of the Index and the Indicator System, source: own (DRAFT) figure

While the exposure component clearly aims to identify the number of people

exposed to selected natural hazards and creeping changes, such as potential

future sea level rise, the other three components – namely susceptibility, coping

and adaptation focus more in-depth on characteristics of vulnerability and

societal responses. Up-to–now, there is still a strong and controversial discussion

regarding the factors and components of vulnerability and adaptation between

and within disaster risk research, climate change school of thought and

development research. This study, however, considers a more comprehensive

concept of vulnerability, encompassing parts of exposure to natural hazards,

susceptibility and coping capacities. One key framework for the vulnerability

analysis in the natural hazard context is the so-called BBC-framework, based on

work by Bogardi and Birkmann (2004) and Cardona (1999/2000), which stresses

EXPOSURE t o natural hazards

e.g.

a) Population exposed b) Land area exposed c) Different natural hazards d) Frequency

SUSCEPTIBILITY L ikel i hood to suffer

e.g. a) Revealed susceptibility – foci on past disasters b) E xisting and future vulnerability, e.g. poverty or dependencies

COPING C apacities to deal with the impacts in an

event e.g.

a) State capacities b) Health c) Insurances

ADAPTATION Capacities to adapt and to

transform e.g.

a) Skills, income earning activities

b) Education c) Health

Structure of the World Risk Vulnerability Adaptation I ndex

Core components of vulnerability

Natural hazard sphere Societal sphere

Global index / indicators with national scale resolution

Local indicators and criteria with sub - national, local and household scale resolution


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that the assessment of interventions and thus adaptation measures are also

important (see Birkmann 2006). In general, vulnerability assessment focuses on

the likelihood of injury, loss, disruption of livelihood and other harm in an

extreme event and/or the unusual difficulties in recovering (Wisner et al. 2004:

13; Wisner 2002: 12/7). This means the focus of vulnerability assessment should

lie on identification of variables that make people vulnerable and show major

differences in the (potential and revealed) susceptibility of people, as well as

those factors that drive and shape the vulnerability. In this context, it is assumed

that vulnerability is not solely a characteristic of a system or society before a

disaster occurs, rather it can also be modified or even intensified during and after

a disaster, for example through inappropriate disaster aid and recovery.

Therefore, the concepts of vulnerability can also be viewed as encompassing

potential and revealed features of susceptibility (see Birkmann and Fernando

2008). The framework used for the World Risk, Vulnerability and Adaptation

Index revealed vulnerability is captured and integrated by means of accounting

for people dead or affected by natural hazards (ex-post focus), while issues of

extreme poverty and/or the dependency ratio – for example - are used to

capture and illustrate potential susceptibility (ex-ante focus) of communities and

nations exposed to natural hazards and climate change.

Additionally, the strong emphasis on coping and adaptive capacities – including

the local-specific interventions - promotes a problem solving perspective, in

terms of the analysis of possibilities to reduce vulnerability and disaster risk. This

general notion of a dynamic process of exposure, susceptibility, coping and

adaptation is also linked to the BBC-framework. This framework developed from

three discussions: (1) how to link vulnerability, human security and sustainable

development; (2) the need for a holistic approach to disaster risk assessment;

and (3) the broader debate on developing causal frameworks for measuring

environmental degradation in the context of sustainable development (see

Birkmann 2006:35). Particularly, the examination of coping capacities and

intervention tools (local assessment), as well as the differentiation of coping and

adaptation, stresses the importance of being proactive in order to reduce

vulnerability before an event strikes society, economy or environment (see

Birkmann 2006:36; Birkmann et al. 2009). Additionally, if a society or

community is not exposed to a certain type of hazard, there is no vulnerability.


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When developing vulnerability indicators, it is sometimes difficult to precisely

distinguish between aspects that decrease the susceptibility and those that

increase coping capacity. Certain overlaps are therefore unavoidable. The BBC-

framework and the concept outlined in this study also acknowledge the original

discussion on vulnerability within development research. The splitting of

vulnerability into two sides was first proposed by Chambers (1989) and then

elaborated more prominently by Watts and Bohle (1993). Thus, also the

conceptual framework presented in this study underlines that societies are not

solely exposed and susceptible to natural hazards, rather they also have abilities

to deal with the changing environmental conditions in forms of coping or

adaptation. The discourse on how to distinguish coping and adaptation, in terms

of extreme events and natural hazards however, is rather new. Therefore, its

measurement is touching an important research boarder in terms of defining

qualities for adaptation (see Birkmann et al. 2009), instead of rather coping with

the impacts of these events.

Definition of key terms used within the framework

Against this background, the following section provides definitions of the key

terms used. Further details regarding the measurement are given thereafter in

the indicator and methodology section.

Exposure

Exposure in its core meaning in natural hazard research encompasses entities

exposed and prone to be affected by a hazard event. These entities include

persons, resources, infrastructure, production, goods, services or ecosystems

and coupled social-ecological systems, etc. Exposure can be further differentiated

in terms of a spatial (geographic exposure) and a temporal component. Often,

communities or regions might be exposed spatially to a certain degree.

Additionally, some people might only be exposed to natural hazards during a

certain time of the day, due to their place of work or place of living. Beside an

inventory of elements exposed to natural hazards, exposure can also be more

precisely assessed in terms of spatial and temporal exposure. Moreover,

exposure can be mapped in terms of hazard events per country, population

exposed or land area exposed. One of the key challenges of mapping and

measuring exposure of countries or societies to different natural hazards and


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creeping changes, such as sea level rise, is in fact the potentially very different

nature of these hazards – such as the different reoccurrence interval (frequency)

and magnitude. In this regard, it would be desirable to consider - besides the

spatial exposure - also the frequency of the different hazard events. Moreover,

the intention to compare different exposure types also requires a standardisation

or normalisation of the respective hazard data.

Overall, the concept developed in this study aims to use either the population

exposed, since it can be compared between countries and communities, or the

land area exposed. The analysis of exposure often has to be based on past

hazard frequencies. However, for the issue of sea level rise, the study also

promotes the integration of exposure data that is based on a GIS analysis.

Susceptibility

Susceptibility, in the understanding of this study, refers to the conditions of

exposed communities or other exposed elements (infrastructures, ecosystems,

etc.) that make them more likely to experience harm and to be negatively

affected by a natural hazard or by climate change. On the contrary, societies or

households that are characterised by a low susceptibility might be exposed,

however, they will face only minor harm due to their low level of susceptibility.

While susceptibility – in the perspective of the authors – is more closely linked to

structural characteristics, coping and adaptation, as societal response capacities,

refer more to the agency and the potential to act. However, it is also evident

that, in practical assessments, susceptibility and coping capacity are overlapping

and are closely linked. For example, the lack of a social network could be seen as

a feature of susceptibility; however, in terms of coping, the availability of the

social network, that might help to cope in stress situations, might also be seen as

a source of coping capacities.

Coping (Coping capacities)

Coping and coping capacities are mainly defined as the ability of a society or

group, organisation or system to use its own resources to face and manage

emergencies, disasters or adverse conditions that could lead to a harmful process

caused by a hazard event (see UN/ISDR 2009). Coping, in the view of Birkmann

(2009), is mainly a response to the impact of a given hazard event. Thus it

comprises the immediate reaction during a crises or disaster. In this context,


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coping is hazard-related and primarily short-term oriented. Coping can also be

classified as more unstructured action, such as swimming during a flood or

eating fewer meals during a drought. This rather short-term and hazard-impact

oriented response clearly distinguishes coping from adaptation (see Birkmann et

al. 2009). Characteristics of coping and coping capacities can be associated with

existing resources that help to face and manage emergencies, natural hazard

impacts and disasters, such as early warning systems, medical care and hospital

capacities or even negatively the lack of these capacities, for example regarding

the provision of an effective civil protection system or social security that covers

health insurances. Overall, a close link between susceptibility and coping cannot

be denied.

Adaptation

Adaptation, within the context of this study, is defined as a long-term strategy

that might be linked to a certain hazard. It can also be oriented towards various

future changes that might occur (multi-hazard perspective). According to the

IPCC (2007), adaptation is defined as:

“Adjustment in natural or human systems in response to actual or expected

climatic stimuli or their effects, which moderates harm or exploits beneficial

opportunities”.

Although the term adaptation, within the IPCC definition, remains relatively

abstract and does not refer to coping or extreme events, the emerging scientific

literature in the field stresses an important difference between short-term coping

and long-term adaptation. O’Brien and Vogel (2003) stress that adaptation is a

more structured behaviour that aims to promote change and transformation. For

example, a farmer who aims to adapt to climate change – in particular, to

drought – might need to change his/her seasonal calendar of cropping and

perhaps the crops themselves, in order to be able to live with the changing

environmental conditions without suffering.

Additionally, the IPCC differentiates between types of adaptation such as

anticipatory, autonomous and planned adaptation. Anticipatory adaptation is

mainly characterised by the fact that it takes place before impacts of climate

change are observed (pro-active focus). Autonomous adaptation characterises

adaptive changes in natural systems or by markets in human systems

constituted by a response to climate change that just happens without planning.


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The last type – planned adaptation – refers to adaptation as a result of a

deliberate policy decision, based on the awareness that conditions have changed

or will change and that respective actions are required to maintain or achieve a

desired state (see IPCC 2007).

Overall, the assessment of adaptation and adaptive capacities is a major

challenge. However, abilities that enable communities to change and to

transform, in the light of environmental and socio-economic changes, are an

important asset and characteristic of these adaptive capacities. In our point of

view, as an example, skills that enable people to shift potential livelihood

strategies, income-earning activities, educational and scientific capacities of

nations or the awareness regarding the need to consider climate change

adaptation in development strategies of the country, are used as first

characteristics to grasp a notion of what adaptation capacities might mean to

various hazards and creeping changes in the future.


