This document summarizes a research project on analyzing the social and economic impacts of climate change in rural Hungary. The objectives are to develop a risk assessment method to evaluate long-term changes in economic and social factors under different climate change scenarios. A literature review establishes that vulnerability to climate change has significant social and economic dimensions beyond direct impacts on agriculture and forestry. Key factors influencing adaptation are identified as access to information, economic resources, institutional capacity, technology, politics, and social trends. The research aims to account for these socioeconomic factors in its risk assessment methodology and scenario analysis of climate change impacts in rural communities and economies.

1. SocialandEconomicImpactofClimateChangeinRuralHungary:AnalysisandMonitoring
Szerkesztő Dr. Kulcsár László
University of West Hungary Press
Sopron
SOCIAL AND ECONOMIC IMPACT OF
CLIMATE CHANGE IN RURAL HUNGARY:
ANALYSIS AND MONITORING
Edited by Dr. László Kulcsár

2. Social and Economic
Impact of Climate Change
in Rural Hungary:
Analysis and Monitoring
Edited by
Dr. László Kulcsár

3. Agroclimate: Impact Analysis of the
Projected Climate Change
and Possible Adaptation in the Forestry
and Agriculture Sector
TÁMOP-4.2.2.A-11/1/KONV-2012-0013
Project Leader
Prof. Dr. Csaba Mátyás, Member of the
Hungarian Academy of Sciences
University of West Hungary
Faculty of Economics
Sopron
2014
Book Reviewers:
Viktória Szirmai
András Ruff
Márton Bruder
ISBN: 978-963-334-210-7
Foto: László Kulcsár

4. “We are not going to be talking about polar bears and butterflies,
we are going to be talking about how this issue of climate impacts
people in their backyards, in their states, in their communities.”
Chris Lehane, Politologist
Los Angeles Times, May 21. 2014

5. Acknowledgments
We are grateful to everyone who contributed to this publication: primarily to academi-
cian Csaba Mátyás, for his advice and evaluation during the project, to the reviewers for
their detailed and thorough opinions, to the students of the University of West Hungary,
Faculty of Economics, for their field work and their assistance in data processing.
We thank all the families of Zala county engaged in agricultural activities for their
contribution and for sharing with us their views and feelings about the climate change
and its impacts.
We thank for the valuable assistance of Mária Csete and Tamás Czira from the
National Adaptation Centre of the Geological and Geophysical Institute of Hungary.

6. Contents
Acknowledgments 4
Foreword 7
Why Does Socio-Economic Impact of Climate Change Matter? 8
László Kulcsár, Csaba Székely
Vulnerability of Society to Climate Change:
Development of the Methodology of Vulnerability Studies
from the Beginning to the ‘Climate Vulnerability Index’ 14
Judit Vancsó Ms Papp, Csilla Obádovics, Mónika Hoschek
Vulnerability of Society to Climate Change: Complex Review of
Social-Economic Vulnerability in Micro Regions of Zala county  25
Csilla Obádovics, Mónika Hoschek, Judit Vancsó Ms Papp
Vulnerability of Society to Climate Change: Analysis of
Vulnerability to Drought in Zala Micro Regions  45
Judit Vancsó Mrs. Papp, Mónika Hoschek, Csilla Obádovics
Vulnerability of Society to Climate Change: Review of Vulnerability to Flash
Floods in Zala Micro Regions  58
Judit Vancsó Ms Papp, Mónika Hoschek, Csilla Obádovics
Climate Change Perception and Responses to the Challenges
Among Agricultural Producers: Results of the Questionnaire-based Survey  67
László Kulcsár
Theory and Methodology Issues of Measuring Environmental Risks 73
Csaba Székely, Csilla Obádovics

7. Application of the Volatility Method
for the Analysis of Changes in Climate Risks 85
Mónika Hoschek, Csilla Obádovics, Csaba Székely
Management of Environmental Risks, Risk Management Methods 98
Csaba Székely
Scenario Analysis: Social-Economic Impacts of Long-Term Climate Changes
Affecting Agriculture, Forestry and Local Communities  115
László Kulcsár, Csaba Székely

8. Foreword
Climate change and its natural and social-economic consequences are among the most well-
known issues of the world. It strongly divides experts, political actors and communicators
in terms of its origin and impacts. Initially mainly natural scientists dealt with the issue,
pointing out the negative changes, risks and process of the climate change that affects the
flora and fauna. Later more stress was also put on the vulnerability and exposure of human
society. The shift in emphasis was boosted by the natural disasters that killed many people
and destroyed their environment in various points of the world.
In Hungary, drought, extreme weather conditions and flash floods represented the
unexpected climatic events, but slower, yet equally risky processes may also be observed
in forestry and agriculture, the consequences of which affect the economy and society.
Our research, which is part of the TÁMOP -4.2.2.A-11/1/KONV project, is not the first
or the only one in the research of the social-economic consequences of climate change. In
our studies we relied not only on that literature, but also used intensively the internationally
available literature.
Social and economic vulnerability to climate change and the difficulties in adapting to
changes are not rapid processes with overnight changes, but historic and cultural specifi-
cities, reflected also in regional differences. In our studies we made an attempt to develop
scenarios based on the reviewed processes which reveal the development options for the
subsequent fifty years. As generally known, a scenario is a vision and not a forecast, which
helps current and future decisions. Social-economic changes stemming from climatic
impacts encourage thinking in scenarios and opening disputes i.e., practically this is their
role. This is also the most important objective of this publication.
Sopron, September 2014
László Kulcsár

9. Location of the Empirical Studies
Zala County in Hungary
Micro Regions in Zala County

10. 9
Why Does Socio-Economic Impact
of Climate Change Matter?
László Kulcsár , Csaba Székely
Research Objectives
There is no doubt about the great deal of uncertainty that may be observed equally
among political actors and within the literature as to how agriculture, forestry and
the respective groups of society can respond to environmental or, in this case, climate
change. It seems certain that the responses depend a great deal on the specificities of
the population, economy, culture, politics and the adaptation capacity, influenced by
these factors. The structure of society and its evolution is simultaneously the cause and
consequence of the change. The question is how the climatic phenomena and impacts
researched by natural sciences modify them and what new movements they launch or
impede in society and in the economy.
The issues associated with natural risks have been intriguing to sciences for a long
time, but they are equally important to politics due to their diversified impact on society.
Forecasts, prevention and management of risk effects have become a current task, which
is listed among the important actions not only by the individual national states, but also
by the EU and other international organization. In 2009 the EU expressed its intention
to take complex action for minimizing impacts of natural disasters and then in 2010 the
Commission initiated internal strategic communication on security with the Member
States. The purpose of this process is to elaborate a consistent risk management policy by
2014 that includes all important components of risk management from the assessment
of threats and risks to decision making (European Commission, 2010).
The TÁMOP-4.2.2.A-11/1/KONV research also covers the issues of environmental
risks and set a goal of elaborating a risk management method to analyze and monitor
the impacts of changes in environmental elements on the economy and society. In order to
implement the research assignment, first we need to clarify the concept of uncertainty
and risk, and then present the theoretical background of risk measurements and risk
estimates. We look at the potential application of a risk measuring methodology, i.e.
volatility calculation, applied in other fields to environmental risks. Following the se-
lection of an adequate database we shall also conduct a statistical methodology analysis
on it. Apart from the databases, we also analyze the results of the questionnaire-based
surveys and interviews conducted in the families of agricultural farmers in Zala county,
which revealed how the farmer families perceived the impacts of climate change and the
various options to reduce their consequences or to adapt to them. Then we present the
potential application of the risk management approach, review the environmental risks

11. 10
and, finally, aim at developing a risk management method, suitable for the analysis and
monitoring of the impact of changes in environmental elements on the economy and
society. Special attention is paid to the identification of the impacts of complex environ-
mental risks with a scenario analysis.
Consequently, our main research objective is to analyze the impact on climate change
on social and economic processes in order to elaborate risk assessment method envisaging
long-term changes in economic and social factors by using various scenarios.
By nature the social and economic impact study is based on risk assessment, the
methodology of which was tested in one test field. The complexity of the risk assessment
procedure is justified by a specific feature, namely that the processes which are simulta-
neously causes and effects of climate and environmental change in the reviewed sectors
need to be specified within the framework of the changes in an otherwise also dynamic
society and economy. Local knowledge about climate change and the assessment of
the factors affecting it are of fundamental importance. The new method, which is in
the center of the scenario analysis, intends to estimate the risk of probability of those
processes and their impacts.
Conceptional background
The previously mentioned factors are necessary, although not enough, for analysing a
social-economic impact analysis. We must understand the phenomena and trends that
generate changes in society and in the economy. We must be aware of the cultural and
historic background that influences the conception, approach and conduct of people. If
those are ignored, our conclusions are reduced to merely simple methodology findings.
The social-economic factors are present more intensively in the literature in the
analysis of climate impacts (McDowell - Ford 2014). Ford and Berrang-Ford (2011) list-
ed the following key factors from the literature which may be taken into account in the
social-economic adaptation process relating to climate change. These factors have been
amended slightly and are described below.
 Reduction of the information deficit. Adaptation requires certain information,
knowledge and skills, with which more efficient responses may be given to the
social-economic challenges of climate impacts. According to experience that in-
formation deficit is greater in disadvantaged areas and population.
 Differentiated economic resources. The different situation of economic resources
in the region and in households has a direct impact on the degree and nature of
vulnerability to climate change.
 Institutional capacity status, standard of knowledge suitable for mobilization in
the existing institutions
 Technology capacity and access to the required technologies in order to reduce
vulnerability.

12. 11
 Political challenges that call for government activity and stronger civil activity
in order to mitigate vulnerability to climate change. The activity of the groups of
population, exposed to vulnerability, their involvement in the allocation of funds
and, in general, their active involvement in decisions would be required.
 Consideration of social trends. Adaptation to climate change is significantly
affected by the social-demographic processes that are inherent in the society of
a particular country or one or two groups or areas thereof. Such processes could
include e.g., aging, reproduction, migration and qualifications.
A considerable part of the literature highlights the increased vulnerability of agricul-
ture and rural areas to climate change due to the currently existing and expected future
difficulties. These studies (e.g., Ford, Berrang-Ford 2011; Parotta, Trosper 2012; Faist,
Schade 2013; Gross, Heinrichs 2010; Black et al. 2013) do not only repeat the usual prob-
lems concerning the uncertainties of production or difficulties of infrastructure, but also
analyze the vulnerability and adaptation difficulties of social groups living in rural areas
and different rural regions depending on their social and cultural situation. Hansjürgens
and Antes (2008) similarly stress the role of social disparities in economic risk analysis,
in which the vulnerability to climate of society and the economy is an important factor.
In this respect vulnerability is identified with exposure to natural risks and threats. The
relations between the social-economic components of vulnerability and their correlation
with climate change are summarized well by Malcomb et al. (2014). We have also adapted
that summary and applied it in our research with slight modifications.
Figure 1 illustrates well that vulnerability to climate change entails a significant
social-economic risks, one of the important factors of which is that it may potentially
strengthen the disparities within society and can undermine efforts for the mitigation
of regional disparities in that respect too.
Social-economic risks of climate
change
Deteriora on of the infrastruc-
ture and built environment
Deteriora on of health
Worse nutri on
Environmental change, environ-
mental impacts, e.g., reduction
of forests, soil degradation
Poverty
Deteriora on in lifestyle and living
standard
Diversifica on of economic rela ons,
agricultural and forestry produc on,
economic ac vity
Source: edited by authors, based on Malcomb et al (2014)
Figure 1: Social-economic vulnerability model and the network
relating to climate change (vulnerability web)

13. 12
Vulnerability is a rather complex phenomenon in social-economic aspects. It covers
significant components of the social status. Consequently, vulnerability cannot be lim-
ited to difficulties associated with economic, forestry and agricultural activity, but must
be extended to a few components of social relations. The figure contains a lot of such
components, yet several were left out. These include cultural knowledge and aspects of
supply of information. The aspect of traditional knowledge, which may contribute to
the reduction of negative climate impacts plays an important role in the literature. It
increases the adaptive capacity of communities and, simultaneously increases the flex-
ibility of social-ecological conditions (Ruiz-Mallén, Corbea 2013; Boillat, Berkes 213).
It is stressed especially in relation to forestry (Trosper, Parrotta 2012) where, according
to the experiences of the author, the local accumulated ecological knowledge still has
an important role in the communities. The correlation and occasional confrontation
between “traditional” and “scientific” knowledge may also create knowledge that stems
equally from traditional and scientific approaches.
Wolf (2011) stressed that climate change cannot be managed in isolation from the
wider social, cultural and economic environment of region. The concept of vulnerability
must be also interpreted in that context. There are three key categories of vulnerability.
According to McCarthy et al. (2001), those three are (1) exposure, which means direct
accessibility of a particular region by the climate threats prevailing there, (2) sensitivity,
which refers to perceptibility of environmental problems and willingness to act, and (3)
available capacity of adaptation, i.e. how people can respond to environmental challenges.
(Wolf 2011, Kovács 2007).
We need to highlight already at this point that two of the three categories i.e., sensi-
tivity and capacity can be studied and influenced through social scientific and economic
factors.
The scenarios developed by us also extend to health problems i.e., climate impacts that
impose a threat to human health and may even cause fatality. Temperature fluctuation,
or health problems caused by heat waves or frost waves are significant even if they do
not cause death directly. Thus, not only old people, suffering from circulatory diseases
but also children and young people are at risk. Let us just think of the higher number of
traffic accidents in such periods, or the consequences of jumping into cold water while
your body is hot.
The developed scenarios, which illustrate the social-economic effects of environ-
mental and climate impacts include a large uncertainty factor. They do not provide
projections or forecasts, but model the social-economic impacts of the analyses made in
natural sciences and also form the indicators with which the changes caused by those
impacts can be monitored in society and in the rural areas of the economy.
Our research and the published studies have convinced us that the correlations be-
tween climate change and natural and social sciences are not independent from each
other. The “social metabolism” or “social regime theories” known from the literature try
to bridge the gap between the two factors (Baerlocher, Burger 2010), and our intention
with the published studies is also to add to that approach.

14. 13
References
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of Biophysical Environment into Social Theory. In: Gross, M, Harald Heinrichs (eds):
Environmental Sociology. European Perspectives and Interdisciplinary Challenges. Springer.
Black, Richard, Dominic Kniveton, Kerstin Schmidt-Verkerk (2013): Migration and Climate
Change: Toward an Integrated Assessment of Sensitivity. In: Faist, Thomas, Jeanne Schade
(eds) (2013): Disentangling Migration and Climate Change. Methodologies, Political Discourses
and Human Rights. Springer.
Boillat, Sébastien, Fikret Berkes (2013): Perception and Interpretation of Climate Change among
Quechua Farmers of Bolivia: Indigenous Knowledge as a Resource for Adaptive Capacity.
Ecology and Society 18 (4): 21.
Faist, Thomas, Jeanne Schade (eds) (2013): Disentangling Migration and Climate Change.
Methodologies, Political Discourses and Human Rights. Springer.
Ford, James D., Lea Berrang-Ford (2011): Introduction. In: James D. Ford, Lea Berrang-Ford (eds):
Climate Change Adaptation in Developed Nations. Springer.
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Springer.
Gross, M, Harald Heinrichs (eds) (2010): Environmental Sociology. European Perspectives and
Interdisciplinary Challenges. Springer.
Hansjürgens, Bernd, Ralf Antes (eds) (2008):Introduction: Climate change risk , mitigation
and adaptation. In: Economics and Management of Climate Change. Risks, Mitigation and
Adaptation. Springer
Kovács, András Donát (2007): A környezettudatosság fogalma és vizsgálatának hazai gyakor-
lata [The Concept of Environmental Awareness and its Practical Analysis in Hungary]. In:
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eling for sub-Saharan Africa: An operationalized approach in Malawi. Applied Geography
48. 17-30.
McCarthy, J.J. – Canziani. O.F. – Leary, N.A. – Dokken, D.J. – White, K.S. 2001: Climate Change
2001: Working Group II.: Impacts Adaptation and Vulnerability.

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McDowell, Graham, James D. Ford (2014) The Socio-ecological Dimensions of Hydrocarbon
Development in the Disko Bay Region of Greenland: Opportunities, Risks, and Tradeoffs
Applied Geography 46 98-110.
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Forest-Related Knowledge. In: Parrotta, John A., Ronald L. Trosper (2012): Traditional Forest-
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Wolf, Johanna (2011): Climate Change Adaptation as Social Process. In: Ford, James D., Lea
Berrang-Ford (2011): Introduction. In: James D. Ford, Lea Berrang-Ford (eds): Climate Change
Adaptation in Developed Nations. Springer.

16. 15
A Vulnerability of Society to Climate Change:Development
of the Methodology of Vulnerability Studies from
the Beginning to the ‘Climate Vulnerability Index’
Judit Vancsó Ms Papp, Csilla Obádovics , Mónika Hoschek
ABSTRACT: Apart from sustainable development, vulnerability is perhaps the other most popular
definition, used in a large number of scientific research studies. In this study we review the devel-
opment of vulnerability as a concept and the evolution of the vulnerability test methodology from
the beginning to the current days by relying on the available international and Hungarian literature,
focusing primarily on the vulnerability of society to the impacts of climate change. In our work we
try to reveal the inadequacies that need to be eliminated in the future and that currently have a
negative effect on the efficient use of the methodology
Keywords: climate change, vulnerability, adaptation, Climate Vulnerability Index
Development of the definition of vulnerability
Vulnerability as a concept has been known in science for a long time: in the past it was
used mostly by medical and biological sciences for a long time (e.g., Traquair, H.M. 1925;
Scharrer, E. 1940; Lewis, W.M. – Helms D.R. 1964), and became an interdisciplinary
concept from the 1980s. These days vulnerability analyses have a key role in environ-
mental risk assumptions, disaster prevention, studies dedicated to public health and
economic development and, especially in research focusing on the correlation between
climate change and adaptation (Füssel, H.M. 2005). Peter Timmerman (1981) was the
first to put the definition into the focus of studies dedicated to climate change as a result
of the then prevailing objectives of the World Meteorology Organization (WMO). WMO
conducted a key research for identifying the factors that make society at different level of
development vulnerable or adaptable to climate fluctuation and change. Timmermann’s
(p. 21.) definition: “vulnerability refers to the degree to what extent a system fails to re-
spond to risky and unfavorable events” has occurred in numerous versions to date, which
shows that the concept is as variable and hard to define as the concepts of sustainable
development and sustainability. In a study, published in 2009 Schroeder, D. – Gefenas,
E. reviewed the majority the previously used definitions (5 versions) and came up with
the following definition (p. 117): “to face the probability of occurrence of a pre-definable
effect without the availability of basic ability or knowledge, required for defence”. In the
end, the negative consequence of the impact and the inability of the system are included
in the latter definition, the same way as in Timmermann’s definition, only in a slightly
more sophisticated way. Consequently, when authors define vulnerability, they always

17. 16
take into account a negative stress effect, which is known and may occur, and a system
that is unable to respond effectively to the impact.
The first report of the second task force of IPCC already used the concept of vulner-
ability, indicating its importance (McG. Tegart, G.W. – Sheldon, G.W. – Griffiths, D.C.
1990), but then the phenomenon was limited more to mapping the effects of climate
change. From the third report besides impacts and adaptations the concept of vulnera-
bility has become an issue of key importance (McCarthy, J.J. – Canziani. O.F. – Leary,
N.A. – Dokken, D.J. – White, K.S. 2001). According to the task force in terms of climate
change a vulnerable system response sensitively also to slight changes occurring in the
climate (harmful effects appear) and the ability to adapt is severely restricted. In con-
trast, a flexible system and society is not sensitive to climate fluctuation or change and
is capable of adaptation.
Review of major experiments to measure vulnerability
Vulnerability is measured with a vulnerability index. The basis of the method was de-
veloped by Lino Briguglio (Briguglio, L. 1993) for establishing the vulnerability of small
developing island states. Briguglio’s index consisted of three components: exposure to
external economy environment, the “island” status and distance, and inclination for nat-
ural disasters. To define exposure to the external economic environment, he developed a
composite index of three elementary indicators (number of population, GDP, size of land),
based on the idea that vulnerability to the external economic environment primarily
depends on population density and the conditions of the economy. In the case of island
status and distance, the share of goods transportation in export revenues was included
in the index, while in relation to the inclination for natural disasters he used the figures
of damages caused by natural disasters as a ratio of GDP, prepared by the UN. Later
Briguglio modified and developed the indicator on several occasions (Briguglio, L. 1995,
1997; Briguglio, L.-Galea, W. 2003). Then the vulnerability indicator began to develop in
several directions and, apart from social, economic vulnerability analyses, the indicator
required for environmental vulnerability analyses, i.e. the Environmental Vulnerability
Index (EVI) was also developed in several projects between 1998 and 2004 (Kaly, U.L. et
al 2004). To define environmental vulnerability, the authors listed fifty indicators from
the areas of weather-climate, geology, geography, resources and services, and human
population. Vulnerability was approached from three aspects - risks, resistance and dam-
ages - while the results were shown on a scale of five (resistant - extremely vulnerable).
With the environmental problems, the first obvious examples relate to the vulner-
ability analyses dedicated to climate change, involving the development of “Climate
Vulnerability Index” in 2002. Wu, S.I. and his colleagues analyzed the vulnerability of
the coasts of New Jersey state in view of floods, coastal storms and sea level variation.
In their work they also analyzed the vulnerability of society, for which they took into
account the age structure of society, its breakdown by nationality and gender, the income

18. 17
figures and the living standards. Several scenarios were prepared for the future changes
of the sea level.
While Wu and his colleagues analyzed the vulnerability of the population of one
state to the variation of sea levels, in her study Katharina Vincent (2004) compared
the vulnerability to shortage of water of certain countries of Africa. In her opinion the
social-economic impact of climate change is a complex correlation of social, economic,
political, technological and institutional factors. She calculated her index from economic
welfare and stability, demographic structure, institutional stability, infrastructure supply,
globalization processes and supply of natural resources.
Sullivan, C. – Meigh. J. (2005) also analyzed the vulnerability of society in relation
to problem associated with water stocks as a result of the climate change and, apart
from a few exceptions, extended their study to all countries of the Earth. The authors
stressed that the CVI index was also suitable for performing regional analyses within
the countries. The components of the index were selected by the authors according to
the following criteria (Table 1).
Table 1. Potential variables for inclusion as sub-components of the CVI
CVI components Sub-components/variables
Resources Assessment of (surface) water (and groundwater) availability
Evaluation of water storage capacity, and reliability of resources
Assessment of water quality and dependence on imported/desalinated water
Access Access to clean water and sanitation
Access to irrigation coverage adjusted by climate characteristics
Capacity Expenditure on consumer durables, or income
GDP as a proportion of the GNP, and water investment as a % of total fixed capital
investment
Educational level of the population, and the under-five mortality rate
Existence of disaster warning systems, and strength of municipal institutions
Percentage of people living in informal housing
Access to a place of safety in the event of flooding or other disasters
Use Domestic water consumption rate related to national or other standards
Agricultural and industrial water use related to their respective contributions
to GDP
Environment Livestock and human population density
Loss of habitats
Flood frequency
Exposure Extent of land at risk from sea level rise, tidal waves, or land slips
Degree of isolation from other water resources and/or food sources
Deforestation, desertification and/or soil erosion rates
Degree of land conversion from natural vegetation
Deglaciation and risk of glacial lake outburst
Source: Based on Sullivan, C. – Meigh. J. (2005), edited by authors

19. 18
The above example shows that the researchers of the topic did not think in a single
framework and that the indicators were selected according to different criteria, depending
on individual problems. It is understandable and acceptable if one thinks about why a rising
sea level generates vulnerability for the economy and environment on the coasts of New
Jersey, and why it is not a problem in the Sahel zone. Reversing the correlation: it is clear
that due to the risk of the population in the Sahel zone is vulnerable and the population of
the coasts of New Jersey are not affected by the problem. Global modeling of vulnerability
to climate change is therefore a problem given the possibility of a multilateral approach
to the issue, and difficulties of comparison. The analyses can capture the problem mostly
according to topics (e.g., concentrating only on water issues or soil degradation, biodiversity
changes, etc.), and not in a complex manner. Another factor that makes the issue more
complicated is that the processes associated with the social-economic impacts of the climate
change and part of the indicators used for measuring them may also change as a result of
factors other than climatic effects.
Table CCIAV assessment
Impact Vulnerability Adaptation Integrated
Scientific
objectives
Impacts and
risks under
future climate
Processes affect-
ing vulnerability
to climate change
Processes affect-
ing adaptation
and adaptive
capacity
Interactions and feed-
backs between multiple
drivers and impacts
Practical
aims
Actions to
reduce risks
Actions to reduce
vulnerability
Actions to im-
prove adaptation
Global policy options and costs
Research
methods
Drivers-pressure-
state-impact-
response (DPSIR)
methods
Vulnerability indicators, past and
present climate risks, risk estimates,
review of the results of develop-
ment/sustainability policy perfor-
mance, relationship of adaptive
capacity to sustainable development
Integrated assessment
modeling, cross-sectoral
interactions, integration of
climate with other drivers,
stakeholder discussions linking
models across types and
scales, combining assess-
ment approaches/methods
Spatial
domains
Top-down
global→local
Bottom-up
local→regional
(macro-economic approach-
es are top-down)
Linking scales (global/re-
gional) often grid-based
Scenario
types
Exploratory
scenarios of cli-
mate and other
factors, norma-
tive scenarios
(stabilization)
Scenarios related
to social-eco-
nomic conditions
Adaptation
analogues
from history,
Exploratory scenarios:
exogenous and often endog-
enous (including feedbacks)
Motivation research-driven research-/stake-
holder-driven
stakeholder-/
research-driven
research-/stakeholder-driven
Source: Based on IPCC 2007. edited by authors