24

Standards for indicators and the selection of relevant data sources

The development of indicators to measure risk, vulnerability and adaptation has

to be based on quality or evaluation criteria that support the selection of sound

indicators. For the World Risk, Vulnerability and Adaptation Index and the

respective indicator system – particularly for the national scale assessment - the

following criteria are taken into consideration:

The indicators – particularly at the national level assessment - have to be

indicators for exposure allowing for a certain comparison of very different

hazard types;

vulnerability and adaptation indicators of a generic nature, in order to be

relevant for different hazards (multi-hazard perspective);

analytically and statistically sound;

reproducible (particularly global index); and

appropriate in scope, in terms of the local level assessment.

Furthermore, the indicators should also consider major goals of this study and

thus should be:

understandable;

easy to interpret; and

comparable.

Additionally, local indicators and criteria should allow for the integration of

context-specific problems, strategies and measures and consequently do not all

require that the above-mentioned criteria are matched. In this context, and in

order to communicate the process complexity, the local indicator and criteria

system can be divided into a) a core set of comparable indicators, such as

extreme poverty, etc., and b) a context-specific set of indicators and criteria that

allow to integrate region- and local-specific features and characteristics.


25

IV Indicators Selected and Methodology

The following section provides an overview of the selected indicators and in

addition, it explains why certain hazards were selected for the World Risk,

Vulnerability and Adaptation Index.

Selection of hazards

The Index focuses on those natural hazard types that occur most often and

account for the most severe impacts on people (fatalities). Although it might be

desirable to consider all potential natural hazards and creeping changes, current

data availability and also the usefulness for a global index concept would be

questionable. Therefore, our concept for a World Risk, Vulnerability and

Adaptation Index clearly prioritises on those hazards that are widely spread

around the globe and also account for major harm in terms of people killed. For

the period of 1970 to 2005, the most frequent and devastating natural hazards

reported were floods, storms, earthquakes and droughts, accounting for 74% of

all events (Fig. 5, UN/ISDR Disaster Statistics) and 88% of all reported fatalities

(see Fig. 6). Epidemics are not regarded as natural hazards in our study, as they

are usually triggered and/or enhanced by other hazards, as for example floods

and thus closely correlated to them.

In addition to the above-mentioned hazard types, this study also takes into

account the effect of global sea level rise caused by climate change. Sea level

rise is one of the major novel transformations emerging with climate change and

posing an additional threat to coastal areas and the people living there. Many

countries have densely populated coastlines including major economic values and

important infrastructures (e.g. harbours of international relevance) close to the

sea, in order to facilitate seaborne trade and commerce. Thus, the World Risk,

Vulnerability and Adaptation Index tries to capture the exposure to potential sea

level rise, in order to take this emerging hazard into account and ensure that it is

dealt with.


26

Fig. 5: Occurrence of different hazard types as percentage of natural disasters by type

1970-2005, source: UNISDR Disaster statistics, based on CRED data, available at:

http://www.unisdr.org/disaster-statistics/pdf/isdr-disaster-statistics-occurrence.pdf

As the figures above show, storms play a major role when it comes to both

frequency of occurrence as well as people killed. It has to be noted however, that

the Index comprises two different indicators on storms. On the one hand, the

population exposed to tropical cyclones and, on the other, the people killed and

affected from past storm (all kinds) events. This is due to the fact that it is not

possible to generate ―pathways‖ and exposure maps for storms other than

tropical cyclones, according to their physical characteristics. This means that

population exposed to winter storms is not represented in the exposure

component. The number of people killed and affected refers to all storms, in

Fig. 6: Number and Proportion of people killed per hazard type for the period of 1970-

2005, own figure, based on CRED EM-DAT, www.emdat.be

http://www.unisdr.org/disaster-statistics/pdf/isdr-disaster-statistics-occurrence.pdf


27

order to partly rectify this problem and consider the societal impacts of all storm

events.

In general, the Index looks at two different, broader categories of natural

hazards: sudden-onset events like storms, floods and earthquakes, and creeping

hazards as droughts and sea level rise. The selection of the indicators needs to

consider this two-fold characteristic of the exposure component and is thus

focusing on variables that determine the overall vulnerability and response

capacities of societies exposed to environmental hazards. This two-fold structure

of exposure is chosen, in order to underline the importance of a comprehensive

understanding of sudden-onset hazards and creeping environmental changes.

28

Table 1: Structural Components of the proposed World Risk, Vulnerability and Adaptation Index

1. Exposure 2. Susceptibility 3. Coping Capacity 4. Adaptive Capacity

POPULATION EXPOSED TO

CERTAIN HAZARDS

A) Population exposed to

earthquakes

B) Population exposed to

tropical cyclones

C) Population exposed to

floods

D) Population exposed to

droughts

E) Population exposed to sea

level rise

HUMAN COSTS

A) Deaths

i) Number of killed from past earthquakes

ii) Number of killed from past tropical

cyclones

iii) Number of killed from past floods

iv) Number of killed from past droughts

B) Affected

i) Number of people affected from past

earthquakes

ii) Number of people affected from past

tropical cyclones

iii) Number of people affected from past

floods

iv) Number of people affected from past

droughts

HUMAN NEEDS

C) Total dependency ratio

D) Extreme poverty (population living on less

than 2 USD/day)

E) Population with access to sanitation

F) Population using an improved water source

G) GDP per capita PPP

STATE CAPACITY

A) Corruption (Corruption

Perceptions Index)

B) Governance (Failed

States Index)

HEALTH

C) Number of physicians

per 10,000 inhabitants

D) Number of hospital

beds per 1,000

inhabitants

EDUCATION

A) Adult literacy rate

B) Combined gross school

enrolment

HEALTH

C) Life expectancy at birth

D) Private expenditure on

health

E) Public expenditure on health

29

IV-A Indicators Global/National Assessment

This section deals with the operationalisation of the framework, in terms of

selection and description, as well as the calculation of the indicators used within

the framework to capture aspects of hazard exposure, susceptibility, coping

capacity and adaptation (see Figure 4). That means the framework of the index

and indicator system will be ―filled‖ with selected indicators. The overview of the

selected indicators’ measurement and weighting, within the index system, is

explained and outlined according to the four major factors: a) exposure, b)

susceptibility, c) coping and d) adaptation. In this context, each of the indicators

represent features of one (or more) of the four factors explained separately in an

indicator sheet (see text boxes below). After having explained each indicator,

additional information is given regarding the methodology and some examples

are provided that illustrate the indicators, based on selected countries for the

concept development. The full implementation and calculation ―for the world‖

was not intended within this study and would also require further efforts

regarding additional quality control of data for some countries.

Calculation of Exposure

In order to calculate exposure to natural hazards at the national scale, several

spatial data sources are needed such as information regarding the gridded

population and frequency of each hazard and its spatial exposure. Current

datasets (EM-DAT, insurance data) often solely encompass the number of

hazards and hazard events per country, while information on respective land

area impacted or exposed, as well as people exposed, is hard to grasp. Munich

Re kindly provided for selected countries, the hazard exposure in terms of land

area exposed. However, the calculation of people exposed is still ongoing and

has to be checked. Therefore, an alternative and globally-available dataset is the

one generated by different UN agencies and the World Bank - the PREVIEW

Global Risk Data Platform. This platform is a multiple agency effort to share

spatial data on global risk regarding natural hazards. The physical exposure data

(see indicator sheet) were obtained from the PREVIEW Platform

(http://preview.grid.unep.ch), which is supported by UNEP, UNDP/BCPR (GRIP),

UN/ISDR and the World Bank. The physical exposure accounts for the people

http://preview.grid.unep.ch/

30

exposed, as well as the frequency of the respective hazard. However, this data

also has certain difficulties, particularly regarding the measurement of drought

exposure. The physical exposure to droughts per country is quite high due to the

input parameters and assumptions made for the calculation. Compared to other

hazards, droughts differ in terms of occurrence periods and the time-span of the

event itself (Peduzzi et al. 2009).

Furthermore, the study of the UNITED NATIONS UNIVERSITY Institute for

Environment and Human Security (UNU-EHS) also considers the hazard sea level

rise using two different datasets: first, the gridded population and second, the

exposed area due to different sea level rise scenarios ranging from 1m to 6m

height. The population data were gathered from UNEP Global Environmental

Outlook (GEO) Data Portal (http://geodata.grid.unep.ch) and the information

regarding the sea level rise scenarios from the Center for Remote Sensing of Ice

Sheets (CReSIS)

(https://www.cresis.ku.edu/research/data/sea_level_rise/h_world.html). Overall,

these two datasets allow for an estimation of people exposed to future sea level

rise.

http://geodata.grid.unep.ch/

https://www.cresis.ku.edu/research/data/sea_level_rise/h_world.html

31

Exposure

Indicator (1A-D)

Physical exposure to earthquakes, cyclones, floods and droughts

Measuring unit

Percentage of expected average annual

population exposed to hazards per country

Spatial and temporal scale

national scale, based on population grids

for the year 2007, provided by

LandScanTM Global Population Database

(30 arc second)

Data sources

Preview database of UNEP Global Risk Data Platform (GRID)

(http://preview.grid.unep.ch/)

Relevancy of indicator

The exposure – measured as the total number of people exposed to the selected hazards

(earthquakes, floods, droughts, cyclones) or rather the share of people exposed to a set

of different hazards - is an important aspect for disaster risk. If not exposed, the country

or population is not at risk.

The knowledge of the population exposed is fundamental for raising awareness and the

development of protection measures (e.g. identification of suitable shelters) and

evacuation strategies (e.g. development of evacuation routes). Additionally, the share of

people exposed to a set of hazards on the total population also provides a first overview

about one problem dimension, in terms of answering the question: how many people are

exposed or might be at risk?

Validity/limitations of indicator

The indicator is based on the estimated number of people exposed to hazards per year.