20. 19
An overall concept, which also provides a framework to vulnerability analysis related
to climate is included in the 4th IPCC report in 2007 (Parry, M.L. et al 2007). Although
the CCIAV climate change impact adaptation and vulnerability (summarized in Table 2)
does not provide any solution to the above problems, it points out that the analyses, which
previously concentrated only on impacts and vulnerability, should also take into account
potential responses and the adaptation capacity of the respective society. Consequently,
the CCIAV table intends to provide a complex framework for the analysis of the various
parameters (impact, vulnerability, adaptation) which are related to climate change and
were often managed separately and not in correlation before.
After the fourth IPCC report, more and more studies dealt also with the analysis of
the adaptation capacity (see e.g., Allison, E.H. et al 2009; Lioubimtseva, E. – Henebry.
G.M. 2009; Wongbusarakum, S. – Loper, C 2011), taking into account numerous related
factors, such as e.g., socio-cultural, economic and political conditions of a community
and related governance and institutional framework. According to the authors it is im-
portant to assess the status of the adaptation capacity because by improving adaptation,
exposure and sensitivity can be reduced.
Below, we shall review the Hungarian studies dedicated to the social and economic
impacts of climate change.
Review of the most important attempts to measure
vulnerability based on the Hungarian literature
The first Hungarian studies dedicated to the impacts on climate change on society
were conducted at the beginning of the new millennium (Budai Z. 2003, Szirmai V.
2004., 2005), but the VAHAVA report, which analyzed the estimated impacts of climate
change (Láng I. – Csete L. – Jolánkai M. 2007.) covered first the issue of adaptation
comprehensively. The team preparing the report was commissioned to assess the
impacts of climate change and vulnerability triggered by it, as well as the correlation
with the required responses. In the report the team presented in detail the potential
impacts of climate change and, underlying the importance of adaptation, made rec-
ommendations to elaborate adaptation strategies in the main documents of the sectors
of the national economy.
After the VAHAVA report, the studies focusing on the social-economic impacts
of climate change reflected traces of research in an increasingly diversified approach.
Apart from the analyses focusing on health impacts (heat stress, air pollution, strong-
er UV-B radiation, increasing allergy symptoms) (Kishonti K. et al 2007. Páldy A.
Málnási T. 2009, Páldi A.-Bobvos J. 2011), analyses describing problems in tourism
(ski tourism) (Szécsi N.-Csete M. 2011), agricultural production (milk production,
variation of yields of cultural plants) (Reiczigel J. et al 2009,), and nature protection
(bird migration routes, changes of Danube phytoplankton,( Kiss A. et al 2009. Sipkay
Cs. et al 2009) also appeared. In 2011 the Sociology Institute of MTA (Hungarian

21. 20
Academy of Science) published a volume of studies (Tamás P.-Bulla M. 2011) dedicated
to “Risk and vulnerability - Environmental dimensions - Social aspects”. The polit-
ical discussion paper (NCCS 2013) prepared in preparation for the Second National
Climate Change Strategy as a response to the questions and recommendation of the
VAHAVA report, which stressed the promotion of adaptation as opposed to the impacts
of climate change already referred to a National Adaptation Strategy. The document
presents in detail the impacts of climate change on natural resources, and on human
and social-economic consequences (human health, agriculture, built environment,
transport, waste management, energy infrastructure, tourism, disaster prevention)
and, then following the presentation of specific vulnerability studies, lays down the
objectives, the direction of actions and tasks related to adaptation. The precedents of
the vulnerability analyses included in the document are described in the studies by
Pálvölgyi T. et al 2010, and Pálvölgyi T. – Czira T. 2011 and Pálvölgyi T. et al 2011.
The vulnerability analyses described in the document (Second NCCS) are based on
the CCIAV assessment, recommended by IPCC and described above and were devel-
oped by an international project CLAVIER (Climate Change and Variability: Impact
in Central and Eastern Europe) concerning, among others, the analysis of the impacts
of climate change on the ecological and built environment. In the course of the study,
the authors conducted district level vulnerability analysis in relation to drought, forest
fires and heat waves in towns.
They applied a multiple approach: the expected impacts were derived from exposure
(e.g., drought, flood) and sensitivity (e.g., response of the vegetation cover to changes
in temperature), then the adaptability to the impacts was identified (the main steps
of the study are summarized in the following table). The degree of sensitivity, expo-
sure and adaptability was illustrated in a map. Vulnerability was determined by the
correlation between the impacts and adaptability: accordingly, the system with a little
climate impact and strong adaptability may be considered robust and has the smallest
vulnerability. In contrast, a system with a strong impact and weak adaptability is the
most vulnerable. The systems with weak adaptability even despite a small impact form
a transition; they are at risk. Systems that have a great expected impact and strong
adaptation are fragile.
The authors noted that the study was a pilot study and that the indicators for the
indices were selected subjectively. The main purpose of this method is to present how
to conduct any territorial vulnerability analyses according to indicators, specifically
designed for a particular problem and to present the results illustratively. Consequently,
the calculation of the indices should be revised and extended within the framework of
the methodology covered by the discussion paper. Following the approach presented by
the authors, we also made an attempt to conduct a vulnerability analysis for drought
primarily by extending the definition of adaptation capacity (more details in the second
part of the study). We deemed it necessary because in the presented examples it was
unclear to us whether we managed to find the most suitable indicators to capture the
problem in the calculation of the adaptation index for drought. The authors prepared

22. 21
the index based on the assumption that bearing and compensation, as well as elimina-
tion of damages depend primarily on the economy of the region. Thus, the index was
calculated from the indicator reflecting the income generating capacity of the sector
and the agricultural support granted for 2003-2008 on one hectare of agricultural
area i.e., in that structure the adaptation capacity for drought would depend only on
economic factors and the knowledge, understanding of the problem of society and
irrigation options, etc. would be disregarded.
Table 3: Main steps of applying the CIVAS model
Phase 1: Impact bearers, indicators and calculation methods
 step Complex climate problems and impact bearing systems. Description of the problems and
their role in the development of local climatic vulnerability.
 step Sensitivity indicators for each complex problem based on literature and expert estimates.
 step Exposure indicators in line with sensitivity indicators based on fine resolution regional climate
model results in the form of regional territorial averages.
 step Decision on the method of calculating the estimated impact. Mathematical representation of
the joint consideration of the sensitivity and exposure indicators (straight line combination)
 step Definition of indicators describing adaptability, separately for each complex problem; based
on the typical social-economic responses to the problem and information of the literature.
 step Vulnerability calculation method. Mathematical representation of the joint consideration of
the estimated impact and adaptability indicators (straight line combination)
Phase 2: Calculation, evaluation, analysis
 step Production of indicators defined in Phase 1. Building a database from the mathematical
values of the indictors defined in Steps 2, 3 and 5.
 step Vulnerability calculation. Building a database according to Steps 4 and 6 of Phase 1.
 step Analysis and evaluation of regional vulnerability. Definition of most vulnerable regions.
Source: Second National Climate Change Strategy (discussion paper) 2013.
Following the review of the Hungarian studies dedicated to vulnerability to so-
cial-economic impacts of climate change, we can conclude that, following international
professional trends, they also appeared in the Hungarian literature taking into account
not only the impacts of climate change, but also the issue of adaptation. Considering that
a complex adaptation strategy may first be presented in the envisaged Second National
Climate Change Strategy and that so far there have been very few studies concerning the
adaptability of society, further work would be required to analyze the knowledge and
general attitude of society to the impacts of climate change and the ideas of individuals
concerning adaptation.

23. 22
Summary
Climate change as an ecological stress is one of the compelling forces that the impact
bearing society must find a way to adapt to. The efficiency of adaptation is determined
by the stability of the respective communities. These days that stability is measured with
vulnerability indices. The initial diversity of vulnerability analyses have developed into
a consistent framework of impacts, adaptation and vulnerability. However, due to the
impacts of climate change that appear in variable phenomena the stability-vulnerability
problem cannot be captured in a complex manner, only by focusing on a specific parame-
ter (e.g., water level change, floods, drought, forest fires). If not globally, at least nationally
it would be important to elaborate composite and complex indices in the methodology of
vulnerability analyses that are capable of simultaneously measuring the instability and
vulnerability of society to climate change.
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Vulnerability of Society to Climate Change: Complex Review of
Social-Economic Vulnerability in Micro Regions of Zala county
Csilla Obádovics, Mónika Hoschek, Judit Vancsó Ms Papp
ABSTRACT This study is dedicated to the possible measuring of the impacts of climate change on
social and economic processes and the review of the indicators and ratios that can be used in the
measurements. The impacts of climate change on society cannot be measured in any straightforward
manner, because social and economic processes exist also independently from the climate change and
are affected by accidental factors. It is practically impossible to conclude whether or not a change in
society is attributable to the climate change or another process, independent from it. Another factor
casting a further shade on the issue is that all this represents two-way interaction: in the long run
social and economic processes also have an impact on climate change (IPCC 2012). During our study
we focused on the factors that determine the general condition of society, assuming that a stable,
well-organised society is able to respond to the challenges of climate change with flexible responses.
KEYWORDS: sensitivity of society, adaptability, exposure, vulnerability, factors affecting the vul-
nerability of society
Introduction
The literature contains numerous publications on the economics and economic aspects
of climate change. There are several approaches to the aspects of climate change that
can be quantified, projected or modeled with various statistical and economic methods.
Given the complexity of climate change, the social-economic correlations may be
revealed only with diversified analyses.
The MTA- (Hungarian Academy of Sciences) Adaptation to Climate Change Research
Group, and Mária Csete in the VAHAVA project have already analyzed the social-eco-
nomic impacts of climate change. However, their final research report does not contain
answers to the objectives outlined in the research concept.
In their studies they approached the economic correlations of climate change from
two aspects. They looked at the frequency, intensity and damages caused by weather
conditions (global warming, drought, torrential rains, mud flood, early and late frosts,
hail storms, hurricane type phenomena, etc.) and also focused on the different sensitivity,
vulnerability, bearing and reconstruction capacity of the sectors of the national economy,
settlements, regions and social groups.
They proposed four analytic and evaluation methods for concluding the character-
istics and size of the various types of damages:
• damages that cannot be expressed in monetary terms (e.g., biodiversity decay);
• damages extending in time and occurring later (e.g., treatment of illnesses);
• indirect damages (e.g., loss of export and markets due to the decay of orchards);
• direct damages (e.g., damages caused by various weather conditions).

28. 27
There is an important conclusion, according to which measures and activities aimed at
decelerating the negative impacts of climate change and growth and development could
be parallel and simultaneous processes. This is facilitated by new technology solutions
and changes taking place in the structure of the economy.
“Exposure and sensitivity to climate change and vulnerability bearing and recon-
struction capacity include elements with an uncertain outcome and a potential impact
on economic risks. The quantification of the migration processes associated with climate
change may also be a challenge to researchers. Further studies may also relate not only to
social but also economic consequences of an increase in the distance between the groups
of society, boosted by climate change. The responses to climate change by a town and
rural areas and the benefits and disadvantages inherent in them.” (Csete 2006)
Various models have been developed to analyze the social-economic correlations of
climate change:
 Local action sample model of adaptation to climate change.
 Assessment of the regional risks of climate change and security.
 Development of economic indicators to monitor the “climate protection perfor-
mance” of the key development trends.
 Adaptation of environmental assessment methods (e.g., evaluation of natural
capital) to assess the “climate print” of local development efforts.
 Development of a methodology for questionnaire-based representative surveys.
The results are contained in the Climate-21 booklets.
Climate change has an effect on numerous areas of social-economic processes, and
therefore there are:
 impacts on tourism
 impacts on health
 the impacts on social-economic processes.
These impacts were studied by various researchers in different ways (Budai 2003,
Csete 2006, Szécsi-Csete 2011, IPCC 2012)
Impacts of climate change on tourism and health
In defining the development trends of tourism, apart from political, social and de-
mographic tendencies the impact of climate change on tourism has an important role
(Budai, 2003). Climate is one of the resources of tourism, because it affects the evolving
tourist services.
In their study dedicated to the same subject Szécsi and Csete (Szécsi, Csete 2011) investi-
gated whether or not the actors of tourism felt the impacts of climate change and how they
adapted to it. In the context of tourism and climate change international research focused
primarily on the potential consequences of any increase in the sea levels and the impacts
of climate change on ski tourism. Sensitivity to climate change is affected by the type of
tourism and the destination of the tourist trips. Business or conference tourism, visits to

29. 28
relatives and health tourism are less sensitive than leisure, vacation, beach or ski tourism.
The “Djerba Declaration on Tourism and Climate Change” is dedicated to that topic.
The literature deals most with the health damaging impact of urban heat waves
within the context of the public health studies of climate vulnerability in small regions
(heat shock, sunstroke, early death) (Páldy – Málnási, 2009, meteoline.hu http://meteo-
line.hu/?m=214). They also touch upon the favorable and unfavorable consequences of
weather changes.
Each person is accustomed to the climate of their place of residence. When temperatures
reach extreme values (high or law), the number of deaths increases. There are age groups and
social groups at risk. The exposure and vulnerability of old people, people with coronary and
lung, as well as circulatory diseases, as well as poorer social groups living in towns increase
in extreme weather conditions. (meteoline.hu). The different areas that have different demo-
graphic characteristics and social structure respond to climate change differently.
Climate change and social-economic processes
In her study Katharine Vincent (2004) made an attempt to measure social sensitivity (vul-
nerability, vulnerability). In her opinion the social-economic impact of climate change
is a complex correlation of social, economic, political, technological and institutional
factors. The author applied the following criteria in the calculation of the index:
 Economic welfare and stability
 Demographic structure
 Institutional stability and community infrastructure supply
 Global concentration: Measuring globalization processes requires indicators which
capture differences between countries.
 Dependence on natural resources: primarily agriculture, the fishing industry and
forestry are affected, where the vulnerability of agriculture is the greatest.
Wongbusarakum and Loper (2011) also set an objective of defining the indicators of
social vulnerability. According to the researchers, social vulnerability depends on three
factors: exposure, sensitivity and adaptation capacity. Exposure refers to what extent the
geographic location and the use of natural resources depends on various climate events
and impacts (e.g., increasing water level).
In their opinion sensitivity reflects the extent by which a particular community is
affected by the negative impact of weather conditions. That sensitivity is greatly de-
termined by the correlation between individuals, households and community and the
use of weather dependent resources. When, e.g., their source of income is agriculture,
which depends a great deal on the weather, than the sensitivity of the individual or the
household and, in particular cases the community will be high.
It should be noted that the income of farmers and agricultural entrepreneurs may
change significantly and unpredictably from one year to the next depending on the
weather. Mitigating the uncertainty caused by droughts and drier conditions is an

30. 29
important economic interest, which can be achieved primarily by introducing irrigation
and with the help of insurance (Kapronczai 2010).
According to the definition of the authors adaptation capacity refers to the extent
by which a particular community can adapt to the changed weather conditions. There
the flow of information and information supply, the leader of the community and the
diversified activities of a community are important factors, because when they have more
information about the impacts of climate change and the community leader is capable
of elaborating a good strategy and making decisions relating to adaptation and when the
sources of income are not limited to agriculture, which is heavily dependent on climate,
the adaptation capacity of the community is also greater than in other cases.
Most indicators relate to the adaptation capacity of the community, which is determined
by numerous relating factors, such as socio-cultural, economic and political conditions of
the community and the respective governance and institutional frameworks. If adaptation
capacity can be increased, exposure and sensitivity can be reduced at the same time.
The analysis of the social-economic impacts of climate change is a problem because
the related processes and the indicators use for measuring them can also change due to
conditions other than climate impacts. Thus, primarily the exposure to climate change
and sensitivity to it, the adaptation capacity and social vulnerability, stemming from the
previous factors, can be defined, together with the timely change of that index, which
suggests some aggravation or mitigation in the circumstances. Figure 1 illustrates the cor-
relation between the three factors and reflects the indicators that form the three factors.
Figure 1: Factors affecting vulnerability
Source: Edited by the authors based on Supin Wongbusarakum and
Christy Loper 2011 as well as Pálvölgyi et al. 2010.

31. 30
The study, interpreted in the context of exposure-sensitivity-adaptation, was per-
formed at micro region level in Hungary first by Tamás Pálvölgyi and his colleagues
(Pálvögyi et al, 2010). The purpose of that research was to develop an objective impact
analysis methodology, with which the complex natural, social and economic vulnerabil-
ity of a region to climate change can be described quantitatively and comparably. In the
course of the regional adaptation in Hungary of the method of climate change vulner-
ability analysis based on regional exposure (CIVAS model), sensitivity and adaptation
capacity several regional complex indicators were developed. The exposure, sensitivity,
adaptation capacity of the micro regions were defined in each studied area and, as a
consequence their complex relative vulnerability level was also established.
As a result of the climate change, regional disparities may increase in Hungary
because various regions, micro regions and social groups have different sensitivity to
change, and the extent of that sensitivity also varies. Those with social needs, regions
and communities in an increasingly disadvantaged situation, i.e. disadvantaged regions
and certain social groups (e.g., poor and old people) are affected especially unfavorably
and their adaptation capacity is also different. As a result of the climate change, the
economic and social disparity between the regions can increase and social differences
may expand (Láng – Csete – Jolánkai 2007).
Analysis of the factors affecting the vulnerability of society
Our study covered the target area of the project supported by TÁMOP1
, i.e. geographically
the territory of Zala county in West Transdanubia. Our objective was to develop a set of
indicators, available for analysis at county and micro region level. Table 1 shows those in-
dicators which are relevant according to the literature and were used in our further studies.
Exposure to climate change is affected by several factors. Some activities are strongly
affected by climate factors and are more influenced by variable and extreme weather.
The exposure of those micro regions is greater to climate change, where the residents
live primarily from agricultural activities. Thus, exposure can be expressed as a ratio
of suburban population, the ratio of rural population (not living in towns), the ratio of
employees working in agriculture, the ratio of income originating from agriculture and
the ratio of green area (agricultural area and forests).
The sensitivity of society to climate change is affected primarily by demographic
characteristics. In an aging population, where the age structure is unfavorable, the ratio
of dependent people is high and therefore the population will be more sensitive to unfa-
vorable impacts of the climate change. (IPCC 2012)
1 TÁMOP 4.2.2.A-11/1/KONV-2012-0013: “Agroclimate: Impact Analysis of the Projected Climate
Change and Possible Adaptation in the Forestry and Agriculture Sector” project, Elaboration of a risk
assessment method for analyzing and monitoring the economic and social impact of changes in abiotic
and biotic environmental elements sub-project

32. 31
Table 1: Indicators defining the vulnerability of society
Partial index Indicators Meaning
Dependence
on natural
resources
Exposure
indicators
Ratio of suburban population
Rural population Ratio of rural population, %
Ratio of green area
ratio of per capita green and forestry area
as a percentage of the total area
Ratio of employees working
in agriculture
Ratio of employees working in agriculture within the
total employees
Ratio of income originating
from agriculture
Ratio of income originating from agriculture within the
total domestic income
Demographic
structure
Sensitivity
indicators
Ratio of children and old
people
Ratio of the population aged less than 5, or more than 60
years as a percentage of the total population
Dependence ratio
Ratio aged below 15 and over 65 years within the total
population, aged 15-64 (%)
Aging ratio and its variation
Ratio of the population aged over 60 within the popula-
tion aged less than 15 and variation of the index in time
Economic
welfare and
stability
Adaptation
capacity
indicators
Ratio of urban population
and its variation
Variation in time of the ratio of urban population
Per capita income Total domestic income / 1,000 residents
HDI Human Development Index
Life expectancy at birth 2007-2012 micro regional average life expectancy at birth
School qualifications Average number of years completed at school
Migration
Migration balance, difference between immigration and
emigration and the index of their difference for 1,000
residents
Source: Edited by the authors based on Katharina Vincent (2004)
The adaptation capacity of the aging population is also lower. If the school qual-
ification of the population is higher, if life expectancy at birth is higher, the health
situation is better, the HDI index, measuring human development is higher, then
the population earning a higher income is likely to make better informed decisions
and respond better to the challenges of climate change. The urban population finds
it easier to adapt to the weather conditions. Basically, the economic processes of an
urbanized area are less dependent on weather conditions, in terms of employment the
population living in urban areas are involved in the service sector in a higher ratio.
The rural economy and rural tourism are greatly influenced by good and bad weather.
If the region has a positive migration balance, the degree of the adaptation capacity

33. 32
of the local residents is likely to be higher. In summary, we can assume that a stable
economy and higher living standards, as well as welfare lead to greater adaptation
capacity, and that society will respond more flexibly to the effects of climate change
compelling adaptation. In summary, we may conclude that where economic stability
and welfare are greater, the adaptation capacity is better and more flexible responses
can be made to climate change.
Figure 1: Changes in the number of population in West Transdanubia
Region between 1981 and 2011, and forecast until 2101
Figure 2: Changes in the number of population Zala county
between 1870 and 2011, and forecast until 2101

34. 33
The vulnerability index is generated from indicators. The data were selected from
the TEIR system, where we used the following CSO files: TSTAR, Census, General
agricultural census, and CSO data (life expectancy at birth, average number of years
completed in schools). The income data were taken from NAV. The figures created
from those data were edited by us.
The population of Zala county is gradually decreasing. While in 1981, the county
had more than 300,000 residents, according to our forecasts by 2050 the total number
of population will be below 250,000.
Figure 3: Changes in the number of population of Zala micro regions in 1870 and 2011
Within the county, the decrease in the population of the micro regions reflects var-
ious tendencies. The population of the Hévíz and Keszthely micro regions is rising, in
Zalaegerszeg and Nagykanizsa micro regions began to fall in the 1980s, while the pop-
ulation of the other micro regions has been shrinking since 1950.
Dependence on natural resources
Exposure indicators
As mentioned earlier, the exposure index is calculated from the ratio of suburban and
rural population, the ratio of agricultural areas and forests and the ratio and income of
employees working in agriculture.

35. 34
Table 2: Ratio of the suburban population and rural population, %
Micro region
Suburban population, % Ratio of rural population, %
1980 1990 2001 1970 1980 1990 2001 2011
Hévíz 10.3 11.2 1.2 73.0 55.9 60.9 63.0 64.5
Keszthely 3.5 2.4 2.2 43.5 36.6 35.6 35.9 39.3
Lenti 3.2 1.7 1.3 78.5 71.8 66.0 63.7 62.2
Letenye 2.0 1.6 0.9 82.3 79.6 76.4 75.4 74.5
Nagykanizsa 1.4 0.7 0.9 32.8 27.1 24.2 24.5 23.9
Pacsa 1.6 0.7 1.0 88.0 85.0 83.4 83.0 83.1
Zalaegerszeg 3.0 2.0 3.1 47.8 37.8 33.3 33.2 33.1
Zalakaros 3.8 2.1 2.3 96.1 95.0 92.5 89.8 85.9
Zalaszentgrót 3.1 1.9 1.5 63.2 60.2 58.5 57.9 58.8
Zala county 2.9 2.0 1.9 56.8 48.4 44.2 43.6 43.5
The ratio of suburban population is not significant in Zala county, and is not a signif-
icant factor in the calculation of exposure either, and therefore that factor is not included
in the index. When the ratio of rural population is lower than 50%, the region is not con-
sidered affected by climate change in that indicator. Between 50% and 80% the region has
moderate exposure, and over 80% the exposure of the micro region is strong.
Table 3: Ratio of agricultural area and forests, %
Ratio of agricultural area
within the total area, %
Ratio of
forests, %
Micro region 2000 2010 2000*
Hévíz 46.5 21.4 22.9
Keszthely 31.1 28.2 29.9
Lenti 23.8 29.9 39.4
Letenye 31.6 29.8 37.4
Nagykanizsa 48.0 37.2 27.5
Pacsa 39.2 39.5 23.1
Zalaegerszeg 43.9 43.5 28.8
Zalakaros 40.2 32.2 22.0
Zalaszentgrót 36.2 35.8 18.4
Zala county 37.3 34.7 29.4
*The forest area data series contained erroneous data for
2010, therefore they were not taken into account.

36. 35
The greater the ratio of agricultural area is in a particular region, the more it is exposed
to the weather conditions, one of the most significant risk factors in agriculture. While
calculating the exposure partial index whenever the ratio of agricultural area was greater
than the county average and it remained so during the studied period, the micro region
was deemed to have strong exposure. Where the ratio of agricultural area was lower than
the county average and it remained so, we deemed the micro region to have a decreasing
tendency or, in the case of stagnation, not to be exposed at all. When the ratio of agricul-
tural area was increasing but did not reach the county average, or it was falling but was still
higher than the county average, the micro region was described as a region with moderate
exposure. The ratio of forests indicator is also one of the exposure indicators. Zala county
is an area within Hungary which is rich in forests. However, the county shows a rather het-
erogeneous picture, as the area of forests in the micro regions of the county varies between
18% and 40%. Areas with less than 25% forests were described as areas with no exposure,
while areas with more than 30% forests were classified as strong exposure.
Based on the ratio and income of employees working in agriculture, the micro regions
were classified into two groups: heavily exposed to weather conditions with values sig-
nificantly higher than the county average and not exposed. Thus, the not exposed micro
regions include Hévíz, Keszthely, Nagykanizsa and Zalaegerszeg, while the remaining
five micro regions were classified as areas with strong exposure.
Table 4: Ratio and income of employees working in agriculture
Ratio of employees wor-
king in agriculture, %
Ratio of income origina-
ting from agriculture, %
1980 1990 2001 2011 1992 2002 2012
Hévíz 6.2 5.0 4.1 3.1 0.0493 0.0371 0.2221
Keszthely 5.2 3.8 2.9 3.3 0.0436 0.0265 0.2274
Lenti 8.5 5.6 4.9 8.5 0.0701 0.1923 0.8294
Letenye 8.6 6.2 6.1 10.0 0.0740 0.1672 0.8845
Nagykanizsa 3.6 3.1 2.1 3.9 0.0182 0.0598 0.1250
Pacsa 6.6 7.1 5.9 10.4 0.0766 0.1661 1.4254
Zalaegerszeg 3.3 2.8 2.2 3.4 0.0268 0.0333 0.3228
Zalakaros 9.9 10.3 4.9 8.6 0.0438 0.2284 0.7483
Zalaszentgrót 7.3 6.2 4.5 7.1 0.0578 0.1701 0.8799
Zala county 5.2 4.2 3.1 4.8 0.0361 0.0727 0.3968
The Hévíz, Keszthely and Nagykanizsa micro regions have no exposure. In these
micro regions people generally do not make a living from agriculture. The Zalaegerszeg,
Zalaszentgrót and Zalakaros micro regions are moderately exposed and fall in the same

37. 36
category also due to their higher agricultural areas and the importance of the income
originating from agriculture. In the most exposed micro regions, both the size of the agri-
cultural area and the ratio of income originating from it are important (Lenti micro region).
Table 5: Exposure index and categories for the micro regions in Zala county
Micro region ratio of
rural po-
pulation
ratio of
agricul-
tural area
forest
area
number of agri-
cultural employees
and their income
exposure index*
Hévíz 1 0 0 0 1 – no exposure
Keszthely 0 0 1 0 1 – no exposure
Lenti 1 2 2 2 7 – strong exposure
Letenye 1 0 2 2 5 – exposure
Nagykanizsa 0 1 1 0 2 – no exposure
Pacsa 2 2 0 2 6 – exposure
Zalaegerszeg 0 2 1 0 3 – moderate exposure
Zalakaros 2 0 0 2 4 – moderate exposure
Zalaszentgrót 1 1 0 2 4 – moderate exposure
*0-1-2 points: no exposure; 3-4 points: moderate exposure; 5-6
points: exposure; 7-8 points: strong exposure
Sensitivity indicators in the micro regions of Zala county
Demographic approach
As indicated earlier, the sensitivity of society is influenced by demographic aspects. The
older population is more sensitive to extreme conditions resulting from climate change,
they adapt more slowly and through more difficulties, and their health problems intensify
as a result of the heat waves.
In Zala county the ratio of the population aged less than 5 fell between 1970 and 2011
to 58% of the number recorded in 1970, while the ratio of the population aged over 60 in-
creased by 25%. (Table 6). According to the projections, the aging index2
is exponentially
increasing, and society in Zala county is aging faster than the national tendency (Table 7).
The maintaining capacity of the regional population can be measured with depend-
ence ratios. Where the ratio of dependent people is higher, the sensitivity of the popula-
tion is also stronger to change (Table 8).
2 The aging index equals the old age population divided by the young population.

38. 37
Table 6: Ratio of the population aged less than 5 and aged more than 60
Micro region Ratio of population, aged less than 5, %
Ratio of the population
aged 60 or more, %
1970 1980 1990 2001 2011 1970 1980 1990 2001 2011
Hévíz 6.3 6.6 5.6 4.7 4.5 20.8 20.3 22.3 22.3 27.2
Keszthely 6.1 7.9 5.7 4.4 4.2 19.6 18.4 20.1 20.9 26.3
Lenti 6.5 7.2 5.6 3.8 3.5 21.1 21.2 24.1 25.5 28.5
Letenye 6.8 7.1 6.0 4.3 3.8 20.0 21.4 25.0 25.2 26.9
Nagykanizsa 6.9 8.9 5.9 4.1 4.2 16.6 15.8 17.6 19.8 24.6
Pacsa 5.8 7.1 5.9 4.6 4.6 22.4 22.8 25.4 24.0 23.9
Zalaegerszeg 7.3 8.7 6.0 4.3 4.3 15.8 14.6 16.9 19.7 23.8
Zalakaros 6.0 6.7 6.3 5.4 5.1 23.8 23.1 25.1 24.8 26.0
Zalaszentgrót 6.8 7.3 5.6 4.5 4.1 21.1 20.9 23.2 23.6 25.6
Zala county 6.7 8.1 5.9 4.3 4.2 18.6 17.8 19.9 21.4 25.2
The ratio of old people is older than the county average only in the Zalaegerszeg and
Nagykanizsa micro regions. The Pacsa micro region is the only micro region where the ratio
of old people is not rising. the figure has been gradually increasing in all other micro regions
since 1970. The rate of increase was especially remarkable in the Hévíz and Keszthely micro
regions over the last 10 years (from 22.3% to 27.2% and from 20.9% to 26.3%).
Table 7: Aging index of Zala micro regions
Micro region
Aging index
(Population aged over 60/popu-
lation aged less than 15, %)
1970 1980 1990 2001 2011
Hévíz 103.7 115.5 114.9 138.9 200.4
Keszthely 96.6 86.2 98.7 135.9 205.4
Lenti 98.2 109.7 128.4 165.6 245.1
Letenye 87.1 107.5 132.3 156.1 210.3
Nagykanizsa 75.4 69.0 82.3 128.0 190.2
Pacsa 108.3 119.0 129.3 141.2 163.3
Zalaegerszeg 68.5 63.0 79.0 127.3 180.2
Zalakaros 113.6 116.8 129.4 135.6 166.9
Zalaszentgrót 94.5 99.3 116.7 144.8 194.9
Zala county 84.7 82.3 96.6 135.8 191.9

39. 38
Table 8: Dependence ratio
Micro region
Dependence ratio (populated aged over 60 and less than
18 as a percentage of the population of working age, %
1970 1980 1990 2001 2011
Hévíz 86.0 70.2 86.0 75.6 77.9
Keszthely 92.2 80.8 86.6 71.7 73.4
Lenti 91.5 78.7 87.2 81.2 74.5
Letenye 94.3 81.0 92.3 85.3 75.5
Nagykanizsa 86.8 78.1 82.4 68.7 69.0
Pacsa 96.6 84.2 98.6 85.2 72.6
Zalaegerszeg 86.9 77.6 82.9 69.2 67.8
Zalakaros 99.5 88.0 96.2 92.5 81.8
Zalaszentgrót 96.8 83.6 91.2 81.2 72.6
Zala county 90.3 79.3 85.9 73.7 71.2
Old age dependence ratio (population aged
over 60/population aged 18-59, %)
Hévíz 38.7 34.6 41.4 39.1 48.3
Keszthely 37.7 33.2 37.4 35.8 45.6
Lenti 40.3 37.9 45.1 46.2 49.7
Letenye 39.0 38.7 48.2 46.7 47.2
Nagykanizsa 31.1 28.1 32.2 33.5 41.5
Pacsa 44.1 42.0 50.4 44.5 41.3
Zalaegerszeg 29.5 25.9 30.9 33.4 39.9
Zalakaros 47.5 43.4 49.2 47.8 47.3
Zalaszentgrót 41.6 38.3 44.4 42.8 44.2
Zala county 35.5 31.8 36.9 37.2 43.1
Young dependence ratio (population aged less
than 15/population of working age)
Hévíz 47.3 35.6 44.6 36.6 29.6
Keszthely 54.5 47.6 49.1 35.9 27.8
Lenti 51.1 40.9 42.1 35.0 24.8
Letenye 55.4 42.3 44.2 38.6 28.3
Nagykanizsa 55.7 50.0 50.2 35.2 27.5
Pacsa 52.5 42.2 48.2 40.7 31.3
Zalaegerszeg 57.3 51.8 52.0 35.9 27.9
Zalakaros 52.0 44.6 47.0 44.7 34.5
Zalaszentgrót 55.2 45.3 46.8 38.4 28.4
Zala county 54.9 47.4 49.0 36.5 28.1

40. 39
Over the last forty years, the aging index more than doubled in the county. The situation
is especially severe in the Hévíz, Keszthely and Letenye micro regions, and is the highest in
the Lenti micro region, where the aging index has gone up by 250% over the last forty years.
In terms of the dependence ratio (Table 8), the figure is the highest in Zalakaros micro
region, while the situation is most favorable in Zalaegerszeg and Nagykanizsa micro re-
gions. The old-aged dependence ratio figures match those described earlier in relation to
the ratio of old people and aging ratio indicators, analyzed above. The dependence ratio of
young people is the highest in Pacsa and Zalakaros micro regions, where it is above 30%,
although there has been considerable decline in each micro region since 1970, and the
tendency has accelerated after 1990. There was a slight increase between 1980 and 1990.
The dependence ratio and the dependence ratio of young people during the 1981 and
2021 actual and estimated period first rose in the first decade, and then began to decline.
Later, according to the projections, that decline with turn into moderate growth (CSO,
TEIR). The old age dependence ratio is likely to be rising evenly. The gravest problem is
the aging index, as it is rising drastically. Over the thirty-year actual period it doubled
and the increase is unlikely to slow down according to the projections.
Figure 5: Sensitivity indicators in Zala county
The components of the sensitivity index are the ratio of children, the ratio of old
people, the aging index and the dependence ratio. The value for the ratio of children
was close or higher than the county figure and the tendency developed favorably during
the reviewed period when the region scored 0 point. When the index generally did not
reach the county figure, the region scored 2 points, otherwise 1 point. The situation is
reverse in the case of the ratio of old people. When the index was generally higher than
the county average and was rising, the region scored 2 points. If the index was mainly
lower than the county figure, it scored 0 point. Otherwise 1 point was given. Among

41. 40
the dependence ratios, we used only full dependence, because there is close correlation
between the ratio of old people and dependence ratio of old people, and ratio of chil-
dren and dependence ratio of young people. The dependence ratio of Nagykanizsa and
Zalaegerszeg micro regions was lower than the county average in each census. Those
micro regions were put into the two-point category, where the dependence ratio was
continuously higher than the county figure. In summary, according to the sensitivity
index, Lenti micro region is mostly at risk, followed by Letenye and Zalaszentgrót
micro regions, given their demographic structure. The Nagykanizsa and Zalaegerszeg
micro regions are not at risk, those regions are not sensitive to the impacts of climate
change in demographic aspects.
Table 9: Sensitivity index and its categories for the micro regions of Zala county
Micro region
Ratio of the
population
aged less
than 5
ratio of the
popula-
tion aged
over 60
Aging index
dependen-
ce ratio
sensitivity index*
Hévíz 0 2 2 1 5 - sensitive
Keszthely 0 2 2 1 5 - sensitive
Lenti 2 2 2 1 7 - strongly sensitive
Letenye 1 2 2 2 7 - strongly sensitive
Nagykanizsa 0 0 0 0 0 - not sensitive
Pacsa 0 1 1 2 4 - moderately sensitive
Zalaegerszeg 0 0 0 0 0 - not sensitive
Zalakaros 0 2 1 2 5 - sensitive
Zalaszentgrót 1 2 2 2 7 - strongly sensitive
*0-1-2 points: not sensitive; 3-4 points: moderately sensitive; 5-6
points: sensitive; 7-8 points: strongly sensitive
Adaptation capacity indicators in Zala county
To what extent society can adapt to new and changed conditions is influenced by several
economic and human factors. Better qualified, healthier and more stable regions change
more easily if required by the conditions. In Table 10 HDI is used to measure adaptation
capacity and we examined its components, i.e. qualifications, life expectancy at birth
and the income indicators.

42. 41
Table 10: Adaptation capacity indicators in the micro regions of Zala county (2012)
Micro region HDI
Average
number
of years
completed
at school
Life ex-
pectancy
at birth
male
Life ex-
pectancy
at birth
female
Life ex-
pectancy
at birth
together
Income/
person
Hévíz 63.7 10.1 73.6 80.0 77.0 658051
Keszthely 64.0 10.5 72.3 79.4 76.0 683931
Lenti 54.1 9.7 71.4 79.2 75.2 710316
Letenye 31.0 9.1 67.6 76.9 72.1 632690
Nagykanizsa 62.3 10.0 71.8 78.8 75.4 815234
Pacsa 32.1 9.0 68.1 78.4 73.0 597773
Zalaegerszeg 68.0 10.3 71.3 79.2 75.3 896517
Zalakaros 24.2 8.8 68.9 76.2 72.5 514688
Zalaszentgrót 39.3 9.5 68.4 78.6 73.3 618620
The micro region of the county seat is in the most favorable situation, with the highest
HDI index and the highest per capita income figure. The Zalakaros micro region is in
the worst situation, where HDI is only 24.2% and the income per capital index is also the
lowest, only 57% of the Zalaegerszeg figure.
The ratio of urban population increased from 43.2 % to 56.5% between 1970 and 2011.
There is great difference between the micro regions containing large towns and the other
micro regions. In the Keszthely, Nagykanizsa and Zalaegerszeg micro regions the urban
population ratio was higher than 50% through the entire examined period with rising
tendencies, although that tendency seems to have come to a halt in the last decade, and in
fact 4 percentage points decline can be observed in the Keszthely micro region (Table 11).
The adaptation capacity of the rural population is weaker, and therefore Pacsa and
Zalakaros micro regions are mostly at risk, followed by the Letenye micro region.
The examined indicators were used for calculating the adaptation capacity. The indi-
cators forming HDI were taken into account according to their simple ranking numbers,
in a declining order. In order to coordinate the scales, the micro region with the highest
ranking numbers scored 1 point, the micro region with the lowest ranking number scored
0 point, the intermediary figures were proportioned, and therefore the ranking order
was established between 0 and 1. Given its importance, the per capita income index was
given a multiplying factor too, therefore the maximum score, that could be achieved with
the HDI components is 4 points. Micro regions with less than 30 % urban population
according to the degree of urbanization were put into the most risky group. These were
Letenye, Pacsa and Zalakaros micro regions.

43. 42
Table 11: Ratio of urban population and migration difference
Ratio of urban population, % Migration difference
Micro region 1970 1980 1990 2001 2011 1980-1989 1990-2001 2001-2011
Hévíz 27.0 44.1 39.1 37.0 35.5 -1628 1259 2338
Keszthely 56.5 63.4 64.4 64.1 60.7 874 2112 748
Lenti 21.5 28.2 34.0 36.3 37.8 -1795 -528 -289
Letenye 17.7 20.4 23.6 24.6 25.5 -889 912 -67
Nagykanizsa 67.2 72.9 75.8 75.5 76.1 -738 641 -1517
Pacsa 12.0 15.0 16.6 17.0 16.9 -1017 -18 -212
Zalaegerszeg 52.2 62.2 66.7 66.8 66.9 1136 1817 365
Zalakaros 3.9 5.0 7.5 10.2 14.1 -858 479 250
Zalaszentgrót 36.8 39.8 41.5 42.1 41.2 -1057 448 61
Zala county 43.2 51.6 55.8 56.4 56.5 -5972 7122 1677
Table 12: Adaptation capacity index* and categories
for the micro regions in Zala county
Micro region
Average num-
ber of years
completed
at school
Life ex-
pectancy
at birth
Per
capita
income
HDI
ele-
ments
total
Urbani-
zation
Migration
balance
Index (after
rounding)**
Hévíz 0.2 0.0 1.2 1.4 1 0 2 – no exposure
Keszthely 0.0 0.2 1.1 1.3 0 0 1 – no exposure
Lenti 0.5 0.4 1.0 1.9 1 2 5 – exposure
Letenye 0.8 1.0 1.4 3,2 2 2 7 – strong exposure
Nagykanizsa 0.3 0.3 0.4 1.0 0 2
3 – moderate expo-
sure
Pacsa 0.9 0.8 1.6 3.3 2 2 7 – strong exposure
Zalaegerszeg 0.1 0.3 0.0 0.4 0 0 0 – no exposure
Zalakaros 1.0 0.9 2.0 3.9 2 1 7 – strong exposure
Zalaszentgrót 0.6 0.8 1.5 2.9 1 1 5 – exposure
*because o the possibility of aggregation with other Indices, the higher value indicates lack
of adaptation capacity, and a lower value reflects existence of adaptation capacity.
**0-1-2: no exposure; 3-4: moderate exposure; 5-6: exposure; 7-8: strong exposure
In the case of the migration difference the adaptation capacity of those micro regions
is the lowest, where the balance was mostly negative in the examined period. The region,
which was previously dominated by emigration, and then by immigration, with a slightly
falling rate in the last decade was classified as slightly exposed. There is considerable
negative tendency in Lenti, Letenye, Nagykanizsa and Pacsa micro regions. The Hévíz,

44. 43
Lenti and Zalaszentgrót micro regions are moderately exposed, their urban population
ratio is lower than 50%. The urban micro regions are Nagykanizsa, Zalaegerszeg and
Keszthely micro regions.
Because of the complexity of adaptation capacity, four categories were used in the classi-
fication. Zalakaros, Letenye and Pacsa micro regions have the weakest adaptation capacity.
The adaptation capacity of Zalaegerszeg and Keszthely micro regions is the best (Table 12).
Vulnerability index
The vulnerability of society depends on exposure, sensitivity and adaptation capacity.
We looked at the indicators forming the various components in the micro regions of Zala
county and calculated partial Indices based on the periods. With the help of the partial
indices, we then prepared the vulnerability index.
Table 13: Vulnerability index and categories for the micro regions in Zala county
Micro region
exposure index
(maximum 8)
sensitivity index
(maximum 8)
lack of adaptation
capacity index
(maximum 8)
Vulnerability index*
(maximum 24)
Hévíz
1
no exposure
5
exposure
2
no exposure
8
moderately vulnerable
Keszthely
1
no exposure
5
exposure
1
no exposure
7
moderately vulnerable
Lenti
7
strong exposure
7
strong exposure
5
exposure
19
strongly vulnerable
Letenye
5
exposure
7
strong exposure
7
strong exposure
19
strongly vulnerable
Nagykanizsa
2
no exposure
0
no exposure
3
moderate exposure
5
not vulnerable
Pacsa
6
exposure
4
moderate exposure
7
strong exposure
17
vulnerable
Zalaegerszeg
3
moderate
exposure
0
no exposure
0
no exposure
3
not vulnerable
Zalakaros
4
moderate
exposure
5
exposure
7
strong exposure
16
vulnerable
Zalaszentgrót
4
moderate
exposure
7
strong exposure
5
exposure
16
vulnerable
*0-5 points: not vulnerable; 6-11 points: moderately vulnerable;
12-17 points: vulnerable; 18-24 points: strongly vulnerable

45. 44
The Zalaegerszeg micro region, which includes the county seats, is not vulnerable, is
the least sensitive to the impacts of climate change and has adequate adaptation capacity.
It is followed by Nagykanizsa micro region, which also has a large town type Centre. The
category of moderate vulnerability includes Keszthely micro region, with its castle, univer-
sity and high tourism potential, forming part of Balaton region and Hévíz micro region,
where the tourism potential and the thermal bath, equally attractive in winter and summer,
compensates for the sensitivity that stems from the older age structure.
The Lenti and Letenye micro regions are the most vulnerable in the county and belong to
the categories of exposure or strong exposure in every aspect. The Lenti micro region falls in
the category of strong exposure both in terms of exposure and sensitivity, and its adaptation
capacity is also low. The Pacsa micro region was classified into the group at risk due to its
high exposure and lack of adaptation capacity, while Zalakaros micro region was classified
there primarily due to lack of its adaptation capacity, even though in terms of exposure is
belonged to the group with moderate risk. Zalaszentgrót micro region belongs to the vul-
nerable group due to its sensitivity and the low degree of adaptation capacity. (Table 13).
Summary
The indirect impacts on climate change on society, such e.g., more frequent heat waves,
extreme weather conditions, forest fires, drought can be described, but the responses to
challenges, the adaptation capacity, the sensitivity to society depend on factors that de-
termine the social-economic processes also independently from climate change. In the
course of the vulnerability analysis applied in the study in the context of exposure - sensi-
tivity - adaptation capacity, we tried to take a look at the indicators that help illustrate the
general condition of a particular society, assuming that a stable community can come up
with flexible responses to compelling conditions that may accompany climate change. On
the basis of the result of the analysis it may be concluded about the micro regions of Zala
county that the majority of them are vulnerable due to their weak adaptation capacity.
Identifying the correlation between the indicators of social-economic processes, which
may be analysed in the long term and the weather indicators, coordinated in time is a
serious challenge for an analyst. The National Meteorology Service established 4 regional
models to estimate the climate change in Hungary and the Carpathian Basin. The cor-
relation between the climate models and the social-economic processes will constitute
the basis of the subsequent research phase.
References
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of Global Climate Change on Tourism]. Tourism Bulletin, 2003/1. pp. 23-27.

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Csete, Mária (2006): A klímaváltozás társadalmi-gazdasági hatásai [Social-economic impacts of
climate change]. MTA-TKI Adaptation to Climate Change Research Group.
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kodása a Szentendrei kistérségben [Adaptation to Climate Change of the Actors of Tourism
in Szentendre Micro Region]. Climate-21 leaflets No. 65.pp. 64-86.
Katharine Vincent (2004): Creating an index of social vulnerability to climate change for
Africa. Tyndall Centre for Climate Change Research and School of Environmental Sciences,
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2004
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környezetegészségügyi vonatkozásai [Environmental Health Aspects of the Health Conditions
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risk and management of extreme climate events. Summary for decision makers. Budapest
December 2011, Ministry of National Development

47. 46
Vulnerability of Society to Climate Change: Analysis
of Vulnerability to Drought in Zala Micro Regions
Judit Vancsó Mrs. Papp , Mónika Hoschek, Csilla Obádovics
ABSTRACT: In the first half of our study we reviewed the evolution of the vulnerability analysis method-
ology from the beginning to the assessment of the potential social impact of climate change based on
both foreign and Hungarian literature. This study presents the analysis of vulnerability, in the context
of exposure, sensitivity and adaptation, to drought of the population living in the rural areas of Zala
and connected to agriculture either in part or in full. Focusing on comparability and looking at the
regional differences, we made our calculations at the level of micro regions.
KEYWORDS: climate change, drought, adaptation, vulnerability
Introduction
Climate change as an ecological stress is one of the compelling forces that the impact
bearing society must find a way to adapt to. As participants of the TÁMOP-4.2.2.A-11/1/
KONV-2012-0013 “Agroclimate”3
project, our responsibility was to assess the potential
social impacts of the projected climate change in Zala county by using the previously ap-
plied vulnerability analyses. On the basis of the results of this questionnaire-based survey
conducted in the county to assess the impacts of the climate change on agricultural soci-
ety, and the indicative national documents describing the estimated impacts of the pro-
jected climate change (Láng I. – Csete L. – Jolánkai M. 2007; Nemzeti Éghajlatváltozási
Stratégia (National Climate Change Strategy - NCCS) and the second planned NCCS)
and the publications (e.g., Pongrácz et al 2009; Sábitz J. et al 2013; Gálos 2014)) we think
that the local population will have to face two significant problems in the future: less an
more unevenly distributed precipitation and more frequent years with drought, caused
by the global warming, as well as increasingly occurring flash floods, also caused by the
uneven distribution of precipitation. This study is dedicated to the review of the problems
caused by increasing droughts affecting the agricultural population.
Exposure and sensitivity
To define vulnerability to drought we relied on the methodology applied in the previ-
ous vulnerability studies described in the first half of this publication (Pálvölgyi et al
2010, Pálvölgyi T. – Czira T. 2011; Pálvölgyi et al 2011; NCCS 2) to which we made some
3 “Agroclimate: Impact Analysis of the Projected Climate Change and Possible Adaptation in the
Forestry and Agriculture Sector”

48. 47
modifications, primarily in the calculation of the adaptation capacity of the society living
in the rural areas of Zala county. The vulnerability of the Zala agricultural population
to drought in the context of exposure - sensitivity - adaptation was defined by using the
following summarized parameters (Table 1).
Table 1. Indicators used in the calculation of sensitivity to drought
Impact Adaptation
Exposure Sensitivity
PaDI
– certain physical and water management
features of soils:
field capacity, dead water content, use-
ful water stock, water absorption capac-
ity and hydraulic conductivity of the soil,
stratification of the soil section, features
causing the special water balance and
water retention capacity of the soil
– knowledge and information concerning
adaptive agriculture (technology and change
of species)
– accessibility of water, available for irrigation
– direct and indirect agricultural support by
farm
– HDI
– Indicator calculated from the above indices
Source: CARPATCLIM; ENSEMBLES EU– FP6; KSH; MVH; NYUDUVIZIG; Pálvölgyi
et al 2010; Pálvölgyi T. – Czira T. 2011; Expert estimate; TEIR database
In the previous studies, exposure was defined with the Ángyán and Pálfai Drought
Index (PAI). In this study, we used a simplified version of PAI, called Palfai Drought
Index (PaDI). The groundwater level data, required for calculating the PAI were not
available to our project for the period until 2100, therefore using the PaDI, which requires
only monthly precipitation and temperature data, seemed a more practical option. In
addition, we also think that the results of the calculations made with various approaches
should also be made comparable. On the basis of the planned National Drought Strategy,
it should be noted that there is no significant difference between the PAI and PaDI val-
ues. The values in our calculations stemmed from the European Union CARPATCLIM
project for the past and another EU project ENSEMBLES EU-FP6 for the future.4
.
In the period of 1981-2010, the average PaDI index was 3.6 °C/100 mm in Zala county,
i.e. it does not even reach the slight drought category. Figures falling within the slight
drought category occurred on 9 occasions, primarily in the 1990s and at the beginning
of the new millennium. Four subsequent years from 2000 to 2003 stood out when, due
to the continuously drier weather, the drought index reached 6.7 °C/100 mm in 2003,
a uniquely high figure for Zala. That figure already falls in the slight drought category.
The diagram also shows that the extreme values are shifted more in a positive direction.
The PaDI, estimated on the basis of future temperature and precipitation data in-
dicates increasing drought in Zala county. Compared to the average of the 1981-2010
period, the drought index is likely to increase by 6.3% between 2011 and 2040, by 13.3
4 Borbála Gálos, responsible for A9 programme within our project supplied the temperature and
precipitation for the PaDI calculations and provided assistance in the methodology used for the analyses.