It results from the combination of the (annual) frequency of hazards (ex-post focus) and

the total population living in the spatial unit exposed for each event. It thus indicates

how many people per year are at risk. The population data is based on the population of

the world in 2007. The indicator is dependent on quality of population estimates and

accuracy of frequency estimation of each hazardous event. (Peduzzi et al. 2009)

Remarks:

The population exposed was calculated for all test countries and the results were

compared with the corresponding risk profile on the prevention web

(http://www.preventionweb.net/english/countries/statistics/risk.php?iso=deu).

Besides the hazard type droughts the results were similar to the structure of exposure

distribution defined within the maps of prevention web. This is based on the calculation

and the thresholds used within the physical exposure for droughts (Peduzzi et al. 2009).


http://www.preventionweb.net/english/countries/statistics/risk.php?iso=deu

32

Exposure

Indicator (1E)

Population exposed to sea level rise (possible from 1m to 6m)

Measuring unit

Percentage of population exposed to 1m

sea level rise


national scale, based on gridded population

of the World, Version 3 consists of

population for the year 2000 by 2.5 arc-

minute grid cells)

Data sources

Population data:

Columbia University, Center for International Earth Science Information Network

(CIESIN)

http://geodata.grid.unep.ch/mod_download/download.php

Sea level rise from 1m to 6m:

Center for Remote Sensing of Ice Sheets (CReSIS)



Sea level rise is clearly a major hazard for the future, in terms of further increase in the

global mean temperature and impacts of climate change. Compared to floods or

earthquakes, sea level rise is a creeping process that also implies irreversible changes. A

population affected by floods might be able to return to the flood-prone area, areas

covered by sea water will hardly be usable anymore for housing or agriculture. Sea level

rise is considered a new hazard that particularly puts coastal populations at risk.


Population exposed to sea level rise is an important indicator for estimation of the

impact climate change might have in the future. This indicator gives a general overview

of people living within the most exposed (low-laying) areas such as coastal zones. It is

desirable, however, to use more recent population estimates in combination with

differentiated projections of sea level rise, in order to evaluate the severity of exposure

with more precision. Including the projected changes it will also be possible to evaluate

the time horizon of the extending exposure.

Remarks:

The assessment of people exposed to sea level rise is possible using GIS analysis, but is

rather time-intensive. Results are dependent on the available data, thus the problem of

scale should always be kept in mind.

The development of the exposure index encompasses the following process chain:

For each hazard, except sea level rise, and for each country, the physical exposure -

which is the expected average annual population (year of reference 2007) exposed -

was derived by calculating the zonal statistic (sum of grid values within the bounds of

each zonal polygon) within each national level.

The population exposed by 1m sea level rise was calculated by extracting the exposed

population information from the 1m inundation file and the population dataset.

The exposed population per hazard was summed up and divided by total population, in

order to obtain one exposure index per country.

http://geodata.grid.unep.ch/mod_download/download.php

33

Overall, the development of the exposure index encompasses the following

process chain:

1. For each hazard - except sea level rise - and for each country, the physical

exposure, which is an expected average annual population (year of

reference 2007) exposed, was derived by calculating the zonal statistic

(sum of each raster values within the bounds of each zonal polygon)

within each national level.

2. The population exposed by 1m sea level rise was calculated by extracting

the exposed population information from the 1m inundation file and the

population dataset.

3. The exposed population-per-hazard was summed up and divided by total

population, in order to obtain one exposure index per country.

34

Calculation of Susceptibility

Susceptibility is calculated in several steps. Figure 7 gives an overview of

different indicators within this factor and outlines the integration process of the

different indicators – including their weighting. The susceptibility factor is

differentiated in two sub-categories: human costs and human needs.

The Human Costs Index encompasses an index regarding fatalities due to

selected hazards and affected population by respective hazard types. More

precisely, the deaths/fatalities per hazard consist of the number of people killed

due to past hazards, based on data from the last 28 years. The Index accounts

the number of affected people with regard to the selected hazards. In order to

get an average annual proportion of people killed and affected by the hazards

selected for each country, those killed and affected per hazards were summed up

and divided by the overall population and then by 28 years. Thereafter, the

respective values were normalised between [0:1] and weighted equally in terms

of the different hazards. Overall, the people killed were weighted stronger since

the consequence of being killed shows a higher degree of susceptibility than just

being affected. That means the value for the people killed was weighted with 0.8,

while the values of people affected were weighted with 0.2.

The second index within susceptibility is focusing on different facets of Human

Needs. This sub-category consists of five indicators, namely 1) the dependency

ratio, 2) extreme poverty, which is the percentage of population living on less

than 2 USD/day, 3) population with access to sanitation, 4) population using an

improved water source and 5) GDP per capita Purchasing Power Parties (PPP).

Since the population with access to sanitation and population using an improved

water source are ―positive‖ characteristics of a community or society, the values

had to be subtracted by 100 to receive the percentage of the population having

no access to sanitation and improved water sources. That means the indicators

were converted to the lack of access to sanitation and improved water sources,

in order to follow the concept that susceptibility captures deficiencies.

Thereafter, each indicator was normalised and weighted equally, in order to

aggregate the indicators to the Human Needs Index.

In order to obtain a singular index for susceptibility, the aggregated indices

Human Costs and Human Needs were combined by variable weighting factors

35

based on the results of expert judgement and on equal weights (comparison of

differences). The following figure outlines the structure and combination of

different indicators within the factor susceptibility.

Fig. 7: Aggregation of Susceptibility Component, source: own figure

Susceptibility

Human Costs Human Needs

D (normailsied) C (normailsed) (1 - E) (normailsed) G (norm) B (normalised) A (normalised)

i)+ii)+iii)+iv)

Population x 28 years

(1 - F) (normailsed)

0,8 0,2 0,2 0,2 0,2 0,2 0,2

0,5 0,5

A) deaths i) number of killed from past earthquakes

ii) number of killed from past tropical cyclones iii) number of killed from past floods iv) number of killed from past droughts

B) affected i) number of affected from past earthquakes

ii) number of affected from past tropical cyclones iii) number of affected from past floods iv) number of affected from past droughts

C) Total dependency ratio D) Percentage of population living on less than 2 USD/day E) Percentage of population with access to sanitation F) Percentage of population using an improved water source

36

Susceptibility

Indicator : 2A

Number of deaths caused by hazards per country

Measuring unit

Average annual proportion of killed population

by hazards per country


Country-based data for 169 countries

(period 1980–2008)

Data sources

Centre for Research and Epidemiology of Disasters (CRED)/www.emdat.be

Disasters entered into the database have to fulfil at least one of the following criteria:





Periodicity of data: annual

Relevance of indicator

The number of deaths caused by natural hazards (i.e. earthquakes, cyclones, floods and

droughts) indicates the most severe harm people can experience from external shocks

and natural hazards in particular. The indicator is thus a measure of the effect hazards

have on people exposed to them (ISDR 2009). In other words, the number of deaths

caused by a natural hazard is a measure for the revealed susceptibility of societies, in

terms of suffering from the impact of a natural hazard.

The average annual average of people killed as a proportion of the total population has

been calculated, in order to obtain an internationally comparable value and indicator for

fatalities caused by hazards. The annual average was calculated, in order to allow a

comparison over time, especially with respect to the intended annual release of the

World Risk, Vulnerability and Adaptation Report.

The reference period covers the time span from 1980 to 2008, since these datasets are

relatively reliable and homogenous, in terms of quality and completeness of information

(Peduzzi et al. 2009).

General and specific trends, by type of hazard, have been further explored using time

series analysis to ascertain relative importance of each hazard.


The indicator shows an average human-life-lost per hazard over the reference period of

28 years.

The data available from EM-DAT for events prior to 1980 is not reliable enough to be

considered, owing to numerous sources and slower reporting technologies at the time. It

can be assumed that the database is not exhaustive, and that a bias in the quality and

accuracy of data towards more developed countries is likely. The limitation to a period

covering a relatively short period of 28 years might lead to an underestimation of

susceptibility to events of low-frequency and high-magnitude, which is especially

relevant for earthquakes.

Unavailability of gender and age characteristics of those killed can be seen as a

limitation for further analysis on the demographic impact of hazards.

Key literature: Guha-Sapir & Below (2002

UN/ISDR (2009) Neumayer & Plümper (2007) Peduzzi et al. (2009)

37

Susceptibility

Indicator : 2B

Number of total people affected by hazards per country

Measuring unit

Average annual proportion of people affected

as a proportion of the total population by

hazards per country



(period 1980-2008)

Data sources

Centre for Research and Epidemiology of Disasters (CRED)/www.emdat.be

Disasters entered into the database have to fulfil at least one of the following criteria:







The total number of people affected from natural hazards listed in EM-DAT comprises

injured, homeless and affected, defined as follows:

- Injured: People suffering from physical injuries, trauma or an illness requiring

medical treatment as a direct result of a disaster.

- Homeless: People needing immediate assistance for shelter.

- Affected: People requiring immediate assistance during a period of

emergency; it can also include displaced or evacuated people.

(http://www.emdat.be/criteria-and-definition)

The average annual proportion of population affected, as a result from natural hazards

(i.e. earthquakes, cyclones, floods and droughts) can serve as an additional indicator for

the severity of hazard impacts people face, complementing the number of people killed.

This approach takes into account, that many people can suffer from harm and be

adversely affected and dependent on external assistance after a hazard even if the

number of people killed by that hazard is comparatively low.

General and specific trends of total affected people by hazard type will be further

explored using time series analysis.


This indicator shows the number of people affected by natural hazards over the

reference period of 28 years. Data quality is a limitation of this indicator: As there is no

standard method to calculate the number of people affected, the EM-DAT relies on

different sources (UN, governments, NGOs). While the number of people killed by a

hazard is usually determined quite accurately, the number of people affected is usually

estimated and may differ from the number of people affected in reality. Despite this

limitation.

The data available from EM-DAT for events earlier than 1980 is not reliable enough to be

considered owing to numerous sources and slower reporting technologies at the time. It

can be assumed that the database is not exhaustive, and that a bias in the quality and

accuracy of data towards more developed countries is likely. The limitation to a period

covering 28 years only might lead to an underestimation of susceptibility to events of

low frequency and high magnitude.

Key literature: Guha-Sapir & Below (2002)

38

Susceptibility

Indicator: 2C

Dependency Ratio by country

Measuring unit

Number of dependents (younger than 15

and older than 65) compared to the

population in working age (15-64)



(2007)

Data sources

www.worldbank.org

Periodicity of Data: annual


A high dependency ratio can indicate, in different ways, a population’s susceptibility to

harm:

As the ratio of the economically dependent population to the income generating

population, a high value increases the susceptibility to harm as more people are affected

if a working person experiences harm (see Schneiderbauer 2007). On the national scale,

a high dependency ration, - can also mean an increase in government expenditures on

social services and support schemes (pension funds, etc.).

As proportion of children and elderly to working age population, it can also give a more

direct measure of susceptible population as children and elderly are often limited in

mobility and thus lack the capacity to individually ―move out of harm’s way‖ in case of a

hazard (Cutter et al. 2003). The dependency ratio of a given population can thus indicate

societal vulnerability, as dependents are more susceptible to harm from disasters.

The total dependency ratio for each country will be calculated:

(Total) Dependency ratio = 1006415

&65140

over


The indicator gives an insight into the amount of people of non-working age, compared

to the number of those of working age. A high rate of dependent population means, that

those segments of the population in working age, and the overall economy, face a

greater burden in supporting both groups, namely children (under the age of 15) and

senior citizens (age 65 and older), economically and socially in stress situations and

when direct and indirect losses due to hazards of natural origin occur.

The working age is commonly 15-64 years (see World Bank), which gives the most

reliable data that can be compared at the global scale. Real working age can differ from

this model however, either due to a large share of youths staying longer in the

educational system or also due to a large share of people working beyond the age of 65.

Key literature: Cutter et al. (2003) Schneiderbauer (2007).

http://www.web.worldbank.org/

39

Susceptibility

Indicator : 2D

Extreme Poverty: Population living on less than 2 USD day

Measuring unit

Population living on less than 2 USD/day



(2007-2008)

Data sources

www.worldbank.org

Periodicity of Data: not on annual basis

Relevance of indicator for World Risk Report

Poverty is the deprivation of essential goods, services and opportunities (ADB 2004). Poor

people are more susceptible to suffer from the impact of natural hazards, as they tend to

live in hazard-prone areas (e.g. in unsafe buildings, on floodplains, etc.) and continuously

have to cope with various shocks related to hazards, in dire conditions with limited assets

(Human Development Report 2008; United Nations 2009). Extreme poverty thus

increases the susceptibility to harm. Therefore, it is important to use this indicator to

identify those people unable to meet their minimal requirements for survival.

National poverty line of individual countries shows the level of income or consumption

needed to be excluded from the poor cohort of people. However, this cannot be used as a

standard measure to compare poverty ratios across countries, as the perceived boundary

between poor and non-poor increases with the average income of a country (World Bank

2008). Therefore, this approach will use the international poverty line developed by the

World Bank, with regard to the definition: “international poverty line in local currency is

the international lines of $1.25 and $2 a day in 2005 prices, converted to local currency

using the PPP (purchasing power parities )conversion factors estimated by the

international comparison program” (World Bank 2008:22).


The Indicator shows the proportion of people with an income of less than 2USD PPP per

day, which is an indication of extreme poverty. Using an income-based indicator to

identify people living under extreme poverty could be a problem, as it does not consider

other assets (human, social, natural and physical) that people possess.

Remarks:

Values for developed/OECD countries were assumed as being below 2% of population

living on less than 2USD/day

Key Literature: ADB (2004) UN/ISDR (2009)

The World Bank (2008) UNDP (2007)

http://www.worldbank.org/

40

Susceptibility

Indicator: 2E

Population with access to improved sanitation facilities

Measuring unit

Percentage of the population



Data sources

Millennium Development Goal Database 2006

Periodicity of Data: Every five years


The population with access to improved sanitation facilities is an indicator of the quality

of basic infrastructure, demonstrating quality-of-life and basic health condition of the

population. Improved sanitation facilities (ranging from protected pit latrines to toilets

with a sewerage connection) cannot only effectively prevent insect and animal contact,

which are agents of diarrhea, but also reduce other non-diarrhea related health

outcomes, such as scabies and helminthiasis (Esrey and Habicht 1986).

In other words, improved sanitation should improve growth rates and reduce child

mortality rates. In this context, it can be concluded that people without improved

sanitation are susceptible to diseases and can become more vulnerable following a

hazard.

It has been identified as a key indicator of vulnerability at the national level by Brooks et

al. 2005.


This indicator shows the percentage of the population with at least adequate excreta

disposal facilities (private or shared, not including public).

Remarks: Disaggregation of data by urban/rural shows more significant variations

Key Literature: Brooks et al. (2005 Esrey & Habicht (1986)

41

Susceptibility

Indicator: 2F

Population with access to an improved water source

Measuring unit

Percentage of the population


Data sources

Millennium Development Goal database, 2007

Periodicity of Data:


The indicator defines the percentage of population with reasonable access (within one

km) to an adequate amount of water (20 litres per person) through a household

connection, public standpipe well or spring, or rain water system (ADB 2004).

Unsafe or unimproved water (sources include among others: vendors, tanker trucks and

unprotected wells and springs) is one direct cause of many diseases.

In other words, people without improved water sources are vulnerable to diseases

caused by unclean water and could become more vulnerable in the aftermath of a

hazard, due to their existing ailments. However, improved water sources (based on the

assumption they are likely to provide safer water) can significantly lower the risk of

water-borne diseases, which, in turn, has in its turn a positive impact on people's health

status (Esrey and Habicht 1986).

Therefore, this variable is recognised as an important indicator for susceptibility to harm

from natural hazards by different authors (e.g. Brooks et al. 2005; Bollin and Hidajat

2006)


This indicator shows the percentage of population with reasonable access to a certain

amount of water. On the national scale, it can be used as an overall proxy for the

general quality of infrastructure and health status.

Remarks: Disaggregation of data by urban/rural shows more significant variations

Key literature: ADB (2004) Bollin & Hidajat (2006) Brooks et al. (2005) Esrey & Habicht (1986)

42

Susceptibility

Indicator: 2G

Gross Domestic Product (GDP) at purchasing power parity (PPP) per capita

Measuring unit

USD



Data sources

The World Health Organization, 2006

Periodicity of Data:


GDP per capita is gross domestic product divided by mid-year population converted to

international dollars, using purchasing power parity rates. An international dollar has the

same purchasing power over GDP as the U.S. dollar has in the United States. GDP at

purchaser’s prices is the sum of gross value added by all resident producers in the

economy plus any product taxes and minus any subsidies not included in the value of the

products. It is calculated without making deductions for depreciation of fabricated assets

or for depletion and degradation of natural resources (The World Bank)

GDP per capita in PPP has been identified as an important determinant of susceptibility

and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi

et. Al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator

for a country’s economic development (e.g. Human Development Index (HDI))


The GDP per capita PPP can serve as an overall measure of economic development and

has often been used as an indicator for economic development and vulnerability. The

determinants of vulnerability are manyfold, however, and some authors have shown, that

GDP per capita is not as significant a vulnerability indicator as, for example, health and

literacy (Brooks et al. 2005). This might lead to a lower weighting of this indicator. It is

still considered useful to estimate a population’s susceptibility to harm, as limited

monetary resources are seen as an important factor of vulnerability.

Key Literature: Peduzzi et al. (2009) Schneiderbauer (2007 UNDP (2004)

43

Calculation of Coping Capacity

The calculation of coping capacity is based on several indicators that determine

the capacity of a given population and/or nation to immediately react to, or

manage the impact of a hazard event. Many variables listed in the process of

concept development (by our own expert judgement, as well as by reviewers and

respondents of the questionnaire) were examined and tested regarding their data

availability and plausibility. Limitations in data availability resulted in the

exclusion of various indicators judged highly relevant by many experts and

practitioners including, in particular, the access to insurance (insurance

coverage) and availability of early warning systems.

The indicators that, at the end, have been used and calculated with existing data

are: A) corruption, B) governance, C) number of physicians per 10,000

inhabitants and D) number of hospital beds per 1,000 inhabitants.

The coping component will be aggregated in two steps: Firstly, the corruption

and governance indicators will be combined into the group ―State Capacity‖

whereas the number of physicians and hospital beds will be combined to the

group ―capacity of the national health system‖. In the second step, the two

groups will be aggregated to build the coping capacity component. The

aggregation is currently made using equal weights; the two-step aggregation

procedure allows the modification of weights of both individual indicators, as well

as groups at a later stage.

Coping Capacity

State capacity Health

C (normalised)B (normalised)A (normalised)

0,5 0,5

0,5 0,5

A) Corruption Perception Index (CPI)

B) Governance (Failed States Index)

C) Number of phyicians per 10000 inhabitants

D) Number of hospital beds per 1000 inhabitants

D (normalised)

0,5 0,5

Fig. 8: Aggregation of Coping Capacity Component, source: own figure

44

The indicators are described in detail in the following text boxes:

Coping Capacity

Indicator : 3A

Corruption Perception Index (CPI)

Measuring unit

Ordinary scale from 0 (corrupt) to 10

(incorrupt), normalised to 0 to 1.



(2008)


Data source:

Transparency International:

http://transparency.org/policy_research/surveys_indices/cpi/2008


This indicator measures the perceived level of corruption of national governments using

13 different sources. In addition to the results, a confidence range is given: it is larger if

there are less source indicators available for calculation.

People living in countries with higher level of corruption are thought to have more

difficulties recovering from natural hazard impacts, due to limited governmental support

reaching affected population compared to states with lower level of corruption.