49. 48
% between 2041 and 2070, and by 17.5 % between 2071 and 2100. In absolute terms it
will mean 0.2-0.6 °C/100 mm increase by the end of the period.
It may be concluded that by the end of this century the average PaDI in Zala county
will rise with continuous variation of negative and positive deviations from the average
and, according to our calculations, the average figures of the 2041-2070 and 2071-2100
periods will fall in the slight drought category. In the agricultural sector, the main
problem is likely to be the occasional high extreme values, especially when they occur
in several subsequent years and not the increase in the averages. Parallel with a slight
increase in the average figures, the extreme values of the fluctuation will also be higher.
The results of the former vulnerability analyses showed that the various drought indi-
ces, extrapolated to national level, indicate a faster increase for areas that are already more
susceptible to drought. Although in a national comparison, the variation of drought in
Zala does not seem to be significant, it should be noted that the changes must not be un-
derestimated at all because previously there was no drought in this region, i.e. it is a new
phenomenon there and, as described later, the local population engaged in agriculture
does not seem to be well prepared for coping with any undesirable effects of droughts.
The sensitivity to drought of soils was defined in the former studies at micro region
and district level based on the criteria described in the first table. Accordingly, there is
a more or less similar distribution in Zala county of extremely moderately and slightly
sensitive soils i.e., compared to the national average, the soils of the county fall in the
third most risky, moderately sensitive category.
Table 2: Overall estimated impacts based on the variation in the drought index
and the sensitivity of soils to drought in the Zala micro regions, 1981-2100
micro region change in the
drought index
soil sensitivity impact
Hévíz moderate increase moderately sensitive moderate
Keszthely moderate increase moderately sensitive moderate
Lenti average increase extremely sensitive increasingly strong
Letenye average increase extremely sensitive increasingly strong
Nagykanizsa average increase moderately sensitive moderately strong
Pacsa average increase slightly sensitive moderate
Zalaegerszeg average increase slightly sensitive moderate
Zalakaros average increase slightly sensitive moderate
Zalaszentgrót moderate increase slightly sensitive moderate
Source: Based on the data published by Pálvölgyi T. et al 2011;
CARPATCLIM and ENSEMBLES EU-FP6, edited by the author

50. 49
The national effect resulting from exposure and sensitivity is neither week, nor ex-
tremely strong. On national scale the increase in drought indices fall in the three weakest
categories on a scale of five, the sensitivity to drought of the soils varies between the two
extreme categories (see e.g., Pálvölgyi T. et al 2011). Consequently, a slight increase in
drought affects strongly and occasionally slightly sensitive soils in the micro regions of
the country. That means that, compared to the national average, local farmers will have
to face occasionally stronger and occasionally weaker impacts in terms of drought. All
in all, the impact on the whole county will be moderately strong.
Calculation of adaptation capacity components
We tried to apply a different method to calculate the adaptation capacity of the agri-
cultural population of Zala county to the one described in the previous studies. In the
studies referred to above, the authors defined the adaptation capacity to drought based
on the assumption that the impacts summarized above cause damage and coping with
them, compensation for them and the elimination thereof depend on the economic
conditions of the region. They used a complex index as an indicator that contains the
agricultural gross added value (GAD) calculated with an expert estimate for the users
of the agricultural area of the district (individual farms, business associations) as an
index illustrating the income generating capacity of the sector and the total agricultural
support granted for one hectare of agricultural area between 2003 and 2008. Obviously,
the factors indicated above are also required for adaptation but, in our opinion, adap-
tation does not depend primarily on the subsequent management of problems (damage
elimination) but on prevention, which is not necessarily cost intensive.
We used the results of the questionnaire-based survey and interviews conducted by
our project among the residents of the rural areas of Zala county to calculate our own
indicator. The survey focused on the sensitivity to problems related to climate change and
adaptation capacity of the local society. In summary, it may be concluded that almost 90
percent of the respondents have experienced size of the climate change, reflected mainly
in summers with drought, more uneven distribution of alteration of the seasons. They
found that these changes had a negative impact on their economic activities. Only 30 %
of the respondents think that they would be able to adapt somehow to the consequences
of climate change. They identify adaptation as an environment aware lifestyle (approx-
imately 20%), irrigation (approximately 60%) and in the alteration of the currently used
business habits5
, i.e. in adaptive agriculture (i.e. 17% of all respondents are aware of and
indicated that latter response).
5 Changes in the sowing structure, the sowing time and the cultivated plants, foil tent against
frost, plant coverage, net against hail, more effective spraying, collection of rainwater, covering the soil
with plants instead of ploughings that promotes drying, drainage gutters. Increasing the ratio of forests,
building water reservoirs for irrigation purposes.

51. 50
On the basis of the responses, first we tried to analyze more the irrigation issue, which
is a complex problem both in Zala county and nationwide. Based on what is stated in the
National Drought Strategy discussion paper, we can declare that due to the likely decline
in the volume of precipitation in future, the volume of surface and sub-surface waters
will decrease. Hence the obvious solution in terms of adaptation would be prevention,
in the form of increasing the water storage capacity of soils, reducing evaporation with
adequate agricultural methods and cultivating plants, capable of adapting to different
volume of precipitation. As the availability of water for irrigation will decrease in future,
its effective, multi-purpose6
forms should be used only where it is absolutely necessary.
As the water retention capacity of hilly areas is much lower than that in flat areas, ad-
aptation through the change of species and technology would be especially important
in Zala county. In total, we can state about irrigation that compared to the solutions of
adaptation to drought listed above, irrigation would be a less ideals solution. However,
the options in that regard have not yet been used widely in Zala county.
On the basis of the NYUDUVIZIG7
data supply, it is clear that irrigation is not a
typical agricultural activity in Zala county. According to the Directorate, the size of ir-
rigated area is only approximately 300 hectares in the whole county, and only 53 farmers
are registered who extract water from surface or sub-surface sources for irrigation pur-
poses, but they actually use only approximately 1/3 of their reserves. Generally, farmers
cultivating more water intensive plants use irrigation (orchards and nurseries growing
decorative plants). In theory, therefore, there are still significant reserves in Zala county
for irrigation. As the simplest way of irrigation is to extract water from surface sources,
those agricultural areas are in an advantageous situation, which are situated close to
surface waters (river, lake, irrigation canal). Water may be extracted from surface sources
with less energy consumption compared to water extracted from sub-surface sources pro-
viding that water is not far from the area to be irrigated. In areas that are far away from
surface waters, sub-surface water extraction and building water reservoirs on hills could
be a solution if there are no irrigation canals. In relation to sub-surface water extraction,
it needs to be noted that when the volume of water extracted exceeds 500 m3/year, the
wells require a licence and aquifer water may be used for irrigation only in justified
cases (Act LVII of 1995 (Vgt.) Article 28 (1) and Decree of the Minister of Environment,
Telecommunications and Water Management No. 18/1996. (VI. 13.) KHVM). It is clear
that irrigation is feasible near surface waters. For the time being water reservoirs estab-
lished in hills for irrigation purposes are used for flood protection purposes.
Based on our questionnaire, it seems that although there would be irrigation demand
in Zala county, the low ratio of currently irrigated areas and parties involved in irriga-
tion suggests that a large number of those interested in irrigation are unable to use the
6 Irrigation is not simple water replacement but it is also ideal for nutrition dosage, as an anti-de-
flation measure, and is also effective for making seeds grow, protecting against frost, delaying flowering,
coloring, refreshing, for noble rot and plant protection purposes.
7 West Transdanubia Water Management Directorate

52. 51
theoretically available options. In our opinion, there are two reasons for that: years with
droughts, when irrigation as a demand occurs only periodically. The other reason is
that irrigation as an investment, as well as the water charges payable by the farmer cause
additional expenses to the business. As for the time being, droughts occur in Zala only
periodically, it is understandable that for the time being farmers do not undertake the
additional expenses of irrigation. Hungary itself falls in the conditional irrigation zone,
where most plan species can be grown without irrigation. These days, the importance
of irrigation is the reduction of the fluctuation of yield and increasing the volume, value
and in most cases, the quality of the product (as stated in the drought strategy). Irrigation
is not a traditional or typical agricultural activity in Zala county. That supports even
more our conclusion that instead of irrigation, primarily adaptation with the change of
species and technology should be encouraged in Zala county. Although very few of the
respondents are aware and use those techniques, those few farmers reported positive
results, higher average yield and successful efforts to overcome any variation in yield
caused by uneven distribution of precipitation.
Based on the above, the most important indicator reflecting the adaptation capacity
of society is the status of knowledge about adaptive agriculture (change of technology
and species). Our survey was the expert estimate in that regard. During the calculation
of the indicator we identified the ideal situation when all farmers are all aware of the
outlined options of adaptation as 100%. However, as only 17% of the respondents are
aware of that, the indicator was low, and 17% at county and micro region level. As it is
the most important indicator, the largest weight is assigned to it during the calculation
of adaptation capacity. It may be modified, but cannot be changed significantly by the
other indicators.
The theoretical feasibility of irrigation is likely to be higher than its current utili-
sation, considering that irrigation in the county is negligible. The size of the currently
irrigated areas may probably be increased not as much by boring wells as by extracting
more water from surface sources. However, due to the reasons outlined above, the
theoretical opportunity cannot be included in the calculation, considering that there
are better solutions than irrigation in the county and the technical conditions are not
in place. Contrary to the areas of the Great Plain, this region did not build irrigation
canals apart from the facilities designed for irrigation on the previously drained ag-
ricultural areas of Kis-Balaton (Small Balaton) (Small Zala irrigation system) and the
Hévíz main irrigation canal, which can no longer be used either. Apart from that,
according to certain calculations, the increase in temperature, which will also affect
evaporation, coupled with a decrease in precipitation, may radically modify the water
yield of rivers: in an extreme case, 3°C average temperature increase and 5-10% de-
crease in precipitation can reduce the volume of rivers on agricultural areas by 50%
(Csáki P. 2013). Based on the above, it also arises as an issue whether or not irrigation
is relevant in Zala county. If we take into account that droughts and the frequency of
drought periods are likely to increase in the future, and that the water intensive cultures
(orchards) are equally present in the Zala hills as certain water intensive field crops

53. 52
(hybrid corn, sweet corn, Zala county regional development concept, TEIR database)
in our opinion there may be more persistent demand for irrigation in future, assuming
that certain plants cannot be replaced by species, more tolerant to drought. As irriga-
tion is primarily associated with surface waters, we calculated the indicator based on
the ratio of surface waters (rivers, canals and stagnant water), and non-irrigated plough
land in the particular micro region. We used the CORINE surface cover data of the
TEIR database in our calculation. The 2.1 ratio of the Nagykanizsa micro region e.g.,
reflects the scope of surface waters calculated as a percentage of the total not irrigated
plough land within the territory of the micro region. Naturally, the indicator is not
perfect because the scope of waters cannot be used as the basis to conclude the volume
of water in them, but it shows well the accessibility and limitation of accessibility to
surface waters. Even now only assumptions can be made in relation to water reserves,
available for irrigation purposes, and therefore we did not endeavor to calculate quanti-
tative limits for the present, and especially for the future. As at present rather technical,
especially financial limitations apply to irrigation rather than the limited availability of
resources, we also looked at the additional income the particular micro regions could
earn with agricultural support. It may be assumed that micro regions able to apply for
support are more competent than the less successful ones. Most applications related
to non-direct standard area-based support, in relation to which readiness to act may
also be assumed, because access to indirect support is often a great challenge due to
the implementation and complexity of the application procedure. The calculation was
made for 2010, because data are available not only for calls, but also for the number of
farmers in that year (our database was also built from TEIR database).
The number of applications by farmer is rather low (Table 3). The county average
is 23%, i.e., only 23% of the total registered farmers receive some agricultural support.
It is probably due to the large number of farmers operating on small farms, than the
lack of successful applications (almost twenty thousand farmers are registered in Zala
county). The distribution around the average is not significant: the ratio is the lowest in
Nagykanizsa micro region (18%), and the highest in Lenti micro region (35%).
The support granted t o one farmer (HUF 540,000 as a county average) varies more
between micro regions, even if the differences are not significant. Accordingly, Pacsa
micro region is in the most advantageous situation (HUF 1,000,000), while Hévíz micro
region is in the least favorable situation (HUF 230,000).
The ratio of direct area-based and indirect agricultural support in the county is
48%-52%, dominated by direct support. The amount of granted support is shared by the
two types of calls similarly. In most micro regions, the direct area-based support ratio
is higher than the ratio of indirect support. The ratio of indirect support is greater than
50% only in the Zalaegerszeg (50.7%), Nagykanizsa (56%) and Lenti (66%) micro regions
i.e., in our approach these three micro regions are considered to be performing better.
In all three cases the indicators were calculated by comparing the performance of
the micro regions to the best performing micro region, which was considered as 100%.

54. 53
Table 3. Support applied by farmer, winner applications and
ratio of indirect support in the Zala micro regions
Micro region Support per farm-
er (HUF)
Ratio of farmers re-
ceiving support among
the total farmers (%)
Ratio of indirect support
within total support (%)
Hévíz 228 149 20 45
Keszthely 578 083 27 49
Lenti 719 633 3-5 66
Letenye 378 965 15 38
Nagykanizsa 498 170 18 56
Pacsa 997 122 21 45
Zalaegerszeg 484 626 25 51
Zalakaros 404 692 21 45
Zalaszentgrót 561 744 24 42
Source: Based on MVH data, edited by the authors
As shown above, it seems that adaptation depends primarily on human factor, knowledge
and preparations; we also took into account those factors in our index by using the Human
Development Index (HDI). HDI an aggregated index, whose components are life expectancy
at birth, school qualifications and per capita GDP. As the latter component cannot be gen-
erated at micro region level, it was replaced by the per capita income (Obádovics, Kulcsár
2003). In our calculations HDI varies between 0 and 100. The HDI value of Zala micro re-
gions vary between 24 (Zalakaros) and 64 (Keszthely), the average of the nine micro regions
is 48, which is much lower than the ideal maximum value. Similarly to the calculation of
support, we used the value of the micro region with the best HDI index as 100% (Table 4).
Adaptation capacity calculation
During the adaptation capacity calculation, we calculated the weight average of the above
indicators, where 60% weight was assigned to the indicator reflecting the status of adaptive
agriculture, 20% weight was assigned to the indicator reflecting accessibility to surface waters
and 5-5 % weight was assigned to the other four indicators (support related indicators and
HDI). In our opinion the first indicator is fundamental in terms of adaptation. Irrigation
may be an increasing demand in future, but traditionally and at present it is not an agricul-
tural activity, typical in Zala county and as droughts increase, the usable water stocks may
reduce, therefore average weight was assigned to it. The support-related indicators are not
dominant factors in this respect, yet they reflect the competence and flexibility of farmers,
which are important characteristics in adaptation and therefore it cannot be ignored. The

55. 54
parameters forming the HDI illustrate well the general state of a particular society. This
affects the adaptation capacity rather indirectly, in our opinion the index can still not be
ignored: former studies (Pappné Vancsó J. 2014) already showed that adaptation to external
stress depends primarily on the stability and degree of organization of the local society.
As for the time being, the knowledge and information about technology and the change
of species required for adaptation are very low, it also determines the value of the indicator.
The last column of the table shows that none of the micro regions of Zala county have good
adaptation capacity, which could be remedied primarily with the training and education
of the farmers. If irrigation becomes considerable demand in the future, the micro regions
may find that they are not prepared for it, which is understandable considering that irriga-
tion for agricultural purposes has no tradition in the region. The state of the population in
the rural areas of Zala county is around the national average in terms of the HDI, which
reflects school qualifications, income and health conditions, but is far from the ideal sit-
uation. Apart from professional training organized for the farmers, a higher HDI could
also contribute to a further increase in the adaptation capacity.
Table 4. Adaptation capacity calculation for the micro regions of Zala county
Micro region
State of
adaptive
agri-
culture
(change
of species
and tech-
nology, %)
Ratio of
surface
water in
the terri-
tory of not
irrigated
plough
land (%)
Support
amount
per farmer
com-
pared to
the best
perform-
ing micro
region (%)
Ratio of
farmers
receiving
support
within
the total
farmers
com-
pared to
the best
perform-
ing micro
region (%)
Ratio of
indirect
support
within
total sup-
port com-
pared to
the best
perform-
ing micro
region (%)
HDI com-
pared to
the best
perform-
ing micro
region (%)
Adap-
tation
capacity
(%)
Hévíz 17.0 1.2 23.0 58.0 68.0 93.66 12.0
Keszthely 17.0 5.0 58.0 77.0 74.0 94.10 16.0
Lenti 17.0 0.2 72.0 100.0 100.0 79.53 18.0
Letenye 17.0 0.9 38.0 42.0 58.0 45.58 9.0
Nagykanizsa 17.0 2.1 50.0 51.0 84.0 91.64 14.0
Pacsa 17.0 5.0 100.0 60.0 67.0 47.24 15.0
Zalaegerszeg 17.0 0.5 49.0 70.0 77.0 100.00 15.0
Zalakaros 17.0 5.0 41.0 59.0 69.0 35.62 11.0
Zalaszentgrót 17.0 0.3 56.0 67.0 64.0 57.74 12.0
Source: Based on the data of NYUDUVIZIG data supplies, TEIR database, MVH public
database, CSO data and field questionnaire based survey, edited by the authors

56. 55
Defining the degree of vulnerability
The degree of vulnerability was established as a relationship between the degree of im-
pacts and the status of adaptation. Following the system of former vulnerability reviews
and the method described in the first part of the study, a distinction is made among
robust, endangered, fragile and vulnerable systems. As in our interpretation, the adap-
tation capacity is weak in each micro region, the robust and fragile category cannot be
used definitely. As practically there is no difference between the micro regions in terms
of adaptation, the degree of vulnerability is determined by the strength of the impacts. If
the impact is mild or moderate, we can only talk about risk and not vulnerability, while
the particular system i.e., in this case the agricultural population, might become vulner-
able if the impact is average or stronger. By increasing the adaptation capacity, risk and
vulnerability may make a system “robust” or “fragile”. In Zala county these objectives
may be achieved by making available the information required for adaptive agriculture,
extending irrigation options and disseminating multi-purpose effective plant cultiva-
tion where absolutely necessary. In relation to the storing of water, nowadays mostly for
regulating surface water courses and flood protection consideration should also be given
to the utilization of the reservoirs for irrigation in the future.
The farmers of Zala county may experience generally strong impacts by the end of this
century, yet for the time being they do not have any understanding or technical skills for
appropriate adaptation. Consequently, despite regional disparities in total the agricultural
population of Zala county should be classified into the category at risk.
Table 5: Vulnerability of the agricultural population
to drought in Zala Micro Regions
Micro region impact (1980-2100) adaptation capac-
ity (at present)
degree of vulnera-
bility (at present)
Hévíz moderate weak adaptation at risk
Keszthely moderate weak adaptation at risk
Lenti increasingly strong weak adaptation increasingly vulnerable
Letenye increasingly strong weak adaptation increasingly vulnerable
Nagykanizsa moderately strong weak adaptation moderately vulnerable
Pacsa moderate weak adaptation at risk
Zalaegerszeg moderate weak adaptation at risk
Zalakaros moderate weak adaptation at risk
Zalaszentgrót moderate weak adaptation at risk
Source: Based on the data of NYUDUVIZIG data supplies, TEIR database, MVH public
database, CSO data and field questionnaire based survey, edited by the authors

57. 56
Summary
On the basis of the results of our survey of vulnerability of the agricultural population of
Zala county to drought, interpreted in the context of impacts-adaptation capacity, we can
conclude that the degree of vulnerability is determined by the strength of the impacts,
because practically there is no difference among the micro regions in terms of adaptation.
The agricultural population of each micro region, which has weak adaptation capacity,
will experience moderate, average and increasingly strong impacts in micro regions and
average or moderately average impacts in the whole county. Thus, the vulnerability of
the individual micro regions may be classified into the increasingly vulnerability and at
risk categories. By increasing the adaptation capacity, vulnerability may reduce, reaching
even the robust category in certain micro regions.
The weak adaptation capacity stems from the generally low standard of information of
adaptive agriculture, lack of irrigation traditions and weak access to irrigation facilities.
The lower than average school qualifications, health and income positions of the popu-
lation only contribute to that overall picture. It is clear therefore that in order to improve
adaptation capacity, farmers should be taught about adaptive agriculture through spe-
cialised training activity and they should also have access to effective irrigation solutions.
References
Csáki P. 2013: A klimatikus jellemzők párolgásra gyakorolt hatásai a felszínborítás függvényé-
ben Zala megye példáján. Diplomamunka (Impacts of Climatic Features on Evaporation
Depending on Surface Cover Based on the Example of Zala County, Thesis]. University of
West Hungary, Faculty of Forest Engineering, Sopron.
Gálos, Borbála (2014):A9. Klímaszcenáriók időszakai, kiválasztott klímamodellekkel (Climate
Scenario Periods with Selected Climate Models]. Manuscript.
Láng, I. – Csete, L. – Jolánkai, M. (ed., 2007): A globális klímaváltozás: hazai hatások és válaszok:
a VAHAVA jelentés ( Global Climate Change: Hungarian Impacts and Responses; VAHAVA
Report]. Szaktudás Kiadó Ház, Budapest, ISBN 978-963-9736-17-7
Obádovics Csilla, Kulcsár László (2003) A vidéki népesség humánindexének alakulása
Magyarországon Területi Statisztika (Human Index of Rural Population in Hungary, Territorial
Statistics] 43 4.
Pappné Vancsó J. 2014: Éghajlatváltozás és emberi alkalmazkodás a középkori meleg időszak-
ban (Climate Change and Human Adaptation in the Hot Period of Middle Ages]. Földrajzi
Közlemények (Geographic Publications] 138. 2. pp. 107-121.