Corruption can further be of particular importance when it comes to the distribution of

and access to emergency relief resources.

The following sources have been used to construct CPI 2008: Asian Development Bank,

African Development Bank, Bertelsmann Transformation Index, Country Policy and

Institutional Assessment, Economist Intelligence Unit, Freedom House, Global Insight

and Merchant International Group. Additional sources are resident business leaders

evaluating their own country; in the CPI 2008, this consists of the following sources:

IMD, Political and Economic Risk Consultancy, and the World Economic Forum.


The CPI assesses the level of corruption using qualitative surveys, as there is no general

quantitative data available. The CPI uses different sources for different countries due to

data limitations. As a result, the confidence intervals for the cases (countries), with few

data sources, are very large. For countries with intervals overlapping more than 29%,

this means the corruption level is indistinguishable (Bell 2008).

The overall reliability of data is demonstrated, however, in the high correlation between

sources, as well as in the use of different independent sources and expert interviews.

Key literature: LAMBSDORFF (2008)

45

Coping Capacity

Indicator : 3B

Governance: Failed States Index

Measuring unit:

Ordinal scale with range of 0 (most stable)

to 120 (critical), scaled to 0 to 1, with 1

(stable) and 0 (critical)



(2009)

Data sources

The Fund for Peace, Foreign Policy http://www.fundforpeace.org/web/index.php?option=com_content&task=view&id=391&Itemid=549 http://www.foreignpolicy.com/articles/2009/06/22/2009_failed_states_index_interactive_map_and_rankings

Relevance of indicator for World Risk Report

The Failed States Index is a vital indicator, as it captures state vulnerability based on 12

variables that can be divided into social, economic and political indicators. Vulnerable

states may have difficulties recovering from natural hazard impacts, owing to their

critical inherent characteristics.

The surveys were conducted using groups of experts that were interviewed separately.

In case of significant differences in their answers, a third, randomly selected expert is

interviewed.


The Conflict Assessment System Tool (CAST) methods are used.

The validation is made by the comparison of the CAST results to local expert opinions.

Remarks:

No correspondence between Failed States and Corruption Perceptions Index for a single country, but there is a correlation in trends (ordinal correlation most failed states are

also corrupt).

http://www.fundforpeace.org/web/index.php?option=com_content&task=view&id=391&Itemid=549

46

Coping Capacity

Indicator: 3C

Number of physicians per 10,000 people

Measuring unit

physicians per inhabitant scaled from 0 to

1, presuming a maximum of 1:20

physicians per inhabitant



Data sources

World Health Organization

Periodicity of Data: data is not available on annual basis for most countries

Relevancy of indicator for world risk report

The number of practicing physicians qualified from medical schools, in proportion to

10,000 inhabitants, allows the international comparison of available health care, which is

a crucial coping measure in the aftermath of a disaster. The general assumption is that

those regions, which have a significantly lower ratio of practicing physicians to 10,000

people, are also those that might face higher difficulties in coping with extreme events

and emergencies. Overall, the indicator can be used to estimate the capacity of a health

care system of a country.


The doctor-patient ratio can serve as a general measure of a health care system. In

order to allow the comparison of this indicator with the number of hospital beds (scaled

to 1000 inhabitants), the ratio is inversed to physicians per inhabitant and then

normalised on a scale from 0 to 1.

Remarks:

Key literature: IDEA 2005 Cardona 2005

47

Coping Capacity

Indicator: 3D

Number of hospital beds per 1000 persons

Measuring unit

hospital beds per inhabitant scaled from 0

to 1, presuming a maximum of 1:10

hospital beds per inhabitant



Data sources

World Health Organization Core Health Indicators

http://apps.who.int/whosis/database/core/core_select.cfm

Periodicity of Data: unknown, data is not available on annual basis for most countries


Hospital beds indicate also the capacity of the medical care infrastructure to help or

support societies in the case of a mass emergency and disaster with respective

treatment. Hospital beds in private, general and specialised hospitals, medical and

rehabilitation centres are included. Although hospital beds do not provide any information

about the standard of these hospitals and their treatment, the general comparison of the

capacities of hospital beds per 1,000 people provides a first overview of those regions

where this infrastructure is significantly lower than in others.


Overall, some experts argue that the indicator hospital beds is rather weak, since it solely

provides information on the health care capacity. Therefore, this indicator should be

supported by an appropriate mix of staff and equipment indicators as well (McKee 2004).

Since this information is not available in global datasets, the respective extension of the

assessment of hospital capacities could not be made in this proposal.

Remarks: Need to disaggregate data by urban/rural to show significant variations

Key literature: McKee (2004)

http://apps.who.int/whosis/database/core/core_select.cfm

48

Calculation of Adaptive Capacity

Indicators for the adaptive capacity of a state need to portray the long-term

response capacities to natural hazards and/or environmental change. They

should grasp the ability of a society or community to transform or adapt, in order

to alter (reduce) the vulnerability to this change.

The indicators specified below were selected based on expert judgement as well

as on data availability. The component on adaptive capacity contains two groups

- education and health status of the population per country - which are set up as

follows:

Education:

A) Adult literacy rate

B) Combined gross school enrolment ratio

Health:


D) Private expenditure on health


Following a similar methodology of aggregation as regarding the other factors

(susceptibility and coping), the sub-variables (A to E) of the adaptation/adaptive

capacity factor are combined to the respective group first and the groups are

combined to ―adaptive capacity‖. In a second step, a weighting for both

aggregated indicators (Education and Health) has been applied. The variables are

currently combined using equal weights.

This leads to a higher weight of health expenditures compared to life expectancy

as two-thirds of the variables ―Health‖ refer to expenditure whereas one-third

refers to life expectancy. It can be argued, that expenditure on health represents

a criterion that is easier to change than a population’s life expectancy. This

means that an alteration of expenditure on health can reveal efforts to enhance

the adaptive capacity sooner than the indicator of life expectancy, which is able

to capture the long-term changes.

49

Fig. 9: Aggregation of Adaptive Capacity Component, source: own figure

Adaptive Capacity

Education Health

D (normalised) B (normalised) A (normalised)

0,5 0,5

0,5

A) Literacy

B) Combined gross school enrolment


D) Private expenditure on health


E (normalised)

0,33 0,33

0,5

C (normalised)

0,33

50

Adaptive Capacity

Indicator: 4A

Adult Literacy rate per country

Measuring unit

Population aged 15 years and over


Country-based data for152 countries

Data sources

Human Development Report 2007-2008

Periodicity of Data: Annually, but based on surveys over a longer period of time


This is defined as the percentage of population aged 15 years and older who can, with

understanding, read and write a short, simple statement on their everyday lives (ADB

2004:19). Adult literacy rate shows the accumulated achievement of primary education

and basic literacy skills of the population crucial for economic, social and political

participation and development, especially in today’s knowledge societies (UNESCO

2006). Moreover, literacy could be an essential indicator, when empowering people on

hazard risk reduction.

Illiteracy rate indicates low quality of primary education and needs for policies in

organising adult literacy programs. Those without literacy skills may have problems

taking advantage of health, educational, political, economic and cultural opportunities

(UNESCO 2006). Moreover, illiterate people may have difficulty in understanding

warnings and access to recovery information (Cutter, et al. 2003).


This indicator shows the adult literacy rate per country. Some countries apply definitions

and criteria different to international standards defined above, which could be a

limitation (UNESCO Institute for Statistics 2008).

Remarks: Adult literacy rate can be presented by gender, in order to show gender

variations.

Key literature:

ADB (2004)

Cutter et al. (2003)

UNESCO (2006)

UNESCO Institute for Statistics (2008)

51

Adaptive Capacity

Indicator: 4B

Combined Gross Enrolment Ratio (GER)

Measuring unit

Proportion of pupils enrolled in a given level

of education



Data sources

Human Development Report 2007-2008

Periodicity of Data: Annually


A good level of educational attainment is important not only to find a secure job, or

climb up the ladder of social mobility to achieve higher socio-economic status, but also

to recover sooner from shocks related to natural hazards. A good level of education also

improves the capacity of a society and different groups to potentially change from one

economic activity (e.g. farming) to another (e.g. small-scale business). In this context,

the gross enrolment ratio is a vital indicator that captures adaptive capacity, as it

measures education access and coverage. It shows the general level of participation in a

given level of education and further indicates the capacity of the education system to

enroll students of a particular age group (UNESCO glossary). It also provides some

indication of internal efficiency of the educational system.

It defines total enrolment in a specific level of education, regardless of age, expressed

as a percentage of the eligible official school-age population corresponding to the same

level of education in a given school year (UNESCO glossary).

Gross enrolment ratio per country can be presented by gender and level of education

(primary and secondary).


A high GER generally indicates a high degree of participation, whether the pupils belong

to the official age group or not. A GER value approaching or exceeding 100% indicates

that a country is, in principle, able to accommodate all of its school-age population, but

it does not indicate the proportion already enrolled (UNESCO glossary).

GER can exceed 100%, due to the inclusion of over-aged and under-aged

pupils/students because of early or late entrants, and grade repetition (UNESCO

glossary).

Remarks: Gross enrolment ratio per each country can be presented by gender and level

of education (primary and secondary), in order to give a clear picture.

Key Literature:

UNESCO Institute for Statistics (nd)

52

Adaptive Capacity

Indicator : 4C

Life expectancy at birth by country

Measuring unit

Years of individual life expectancy



(2007)

Data sources

www.worldbank.org

Periodicity of Data: Annually


Continuous hazards, in general, lower the life expectancy. Nevertheless, life expectancy

at birth reflects the overall mortality level of a population. It summarises the mortality

pattern that prevails across all age groups – children and adolescents, adults and the

elderly (World Health Organization’s Statistical Information System - WHOSIS). This

indicator also reveals the general health standards of a country, therefore, vital to

include it.