58. 57
Pálvölgyi, T. – Czira, T. – Dobozi, E. – Rideg, A. – Schneller, K. (2010): A kistérségi szintű égha-
jlat-változási sérülékenység vizsgálat módszere és eredményei (Micro Region Level Climate
Change Vulnerability Analysis. Method and Results.]. Climate-21 Leaflets, 2010. No. 62. pp.
88-103.
Pálvölgyi T. – Czira T. 2011: Éghajlati sérülékenység a kistérségek szintjén (Climate Vulnerability
in Micro Regions]. In: Tamás P.-Bulla M. (ed.): Sebezhetőség és adaptáció - A reziliencia esélyei
(Vulnerability and adaptability - Chances of Resilience]. MTA Research Institute of Sociology,
2011. pp. 237-253.
Pálvölgyi T. – Czira T. – Bartholy J. – Pongrácz R. 2011: Éghajlati sérülékenység a hazai kistérségek
szintjén (Climate Vulnerability in Hungarian Micro Regions]. In: Bartholy J. – Bozó L. – Haszpra
L. (ed.): Klímaváltozás – 2011. Klímaszcenáriók a Kárpát-medence térségére (Climate Change -
2011. Climate Scenarios for the Carpathian Basin]. MTA and ELTE Department of Meteorology,
Budapest. pp. 235-257.
Pongrácz R, Bartholy J, Gelybó Gy, Szabó P. 2009: Detected and Expected Trends of Extreme
Climate Indices for the Carpathian Basin. Bioclimatology and Natural Hazards pp. 15-28.
Sábitz J. - Pongrácz R. - Bartholy J 2013: Az aszályhajlam várható változása a Kárpát-medence
térségében (Expected Variation of Inclination to Droughts in the Carpathian Basin]. In:
Pajtókné Tari Ilona, Tóth Antal (ed.): Változó Föld, változó társadalom, változó ismeretszerzés,
2013: a megújuló erőforrások szerepe a regionális fejlesztésben: nemzetközi tudományos
konferencia (Changing World, Changing Society, Changing Learning, 2013: Role of Renewable
Energy Sources in Regional Development: international scientific conference]. 242 p. Venue
and date of the conference: Eger, Hungary, 10.10.2013.-12.10.2013. Eger: EKF Department of
Geography; Agria-Innoregion Knowledge Centre; Agria Geography Public Benefit Foundation,
2013. pp. 82-86.
Internet references:
National Climate Change Strategy 2008-2025.
source: http://www.kvvm.hu/cimg/documents/nes080214.pdf
Downloaded: 23 July 2014
National Drought Strategy - discussion paper
http://2010-2014.kormany.hu/download/7/0a/90000/Aszalystrategia.pdf
Downloaded: 15 July 2014

59. 58
Second National Climate Change Strategy 2014-2025 with an outlook to 2050. Discussion paper.
September 2013. http://www.kormany.hu/download/7/ac/01000/M%C3%A1sodik%20
Nemzeti%20%C3%89ghajlatv%C3%A1ltoz%C3%A1si%20Strat%C3%A9gia%202014-
2025%20kitekint%C3%A9ssel%202050-re%20-%20szakpolitikai%20vitaanyag.pdf
Downloaded: 14 February 2014
Status analysis of Zala county regional development concept and programme
http://www.zalamegye.hu/koncepcio/1Helyzetelemzes.pdf
Downloaded: 02.01.2013
Data source: CARPATCLIM, ENSEMBLES EU– FP6, CSO, MVH NYUDUVIZIG, TEIR
Laws, decrees:
Act LVII of 1995. on water management (Vgt.) Ar cle 28(1)Decree of the Minister of Environmental
Protec on, Communica ons and Water Management No. 18/1996. (VI. 13.) KHVM

60. 59
Vulnerability of Society to Climate Change: Review of
Vulnerability to Flash Floods in Zala Micro Regions
Judit Vancsó Ms Papp, Mónika Hoschek, Csilla Obádovics
ABSTRACT: In this study we present an analysis of vulnerability of the population to flash floods
in the context of exposure, sensitivity and adaptation. By focusing on comparability and regional
differences, we made our calculations at the level of micro regions.
KEYWORDS: climate change, flash flood, adaptation, vulnerability
Introduction
As participants of the TÁMOP-4.2.2.A-11/1/KONV-2012-0013 “Agroclimate”8
project,
our responsibility was to assess the potential social impacts of the projected climate
change in Zala county by using the previously applied vulnerability analyses. On the
basis of the results of our questionnaire-based survey conducted in the county to assess
the impacts on the climate change on agricultural society, and the indicative national
documents describing the estimated impacts of the projected climate change (Láng
I. – Csete L. – Jolánkai M. 2007; Nemzeti Éghajlatváltozási Stratégia [National Climate
Change Strategy]: NCCS; National Disaster Risk Assessment and the planned second
NCCS) and publications (see e.g., Pongrácz et al 2009; Czigány Sz. et al 2010); we think
that, apart from the drought previously discussed in detail, rural society will have to face
more frequent conditions with potential floods, groundwater and flash floods in future
due to the uneven distribution of precipitation. This study is dedicated to the increasing
problems of flash floods, affecting society.
Floods, inland inundation and flash floods as threats
In relation to the threats of floods and inland inundation we found hardly (Vári A. –
Ferencz Z. 2011) any literature, while in the case of vulnerability to flash floods we did
not find any study at all in the Hungarian literature. On the basis of the documents
and publications referred in the introduction in terms of precipitation projections in-
volve a great deal more uncertainty than in the case of temperature or droughts, yet we
can say that in the long term we can expect more extreme weather with precipitation
8 “Agroclimate: Impact Analysis of the Projected Climate Change and Possible Adaptation in the
Forestry and Agriculture Sector”

61. 60
leading to floods and inland inundation. This opinion is also supported with the ques-
tionnaire-based survey described in the previous study, because the majority of the
respondents (72% of the total respondents) indicated changes in the annual precipitation
distribution: typically there is no precipitation in summer or rainfall occurs suddenly,
often in the form of torrential rain.
While flood and inland inundation are well known expressions, the concept of a flash
flood is relatively new in the science. Nevertheless, researchers of the University of Pécs
already conducted numerous studies primarily in the settlements of Transdanubia (see
e.g., Czigány Sz. et al 2010; Czigány, S. et al 2011; Pirkhoffer E. et al 2009). Compared to
river floods, a flash flood generally occurs on sloppy hilly areas, mostly in relation to
small water flows, when a large amount of rain falls. It usually occurs intensively, between
half an hour and six hours. A flash flood is not associated with any season, it may occur
at any time from early spring to late autumn, and may cause significant damage to built
environment and in the agricultural sector according to experiences to date.
According to the maps contained in the National Disaster Risk Assessment docu-
ment, in terms of floods, Zala county is a low risk area, (with the exception of the high
risk region surrounded by Letenye-Murakeresztúr and Nagykanizsa), with a low risk
of inland inundation, but it is extremely at risk of flash floods. As exposure to flash
floods is well documented according to the flash flood risk map, the problem should
also be reviewed in the context of impacts - adaptation - vulnerability, as illustrated in
the method presented in the first half of the study.
Exposure and sensitivity
To define vulnerability to flash floods we relied on the methodology applied in the
previous vulnerability studies described in the first half of this publication (Pálvölgyi et
al 2010, Pálvölgyi T. – Czira T. 2011; Pálvölgyi et al 2011; NCCS 2). Our indicators were
calculated according to the parameters presented in Table 1. The exposure was calculated
with more than 30 mm daily precipitation, considering that insurance companies also
used that threshold and compensate for flood damage when it is exceeded and provid-
ing that it is also certified by OMSZ (National Meteorology Service) (Czigány Sz. et al
2010). As flash floods develop in a very short time, measured in hours and not in days,
the 30 mm/day may be taken into account only with a compromise, considering that if
precipitation is distributed evenly across a day, the water flow will not necessarily lead to
inundation. However, there are very limited options to predict precipitation with hourly
accuracy. However, according to climate models for the future, it may be projected for
a longer term (e.g., 30-year periods) how many times the daily total precipitation will
be over 30 mm each year during the particular period. Although this cannot be used to
project the probability of precipitation potentially leading to flash floods in future, but
the tendency of occurrence of a relatively large amount of precipitation falling within
one day may be concluded (increase, decrease of stagnation).

62. 61
The values in our calculations stemmed from the European Union CARPATCLIM
project for the past and another EU project ENSEMBLES EU-FP6 for the future. Based on
that it may be concluded that in Zala county the volume of daily precipitation exceeded
30 mm on 50 occasions between 1981 and 2010, which between 2011 and 2040 is likely
to go up to 55 days (10% increase), for the period of 2041-2070 to 60 days (20% increase),
and for the period of 2071-2100 to 65 days which, compared to the reference period, will
result in 30% increase by the end of the period. On the basis of the potential increase in
the occurrence of a large amount of precipitation falling within a relatively short time,
therefore flash floods are likely to occur more frequently in the examined period.
Table 1. Vulnerability to Flash Floods among the
agricultural population in Zala (1980-2100)
Impact Adaptation
Exposure Sensitivity
probability
>30 mm/day
precipitation
(day/year)
 surface cover:
 forests, bare surface ratio
 carbonate rocks close to the surface
 ground thickness, physical features of soils
 slope parameters:
 average steepness of the slope on the water
catchment area
 slope range
 valley density
 water system parameters:
 confluence points, water flow density, water
network
 availability or lack of plans to
eliminate water damages
 conformity of the plan for elim-
inating water damage to regu-
lations
 infrastructure relating to water
damage elimination (e.g., built
rain reservoirs)
Source: CARPATCLIM; ENSEMBLES EU– FP6; NYUDUVIZIG; National Disaster Risk Assessment
in Hungary 2011. Based on the data disclosed by Szabolcs Czigány, edited by the author
To define sensitivity, we used the values of the flash floods risk map, broken down by
settlement.9
. In defining the risk levels the first step of the research was to identify the
mountains and hilly areas, as well as water catchment areas in Hungary, to which the
authors always assigned an exit point, i.e. a settlement, or a part of a settlement (Czigány
et al 2010). The authors agree that a water catchment area arbitrarily defined for a settle-
ment is not a natural demarcation, but the truly serious damages are always associated
with a particular settlement. Within the marked areas, the authors classified the defined
areas into risk categories according to the passive factors indicated in Table 1. In the flash
flood risk map, included in the National Disaster Risk Assessment document, referred
to above, the authors defined four categories (high, average and low risk, and no risk).
9 The high resolution maps for Zala county and the related information were provided by Szabolcs
Czigány, a researcher of the University of Pécs, involved also in flash floods.

63. 62
The entire Zala county falls in the average and high risk category, while the figures of
the micro regions are illustrated in Table 2.
Table 2. Overall estimated impacts based on the probability of
occurrence of >30 mm/day precipitation and the risk category
of flash floods in the Zala micro regions 1981-2100
micro region variation in the
frequency of >30
mm/day precipita-
tion (day/period)
flash flood
risk category
Probability of
occurrence
impact
Hévíz moderate increase high negligible moderately strong
Keszthely moderate increase average negligible moderate
Lenti moderate increase average negligible moderate
Letenye moderate increase high negligible moderately strong
Nagykanizsa moderate increase average negligible moderate
Pacsa moderate increase high negligible moderately strong
Zalaegerszeg moderate increase average negligible moderate
Zalakaros moderate increase average negligible moderate
Zalaszentgrót moderate increase high negligible moderately strong
Source: CARPATCLIM; ENSEMBLES EU– FP6; based on the data
disclosed by Szabolcs Czigány, edited by the author
Despite the relatively high risk, one can still not talk about strong impacts especially
by taking into account the extremely low probability of the occurrence of the event. It
was already indicated above that during the 30 years of the reference period, precipita-
tion exceeding 30 mm a day occurred on average on 50 occasions in Zala. According to
the data of the meteorology stations of 15 large towns (average of 1985-2013; CSO), we
can conclude that as a national average, there are 128.5 days with precipitation in a year.
Consequently, there were only 55 days among the 3,855 days with precipitation of the
thirty years when the volume of precipitation exceeded the threshold referred to above. In
a year on average precipitation more than 30 mm occurred on 1.8 days within the county.
There is therefore very little probability that more than 30 mm precipitation would fall
on the water catchment area of a particular settlement in just a few hours. Another factor
confirming the above idea is that over 25 years (between 1980 and 2005 based on the
reports from 41 settlements) on average 1.2 flood claims were submitted in relation to
the county10
, and in fact not all claims stemmed from flash floods. If we accept the 1.2
10 Based on the data disclosed by Szabolcs Czigány (PTE).

64. 63
cases/25 years, and assume that it will change according to an increase in the exposure
(30% increase by the end of the period), then number of cases may rise to 1.32 for the
first phase, 1.44 for the second phase and 1.6 for the last phase of the reviewed period by
settlement, i.e. the probability of occurrence of the event is still negligible.
If probability was not taken into account during the establishment of the strength
of the impacts, strong or at least moderately strong impacts could occur according to
exposure and sensitivity. As the probability of occurrence of the evens is negligible, the
strength of the impacts was deemed weak, classifying them into moderately strong and
moderate categories.
Adaptation capacity calculation
To define the adaptation capacity we tried to find out what the individual settlements
did to prevent potential damages. Generally water management directorates inform the
municipalities of settlement at risk of the need to prepare flood prevention plans (based
on 11
information from NYUDUVIZIG). Although the guide containing the local flood
prevention tasks of the settlement (Szunyog, Zalányi 1998) does not specifically refer to
flash floods, which is understandable because it is a relatively new concept, it defines
exactly the phenomenon which is identical with a flash flood. The document describes
in detail the task of the local management while preparing the plans, and the tasks to be
performed when the events occur. Having studied the guide, it may be concluded that
if a settlement prepares its flood prevention plan according to the requirements, it will
most likely be able to protect itself against flash floods. The settlements were classified as
to whether or not they had a flood prevention plan and whether the plans complied with
the guide, whether infrastructure projects were completed or plans are in place that can
mitigate the impact12
(e.g., rainwater reservoirs). A settlement scored 0% if they did not
have any flood prevention plan or related infrastructure project. As each Zala settlement
is at least moderately at risk according to the flash flood risk map, the need for protection
was considered fundamental for each settlement. If a particular settlement had a flood
prevention plant, but it did not comply with the requirements, the adaptation capacity
was deemed 50%. In such cases the settlement is fully aware of the threat and also took
steps to eliminate it, but the plan does not fully comply with the requirements. When we
did not find any plan for flood prevention but found planned or existing infrastructure
projects, the adaptation capacity was also considered 50%. For a settlement possessing
a flood prevention plan complying with the requirements, the adaptation capacity was
deemed 90%, if the settlement has already been hit by a flood at least once. In that case
it may be assumed that, based on the experience, the municipality revised and updated
its plans for defence. The adaptation capacity was deemed 80%, when the settlement had
11 West Transdanubia Water Management Directorate
12 The respective database was provided to us by NYUDUVIZIG.

65. 64
flood prevention plans that satisfied the requirements, but had not yet been hit by any
flood. The ideal situation, when it is certain that a particular settlement will be able to
protect itself against the consequences of a potential flash flood was considered 100%.
As it is unlikely that the volume of water generated by a flash flood or the direction of
the flow can be perfectly modeled, in our opinion 100% adaptation capacity cannot be
assumed in the case of any settlement, even if the particular settlement already expe-
rienced a flood, because the next flash flood may involve twice or multiple times the
volume of the water experienced in the previous flood.
The micro regional adaptation capacity was calculated as the average of the respective
figures of the settlements of the particular micro region (Table 3). The relatively weak
adaptability of the micro regions stems from the fact that only 74 of the nearby 260 set-
tlements have any version of adaptation and 80% or 90%, indicating good adaptation,
was found only in 25 settlements. Generally, 80% of the settlements already experienced
a flood have some adaptation capacity. As the concept of a flash flood and the map indi-
cating the risk category are new, settlements often do not fully understand whether they,
or any part of them is/are situated on a potential flood plain because generations may
pass before a small water flow crossing the settlement would cause problems. Perhaps
that is the explanation for the relative unpreparedness
Table 3. Adaptability to flash floods in the micro regions of Zala micro region
Micro region adaptation capacity (%)
Hévíz 10.0
Keszthely 21.3
Lenti 27.4
Letenye 18.5
Nagykanizsa 12.6
Pacsa 7.5
Zalaegerszeg 11.9
Zalakaros 13.3
Zalaszentgrót 17.9
Source: Based on NYUDUVIZIG data, edited by the author
Defining the degree of vulnerability
The degree of vulnerability was established in the manner already described in the
second part of the series of the studies, as a relationship between the degree of impacts
and the status of adaptation. Following the system of former vulnerability reviews, a

66. 65
distinction is made among robust, endangered, fragile and vulnerable systems. As in
our interpretation, the adaptation capacity is weak or moderate in each micro region,
the robust and fragile category cannot be used definitely. There is no great difference
between micro regions in terms of adaptation or impacts, generally moderate or aver-
age impacts reach relatively weakly adapting communities, vulnerability therefore is
no more than average and there is only a risk in the case of a moderate impact (Table
4). However, weak adaptation could be remedied fast, if the individual micro regions
recognised their vulnerability. Perhaps these ideas are confirmed by the fact that within
the micro regions, those settlements adapt well which are forced to protect themselves
against sudden floods at least once. By increasing the adaptation capacity, vulnerability
could be reduced significantly in the case of flash floods.
Table 4: Vulnerability of society to flash floods in Zala micro regions
Micro region impact (1980-2100) adaptation capac-
ity (at present)
vulnerability
(at present)
Hévíz moderately strong weak adaptation moderately vulnerable
Keszthely moderate moderate adaptation at risk
Lenti moderate moderate adaptation at risk
Letenye moderately strong weak adaptation moderately vulnerable
Nagykanizsa moderate weak adaptation at risk
Pacsa moderately strong weak adaptation moderately vulnerable
Zalaegerszeg moderate weak adaptation at risk
Zalakaros moderate weak adaptation at risk
Zalaszentgrót moderately strong weak adaptation moderately vulnerable
Source: CARPATCLIM; ENSEMBLES EU– FP6; NYUDUVIZIG; based on
the data disclosed by Szabolcs Czigány, edited by the author
Summary
On the basis of the results of our study assessing the vulnerability of the population of
Zala county to flash floods in the context of impact adaptability, we can conclude that
vulnerability is moderate or, as an overall result of moderately strong effects and weak
and moderate adaptation capacity, average, or it reaches only the degree of a risk in
certain micro regions. By increasing the adaptation capacity, vulnerability may reduce,
reaching even the robust category in certain micro regions.
Weak adaptability stems from the lack of recognizing the problem. Typically those
settlements adapt properly, which had to protect themselves against a flood at least

67. 66
once. Hopefully, increasing research in the topic and the presentation of the problem to
responsible organizations can significantly increase the number of settlements which
can protect themselves properly against flash floods.
References
Czigány Sz. – Pirkhoffer E. – Balassa B. – Bugya T. –Bötkös P. – Gyenise P. – Nagyváradi L. – Lóczi
D. – Geresdi I. 2010. Villámárvíz, mint természeti veszélyforrás a Dél-Dunántúlon [Flash Flood
as a Natural Risk in South Transdanubia]. Geographic Publications 134. 3 pp. 281-298.
Czigány, S – Pirkhoffer, E – Nagyváradi, L – Hegedűs, P – Geresdi, I 2011: Rapid screening of
flash flood-affected watersheds in Hungary, Zeitschrift für Geomorphologie 55: (1) pp. 1-13.
Láng, I. – Csete, L. – Jolánkai, M. (ed., 2007): A globális klímaváltozás: hazai hatások és válaszok:
a VAHAVA jelentés [Global Climate Change: Hungarian Impacts and Responses; VAHAVA
Report]. Szaktudás Kiadó Ház, Budapest, ISBN 978-963-9736-17-7
Pirkhoffer E.–Czigány SZ.–Geresdi I. 2009a: Impact of rainfall pattern on the occurrence of flash
floods in Hungary. – Zeitschrift für Geomorphologie 53. pp. 139–157.
Pálvölgyi, T. – Czira, T. – Dobozi, E. – Rideg, A. – Schneller, K. (2010): A kistérségi szintű égha-
jlat-változási sérülékenység vizsgálat módszere és eredményei [Micro Region Level Climate
Change Vulnerability Analysis. Method and Results.]. Climate-21 Leaflets, 2010. No. 62. pp.
88-103.
Pálvölgyi T. – Czira T. 2011: Éghajlati sérülékenység a kistérségek szintjén [Climate Vulnerability
in Micro Regions]. In: Tamás P.-Bulla M. (ed.): Sebezhetőség és adaptáció - A reziliencia esélyei
[Vulnerability and adaptability - Chances of Resilience]. MTA Research Institute of Sociology,
2011. pp. 237-253.
Pálvölgyi T. – Czira T. – Bartholy J. – Pongrácz R. 2011: Éghajlati sérülékenység a hazai kistérségek
szintjén [Climate Vulnerability in Hungarian Micro Regions]. In: Bartholy J. – Bozó L. – Haszpra
L. (ed.): Klímaváltozás – 2011. Klímaszcenáriók a Kárpát-medence térségére [Climate Change -
2011. Climate Scenarios for the Carpathian Basin]. MTA and ELTE Department of Meteorology,
Budapest. pp. 235-257.
Pongrácz R, Bartholy J, Gelybó Gy, Szabó P. 2009: Detected and Expected Trends of Extreme
Climate Indices for the Carpathian Basin. Bioclimatology and Natural Hazards pp. 15-28.

68. 67
Szunyog Z. – Zalányi T. 1998: Települések helyi vízkárelhárítási feladatai. Útmutató [Local Flood
Prevention Tasks of Settlements. Guide]. General Directorate of Water Management,
Budapest.
Vári A. - Ferencz Z. 2011. Az árvízi sebezhetőség társadalmi indikátorai: esettanul-
mányok két Felső-Tisza-vidéki területen [Social Indicators of Vulnerability to Floods:
Case Studies in Two Areas in the Upper Tisza Region] In: Tamás P.-Bulla M. (ed.):
Sebezhetőség és adaptáció - A reziliencia esélyei [Vulnerability and adaptability - Chances
of Resilience]. MTA Research Institute of Sociology, 2011.
Internet references:
National Climate Change Strategy 2008-2025.
http://www.kvvm.hu/cimg/documents/nes080214.pdf
Downloaded: 23 July 2014
National Disaster Risk Assessment, Hungary 2011.
http://vmkatig.hu/KEK.pdf
Downloaded: 13 August 2014
Second National Climate Change Strategy 2014-2025 with an outlook to 2050. Discussion paper.
September 2013. http://www.kormany.hu/download/7/ac/01000/M%C3%A1sodik%20
Nemzeti%20%C3%89ghajlatv%C3%A1ltoz%C3%A1si%20Strat%C3%A9gia%202014-
2025%20kitekint%C3%A9ssel%202050-re%20-%20szakpolitikai%20vitaanyag.pdf
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CARPATCLIM
ENSEMBLES EU– FP6
NYUDUVIZIG

69. 68
Climate Change Perception and Responses to
the Challenges Among Agricultural Producers:
Results of the Questionnaire-based Survey
László Kulcsár
ABSTRACT: In relation to climate change, the questionnaire-based survey conducted among agricul-
tural producer families focused on two issues: the opinion about climate change and the views on
protection against the impacts of climate change. Both issues were tested with answers given to a
series of statements, which were then processed with multi-variable methods. The results showed the
different types of climate change perception and the characteristics of the responses to challenges.
KEYWORDS: opinion about climate change, adaptation, perception, mitigation
Introduction
The social-economic processes are reflected in the conduct and behaviour of individuals
and group, who are influenced by the culture that dominates their social and natural
environment. The behaviors which are reactions to climate effects as well as attitudes
and opinions are the results of a cognitive filter which are practically coded in the social
status of an individual or family. It should be stressed because any sectoral policy decision
can gain legitimacy and exert a sufficient effect if they take into account the economic
and cultural position of the particular social groups.
Our questionnaire-based survey conducted among agricultural producers13
covered the
perception of climate change i.e., whether or not the respondent families felt those impacts
and to what extent they apply them to their own environment. The survey also tried to
identify whether the perception of risks, potential responses to risk mitigation and the ex-
pression of the importance of risk are related to the people’s social and demographic features.
Literature
The opinions on climate risks and related attitudes are frequently covered by the litera-
ture. A Swedish study (Sundblad, Garling 2007) e.g., analyzed the impact of numerous
demographic factors (gender, age, place of residence, qualification) in relation to the un-
derstanding of climatic change and perception risks. Information and knowledge-based
13 The questionnaire-based survey was conducted on 217 Zala county families in the autumn of
2013. We wish to take the opportunity here to thank for the contribution of the students of the University
of West Hungary Faculty of Economics and tutors Dr. Ferenc Jankó, Dr. Csilla Obádovics, Dr. Judit Vancsó
Mrs. Papp.