In terms of definition: average number of years that a newborn is expected to live if

current mortality rates continue to apply (WHOSIS).


Life expectancy can indicate general health standards and overall living conditions in a

country (WHO 2008).

Remarks:

Key Literature:

WHO (2008)

WHOSIS (2007)

http://www.web/

53

Adaptive Capacity

Indicator 4D

Private per capita expenditure on health (percentage of total health expenditure)

Measuring unit

USD PPP, logarithmised and normalised to

0 to 1



Data sources



Relevancy of indicator for world risk report

The proportion of private expenditure on health can be used as an indicator for the

general structure of the health system of a state and determines whether equal access

to health services is granted. It is presumed that high proportions of private expenditure

on health indicate the lack of a reliable public health system and thus determine the

adaptive capacity.

Equal access to health services would be very important when it comes to the recovery

from hazard impacts as people might not only suffer from the actual impact, but also be

restrained economically if they have to cover medical expenses with private means. The

lack of access to adequate health services would thus lead to a large proportion of

people with poor health who are not able to adapt to the risk of a novel hazard impact.

The indicator comprises the following types of expenditure: ”The sum of outlays for

health by private entities, such as commercial or mutual health insurance, non-profit

institutions serving households, resident corporations and quasi-corporations not

controlled by government with a health services delivery or financing, and households”

(WHO Indicator Compendium 2009 on private expenditure on health).

In order to ensure the comparability with the second health expenditure indicator, the

value is transformed from the percentage into USD PPP (calculated from private

expenditure on health as a percentage of total expenditure on health and per capita total

expenditure on health) and then logarithmised and normalised.


The usefulness of the indicator relies largely on the quality and accuracy of input data.

According to the indicator compendium of WHO “[t]he most comprehensive and

consistent data on health financing is generated from national health accounts that

collect expenditure information within an internationally-recognized framework. Not all

countries have or update national health accounts and in these instances, data is

obtained through technical contacts in-country or from publicly-available documents and

reports. Missing values are estimated using various accounting techniques depending on

the data available for each country“.

Key Literature:

Cutter et al. (2003)

Brooks et al. (2005)

WHO (2009)

UNDP (2007)

54

Adaptive Capacity

Indicator: 4E

Per capita government expenditure on health

Measuring unit

USD PPP, logarithmised and normalised to

0 to 1



Data sources




High government expenditure on health is understood to be an indicator for the quality

of the health system, which is an important factor of adaptive capacity because medical

services represent important sources of post-disaster relief. “The lack of proximate

medical services will lengthen immediate relief and longer-term recovery from disasters”

(Cutter et al. 2003). In our understanding, the lack of medical services is not only

expressed by direct capacities as hospital beds and physicians, which are responsible for

coping, but also by the lack of access to these services, which are determined by the

health system. While the proportion of private expenditure measures the equality of this

access within a country, the per capita government expenditure on health gives a

measure on the amount of the health expenditures and thus allows the comparison of

the quality of the health system among countries.

The indicator comprises the following types of expenditure: ”The sum of outlays for

health maintenance, restoration or enhancement paid for in cash or supplied in kind by

government entities, such as the Ministry of Health, other ministries, parastatal

organizations or social security agencies (without double counting government transfers

to social security and extrabudgetary funds). It includes transfer payments to

households to offset medical care costs and extrabudgetary funds to finance health

services and goods” (WHO Indicator Compendium 2009).


The usefulness of the indicator largely relies on the quality and accuracy of input data.

According to the indicator compendium of WHO “[t]he most comprehensive and

consistent data on health financing is generated from national health accounts that

collect expenditure information within a internationally-recognised framework. Not all

countries have or update national health accounts and in these instances, data is

obtained through technical contacts in-country or from publicly-available documents and

reports. Missing values are estimated using various accounting techniques depending on

the data available for each country“.

55

Calculation of the World-Risk-Vulnerability-and-Adaptation Index

Every major factor - exposure, susceptibility, coping and adaptation - of the

World Risk, Vulnerability and Adaptation Index is calculated individually. In order

to have one common index that can be illustrated and displayed cartographically,

the following aggregation process was conducted and processed (see Figure 10).

The coping capacity and the adaptive capacity were subtracted by one, because

positive values represent good coping and adaptive capacities (desirable).

However, the Susceptibility Index indicates deficiencies, thus also the positive

coping and adaptive capacities had to be transferred into the reverse information

on the lack of coping and adaptive capacities. This was done by reversing most

of the indicators – if, e.g., 30% of the total population have access to sanitation,

then the reverse means that 70% of the population are having no access or a

very limited access to sanitation. This is essential for the combination with

susceptibility, since high values represent high susceptibility. Thereafter, the

combined susceptibility, lack of coping and lack of adaptation index was weighted

or multiplied with the Hazard Exposure Index (see Figure 10).

World Risk Vulnerability and Adaptation Index

Susceptibility (1 - Adaptive Capacity)

0,33 0,33

(1 - Coping Capacity)

0,33

Exposure X

Fig. 10: Aggregation of World Risk Vulnerability and Adaptation Index, source; own figure

56

Further Explanations to the Overall Methodology Used

Different aggregation methods were considered for the composition of the World

Risk, Vulnerability and Adaptation Index.

In general, there are three possible ways to combine the variables to the

proposed index. As stated in Chapter II, it was considered to use a principal

component analysis (PCA) to identify variables that are responsible for the

greatest variability, thus giving a measure of statistical significance for the

different variables.

A major constraint of such an aggregation would be, that it would not necessarily

reflect the argumentative relevance of the indicators and lead to a static model

that is not comparable over time, if input variables are changed. As the World

Risk, Vulnerability and Adaptation Index is supposed to be published annually,

allowing for the potential integration or alteration of indicators in subsequent

editions, the PCA is not the best option.

It was decided to generate different weights using the Analytic Hierarchy Process

(AHP) statistical technique based on expert questionnaires. The AHP statistical

technique allows an evaluation of the consistency of experts’ answers. It detects

contradictions, so that inconsistent values can be double-checked or excluded

from the weighting process. In the end, the average of weights created by the

individual experts can be used as weights for different variables (for more

detailed description see Saaty 2003²).

Owing to the modular structure of the index (see Table 1, Fig. 4) it is possible to

modify variables and weights of components in later editions, if the expert

opinions change or, in case the risk context changes.

57

Expert Evaluation and Weighting

In the process of indicator selection, a questionnaire on the relevance of

suggested indicators was distributed to several experts and practitioners with

different backgrounds and working experience in various countries. Apart from

two scientists consulted to review the conceptual approach, the professional

background of the respondents lies within the field of development cooperation,

so that they are familiar with topics of risk and vulnerability from a more applied

vantage point (e.g. disaster relief operations). In addition to judgement on

importance of single indicators, respondents had the opportunity to suggest new

potential indicators or relevant criteria that were missing in the provided list. This

feedback ensured the relevance of proposed indicators not only from a

theoretical but also from a praxis-oriented viewpoint.

The respondents evaluated none of the suggested variables as irrelevant (see

Annex 3 for detailed illustration), but some were, of course, judged more

important than others. The population exposed to sea level rise, for example,

was judged as less important – for the calculation of the Index - than the

population exposed to other hazard types. Also, the GDP per capita in

purchasing power parity received lower values than other indicators suggested

for susceptibility (e.g. extreme poverty, dependency ratio).

However, the expert weights relating different indicators to each other were, for

most cases, not significantly different from equal weights. It was hence decided

to use equal weights in many of the components at the moment and to reassess

the weighting of the indicators at a potential later project stage, within the

process of fully testing the proposed index.

Reliability analysis using Cronbach’s Alpha showed good reliability results, which

means the information of the single indicators is adequately represented in the

final index. In the potential follow-up phase, a Monte Carlo simulation, could also

test the robustness of the indicator.

58

IV-B Indicators Local Assessment

The global/national resolution assessment focuses primarily on a ranking of

different countries and the identification of the major deficits that the indicators

allow to outline.

The local resolution assessment serves also as a basis for identifying potential

measures to improve disaster risk reduction (e.g. preparedness) and climate

change adaptation. However, the local indicator set also encompasses a core set

of indicators that should allow comparing general patterns of hazard exposure,

vulnerability and adaptation and adaptation capacities. In this context, the set of

exposure indicators remains the same as for the global assessment, although it

might be more difficult to obtain this data with a fine resolution. Furthermore,

many indicators refer also to the asset and capacities individual households have

to face and manage consequences of natural hazards and climate-related

creeping changes, such as sea level rise.

Distinctly different is the part of the indicator set that characterises susceptibility,

coping and adaptation. For example the local assessment – which would also

require input from local partners for selected communities – encompasses

indicators such as the access to information and resources by ethnicity or the

access to infrastructure in urban versus rural communities. Also the type of

house the respective household is living in is a local- and context-specific

indicator, since it has to take into account the region-specific housing types and

thus classifications might have to be based on local-specific features. In this

context, the local indicator set has still to be operationalised, in some cases,

according to the cultural- and geographical-context of the community or

municipality examined. However, the local assessment also captures more

standardised indicators, such as the dependency ratio, extreme poverty or life

expectancy at birth. These more standardised indicators allow for the comparison

between the local and national conditions, thus a first estimation of whether the

community ranks above or below the national average or can be obtained.

The focus on societal response capacities at the local level – particularly a)

coping and b) adaptive capacities – is broader. It captures - besides some

standard indicators - also measures and tools are linked to the level of

preparedness and the ability to deal with negative impacts of natural hazards

59

and changing environmental conditions. These additional indicators encompass,

for example, in the category of coping aspects such as job security,

landownership, debt and insurance protection or government assistance for

vulnerable groups. These indicators clearly have to be further operationalised,

since, e.g., insurance protection regimes are often specific in terms of the

national or even sub-national context. Nevertheless, these indicators point

already into a direction where action and further improvements might be needed,

such as the access to land, insurance protection or governmental assistance for

vulnerable groups.