70. 69
cognitive risk perception (the probability they attribute to the occurrence of the risk) in
fact had positive correlation only with qualifications, while affective risk perception fo-
cusing on emotions (fear) was related to gender, as women generally judged any problem
caused by climate change on emotional basis. The perception of climatic changes derives
from cultural background factors according to Kahan (2011) as well, who emphasizes
cultural polarization, according to which social groups with more decisive rationality
are likely to accept or ignore the evidence of climate change if they confirm/contradict
their existing beliefs.
The central position in the mitigation of the risks of climatic effects of the importance
of traditional knowledge in numerous studies also confirms the importance of the cul-
tural context. This happens despite the fact that traditional knowledge shows a declining
tendency in a global context. Gómez-Baggethun, Corbera and Reyes-García (2013) wrote
about the increasing role of traditional knowledge in local communities, and also suggest-
ed that we were witnessing a “hybridization” process, where traditional knowledge can
make the modernization process more flexible. In addition, traditional knowledge has a
localization feature and is closely related to agriculture or, in a slightly wider context, to
the rural economy. In 2004, the “Ecology and Society” journal dedicated a separate issue
to the correlation between traditional knowledge and climate change, emphasizing the role
of traditional knowledge in the mitigation of the negative effects of climatic changes. In
the introduction Folke (2004) also referred to problems and positive facts resulting from
the complexity of traditional knowledge, local institutions and various cultural effects.
Leclerc et al (2013) and their co-authors stress that traditional knowledge is applied purely
only in few cases and that it is rather mixed with “scientific” knowledge, which makes the
mitigation of risks more difficult in numerous cases. Boillat and Berkes (2013), as well as
Ruiz-Mallen and Corbera (2013) all stress the importance of traditional knowledge in the
perception and interpretation of climatic changes. In terms of the Hungarian traditions
it is clear that there is no uniform opinion about the role or applicability of traditional
knowledge. The transformation of the institutions of the agriculture and a new economic
structure created significantly different situations for producers and may also have triggered
different opinions and reactions in relation to climate effects.
In our questionnaire-based survey, we asked the respondents to evaluate eleven
statements, which focused on the perception of climate effects and various dimensions
of perception. Our other topic summarizes the opinions about adaptation, where we
processed the responses to seven statements. The applied Likert scales had five grades,
where the higher grade indicated a higher level of agreement.
Results
Let us take a look at perception first. The eleven statements offered to Zala farmers
represented different dimensions of climatic effects. The statements were classified with a
factor analysis, and the structure was illustrated in the following table. The reviewed aspects

71. 70
were as follows: (1) climate change and agriculture and the relevance of that relationship,
(2) distancing climate change from local conditions, (3) causes of climate change not attrib-
utable to people, which may also suggest a kind of fatalism, because it detaches the causes
from people, (4) unreliability of mass communication concerning climate change and (5)
skepticism about the performance of science in terms of climate change.
The respondent agricultural producers think that the impact of climate change is
already present and important, although they are skeptical about scientific projections.
It is a very important result that people do not think that they are not affected by climate
change or that it was only a remote thing. They think so despite the fact that mass com-
munication strengthens that distance by presenting far away disasters and problems of
remote places. The prestige of television is very small in the area.
Table 1. Perception of climate change among the agricultural
producers of Zala county. Result of the factor analysis.
Significance of items in terms of demographic features
Factor/statement Average
(1-5)
Deviation Sign.
0.05
F1. The impact on climate change on agriculture is significant
and relevant (explained variance: 29.491%)
 Climate change will alter production and product structure
in agriculture
 Climate change will cause a great deal of problem to farmers
 Confidence that climate change has started
4.28
4.35
4.34
1.028
0.98
1.051
ns
F2. Climate change is a remote phenomenon, we are not affected
by it (explained variance 14.899%)
 Climate change affects only faraway places but not us
 I do not think that climate change would be a real problem
 Climate change is so far away in time and it will happen so far
ahead in the future that it is not worth talking about it
1.56
1.74
1.90
1.035
1.217
1.097
ns
F3. Climate change is not the result of a human effect (explained
variance 12.277%)
 The climate change experienced these days is the result of
natural causes and is not caused by people
 Climate change is cause more by human activity and not by
nature
2.56
(-)3.60
1.230
1.146
low
qualification
F4. Lack of trust in mass communication (explained variance
9.167%)
 TV creates too much ado about climate change
 TV often spreads rumors about climate change
2.95
2.99
1.393
1.525
ns
F5. Science skepticism (explained variance 7.408%)
 If scientists cannot forecast weather for the next week, how
would they would be able to predict what will happen in the
next 50-100 years? 3.83 1.347
low
qualification
women

72. 71
Among the demographic features, the effect of qualifications and gender turned out to
be significant. Women respondents with lower qualifications were more skeptical about
the role of science and accepted in a higher number the opinion that climate change was
not caused by human activity.
Consequently, climate change was significant and relevant for the majority of agricul-
tural families of Zala county who felt that it was close to them. Perception is also significant
and that is the reason why it is important to analyze their adaptation skills and ideals and
how they envisage reducing the negative consequences of climate change in agriculture.
Table 2. Adaptation ideas about climate change among
the agricultural producers of Zala county.
Result of the factor analysis. Significance of items with demographic features
Factor/statement Average
(1-5)
Deviation Sig.
0.05
F1. Positive adaptation skill (explained variance: 25.042%)
 Climate change is unavoidable in agriculture and we must
learn to adapt to it
 I would be willing to change the established farming meth-
ods, if required by climate change
 I do not think that climate change will reach a degree that
I would have to change my farming habits
4.22
3.55
(-) 2.23
1.084
1.417
1.273
ns
F2. Failed adaptation due to lack of funding (explained var-
iance 19.151%)
 Adaptation to climate change is very costly, not everyone
can afford it 3.84 1.204
low quali-
fication
women
F3. Failed adaptation due to adaptation skill (explained var-
iance 15.632%)
 Most people find it difficult to change their habits
 I do not think that climate change will reach a degree that
I would have to change my farming habits
4.01
(-)2.23
1.123
1.273 ns
F4. Traditional agricultural knowledge (explained variance
12.775%)
 If more people had the knowledge of peasants, agriculture
would find it easier to cope with the challenges of climate
change
 Even the farmers’ skills could not help in the adaptation
to climate change because weather is becoming more and
more extreme
3.35
(-) 3.31
1.394
1.372
women
The strongest factor14
was clearly the positive adaptation skill, according to the results of
the analysis, but it is also clear that the adaptation skill, inadequate due to various reasons
14 The strength of the factor is indicated by the size of the explained variance in this case too

73. 72
had overall stronger explained variance which definitely suggests adaptation problems
among agricultural farming families. Traditional agricultural knowledge as an adaptation
opportunity is similarly important, as seen in the literature, yet very thorough support is
required for sufficient implementation. The data also indicate that demographic features
were not important. Therefore cultural effects and personality features may come to the
forefront as factor affecting people’s opinion. The role of school qualifications matches the
findings of the Swedish study referred to above in this aspect too.
We then further analyzed the impact of school qualifications in relation to the ap-
proaches represented by the various factors. We applied discriminate analysis, which
shows how far the various dimensions (functions) representing the individual factors
place the different qualification groups from each other. The further away those groups
are from each other in a system of coordinates, the stronger is the influence of qualifi-
cations on the level of perception and adaptation views. Accordingly, among the various
demographic factors, we only looked at the impact of school qualifications with that
method, because the other demographic and social factors did not show enough influ-
ence on the attitude and understanding of agricultural producers about climate change.
Table 3: Result of a canonical discriminate analysis: perception of
climate change in groups with various school qualifications
School qualification
Dimensions (functions)
The climate change is a remote
phenomenon, the scientific
results are not convincing.
Human activity is involved in cli-
mate change and there is no trust in
mass communication (television)
Primary school or less 0.573 -0.145
Secondary school 0.066 0.101
College, university -0.406 -0.372
Table 4: Result of a canonical discriminate analysis: adaptation options
to the consequences of climate change by school qualifications
School qualification
Dimensions (functions)
High adaptation skill,
successful adaptation
Failed adaptation, bad
adaptation skill
Primary school or less -0.561 -0.051
Secondary school 0.057 0.032
College, university 0.442 -0.110
With higher school qualifications the ratio of those who feel that climatic changes are
close to them and who positively acknowledge scientific results is likely to increase. They

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are the ones who are the least critical about mass communication (television), although
the degree of trust is little in that group too. The group of agricultural producers with
low school qualifications generally feel that they are far away from climate change and
its problems and they do not consider scientific prediction results convincing either.
In terms of adaptation skill, agricultural producers with low school qualifications put
themselves into a more disadvantaged group. In the group who completed secondary
schools, their qualifications were not enough for being optimistic. Only those with the
highest school qualifications were optimistic about the possibility of successful adapta-
tion in their own situation.
Summary
The results also confirm the conclusion that the perception of climate change and the
adaptation categories are present differently among the agricultural population which
supports a need for similarly differentiated decisions concerning state and local inter-
ventions by taking into account social and cultural disparities.
References
Boillat, S., and F. Berkes. (2013). Perception and interpretation of climate change among Quechua
farmers of Bolivia: Indigenous knowledge as a resource for adaptive capacity. Ecology and
Society 18 (4)
Gómez-Baggethun, E., E. Corbera, and V. Reyes-García. (2013). Traditional ecological knowledge
and global environmental change: research findings and policy implications. Ecology and
Society. 18 (4): 72.
Kahan, Dan M. (2011): The Tragedy of the Risk-Perception Commons: Culture Conflict,
Rationality Conflict, and Climate Change. Cultural Cognition Project Working Paper No.
89 Yale University
Leclerc, Christian, Caroline Mwongera, Pierre Camberlin, Joseph Boyard-Micheau (2013):
Indigenous Past Climate Knowledge as Cultural Built-in Object and Its Accuracy. Ecology
and Society 18 (4)
Ruiz-Mallén, I. and E. Corbera. (2013): Community-based conservation and traditional ecological
knowledge: implications for social-ecological resilience. Ecology and Society 18 (4)
Sundblad, Eva-Lotta, Anders Biel, Tommy Garling (2007): Cognitive and affective risk judge-
ments related to climate change. Journal of Environmental Psychology 27 97–106.

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Theory and Methodology Issues of
Measuring Environmental Risks
Csaba Székely - Csilla Obádovics
ABSTRACT : The uncertainty factor has an important place in the concept of environmental risks.
In risk assessment, time horizon is an important factor. Consequently, the study is dedicated to the
risk management methods, a matrix type approach of risk assessment and the steps of volatility
calculation.
KEYWORDS environmental risk, volatility, risk assessment
Uncertainty and risk
Risk relates to the future vision, and certainty or, the contrary, uncertainty related to
it. Human thinking covers not only the dimension of the present, but people have also
become a capable of projecting and planning the future to a certain extent. However, as
the time horizon expands, the projections and forecasts become more uncertain.
Depending on the scientific discipline and objectives of the analysis, the concept
of risk is described according to various criteria. In this study, we think that a more
general approach and classification should be applied to the concepts, over-viewing
the whole system. Consequently, the related concepts will be described by also taking
into account the logical relations and correlations attached to them. Apart from the
terms generally used for this concept, we rely on the complex analysis of risk and in-
surance prepared by Williams et. al. (1995) and a document prepared by the European
Commission (EC, 2010).
Certainty: clear future situation without any doubt.
Uncertainty contrast to certainty: the lack of ability to project the future outcome of
present actions and events. Consequently, uncertainty develops when the individual feels
that the outcome cannot be certainly identified.
Risk is the potential variation of outcomes and results. The outcomes cannot be pre-
dicted exactly when there is risk. Risk exposure occurs when a certain activity may lead
to potential benefit (positive outcome) and losses (negative outcome).
Basic and speculative risk: basic (pure) risk occurs when there is a chance for a loss
but not for any benefit. In the case of a speculative risk, both can occur.
Uncertainty may be mitigated by increasing the information. The degree of uncertain-
ty depends on the quantity, type and quality of the information available for identifying
the potential outcomes and assessing the probability of their occurrence.
First we shall take a look at risk measurement and estimates.

76. 75
Table 1. Certainty-uncertainty continuum
Degree of uncertainty Characteristics Examples
No (certainty) The outcomes and results can be predict-
ed exactly
Physical laws, natural sciences
Level 1 (risk, objective
uncertainty)
The outcomes can be identified and the
probability of their occurrence is known
Natural sciences, gambling games,
simple economic decisions
Level 2 (subjective un-
certainty)
The outcomes can be identified but the
probability of their occurrence is not
known
Natural disasters, accidents, the
majority of business decisions
Level 3 The outcomes cannot be fully identified
and the probability of their occurrence is
not known either
Spatial research, genetic research,
wars
Source: Williams et al, (1995), modified
Risk measurement, risk assessment
Identification of risks and their sources
In order to recognize and manage risks it is indispensable to classify and regularly
identify risks and uncertainty. Risk identification involves the collection of adequate
information about the sources and factors of risks, threats and exposures to threats.
Risk assessment refers to the activities that facilitate the identification, measurement,
analysis and evaluation, as well as projection of the potential impacts of the risk and
uncertainty. Risk identification means the detection, identification and evaluation of
the risk, the analysis reveals the nature and level of the risk, while evaluation means the
comparison of the results to conclude whether or not a risk or its impacts (scope) is still
acceptance and tolerable.
The sources of risks are external and internal factors that may contribute to positive
or negative outcomes. Threats are the causes of negative outcomes.
The sources of risks can be classified in several ways. The following classification is
aimed at the general assessment of external factors:
 physical environment,
 social environment,
 political environment,
 legal environment,
 operational environment,
 economic environment,
 cognitive environment.

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Risk factors: the causes and conditions that create or affect the positive or negative
outcome of a risk.
Threats are the factors that increase the chance and severity of threats (negative outcomes).
These threats are phenomena, substances, human activities and conditions, which may
cause harm to human life, health, assets, services, operation of the economy or society, or
the nature. The causes of natural threats stem from natural processes or phenomena, while
the causes of technology threats stem from technical and industrial conditions.
Risk measurement
The process of risk measurement defines the losses arising from risk exposure and
their consequences.
Exposure to threats may affect the following groups (risk object):
 people (human resources),
 social conditions (relational system),
 assets (economic resources),
 man created systems, infrastructure,
 natural environment.
The risk impacts (loss, damage, negative outcome) also depend on vulnerability and
may be classified into several categories:
 individual losses (unfavorable turn in life, loss of trust, etc.),
 violation of law,
 economic losses,
 social discrepancies,
 crisis,
 natural disasters,
 human victims.
The human impacts may be measured with the following quantitative indicators:
number of deaths, number of severely injured or ill people, number of temporarily re-
moved, relocated people, etc.
The economic impacts result from the total costs of nursing, direct or longer term
protective measures, infrastructure, reconstruction of buildings, cultural heritage, the
construction of economic activities, insurance payout, indirect social expenses, etc.
The political/social impacts may be measured with quantitative or qualitative scales.
The most frequent impacts include social psychological consequences (fear, uncertainty),
loss of territory, weakening of international position, harm to the democratic system,
deterioration of public safety and public order, political changes, damage to cultural
goods and other similar phenomena.
Risk assessment means the identification of threats, the assessment of vulnerability
(risk exposure) of risk objects and the analysis of the potential or actual impact on risk
objects. The threats may entail negative consequences of various degrees:
 Irreversible and irreplaceable (losses of human life, loss of economic opportunities,
distinction of species).
 Reversible and replaceable (recovery after the illness, loss of assets or flood)

78. 77
The aspects of risk exposure may be summarized
according to the following structure:
SERIOUSNESS
FREQUENCY LOW SERIOUS
rare 1 2
frequent 3 4
Source: Williams et al., (1995), modified
Figure 1 : Risk dimensions
The measurement and assumption of risks with such a simple approach is not enough
for practical risk management. Often a lot more detailed risk assumption matrices and
evaluation color scales are developed for the practical management of various risks, which
may become a standard evaluation system in a particular field. This can include the known
flood protection of weather forecast systems. Figure 2 shows a two-dimensional risk assess-
ment matrix with different degrees of seriousness and probabilities, also illustrated by color.
SERIOUSNESS PROBABILITY
Definition Degree Frequent Probable Occasional Rare Unlikely
Disaster 4 EH EH H H T
Critical 3 EH H H T L
Marginal 2 H T T L L
Negligible 1 T L L L L
Degree EH – extra high H - high T - tolerable L - low
Source: Based on ISO 31010, modified (IEC/FDIS 31010, 2009)
Figure 2: Risk assessment matrix
In a single case risk assessment concentrates on the assessment o one risk, or its
consequences (single risk assessment). That can include e.g., the assessment of a flood
or its consequences at a particular time in a particular geographic region. In the case of
multiple risk assessment the probability of threats occurring concurrently or immediately
one after the other it established, because they are often caused by the same factors or
simply the same risk components are analyzed without any chronological concurrence.

79. 78
A multi-hazard assessment is used when different threats occur shortly after one another,
because they depend on each other or they stem from the same causes. Later we shall
cover the importance of multiple risk assessment in environmental risks.
Quantitative methods for risk measurement
Probability
The classification in Table 1 made it clear that risk is closely related to probability.
According to the table with objective certainty, the outcomes can be identified and the
probability of their occurrence is also known. Consequently, establishing probability is
one of the fundamental aspects of risk measurement.
Mathematics refers to probabilities in relation to accidental or stochastic variables. If
the outcome of a particular event is clearly determined by the observed conditions, then
it is a deterministic phenomenon, while when accidental factors also affect the outcome,
it is also influenced by accidental factors i.e., it is a stochastic phenomenon. Stochastic
and uncertain variables of simply accidental variables are those variables to which no
clear values can be assigned.
The literature mentions three options for establishing probability. Accordingly, there
may be
 classic (mathematical),
 statistical and
 assessment (subjective) probability.
Classic probability
Classic (mathematical) probability is a figure which is assigned to an E event with a spe-
cific P(E) probability value for each event. The classic definition of probability assumes
that the event space consists of a finite number of elementary events, which mutually
exclude each other.
The probability of a specific event can be established by defining the relevant segment
of the elementary events by establishing the ratio of the relevant segment of elementary
events within the total event system.
Classic (mathematical) probability can be clearly defined with theoretical mathemat-
ical (combinatory) methods.
Statistical probability
Statistical probability is practice oriented, because it is based on practical experience.
In relation to the chance of probability of any event (or natural condition) information
may be gained by performing an experiment relating to the event on several occasions
and observing how many times the event occurs.

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The probability of the event is the number that shows the portion of the experiments
when the event occurred. That figure is known as relative frequency. If the series of
experiments are repeated enough times, one can see that the relative frequencies show
greater stability. A figure develops around which the relative frequency values “vary”
and that variation will be smaller and smaller as the number of experiments increases.
The probability of an event is the real number around which the relative frequency
varies.
Subjective probability
Classic and statistical probabilities are also known as objective probability. In addition,
if there is no adequate database, in risky and uncertain situations the probability of
occurrence of events can also be estimated based on confidence, if the events cannot be
measured with any objective indicator or there are no adequate data, and no prelimi-
nary experiments can be conducted. Such subjective ideas may also be integrated into
stochastic decision models, in which these confidence-based values may be quantified as
subjective probability. Consequently, personal experience and intuition-based subjective
probability may also be taken into account.
The concept of subjective probability intends to quantify the ideas that stem from
personal confidence in order to explicitly reflect the experience of the decision maker
in the calculations.
Subjective probability can be defined with two methods. The decision maker can
be asked directly about their probability ideas and subjective probability can also be
concluded indirectly, from decisions.
Subjective probability, similarly to subjective ideas, made be very different in rela-
tion to individuals. That is why the application of subjective probabilities needs to be
certified. Another reason justifying it is that subjective probabilities cannot be reviewed
objectively. Many people think that they cannot be even taken into account in risk man-
agement models.
At the same time, the question may also be asked whether there is any alternative
to the concept of subjective probability. Facts show that in most practically important
cases, no objective probability can be defined at all. It could cause a significantly greater
problem, if in such situations we would totally waive the consideration of probabilities.
Consequently, information derived on subjective basis must not be waived uncondi-
tionally.
Another fact supporting the application of subjective probability is that the concept
does not exclude the review and continuous improvement of probability assessments.
However, the decision maker can generally obtain additional information and review
any former judgment on probability according to the new information. Naturally, ob-
taining new information may be costly, therefore a decision is also required whether or
not obtaining additional information makes any sense.

81. 80
The concept of conditional probability is important too in terms of events that assume
one another (related events). In relation to two events, conditional probability occurs
when, for the occurrence of E2
event only those outcomes are taken into account, which
also belong to E1
event.
The probability of occurrence of E2
event is identified with the condition that previ-
ously an E1
event already occurred. It may also be deemed a new experiment in which
the original event plant is reduced to the set of elementary events constituting the E1
event
i.e. the E1
event will be the new event space.
Estimated value of the probability variable, distribution and variance
When the series of experiments, already mentioned during the introduction of the
concept of probability was repeated on several occasions, we found that relative frequency
varied around the probability of the event. The figure around which the observed average
probability variable varies is known as estimated probability variable.
Apart from probability and the estimated value distribution is also required for ana-
lysing the stochastic correlations. The estimated value corresponds with the average value
“in a common, everyday definition”. However, it does not provide enough information
about the probability variable or the analyzed data. The deviations from the estimated
value should also be revealed. Distribution is the estimated value of deviations from the
estimated value.
Variance or the distribution square is an important parameter describing probability
distribution. In the economic risk models, variance and its square root, the standard
deviation is often used to measure risks (Obádovics, 1965).
Distribution of probability variables
Random variables may take several values. In the case of the probability variable the 1
probability of a certain event “is distributed” across all potential values of the variable.
In that respect there are two extreme cases: the discreet and continuous random variables
(Szelényi-Tóth, 2010). There are some variables which may take a limited number of
statuses that can be described exactly. These variables are known as discreet stochastic
variables. The value set of the discreet probability variable is either finite or has a count-
able number of values.
A discreet probability distribution can be illustrated with a distribution function and
(line) diagram.
The distribution function shows the probability when the probability variable takes
a value, lower than x. Its potential values are the real numbers, which were assigned to
the outcomes of the experiment.
The continuous random variables may take any value within a limited data field. Thus,
in theory an infinitely large number of statuses can be achieved.
In the case of continuous random variables, the probability of occurrence cannot
be assigned to one specific occurrence, and therefore it needs to be defined in intervals.

82. 81
In order to define continuous random variables, the probability needs to be estimated
as to whether the actual x is greater than a specific x0
, or the probability that the actual
x is lower than that x0
. The probability analyses may also be performed for several ran-
domly selected points within the potential timeframe. The distribution function may
be derived accordingly.
Volatility
A large number of natural risks has recurring features due to cyclicity (days, years, solar
flare, etc.) (e.g., frosty days, drought or tornadoes). That is why, as time passes, certain
processes may become stronger or weaker. That is why the observations of natural events
and the analysis of time series are of major importance. Long-term meteorological ob-
servations are typical examples for that.
By analyzing the time series, long-term trends can be established and the specifi-
cities of the cycles can be analyzed. In addition, the timely changes of risks may also
be analyzed, with the help of a special analytic method, the volatility analysis, which
is used in economics. That analytic method should also be expanded to the review of
natural events.
Source: Central Statistical Office (CSO)
Figure 3: Number of sunny hours in Budapest 2009-2013
Volatility in economics
The term volatility used in economics when an economic indicator changes and
fluctuates often and extensively.
In the context of the stock exchange, volatility refers to the fluctuation of exchange
rates (more specifically, the logarithm of the yield i.e., the quotient of the exchange rate
of two subsequent trading days).

83. 82
Source: Portfolio financial, 2013
Figure 4: EUR/HUF exchange rate volatility between 2009 and 2013
A distinction must be made between historically measured (calculated) and future pro-
jected (forecast) volatility. Historic volatility practically shows “caprice” and variability of
the analyzed phenomenon, whether it is an exchange rate or the daily average temperature.
At the same time, it can also indicate the status of the current trend. Low volatility means
that the trend is stable, but increased volatility indicates the vulnerability of the trend.
Future volatility is projected volatility. Such projected values are the VIX index, cal-
culated at the Chicago Stock Exchange, indicating the volatility of the American S&P500
index forecast for 30 days.
In finance, volatility is used to measure the fluctuation of the price of financial
instruments (e.g., equities). “Historic” volatility is derived from the time series of the
market prices of the past. The implied volatility is derived from the market price of the
derivatives, traded on the market (specifically an option). Volatility is also expressed with
the σ symbol, which corresponds with the standard deviation , introduced earlier (which
cannot be mistaken by variance, which is the square of the former (σ2
)) (Jorion, 1995).
Volatility in this context expresses the actual current volatility of the equities for a
particular period (e.g., 30 or 90 days). Its expresses the volatility of an equity for a specific
period with the latest price of the last observation. That expression is used especially
when they intend to distinguish between the actual current volatility and one of the
following volatilities:
 actual historic volatility - volatility of the equities over as specific period based on
the latest observation of historic data,
 actual future volatility - volatility of the equities over as specific period from the
current time until a future date (generally the maturity date of the option),
 historic implied volatility - refers to the implied volatility that was established from
the historic price of the equities (normal options),
 current implied volatility - refers to the implied volatility which is calculated from
the current prices of the equity,

84. 83
 future implied volatility - refers to the implied volatility that is calculated from the
future price of the equity.
Mathematical definition
The σ volatility, calculated for the year is the standard deviation of the annual logarithmic
yield of the share (Andersen et. al. 1998).
The σT
volatility for a T period, expressed in years as follows:
Consequently, if the standard deviation of the daily logarithmic return on an equity
is σSD
the time period of the yield is P, the volatility calculated for the year is:
According to the general assumption P = 1/252 (252 is the number of equity trading
days in the particular year). Then when σSD
= 0.01, the annual volatility is:
=0.1587
Monthly volatility (i.e. 1/12 of the year, T = 1/12):
The yields, or the above formula used for calculating volatility from one period to
the next assumed a special model or process in the background. Those formulae are the
exact extrapolations of the random walk, or the Wiener process, the steps of which have
finite variance. However, more generally, in the natural stochastic processes the exact re-
lationship among the volatility degrees of various time periods is even more complicated.
Therefore, more people use the Lévy stability exponent to extrapolate natural processes:
By simplifying the above formula, the annual volatility assumption becomes possible
even based on only approximate observations. Let us assume that the market price index,
the current value of which is 10,000, varies on average by 100 points over several days.
That would mean 1% daily movement up or down.
To annualize it, the “16 rule” can be used for the calculation i.e., by multiplying it by
16, we can reach 16% annual volatility. 16 is the square root of 256, and 256 is only slightly
different from the number of annual daily days (252). This simplification uses the fact
that the standard deviation of n independent variables (with equal standard deviations)
is the square root of n of the standard deviation of the individual variables .

85. 84
Naturally, the average observations can be used only to calculate an approximate value
of the standard deviation of the market index. Assuming that the daily changes of the
market index have normal distribution with zero average and σ deviation, the estimated
value of the size of observations is *σ = 0.798σ. As an actual effect, a rough estimate
underestimates the actual volatility by approximately 20%.
The volatility analysis will then be extended to natural events. The next study presents
the background of this statistical database and the application of the method.
References
Andersen, Torben G.; Bollerslev, Tim (1998). “Answering the Skeptics: Yes, Standard Volatility
Models Do Provide Accurate Forecasts”. International Economic Review 39 (4): 885–905.
JSTOR 2527343.
Cumby, R.; Figlewski, S.; Hasbrouck, J. (1993). “Forecasting Volatility and Correlations with
EGARCH models”. Journal of Derivatives 1 (2): 51–63. doi:10.3905/jod.1993.407877.
European Commission, (2010): Risk Assessment and Mapping Guidelines for Disaster
Management. Commission Staff Working Paper, Brussels, 21.12.2010. SEC 1626 final.
Goldstein, Daniel and Taleb, Nassim, (March 28, 2007) “We Don’t Quite Know What We are
Talking About When We Talk About Volatility”. Journal of Portfolio Management 33 (4),
http://www.portfolio.hu/deviza_kotveny/deviza/ (28 July 2013)
http://www.wilmottwiki.com/wiki/index.php?title=Volatility: “Calculating Historical Volatility:
Step-by-Step Example”. 2011-07-14.
IEC/FDIS 31010 (2009): Risk management – Risk assessment techniques.
Jorion, P. (1995). “Predicting Volatility in Foreign Exchange Market”. Journal of Finance 50 (2):
507–528. JSTOR 2329417.
Obádovics, J. Gy.(1965): Mathematics. Műszaki Könyvkiadó, Budapest.
Szelényi L., Tóth Z. (2010). Valószínűségszámítás [Probability calculation]. BKF, Budapest, p, 193.
Williams, C.A. Jr., Smith, M.L., Young, P.C.: Risk Management and Insurance. McGraw-Hill,
Inc., 1995.
www.ready.gov/risk-assesment/01.29.2014

86. 85
Application of the Volatility Method for the
Analysis of Changes in Climate Risks
Mónika Hoschek, Csilla Obádovics, Csaba Székely
ABSTRACT The assumption, according to which climate change also has an impact on social and
economic processes can only be studied over a long period. The climate change, global warming
and more extreme weather conditions cannot be identified from one year to another. The increase
in the annual average temperature can clearly be detected from the data, available for the last 113
years (1901-2013). However, the appearance and more frequent extreme weather conditions, i.e.
increasing fluctuation of daily temperature, number of heat wave days, number of droughts, etc.,
cannot be clearly proven. We conducted a volatility analysis of the climate factors to confirm this
extremism or to reject the assumption.
The weather forecast models show that climate volatility is increasing as more and more extreme
weather conditions occur, which will entail severe consequences in agricultural countries and in
areas in Hungary where economy is based on agriculture. The volatility tests were run on annual
average temperature, daily minimum and maximum temperatures and the fluctuation data series.
The average annual temperature began to rise significantly in the second half of the last century.
The fluctuation of daily temperature, i.e. the difference between the daily maximum and minimum
temperatures, is also clearly rising, although to a lesser extent.
KEYWORDS: annual average temperature, daily temperature fluctuation, frosty day, harsh days,
heat wave day, hot day, volatility
Introduction
Volatility analysis as a method has been used by researchers mainly to analyse eco-
nomic processes, primarily stock exchange processes. The stock exchange processes
vary according to time series and are influenced by accidental factors. (Farkas, 2010.,
Hull J. and A. White, 1987.) The change in climate volatility has a significant impact on
the poverty of countries engaged in agricultural production too (Syud Amer Achmed
et al 2010). Climate change has a huge impact of the prices of agricultural products,
thus affecting the financial position of the population living in primarily rural and
agricultural areas.
The purpose of our study is to illustrate that the volatility analysis as a method may be
applied not only to analyze economic data series, but also to other data series, reflecting
volatility in time and affected by accidental factors. By relying on the similar features of
natural and stock exchange processes, we applied the volatility analysis to the analysis of
weather conditions. ‘Capricious, like the weather’. This saying also shows why we decided
to apply the volatility calculation method to weather data. In the course of our analyses
we calculated and analyzed historic volatility data.

87. 86
Extension of the volatility analysis to natural phenomena
In economic sense volatility is the indicator of the risk of an investment. Volatility refers
to the variability and fluctuation of estimated or historic yields which, translated into
weather conditions, reflect the variability and fluctuation of forecast of historic e.g., daily
average temperature. In fact, when we want to know whether our weather has become
more capricious, what we would like to know is if its volatility is increasing. If e.g., the
daily average temperature increases or decreases continuously by 1-2 degrees from one
day to another, its volatility is not significant (irrespective of the direction of the change).
However, when the daily average temperature is increasing at one time and falling at
another time, it has greater volatility. That means that we can talk about the volatility
of the weather when it shows sudden changes within as short period as a result of any
unexpected or extraordinary weather condition. The unexpected weather conditions of
that nature and the consequential volatility is an uncertainty and the risk, and therefore
volatility may also be described as an indicator of risk in that sense. (Zsembery, 2003)
Used data and applied methods
The “Agroclimate”15
project focuses on a designated area within the country i.e., Zala
county, as a pilot programme. Zala county is part of the West Transdanubia Region.
The long time series data used for the analysis of the weather and climatic factors were
available at the Regional Centre of the Meteorology Service in Szombathely. So, these
data series were used for our analysis, and our figures were prepared accordingly.
The temperature data series were available from 1 January 1901 to 31 December
2013, and therefore we were able to analyze a sufficiently long time series. Apart from
the daily average temperature, the minimum and maximum temperature data and daily
temperature fluctuation were analyzed for the reviewed 113 years.
In the case of temperature data prior to calculating volatility we had to make one more
conversion, which is not required for stock exchange data. All stock exchange indices are
based on a positive number. The indices only increased from the initial point. This fact
is important because when subsequent logarithmic yield figures must be divided by one
another in order to calculate volatility, it is easily feasibly mathematically. However, 0°C
may also occur among temperature data, which prevents the division. In order to eliminate
the problem, we analyzed the temperature data series of the Kelvin scale instead of using
the scale of Celsius degrees.
As mentioned above, the stock exchange indices only increased from the initial value.
The increase reflected an almost exponential curve. A logarithmic scale had to be applied
to the yield values in the stock exchange volatility calculation because of the nature of
15 “Agroclimate: Impact Analysis of the Projected Climate Change and Possible Adaptation in the
Forestry and Agriculture Sector”

88. 87
the curve. No such conversion is required for the temperature data, forming the subject
of our study, because here there are no differences in volume.
Deciding on the time horizon is a very important step in relation to a long time se-
ries, like the one available for our analysis. The obvious choice for the shortest period is
an annual time horizon. To calculate annual volatility, the spread, calculated from the
quotient of the daily temperature data had to be annualized. In order to do that, we had
to multiply the spread figures, calculated for the daily data by the root of the number of
days, examined during a specific period. That meant that the data had to be multiplied
by x-times. For leap years we used the X multiplication factor.
We performed analyses for 10-year periods with annual volatility. In that case we also
had to pay attention to leap years in the multiplication factors. In decades containing
two leap years, the multiplication factor was, while in other decades containing three
leap years, the factor was.
Volatility of daily average temperatures
The series of annual average temperatures of Szombathely over the analyzed more than
one hundred years showed a great deal of volatility each year. The difference between
the hottest and coldest years is 3.8o
C, while the average temperature of the individual
years is on average 0.74o
C different from the average 9.5o
C.
In the first half of the last century, no change or increasing or decreasing trend can
be observed in the average temperature data series16
(Figure 1.)
Figure 1. Annual average temperatures 1901-2013
16 However, it should be noted that the variation of the measuring points led to inhomogeneous data
series. In the climate analysis, available on the website of the National Meteorology Service (OMSZ) the
data reflected 0.6o
C increase following homogenization (Domokos, 2008, Szalai et al 2005) and a straight
line trend adjustment. It is especially important for our research area, because Alpokalja is one of the most
intensively warming up areas of Hungary. (OMSZ, Szalai et al 2005)

89. 88
The coldest year of the analyzed period in Szombathely was 1940 when the annual
average temperature was only 7.4o
C. The second coldest year was 1956, when the annual
average temperature was only 0.3o
C higher. In the ranking order of cold years the average
temperature of the third subsequent years was higher than 8o
C. Only two years of the
last quarter of the century were included among the ten coldest years.
The last year of the century i.e., the year of 2000, turned out to be the hottest, with
11.2o
C average temperature. The second hottest year was in that millennium, when the
average temperature in 2008 was only 0.01o
C lower than the average recorded for 2000,
followed by 1994 (11.0o
C). The hottest 10 years included only three years from the 1900s,
and only one from the first half of the century (1934).
The curve starts to change from the 1960s, as parallel with the global changes, the
time series reflect a clearly warming up trend (Figure 2).
Figure 2. Annual average temperatures 1960-2013.
From the 1960s the increase in the average temperature is clearly obvious also with the
high reliability linear trend line. If the rise continues in a straight line in the future too,
the average temperature will increase by more than 1o
C in every thirty years. However,
this tendency may not change in a straight line, in which case the consequences of the
global warming will demand fast adaptation and changes from society.
The annual volatility figures of average temperatures varied between 12.7% and
18.8%, i.e. within a range of 6.1% over the analyzed period. It may be concluded that
the average difference from the average annual volatility figures was only 7.2%, which
reflects very low distribution (Figure 3.).
These days we often hear that weather conditions have become more variable. The fact
that 1929 was the year with the greatest volatility of 18.8% seems to slightly contradict
to that statement. In that year people had to suffer the greatest changes from one day to
another. Looking at further elements of the ranking order, apart from one exception,
there are only years prior to 1950 among the ten years with the greatest volatility. Of
the years of the new millennium 2012 was the most extreme, but it still lies only in 10th
place in the order with its 16.9%.

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Figure 3. Annual volatility of daily average temperatures 1901-2013.
If we take a look at the figures from the opposite side and look at the least volatile
year, then 1974 strikes out (12.7%). The changes from one day to another were the
smallest in that year. Looking at the ranking order of ten in that respect too, from the
fifty years prior to 1950 only 1916 is included in ninth place (13.7%). In our millenni-
um 2013 had the lowest volatility. With its 13.3% figure, it shares fourth place in the
list with 1972.
Figure 3 illustrates well that some change occurred at the beginning of the 1950. Since
then the data have been lower than before. The phenomenon is known as a level shift,
which is one of the typical examples of outstanding values. In the case of a level shift the
data are shifted equally in a positive or negative direction from a particular time. Among
the examined volatility data that shift took place in the negative direction. The reasons
behind the shift are not known (most probably they were caused by the changes in the
measuring method).
Apart from analyzing the volatility figures, we should also take a look at which years
showed the largest differences compared to the preceding year. If we look at the shifts
upwards, i.e. the largest positive differences, then 1929 is in first place again. In that year
not only volatility was high, it increased significantly even compared to the preceding
year (by 23.7%). There are in total five years, which are included among the years with
the greatest volatility and the highest positive change. 2012 was another year of the same
category. Although it was last in the previous order, it increased by 16.6% from 2011,
which landed in fourth place.
1951 brought the largest negative change from the preceding year (23.7%), when
volatility was also very low (13.1%, the second). Of the years of the 21st century, 2013
and 2010 are both included in the list of the years reflecting the greatest decrease. The
first is in second place, the latter is in eighth place with 20.9% and 12.9% decreases
respectively.

91. 90
Figure 4. 10-year volatility data for the daily average temperature
The volatility of the daily average temperature data, calculated for ten-year peri-
ods are shown in Figure 4. The figure shows the level shift, observed also for annual
volatility, starting from the 6th decade. If the tendency of the first five decades had
continued i.e., if the shift had not occurred the volatility data could be described re-
liably with a straight line slightly increasing trend. For the actual data, if we intended
to apply a straight line trend, it would be declining and the accuracy of the adjustment
would reflect a relatively low figure (r2
=37.4%). Choosing polynoms from the analytic
trends, however, will lead to relatively good correlation. It is generally true that by in-
creasing the number of degree of polynoms of the accuracy of the curve also improves.
Compared to a third degree polynom, a fourth degree polynom results in hardly any
increase in the data series forming the basis of our analysis (r2
=66.8% to r2
=66.9%), but
in the case of a fifth degree polynom, the improvement becomes significant (r2
=79.6%).
These results may be important for a future model.
Volatility of daily maximum temperatures
The year-on-year volatility is significant also in terms of annual absolute minimum
and maximum temperatures, but both the minimum and maximum temperatures are
undoubtedly rising.
In terms of the annual absolute maximum temperatures we can observe that only one
of the top ten years was from the first half of the last century, and three were from the
2000s. The absolute peak so far was 2013 with 39.7o
C. Looking at maximums, seven from
the ten “coldest years” were before 1950, with a negative peak of 28.0o
C in 1926 (Figure 5.).

92. 91
Figure 5. Annual maximum temperatures 1901-2013.
We began analyzing the volatility of daily maximum temperatures on an annual ho-
rizon too (Figure 6). The volatility figures spread between 18.2% and 25.7%, i.e. within a
range of 7.4%. The annual average volatility data are on average 6.4% different from the
average figures, which is smaller than the small distribution observed for daily average
temperatures.
Figure 6. Annual volatility of daily maximum temperatures 1901-2013.
Looking at Figure 6 it is clear immediately that at the end of the analysed period vol-
atility figures surpass a threshold (25.0%), which was never exceeded by the volatility of
any year in the previous period. The list of ten years with the greatest volatility is led by
2012 (25.7%). (That year was only 10th in the order of annual volatility figures, calculated
from the daily average temperatures.) Four more years from the 21st century are also
included in the top six places of the list. That means that the increasingly extreme weather
conditions these days may be verified more in terms of the daily maximum temperatures.

93. 92
Concerning the least variable years, 1972 is at the top of the list with 18.2% volatility.
The other years of the list include five years prior to 1920 and none after 2000.
In terms of changes between individual years the list of positive changes is topped by
2011 with 24.3% rise over 2010. 2012, which showed the highest volatility, is not included
in the list, because volatility was already high in 2011. However, there are two other years
in the 21st century that showed remarkably high increase over the previous year. The
increase in 2001 was 2,.% and in 2009 it reached 10.7%.
Concerning negative changes, 2013 leads the list with 18.8% decrease. There are four
years when low volatility was also the result of a major decline. In 1996 volatility turned
out to be the third lowest figure (18.5%) following a drop of 15.7%. The volatility in 1910
reached the fifth lowest figure (18.7%) after a decrease of 14.4%. A fall of 13.3% could
be observed in 1972, which had the lowest volatility. 1978, which had the second largest
volatility, produced a decrease of 11.5%.
If we intend to capture the trend of the volatility time series, we should opt for a
polynomial trend again. However, in that case even with a fifth degree polynom only
a very weak (r2
=30.0%) explanatory power could be achieved. The trend, however, is
definitely increasing.
The same may be said for volatility, calculated over a period of 10 years (Figure 7).
In other words, the figures reflecting the volatility of daily maximum temperatures are
increasing in the time series with a trend that can be described well with a fifth degree
polynom (r2
=92.0%).
Figure 7. 10-year volatility data for daily maximum temperatures
It should be noted that the level shift, which could clearly be detected in the average
temperatures could not be found for the daily maximum temperatures either in the
annual, or the ten-year volatility time series.

94. 93
Volatility of daily minimum temperatures
Analyzing the absolute minimums, it is clear that 1929 was the coldest year when
-29.3o
C was measured. On the list of the ten coldest days 1985 represents the last third
of the 20th century with -21.9o
C, which puts it into 7th place. The last of the negative
record holders, i.e. the years with the fewest cold days include only 1910 and 1911 from
the first half of the last century, and also two years from the 21st century. The lowest
minimum temperature of -5.5o
C was measured in 1974 (Figure 8).
Figure 8. Annual minimum temperatures 1901-2013.
The annual volatility of minimum temperatures is distributed more (23.7%-15.5%),
than in relation to the other two weather data. The relative distribution is still only 8.3%
(Figure 9).
Figure 9. Annual volatility of daily minimum temperatures 1901-2013.

95. 94
The volatility of minimum temperatures was the lowest, 15.5%, in 1916. The ranking
order shows that the five years with the lowest volatility were prior to 1920, but even
the list of ten years includes only 2011 from the years after 1951. As the figure also
shows, the volatility of the weather in terms of minimum temperature was very low in
the first third of the 20th century. The highest volatility was measured in 1927 (23.7%).
In the declining order based on the annual volatility of daily minimum temperatures
there are years only from the 20th century. However, three years of the last decade of
the century are included in the list. Volatility was 22.3% in 1996, 21.9% in 1997 and
21.7% in 2000.
Examining the changes, 1952 was an outstanding year, which also tops the list of the
positive changes with 40.1% increase. The most volatile year of 1929 is in second place,
with 37.0%. 2012 takes the fourth place in the list, structured according to the increase,
when annual volatility increased by 17.3% over the preceding year.
Rather large changes can also be observed downwards too. In 1936 the volatility
dropped by 20.4% compared to the preceding year. This is a negative record. The decline
in 1930 (20.0%) is only slightly behind. In our current century 2011 and 2013 were the
two years with a major drop (12.8% and 10.6%) in the volatility of subsequent years.
The trend of volatility data could be captured with a straight line method only with
very little accuracy. The accuracy of the trend reflecting a slight increase hardly reaches
r2
=28.1%. The low determination coefficient also shows that a polynomial trend would
be more suitable for such purposes too. The accuracy of the third and fourth degree
polynomial trends is hardly different (r2
=43.2% and r2
=43.4%) and is significantly lower
than the result with the fifth degree polynom (r2
=51.4%). As previously observed, the
most appropriate trend was falling until the 1920s, and then increasing until the 1940s.
It remained the same until 2000, and then started to decline.
The volatility of minimum temperatures over a period of ten years shows a very
similar picture (Figure 10). A fifth degree polynom can almost fully capture that time
series (r2
=98,.0%).
Figure 10. 10-year volatility data for daily minimum temperatures

96. 95
Volatility calculated from the data series of temperature fluctuation
The daily temperature fluctuation could put a strain on the human body, therefore
it is important to know the difference, i.e. the range between the maximum and mini-
mum temperatures. The annual maximum figures of such differences were falling until
the 1930s, then stagnating until the mid-1980s, but began to rise since then. The largest
difference occurred in 1943. There was a day in that year with a temperature fluctuation
of 23.8o
C In 2011 the same figure was “only” 23.5o
C, followed by 1911 and 1990 with the
third highest fluctuation (23.4o
C). In that respect 1927 and 1982 were the most fortunate
years with only 17.4o
C temperature fluctuation (Figure 11.).
Figure 11. Maximum daily temperature fluctuation, 1901-2013
The volatility figures, calculated for the intra-day temperature fluctuation, i.e. the
difference between the daily maximum and minimum temperatures, vary between 30.8%
and 22.1%, 7.6% relative distribution (Figure 12.).
Figure 12. Annual volatility of daily fluctuation, 1901-2013

97. 96
The greatest volatility of 30,8% occurred in 2012. Volatility above thirty percent oc-
curred only three times over the examined 113 years, and all three respective years were
after the year of 2000 (2003, 2011, 2012). The analysis of the ten years with the greatest
volatility shows that five years were after the turn of the millennium. The lowest volatility
(22.1%) was observed in 1916, but volatility was only 0.1 percentage point great in three
other years: in 1903, 1907 and 1910. 1926 was the latest year included among the ten years
with the lowest volatility calculated from the daily maximum temperature fluctuation.
The degree of volatility increased most in 2011 (22.1%) i.e., that year was outstanding
not only due to the size of volatility, but also due to the size of the change. There are four
other years (1929, 1942, 1952, 1970), when similar phenomena could be observed, i.e. high
volatility and also a great deal of change over the previous year.
The list containing the year-on-year negative changes is topped by 1972. In that year
volatility was 15.5% lower than in the preceding year. In 1951 the decrease was only 0.5
percentage point smaller. There are only two years in the list (1910, 1926), which had
extremely low volatility and, simultaneously, the largest decline over the previous year.
Figure 13. 10-year volatility data for the daily temperature fluctuation
The time series of volatility, calculated for a ten-year period shows (Figure 13) that
a fifth degree polynom would almost perfectly capture it (r2
=98.3%), because following
the decline after the initial increase the volatility of the last three decades shows a clear
increase.
Summary
The volatility of daily average temperature data does not reflect an increase. Although
an increasing tendency can be derived from the daily average temperatures, the even
increase was not affected by volatility. Volatility would increase, if the daily average
temperature data series showed a great deal of fluctuation.

98. 97
However, the increasing trend in the variability and volatility of the daily maximum
temperatures is an important result. The volatility of the daily minimum temperature
data series has shown a decline over the last few years. However, in the case of the daily
temperature fluctuation the increased volatility is obvious.
The volatility of daily average temperatures over a period of ten years varied between
45.2% and 51.9%, that of daily maximum temperature was between 62.9% and 72.0%,
that of the daily minimum temperatures was within the range of 54.4% and 66.1%, and
that of daily temperature fluctuation ranged between 74.0% and 90.0%. We can conclude
that while the volatility of average temperature data over a period of ten years varied
within a relatively small range (6.7%), the same range was much wider for the other three
indicators (9.1%, 11.8%, 14.0%). The greatest deviations from average (5.6%) could be
measured among the daily fluctuation. The smallest differences can be observed among
the average temperatures, where the data departed from the average ten-year volatility
on average by 2.1%.
It was proved that volatility as method is suitable for capturing an increase in ex-
tremes not only in economics, in the analysis of stock exchange data or price fluctuation.
Just like prices, weather conditions are also data that can be described with increasing
or decreasing tendencies in time and the increase or decrease in the distribution or var-
iability of which can be described well with the volatility calculation.
References
Domonkos, Péter (2008): Homogenizáló módszerek alkalmazásának hatása a detektálható
hômérsékleti trendek megbízhatóságára [Impact of the Homogenisation Methods on the
Reliability of Detectable Temperature Trends]. Légkör Volume 53. No. 1.
Farkas, Péter (2010): „Devizaárfolyam-volatilitás a pénzügyi válság idején” [“Exchange Rate
Volatility during the Financial Crisis”], Gyula Kautz, Commemorative Conference publication
OMSZ: http://owww.met.hu/eghajlat/eghajlati_adatsorok/szh/Navig/Index2.htm
Syud Amer Ahmed, Noah S. Diffenbaugh, Thomas W. Hertel, David B. Lobell, Navin Ramankutty,
Ana R. Rios and Pedram Rowhani (2010): Climate volatility and poverty vulnerability in
Tanzania. In Global Environmental Change. Volume 21, Issue 1, February 2011, Pages 46–55
Szalai, Sándor, Konkolyné, Bihari, Zita, Lakatos, Mónika, Szentimrey, Tamás (2005):
Magyarország éghajlatának néhány jellemzője 1901-től napjainkig [Features of Hungary’s
climate from 1901 to the current days]. National Meteorology Service.
Zsembery, Levente (2003): „A volatilitás előrejelzése és a visszaszámított modellek” [“Volatility
Forecast and Denormalisation Models”], Közgazdasági Szemle, Volume L. pp. 519-542.

99. 98
Management of Environmental Risks,
Risk Management Methods
Csaba Székely
ABSTRACT: Risk assessment includes the risk identification, analysis and assessment phases. In relation
to environmental risks it refers to the survey of the probability of occurrence of events which appear
as a result of changes in the environmental conditions, caused by human activity. The risk analysis
of the increasingly complicated environmental problems calls for considerable development in the
methodology. These days quantitative and qualitative information may also be used in most methods.
By projecting environmental risks, important information can be supplied for decisions on sustain-
able development, but such information is often missing. The primary objective of environmental
risk management is to satisfy the information requirement of decisions. Risk management means
the selection and application of options which facilitate planned changes in the probability of
occurrence and risk impacts and the implementation of options. The proposed strategy in general
may result in the termination, reduction, transfer or bearing the risks. The implementation of the
strategy must be monitored in order to keep the risk at an acceptable level. If it does not happen,
the risk assessment and risk management processes will need to be repeated as required.
KEYWORDS: Risk management, environmental risk types, methodology
Environmental Risks
The risks discussed in the research assignment always originate from the environ-
ment. They actually emerge in space, the air, water, in the ground, the soil, or the bio-
logical food chain, or they convey the risk to people.
However, their reasons and characteristics may be very different. Some are created
by people introducing new technologies and products, others are the results of natural
processes and, as natural risks, are connected to human activity or settlements. Yet an-
other group of risk emerged totally unsuspectingly, in the period when the technology
or activity was developed (e.g., impact of fluocarbon spray on the ozone layer).
Environmental risks generally cause damage to people who are totally innocent: they
suffer the consequences but not as a result of their own decisions. The consequences may
exert their harmful impact in later periods on subsequent generations too (e.g., unrea-
sonable management of natural resources).
Types of environmental risks
The majority of environmental risks were brought into the centre of attention through or-
ganization and industrialization; they are the consequences of economic development. It is not
accidental that these risks are associated with countries and regions that are highly industrial-
ized. Other risks spread more in poorer countries with insufficient nutrition and housing, etc.

100. 99
The most important risks can be derived from lists created from international obser-
vations. Table 1 presents the internationally observed risks that are considered the most
important according to the SCOPE 15 classification (1980).
The European Union also classified the environmental risks that it deems most im-
portant and assigned “euro codes” to such natural and industrial disasters as well (EC,
2010). The following table lists the disasters described by the EU and their codes (Table
2). The environmental risks occurring as a result of climate change may occur in several
dimensions, at several levels and in different scopes and may have different causes.
These days climate change is primarily associated with the global warming, which re-
lates to an increase in the emission of greenhouse gases. However, over the course of history,
the climate of the earth changed due to various reasons and with different consequences.
Certain analyses describe cyclical changes involving cooling and warming periods.
Table 1: Main internationally observed risks
ECOLOGICAL MONITORING soil degradation - global
tropical forest cover reduction
rangelands
river and sediment discharge
world glacier inventory
isotope concentration increase in precipitation
BIOSPHERE Wildlife sampling and monitoring
Impact of pesticide residues
Living marine resources
POLLUTANTS Air quality monitoring
water quality
eutrophication in inland water
food and animal feed contaminants
ionising radiation
CLIMATE climatic variability
World weather watch
solar radiation
atmospheric ozone
climate change
glacier mass balance and fluctuation
atmospheric pollutants and effects
OCEANS Pollutants in regional seas
Open ocean waters
Marine oil pollution
Ocean-bed contaminants
NATURAL DISASTERS Tropical cyclones
Tsunami
Flood
Earthquake
Volcanic eruption
Source: SCOPE 15, 1980, modified

101. 100
Table 2: EURO codes of different types of natural and industrial disasters
Type of disaster Technical/normative framework
Forest fires Eurocode 1 (actions on structures) defines protective design measures
against fire for buildings made of various materials (steel, concrete,
wood, masonry)
Ground movements Eurocode 7 defines calculation and design rules for stability of buildings
according to Geotechnical conditions of construction site (XP ENV 1997,
PR EN 1997-2, ENV 1997-3)
Earthquakes Several rules were worked out in the framework of Eurocode 8: EN 1998-
1 (general rules, seismic actions), EN 1998-3 (assessment and strengthen-
ing of buildings), ENV 1998-4 (reservoir, pipes), EN 1998-5 (foundations,
structures), EN 1998-6 (towers, masts …)
Storms, hurricanes Wind resistant design of buildings is covered by Eurocode 1 - EN 1991-1-4
Cold waves Eurocodes cover protection against cold and snow
Heat waves and drought EN 1991-1-5 includes design to resist heat waves
Partly covered by Eurocode EN 1997-1-1 (Geotechnics)
Industrial and technological
hazards
Eurocode 1 (EN 1991-2-7) also defines building design rules against ex-
plosions
Marine pollution and oil spills Technical norms for vessels
Source: EC, 2010
These days in relation to global warming science generally focuses on the various
changes and the risks associated with them. These risks also form a cause and effect
relationship, as indicated below:
 global temperature increase,
 melting of the arctic ice layer and mountain glaciers, as well as permafrost areas,
 modification in the direction of the sea currents,
 increase in the sea level and flooding the areas on the shore,
 more frequent extreme climatic events (heat wave, droughts, storms, floods, etc.),
 damages to the biosphere, deterioration in the conditions of survival of the flora
and fauna,
 compelled restructuring in terms of grown plants and bred animals,
 threat to human health (sunstroke, impacts of increasing UVB radiation, epidem-
ics, sensitivity to weather fronts, etc.),
 increased migration, refugees,
 increasing tensions between countries, fight for resources,
 instability of the economy, economic losses.