Additionally, within the category of adaptive capacity, also the job diversity or

forced migration are considered as important local indicators. Particularly, major

disasters in the past have revealed that societal opportunities for transformation

towards more resilient communities also depend on the portfolio of skills local

people have to be able to shift into another livelihood or income earning activity.

Particularly, the recovery and reconstruction process after a tsunami is an

example that, in most cases, the opportunity for change that the disaster offered

had not been taken into account. This is largely due to the fact that many fishing

communities, for example, were just educated and skilled in fishing, thus a shift

to another field of employment would have proven particularly difficult and was

often also not promoted by various agencies involved in the reconstruction

process. Furthermore, the indicator ―forced migration‖ should also give a hint to

failed adaptation. It should, for example, point to the fact that with increasing

risks of droughts and floods or with the expected sea level rise migration

stimulated also by environmental degradation and intensified hazards should be

considered. Since the global data is not sufficiently adequate for measuring this

aspect of failed adaptation, the local assessment should start to consider this

aspect, in order to indicate that these questions are a new problem dimension.

Lastly, this indicator should outline that disaster risk reduction cannot be limited,

in the future, to emergency response and disaster management at the local

level. Rather, local and sub-national perspectives are needed, in order to be able

to address the issue of forced migration in a useful manner.

Overall, the local indicator set is a first draft to be modified and tested in

selected communities, in order to finalise the procedural recommendations for

context and local-specific indicators, such as ―access to information‖ or

―insurance protection‖. Moreover, the assessment of the draft of indicators by

60

experts of the Bündnis Entwicklung Hilft network, also encompassed a section in

which additional indicators could be mentioned. These additional indicators and

criteria have to be further discussed and evaluated, in terms of their indicative

function for risk, vulnerability and adaptation, as well as regarding their

applicability and measurability in different locations. Recommended indicators by

the experts and practitioners from the network of Bündnis Entwicklung Hilft

were, among others:

Exposure

- Tidal surges

- Cold waves

- Epidemics

- Volcanic eruptions

- Flash floods

Susceptibility

- Post-disaster trauma

- Extent of damage of embankment

- Political and religious polarisation

- Leadership on local level (good/bad)

- Population density

- Unemployment

Coping

- Snapped communication

- Income out of remittances

- Livestock

- Community-based response structures

- Communication means

- Television sets per 1000 people

- Quality and capacity of NGOs

Adaptive capacity

- Community-level disaster management capacity

- Gender-related Development Index, GDI

61

- Social expenditure on pensions, health and education as percentage

of GDP

The list of additional indicators and criteria is interesting and should be

considered in the first application phase. However, some of the indicators have

also be handled with care, since, for example, the social expenditure on

pensions, health and education might increase (e.g.) in Europe also due to the

increasing ageing population and, therefore, it is not really an indicator for

adaptive capacity in these countries (see Birkmann 2007).

The following figure gives an overview of the suggested indicators for the local

assessment (see Table 2). It has to be noted however, that there has not yet

been developed a comprehensive concept for the local scale. Emphasis has so far

been put on the development of the national scale index approach.

62

Table 2: Structural Components: Local Level (Local Resolution) as suggested in questionnaire

1. Exposure 2. Susceptibility 3. Coping Capacity 4. Adaptive Capacity

A) Hazard events per country

i) Earthquakes

ii) Tropical cyclones

iii) Floods

iv) Droughts

B) Population exposed to certain

hazards

i) Population exposed to

earthquakes

ii) Population exposed to

tropical cyclones

iii) Population exposed to floods

iv) Population exposed to

droughts

v) Population exposed to sea

level rise

C) Land area exposed to certain

hazard

i) Land area exposed to

earthquakes

ii) Land area exposed to tropical

cyclones

iii) Land area exposed to floods

iv) Land area exposed to

droughts

v) Land area exposed to sea

level rise

A) Dependency ratio

B) Extreme poverty (<2USD)

C) Access to information and

resources by ethnicity

D) Female-headed household

E) Type of house (permanent,

semi-permanent or temporary)

F) Life expectancy at birth

G) Population access to sanitation

H) Population access to clean

drinking water

I) Population without electricity

J) Access to infrastructure

urban/rural population

K) Number of physicians per

1,000 inhabitants

A) Job security based on sector of

employment (government,

private, informal or non-

governmental)

B) Number of income earners

C) Land ownership

D) Savings

E) Debt

F) Availability of local early

warning system

G) Corruption Perceptions Index

H) Failed State Index

I) Type of family (nuclear or

extended)

J) Insurance protection (life,

property, etc.)

K) Government assistance for

vulnerable groups (e.g. single

mothers, low-income families,

etc.)

A) Job diversity

B) Expenditure on education

C) Educational achievement

D) Schooling children (percentage

currently attending)

E) Membership in community-

based organisations

F) Forced migration

63

IV-C Overview of Other Reviewed Indicators that were not Included in the Concept Indicator Reason for Elimination Annual

Data

Data

Source

Hazard events per

country

information on frequencies already

incorporated in the model used to calculate

population exposed

yes CRED

Land area exposed to

hazards

no ready-to-use data available, possible to

calculate using GIS, but judged less

important by experts, might lead to

overestimation of risk in large sparsely

populated countries

no

Past economic losses as

proportion of GDP

lack of reliable data, estimated economic

losses not available for many events

yes CRED

Population without

access to electricity

data was not available for enough

countries, only of intermediate relevance

according to experts

no WB

Insurance protection lack of data availability (no public access) unsure MunichRe

Availability of early-

warning system

lack of data, there is no standard database

accounting for the different running early-

warning systems

no

Social benefits lack of data availability

Traditional knowledge lack of data availability, although it might

be important especially for local level

assessments

Ecological situation a wide range of environmental indicators

are available in the Environmental

Performance Index (EPI), it is unknown

however if/how often the input data is

updated, the EPI 2008 combines data

mostly from the period of 2000-2008, some

indicators give a longer-term annual

average though

no Yale/Colu

mbia

Universiti

es

Job diversity ILO data is available, but no standard

reporting is applied, the number of sectors

indicated by the countries range from two

to 18, comparability is not granted

yes ILO

Expenditure on

research and

development

data was not available for enough countries yes UNESCO

Expenditure on

education

data was not available for enough countries yes UNESCO

States with adaptation

strategies

lack of data availability

Gender-related

development index

(GDI)

though the GDI was evaluated as important

by the experts, the GDI did not give

significant results, its quality as a measure

for gender inequalities might be limited

yes UNDP

64

V Presentation of Selected Results


whole complexity of hazards and their generation, as well as the various and

context-specific features of vulnerability. Rather, it should give a first

introduction and overview. In this context, it should stimulate further discussions

on how to improve coping capacities and adaptation strategies towards extreme

events and natural hazards – with a special emphasis on the societal

vulnerability and response capacity. Chapter IV described the calculation of

exposure, susceptibility, coping capacity and adaptive capacity. The following

three maps (Figure 11-13) display the cartographic implementation of these

indices. Since the exposure was just calculated for the eight selected country

case studies, the World Risk, Vulnerability and Adaptation Index will be

presented for the first time (see Figure 14).

Map of the Susceptibility-Index (Draft)

Fig. 11: Results of the calculation of the susceptibility component, source: own figure

based on input data as indicated in the respective indicator sheets

65

Map of the Index Regarding the Lack of Coping Capacity-Index (Draft)

Fig. 12: Results of the calculation of the coping capacity component, source: own figure


66

Map of the Index Regarding the Lack of Adaptive Capacity Index (Draft)

Fig. 13: Results of the calculation of the adaptive capacity component, source: own

figure (DRAFT version) based on input data as indicated in the respective indicator sheets

67

Test of the Index Within Eight Selected Countries With regard to the development and testing of the methodology and concept

developed for a World Risk, Vulnerability and Adaptation Index, the applicability

of the indicators and their indicative value was tested in eight selected countries.

The result of this index application (combination of the exposure, susceptibility,

coping and adaptation indices) is shown for the selected countries within the map

(see Figure 14). Additionally, some selected indicators of the Index are outlined,

in order to provide an impression of some of the basic data variables used within

the Index.

Figure 14: Results of the calculation of the World Risk, Vulnerability and Adaptation Index

for selected countries, source: own figure based on data as indicated in the respective

indicator sheets

68

Table 3: Overview of selected indicators for the eight case studies, source: own table


Exposure Deaths Extreme Poverty

(%)

Dependency Ratio

Corruption Index (CPI)

Life Expectancy

Health Expenditure ($ per capita

PPP)

Sri Lanka 0,2895 72692 39,7 42,99 3,1 72,42 189

Germany 0,2169 249 2,0 50,57 8,0 79,86 3250

Honduras 0,3759 15575 34,8 75,49 2,5 70,20 226

Mozambique 0,2684 101677 90,0 90,80 2,5 42,07 47

Peru 0,2327 2820 19,4 57,20 3,7 71,41 274

South Africa 0,2369 1289 42,9 56,89 4,7 50,46 811

Viet Nam 0,3886 29314 48,4 50,86 2,7 74,22 221

Indonesia 0,3440 367130 53,8 50,23 2,8 70,61 78

69

VI Challenges and Barriers

To be discussed in the Expert Workshop on 2 December 2009 in Berlin.

- Problem cascade effects

- Problem weighting


whole complexity of hazards and their generation, however, it clearly outlines for

the different factors defined: exposure, susceptibility, coping and adaptation that

countries are different and also various patterns can be identified. For example it

is interesting to note that Vietnam is highly exposed to various natural hazards,

however, it has rather a medium susceptibility, but a very low coping capacity

(thus a large deficit of coping capacities) and a medium adaptive capacity.

Overall, the Index gives a first introduction and overview and, in this context, it

should stimulate further discussions on how to improve coping capacities and

adaptation strategies towards extreme events and natural hazards – with a

special emphasis on the societal vulnerability and response capacity.

70

VII Recommendations and Outlook

The proposed concept of the World Risk, Vulnerability and Adaptation Index

shows that - although many data constraints were identified – the index provides

a first overview of the relative vulnerability and risk countries are facing in terms

of selected natural hazards. In this context, it is important to note that

quantitative approaches, at the global or national scale, have a high potential to

measure exposure and susceptibility, as well as parts of coping and adaptation

using surrogate indicators and indicators that outline revealed vulnerability of

past events. However, the ability of these global tools and datasets to capture

local-specific coping and adaptation strategies is limited and therefore, a second

layer of a set of local indicators should allow for considering these more context-

specific features and characteristics of vulnerability and adaptation, such as the

access to information, the government assistance for vulnerable groups or the

insurance protection – to name just a few. In this context, quantitative

approaches and indicators can be combined at the local level with semi-

quantitative and qualitative information – capturing, for example, issues of risk

governance and the performance of disaster risk reduction.

However, we also acknowledge the limitations of current knowledge and

measurability. Especially, the coping and adaptation strategies to sea level rise –

which has not been experienced in most countries yet – is difficult to project.

Also, communities in delta regions, such as the Mekong Delta in Vietnam – which

is seen as a global hotspot for sea level rise – are not fully aware of the potential

future risks that sea level rise implies. Thus, the identification of coping and

adaptation strategies using household questionnaires might be limited if we deal

with ―unexperienced‖ events – event types that the local population has not yet

experienced.

Overall, the Index is not intended to capture the whole complexity of hazards

and their generation, as well as the various and context-specific features of

vulnerability. Rather, it should give a first introduction and overview and, in this

context, should stimulate further discussions on how to reduce exposure and

71

susceptibility and increase coping capacities, as well as improve adaptation

strategies towards extreme events and natural hazards.

Lastly, the indicator and index concept proposed here encompasses both hazard-

independent and hazard-dependent indicators, with a stronger emphasis on

hazard-independent indicators, since these allow more easily the consideration of

multiple hazards.

72

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Annex 1: Expert Questionnaire

76

Questionnaire on potential indicators for the development of a “World Risk, Vulnerability and Adaptation Index to Natural Hazards”

Dear Colleague,

The Bündnis Entwicklung Hilft (Alliance Development Works) together with the UNITED NATIONS UNIVERSITY – Institute for Environment and

Human Security (UNU-EHS) is currently in the process of developing a concept for a “World Risk, Vulnerability and Adaptation Index to Natural

Hazards”. To achieve a comprehensive review of the selected indicators used for this index, we would like to draw on your expertise and kindly ask

you to fill in the following short questionnaire. The main task is judging the variables proposed so far and possibly also pointing out additional

indicators or criteria (qualitative) which might be useful to measure societal vulnerability or adaptation capacities to natural hazards. This should

take about 15 minutes.

Please save the questionnaire using a new file name to include your answers and kindly send it back to [email protected] and

[email protected] at your earliest convenience. We greatly appreciate your valuable input!

Some information on your background:

Profession:

Organization:

Age:

Gender: female: male

Current place of work (country):

Years of work experience in developing countries:

Please note that the

indicator and criteria list

is still under development

– therefore some

indicators might need to

be specified at a later stage

mailto:[email protected]

mailto:[email protected]


77

1) Index for the global mapping (national-; sub-national resolution)

First of all, we would like to know your opinion on the relevance of the following variables to describe people’s vulnerability to natural hazards at a

global level. The four columns represent the structural components (i.e. exposure, susceptibility, coping and adaptive capacity) of the index.

Please rate the listed variables on a five point scale as follows:

Exposure 5 4 3 2 1 99 Susceptibility 5 4 3 2 1 99

1) Hazard events per country 1. Number of killed from past hazard events

earthquakes 2. Number of people affected from past hazard events

tropical cyclones 3. Extreme Poverty (<2US$)

floods 4. Dependency ratio (number of dependents/working age

population)

droughts 5. Life expectancy at birth

sea-level rise 6. Gender-related development index

2) Population exposed to certain

hazards 7. Adult literacy rate

pop. exposed to earthquakes 8. Gross school enrolment

pop. exposed to tropical cyclones 9. GDP per Capita PPP

pop. exposed to floods 10. Public expenditure on health

pop. exposed to droughts 11. Private expenditure on health

pop. exposed to sea-level rise 12. Number of physicians per 1000 inhabitants

3) land area exposed to certain

hazards 13. Population access to clean drinking water

land area exposed to earthquakes 14. Population with access to sanitation

land area exposed to cyclones 15. Population without electricity

land area exposed to floods 16. Past economic losses as proportion of GDP

land area exposed to droughts

land area exposed to sea-level rise

Overview:

5= Highly Relevant 4= Relevant 3= Intermediate 2= Irrelevant 1= Highly Irrelevant 99= no idea


78

- continuation - Index for the global mapping (national-; sub-national resolution)

First of all, we would like to know your opinion on the relevance of the following variables to describe people’s vulnerability to natural hazards at a

global level. The four columns represent the structural components (i.e. exposure, susceptibility, coping and adaptive capacity) of the index.

Please rate the listed variables on a five point scale as follows:

Overview:

5= Highly Relevant 4= Relevant 3= Intermediate 2= Irrelevant 1= Highly Irrelevant 99= no idea

Coping 5 4 3 2 1 99 Adaptive Capacity 5 4 3 2 1 99

1. Insurance protection (life, property etc) 1. Expenditure on education

2. Corruptions Perceptions Index (by Transparency

International)

2. Expenditure on research and

development

3. Failed States Index (by Fund for Peace) 3. Job diversity

4. availability of a national early warning system 4. States with adaptation strategies

5. Number of hospital beds per 1000 inhabitants


79

2) Index for local mapping (local resolution)

Here, we would like to know your opinion on the relevance of variables listed under each structural component (i.e. exposure, susceptibility, coping

and adaptive capacity) that you might think are important to describe people’s vulnerability to natural hazards at a local level.

Please rate the listed variables on a five point scale using the following guide line:

Exposure 5 4 3 2 1 99 Susceptibility 5 4 3 2 1 99

1) Hazard events per country 1. Dependency ratio (number of dependents/working age

population)

earthquakes 2. Extreme Poverty (people living on less than <2US$ PPP

per day)

tropical cyclones 3. Access to information and resources by ethnicity

floods 4. Female Headed household (still under discussion)

droughts 5. Type of House (permanent, semi permanent or temporary)

sea-level rise 6. Life expectancy at birth

2) Population exposed to certain

hazards

7. Population with access to sanitation

pop. exposed to earthquakes 8. Population access to clean drinking water

pop. exposed to tropical cyclones 9. Population without electricity

pop. exposed to floods 10. Access to infrastructure urban/rural population

pop. exposed to droughts 11. Number of physicians per 1000 inhabitants

pop. exposed to sea-level rise

3) land area exposed to certain

hazards

land area exposed to earthquakes

land area exposed to cyclones

land area exposed to floods

land area exposed to droughts

land area exposed to sea-level rise

Overview: 5= Highly Relevant 4= Relevant 3= Intermediate 2= Irrelevant 1= Highly Irrelevant 99= no idea


80

- continuation - Index for local mapping (local resolution)

Here, we would like to know your opinion on the relevance of variables listed under each structural component (i.e. exposure, susceptibility, coping

and adaptive capacity) that you might think are important to describe people’s vulnerability to natural hazards at a local level.

Please rate the listed variables on a five point scale using the following guide line:


Coping 5 4 3 2 1 99 Adaptive Capacity 5 4 3 2 1 99

1. Job Security based on Sector of employment

(government, private, informal or non-governmental)

1. Job diversity

2. No. of income earners 2. Expenditure on education

3. Landownership 3. Educational achievement

4. Savings

4. Schooling children (% currently

attending)

5. Debt

5. Membership in community based

organizations

6. Availability of a local early warning system 6. Forced Migration

7. Corruption index

8. Failed state index

9. Type of family (nuclear or extended)

10. Insurance protection (life, property etc)

11. Government assistance for vulnerable groups (e.g.

single mothers, low income families etc)


81

3) Missing Fields/potential additional indicators

After you had the possibility to judge the previously listed indicators, we would like to encourage you at this point to suggest new potential

variables or criteria not yet included in our list which you find relevant and important for the creation of a Global Risk, Vulnerability and

Adaptation index. Please specify and rate the variables you propose inserting a number (#) according to five point scale:

a) additional variables for index at national level Exposure # Susceptibility # Coping # Adaptive Capacity #

b) additional variables for index at local level Exposure # Susceptibility # Coping # Adaptive Capacity #

4) General Comments

Lastly, we are looking forward to receive some general feedback and comments on our conceptualization to measure global risk, vulnerability and

adaptive capacity to natural hazards using the four structural components outlined above, if any:

.

Thank you very much for your participation!


Annex 2: DRAFT figures on “Human Costs” indicators (own Figures based on CRED data)

82


83

Germany; 0,11

South Africa; 0,96 Peru; 3,61

Honduras; 78,30

Sri Lanka; 64,87Mozambique; 169,91

Indonesia; 29,06

Average annual deaths (1980-2008) caused by natural hazards per 1,000,000 inhabitants

Germany

South Africa

Peru

Honduras

Sri Lanka

Mozambique

Indonesia


84

Germany; 696,09

Honduras; 54.784,25

Peru; 24.785,10

Indonesia; 6.146,58

Sri Lanka; 133.137,38

South Africa; 38.279,00

Mozambique; 83.666,83

Average annual total affected per 100,000 inhabitants

Germany

Honduras

Peru

Indonesia

Sri Lanka

South Africa

Mozambique

Annex 3: DRAFT figures illustrating the expert judgement on selection of indicators for the national scale(own Figures)

85

Annex 3: DRAFT figures illustrating the expert judgement on selection of indicators for the national scale(own Figures)

86

Documents

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