102. 101
Analysis of environmental risks
The analysis of environmental risks is the responsibility of each Member State within
the European Union, for which the EU develops various directives and provides meth-
odology assistance. According to one principle, Member States must try to conduct a
risk assessment for at least the most important 10-20 environmental risks and to develop
scenarios as the first step. Another principle suggests that the analyses should rely more
on quantitative data and the quantitative models based on them.
According to the directives issued by the EU Commission (EC, 2010), the national
risk assessments must apply the following criteria. The principles were developed by
GTZ (GTZ, 2004).
1. Risk analysis:
 geographic analysis (geographic location, size)
 timely analysis (frequency, duration),
 analysis by dimension (size, intensity),
 probability of occurrence.
2. Vulnerability analysis:
 people potentially affected by the risk and identification of the elements (exposure),
 identification of the factors/impacts causing vulnerability (physical, economic,
environmental, social/political),
 assessment of the probable effects,
 analysis of the self-protection capabilities, which can reduce exposure or vulner-
ability.
Multi-risk assessment
The purpose of a multi-risk assessment is to properly take into account the potential
knock-on effects of the individual events (consequences, domino effects, cascade effects).
As an example, an earthquake may damage a gas pipeline, which may then blow up, or
may cause an industrial fire. The joint management facilitates the analysis of the mutual
correlation between several threats and risks.
A multi-risk assessment involves the approach of multiple exposure and multiple
vulnerability. Such risk assessment type can illustrate the potential increasing and ex-
panding impacts of interactions with other threats. A particular risk may increase the
occurrence of another risk, or a certain event can significantly change the vulnerability
of the whole system. The multi-risk assessment makes statements about the vulnerability
of the sensitive areas exposed to risks (e.g., population, transport system and infrastruc-
ture, buildings, cultural heritage) and shows the various types of injuries and damages
that occur as a result of various threats, the prevention and combating of which requires
different methods and resources.
Concurrently implemented several single-risk analyses may also take into account
the complexity of the various reasons of a particular risk. However, this method often

103. 102
separates risks of different origin (including different natural disasters, risks caused
by people, or certain combinations of natural risks and risks caused by people). Several
difficulties must be overcome in order to transform single risk analyses into a multiple
risk assessment. Among others, it should also be taken into account that the acces-
sible data of various risks may refer to different periods in time, and the impacts are
measured with different methods too, which makes comparison or ranking difficult
or even impossible.
In practice, other types of challenges also make multiple risk assessment difficult.
Such difficulties include cumbersome coordination and cooperation between various
experts, authorities and agencies. Each expert and authority deals with certain threats
and risks without having an actual overview of the estimated situation, the “domino
effect” or the gradual consequences. Example: the engineer designing a gas pipeline may
not take into account the potential 10 cm ash layer that can be the result of a volcano
eruption and may cause the bridge holding the gas pipeline collapse. Similarly, it is un-
likely that a forestry fire-fighter understands the chance of a forest fire occurring as a
result of an industrial accident.
The ESPON project, launched by the EU, prepared a general and, in certain aspects,
light analysis of the main risk interactions for each European NUTS3 region and pre-
pared maps accordingly. The project report illustrates well how to build a quantitative
risk assessment for the entire EU. The ESPON report describes the identification of the
“threat clusters”.
The Commission service intends to analyse the multiple risk assessment field and
methodology in terms of the risks affecting the EU. However, those guidelines do not
recommend a special method for joint risk scenarios.
The national risk assessments must also take into account the multiple risk scenarios.
The following steps are recommended (EC, 2010).
1) Identification of the potential multiple risk scenarios, which begins with the anal-
ysis of the currently most important event and potentially triggers events that may
lead to further threats.
2) Exposure and vulnerability analysis, performed for various threats and risks
within the various branches of the scenario.
3) Risk assessment for each risk and unfavorable events for joint risk scenarios.
Methods and techniques used for environmental risk assessment
The techniques used for environmental risk assessment can be classified in many ways.
One type of classification is based on the phases of risk management (IEC/FDIS 2009):
 techniques used for the identification of risks,
 analysis of risks and
 assessment of risks
can be distinguished.

104. 103
A consequence analysis, a qualitative, semi-quantitative and quantitative probability
estimates or risk level estimates may be applied for the analysis of risks.
The following table shows the individual techniques according to what extent the
respective method is suitable for risk assessment and measurement for different purposes.
Table 3: Main risk assessment techniques and applicability
Methods and techniques Risk assessment process
risk iden-
tification
Risk analysis Risk as-
sessment
conse-
quence
proba-
bility
risk level
Brainstorming SA NA NA NA NA
Interview method SA NA NA NA NA
Delphi method SA NA NA NA NA
Checklist SA NA NA NA NA
Primary risk analysis SA NA NA NA NA
HAZOP SA SA A A A
HACCP SA SA NA NA SA
Environmental risk assessment SA SA SA SA SA
SWIFT SA SA SA SA SA
Scenario analysis SA SA A A A
Business impact analysis A SA A A A
Failure effect analysis SA SA SA SA SA
Fault tree analysis A NA SA A A
Event tree analysis A SA A A NA
Decision tree NA SA SA A A
Human reliability analysis SA SA SA SA A
Consequence/probability matrix SA SA SA SA A
Cost/benefit analysis A SA A A A
Multiple criteria decision analysis (MCDA) A SA A SA A
Explanation:
SA: strongly applicable
A: applicable
NA: non-applicable
Source: IEC/FDIS 2009

105. 104
The method selection is affected by several factors. The following list presents the
factors that may affect the application.
 problem complexity,
 methods suitable for analysing the particular problem,
 nature and degree of uncertainty,
 quantity of available information,
 quantity of available resources (money),
 time requirement,
 degree of professional expertise,
 ability of the method to provide quantitative results.
The most important risk assessment and analysis techniques are described below
according to IEC/FDIS (2009).
Brainstorming
The brainstorming method was introduced in the 1950s to collect original ideas. It
is an association-based creative technique, aimed at effectively collecting the ideas of a
12-15-member strong expert group. The approximately one hour discussion is held in a
tied framework, involving various techniques to encourage the participants to think. The
moderator has a very important role in promoting a new “wave of ideas” by approach-
ing the problem from a different aspect. It is prohibited to criticise any opinion even if
certain participants consider them extreme. The collected opinions are evaluated and
classified after the meeting.
The brainstorming method may be applied in several phases of risk management to
identify risks and consequences and to come up with potential actions.
Interview method (structured and semi-structured interviews)
The structured interview method is widely used in economic and social sciences to
collect information and to form expert opinion. The views of the interviewees are col-
lected with the help of predesigned questions. The semi-structured interviews provide a
greater room for maneuver to discuss the issues and to collect the opinions.
The interview method becomes useful when complicated questions need to be asked
from the interviewees, or the experts cannot be called together at one time, or when the
traditional questionnaire-based surveyed are not effective. This method is ideal primarily
to identify the impacts of environmental risks on those concerned.
Delphi method
This method is also useful for collecting and evaluating expert opinions. It is a group
technique, even though the experts are not gathered at one time. It may also be used
to reach a consensus among the experts, providing also the most acceptable solutions

106. 105
and best proposals of a particular period. Experts are requested to respond and resolve
a particular problem (via e-mail). Semi-structured questionnaires may also be used to
describe the problem. The experts come up with their answers independently. Then the
collected opinions are evaluated (e.g., statistical analysis), in the course of which the most
frequent solutions, or the solutions reflecting the average of the opinions and the differ-
ences in opinion are analyzed (e.g., average and spread calculation). Then the results are
returned to the experts, who may modify their position based on the aggregated result.
The step can be repeated several times, but it should only be continued until no more
difference can be detected between two subsequently returned sets of opinions. This may
be considered a consensus-based opinion or solution.
Checklists
Checklists are lists of risks prepared by experts generally based on former experience,
or errors made in the past. It helps consistent error detection, standard control but it
cannot be used for identifying new threats and risks.
Preliminary hazard analysis, PHA
The PHA is a simple, inductive method to analyze threats and risky situations that
may cause damage to a particular system: in some equipment or an activity. In general,
it is used in an early phase of projects, when still little information is available about the
details of plans or operation. Consequently, it is the starting point of further studies and
may also assist in the details of a system design (specifications). It may also be used to
analyze existing systems when circumstances prevent the application of more complex
methods. It uses the accessible and recognizable information of the system and t hose
elements of the system design that are accessible and important. The results (the list of
risks and threats) are illustrated in tables or in a tree structure. The findings should be
regularly reviewed during the progress of the project.
Hazard and Operability Study (HAZOP)
This method is used for the structured and systematic review of a designed or existing
product, process, procedure or system. It may also be used to identify risks associated
with people, equipment, organizational and/or natural environment. It is also expected
from the expert team to also come up with solutions for risk management to the extent
possible.
Originally the HAZOP technique17
was used to analyze chemical processes, but later
its application was also extended to other types of systems and complex operations (e.g.,
technical and electronic systems, software systems, organizational changes, complicated
17 IEC 61882, Hazard and operability studies (HAZOP studies) – Application guide

107. 106
legal contracts, etc.). The HAZOP process may register any form and method of any
deviation from the plan.
A HAZOP study is generally prepared in the phase of detailed planning when the
complete process chart of the planned process is already available but some details may
still be modified. It may also be elaborated in the implementation phase, but in that case
the implementation of the required changes will be more expensive.
HAZOP studies the design and specifications of a process, procedure or system: it
analyses each component where any deviation from the planned may be assumed or
detected. It is achieved by using “guide-words”, suitable for such purposes, based on
which the system, the process or procedure can provide responses to change in the key
parameters. Those guide-words are calibrated to the respective system (e.g., technical
systems: more (higher), less (lower), the same, contrary, different from etc.; in HR: too
early, too much, too little, too short, too long, wrong direction or wrong action, etc.).
Normal steps in a HAZOP study:
 nomination of a person with the necessary responsibility and authority to con-
duct the HAZOP study, and to ensure that any actions arising from the study are
completed,
 definition of the objectives and scope of the study,
 establishing a set of key or guide-words for the study,
 defining a multi-disciplinary HAZOP team to prepare the study, which should
include persons who did not take part in the design of the system,
 collection of the required documentation,
 splitting the system into smaller elements, or sub-systems, or sub-processes, etc.,
 identification of possible deviations affecting various elements, sub-system,
sub-processes, etc. based on the guide-words which will have undesirable out-
comes,
 where an undesirable outcome is identified, agreeing the cause and consequences
in each case and suggesting how they might be treated to prevent them occurring
or mitigate the consequences if they do,
 documenting the discussion and agreeing specific actions to treat the risks iden-
tified.
The outputs of HAZOP are the minutes of the meetings, which should include the
guide-words used, the deviations, the possible causes of problems, the actions to address
the identified problems and persons responsible for the action. For any deviation that
cannot be corrected the risks for the deviation should be assessed.
Hazard analysis and critical control points (HACCP)
Originally HACCP process was developed to ensure the quality of the NASA space
programmes. It is now used widely to ensure the safety of the food chain and to prevent

108. 107
physical, chemical and biological contamination (ISO 22000)18
, as well as in other areas
important in terms of preservation of human life (manufacture of pharmaceuticals,
medical devices, etc.). The primary objective of HACCP is to minimize risks by super-
vising the processes all the way. In the course of the procedure all factors are identified
that may affect that quality of the product, and define the points of the process where
critical parameters may be observed and risks can be controlled. They are also generally
elaborated principles in a HACCP, which may be applied to any system to be analyzed.
The HACCP procedure is used in an extremely strict system by the accredited or-
ganizations, prepared for it.
Environmental risk assessment
The environmental risk assessment is used to assess various environmental risks
affecting plants, animals and people. A special form of it is called toxicity assessment.
The method involves analyzing the hazard or source of harm and how it affects
the target population and its impact on it. This information is then used for assessing
the likely extent and nature of harm. In the case of chemical risks, different levels are
established and dose-response curves are established by using the results of animal ex-
periments or some other experimental systems (e.g., tissue or cell cultures).
This method requires very accurate data and thorough understanding of the risks
and the specificities of the target population, as well as any potential interaction between
them.
Structured “What-if” technique (SWIFT)
SWIFT was originally developed as a simpler alternative to HAZOP. It is a systematic,
team-based study in the course of which the facilitator uses various “prompt” phrases
during a workshop to encourage participants to identify risks. The moderator uses
standard “what if” type phrases in combination with the prompts to investigate how a
system, organization or a particular procedure will be affected by deviations from normal
operations and behaviour. SWIFT is normally applied at more of a system level with a
lower level of detail than HAZOP.
While SWIFT was originally designed for chemical and petrochemical plant hazard
studies, the technique is now much more generally and widely applied.
In the course of the procedure the facilitator asks the participants to discuss the
following:
 known risks and hazards,
 previous experience and incidents,
 known and existing controls and safeguards,
 regulatory requirements and constraints.
18 ISO22000,Foodsafetymanagementsystems–Requirementsforanyorganizationinthefoodchain

109. 108
It is then followed by a discussion facilitated by creating a question using a “what if”
phrase and a prompt word or subject. The possible phrases are as follows: “what would
happen, if...”, “could someone or something...”, “has anyone or anything ever...” The
intent is to stimulate the study team into exploring potential scenarios, their causes and
consequences and impacts.
The output of the SWIFT study is a risk register with risk-ranked actions or tasks.
Scenario analysis
The scenario refers to a future vision that results from logically related assumptions.
The scenarios describe the hypothetical consequences of related events in order to identi-
fy the cause and effect relationships and the decision making situations. Various versions
and options need to be outlined, which represent characteristic development trends.
In the course scenario analysis, alternative future trends need to be described that
lead to future situations. The scenario technique is based on the analysis of extreme
situations. By analyzing the best, worst and expected cases, potential consequences and
their probability may be identified in the form of sensitivity analyses.
Given its outstanding importance, the scenario analysis is described in a separate
study.
Business impact analysis (BIA)
A business impact analysis analyses how key disruption risks could affect and organ-
ization’s operations and identifies and quantifies the capabilities that would be needed
to manage them.
BIA is used to determine the criticality and recovery timeframes of processes and as-
sociated resources (people, equipment, information technology) to ensure the continued
achievement of objectives. Additionally, the BIA assists in determining interdependences
and interrelationships between processes, internal and external parties and any supply
chain linkages.
Questionnaires, interviews and structured workshops are used for the impact anal-
ysis in order to better identify the critical processes, impacts of losses and the required
recovery timeframe, as well as supporting resources.
Failure effect analyses
The failure effect analyses are similar to the failure prevention analysis method, used
in technical disciplines. Apart from the basic version (Failure modes and effects analysis,
FMEA) the extended version also contains a ranking order of various effects according
to importance or criticality (Failure modes and effects and criticality analysis, FMECA).
FMEA/FMECA are used on several areas: in the design of components and prod-
ucts, in the system analysis, in production, assembly and servicing and in software

110. 109
development. The method helps selecting design options, to take into account all possible
faults, to identify the impacts of human errors and mistakes, to ensure the background of
design, testing and maintenance of physical systems, to improve the design of procedures
and processes and to obtain qualitative and quantitative information for future analysis
methods to be presented later.
Fault tree analysis (FTA)
This procedure uses a graph to present the factors that can contribute to a specified
undesired event (“top event”) with a tree diagram. The causal factors are deductively
identified, organized in a logical manner and were presented in as system. Standard
symbols are used for reflecting the causal factors. (IEC 60300-3-9).
Event tree analysis (ETA)
The event tree analysis is a similar technique to the fault tree. It is used for repre-
senting mutually exclusive sequences of events to mitigate the detrimental consequences
of functioning/non-functioning of various systems. It may be used as a qualitative and
quantitative procedure.
Decision tree analysis
A decision tree represents decision alternatives and outcomes in a sequential manner,
which takes account of uncertain outcomes. It is similar to an event tree analysis but it
models not only events, but also decision possibilities. This method is often applied and
illustrated in economic decision making.
Human reliability assessment (HRA)
HRA analyses the influence and impact of people on the performance of systems,
concentrating primarily on errors made by people. It has been developed primarily be-
cause in many cases disasters occurred as a result of critical human faults.
Consequence/probability matrix
The procedure, also known as risk assessment matrix, ranks consequences and prob-
abilities quantitatively or semi-quantitative in order to establish the risk level. In general,
it is used in the first phase of the analysis is order to establish the severity or hazard
associated with the risk without any delay. More detailed and deeper analyses may also
be required in order to manage risks effectively. The method may be used extremely well
to communicate the level of risks and the degree of the hazard within the organization.

111. 110
Cost/benefit Analysis (CBA)
It is primarily used for assessing risks economically (financially) and for identifying
the most profitable alternative by comparing the estimated total costs to the estimated
total benefits. The future economic values are weighted with net present value (NPV)
calculation. Consequently, the greater economic sacrifices made in future are assigned
a lower weight in the calculations than the identical economic sacrifices of the present.
The same also applies, in a contrary direction, to benefits. They also try to identify all
the costs (direct and indirect costs, opportunity costs, etc.) and indirect benefits.
Multi-criteria decision analysis (MCDA)
Several important criteria are applied in the analysis for the issue to be decided upon
in order to judge particular options objectively and transparently. The general objective is
to establish an order of preferences among the potential options for the decision makers.
In the course of the analysis an option/criteria matrix is elaborated in order to establish
the ranking order. The difficulty of this method is to identify the weight assigned to
various criteria and to establish an order of importance.
Management of environmental risks
Risk management performs the overall tasks of analysis, evaluation and management of
risks. Its concept may be illustrated with several approaches. The model in Figure 1 presents
risk management completely by applying the concepts of risk estimates. The focus is on the
identification of the risk, risk analysis and risk assessment, which phases are preceded by
the establishment of the context and the criteria related to the problem. Finally, the process
ends with risk management. Consultations with the stakeholders and control i.e., monitoring
and review, are indispensable and important parts of the whole risk management process.
According to the management theories dedicated to the control of organizations,
three overall management areas can be separated: strategic management, operational
management and risk management. The same may also apply to the control and in-
fluence of society and global systems. These areas cannot be fully separated from each
other (Williams, 1995).
Strategic management is dedicated to building, designing and implementing the future
vision, mission and objectives of an organization and the related strategies. Operational
management sets out plans, schedules and implements short-term tasks for the implemen-
tation of the strategies. Both functions relate to the future, setting targets for the future
and implementing them i.e., the correlation between the risks and uncertainty relating
to the future must be understood under all circumstances. During the performance of
strategic and operational management tasks, risks are detected, their impacts are assessed
and risk management also takes place.

112. 111
Strategic organisational
and risk management
aspects
What can happen?
How can it happen?
Comparison with criteria
Establishment of risk priorities
Identification of options
Assessment of options
Elaboration of plans
Implementation of the plans
Communication and
consultation
Monitoring and
review
Definition of criteria
Identification of risks
Risk analysis
Proba-
bilities
Conse-
quences
Risk level
Risk assessment
Acceptable
risk?
Yes
No
Risk management
Source: ISO 31000, supplemented
Figure 1: Risk management model
The concept of risk management has developed in relation to the need for joint
classification and review of risk-related tasks. However, risk management assumes the
existence of two other management functions, as operational management cannot be
separated from strategic management either, they also have overlaps. This also means
that risk management may be strategic or operational. The difference between the two
is in the time horizon and in the more or less complex or specific nature or the factors

113. 112
to be analyzed. The strategy looks at a longer term and wider time horizon and operates
with more general statements and more complex actions.
There is more uncertainty in a longer term, and therefore there is less specific clear
information about the future.
Figure 2 reflects the increase in the degree of uncertainty relating to time, based on
which it can be assumed that uncertainty exponentially increases with the extension of
the time horizon.
Source: edited by the author
Figure 2: Increase in the degree of uncertainty depending on time
Wanner proposes a four-degree model to manage more complex risks with more
severe consequences (Wanner, 2009). The proposals, built on one another, are as follows:
1. risk avoidance,
2. risk mitigation,
3. risk sharing,
4. preparation for risk bearing.
The four degrees also refer to a specific order as illustrated in the figure below.
Source: Wanner (2009)
Figure 3: Degrees of risk management

114. 113
According to the Wanner model first it should be analyzed whether the risk can be
avoided or not. In order to do that the causes of the risk must be terminated or eliminated,
if possible (e.g., if there is a flood risk, another site should be selected).
However, not only the time horizon, but also an increase in the number of factors
affecting the specific process or the lack of identification of their mutual effect increase
uncertainty. The following table describes situations relating to the degree of uncertainty
and classifies the method that can be used for analyzing it.
Table 4: Degree of uncertainty and available analytic methods
Degree of uncertainty Features Analytic methods
clear and bright future clear development direction,
specific forecast is possible
traditional mathematical-statistical
methods (regression analysis, trend
analysis)
alternative develop-
ment options
alternative development trends may
be identified and described
scenario technique, optional models
continuous develop-
ment
there are several potential directions
which may be limited
scenario technique, gaming theory, de-
cision models
total ambiguity no development trends and corre-
lations can be identified, no clear
statements can be made
scenario technique, gaming theory, de-
cision models
Source: Hungenberg, 2012.
Risk management may be a fundamental approach to environmental risks not to
environmental risks not only because of the combination of strategic and operational
approach and the long-term uncertainty analysis, but also as a result of the complex risk
management concept.
If risks stem from persistent causes that cannot be influenced, attempts must be made
to mitigate risks as much as possible. It can be achieved by reducing the probability of
occurrence and harmful effects. The reduction of the probability of occurrence can in
general be achieved at a high expense (e.g., raising the flood protection dam). Similarly, the
mitigation of harmful effects may also demand financial sacrifice, e.g., when a skyscraper
needs to be reinforced with technical solutions that will protect it against earthquakes.
The next degree can be risk sharing, for which risk communities can be created (in-
volvement of more actors in risk management, e.g., insurance). Risk sharing is a widely used
method for managing various economic and social risks (home insurance, life insurance, etc.).
If risk sharing is not possible or it does not provide enough security to avoid damage,
preparations must be made for bearing the risks. Among others, it may also mean devel-
opment of plans but can be used in risks. Society, economic organisations and families
can develop such plans and adopt such measures in order to successfully implement
the plans (e.g., sand bags, storage of other flood protection equipment, operation and
financing of disaster prevention organization, etc.).

115. 114
Summary
On the basis of the previous chapters, it has become clear that there is no exclusive
method to observe or analyze the risks arising from changes in the environmental
elements, or their economic-social impacts. Each of the methods described in the inter-
national literature was developed for the analysis of a specific environmental risk, or on
the contrary, they can also be considered analytic methods that can be used more widely
for several purposes. Only different complementary analytical methods can provide
the responses for the assessment of economic and social impacts, induced by factors,
which even influence each other in a complicated system, and for the related scientific
conclusions and statements.
In relation to the objectives of the research assignment, in the subsequent study we
shall move on to the application of the scenario analysis, which integrates several methods
in order to analyze the complex impact of environmental risks.
References
Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ), 2004: Risk Analysis – a Basis for
Disaster Risk Management
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1995.

116. 115
Scenario Analysis: Social-Economic Impacts
of Long-Term Climate Changes Affecting
Agriculture, Forestry and Local Communities
László Kulcsár - Csaba Székely
ABSTRACT: Scenario analysis is a method generally used for the analysis of the impact of climate
change. It cannot be confused with projections. A scenario analysis provides an alternative future
vision, the actual occurrence of which heavily depends on the occurrence of the identified key
factors. The study deals in detail with the scenario analysis methodology and then outlines three
alternative scenarios by relying on the background studies described in the book, estimating also
the probability of their occurrence.
KEYWORDS: scenario analysis, climate change, local community, social, economic change, agricul-
ture, forestry
Introduction
The methodology study dedicated to risk analysis (Székely 2014) clearly showed that un-
certainty makes it very difficult to properly forecast the future development processes of
individual environmental factors. That uncertainty equally applies to natural and social
sciences. The results and assumptions of natural sciences gain true importance when they
are applied to the development of society and economy. That is why, as also mentioned
in the introductory study, interdisciplinary research dedicated to climate change proved
to be very successful across the world.
The scenario analysis has been recently used more extensively to analyse complex
global problems. Bohensky and his partners (Bohensky et al 2011) pointed out that
scenarios should not be mixed up with projections even though they have some simi-
lar features. A scenario is an alternative vision for the future, which lies on qualitative
and quantitative observations. In other words, the scenarios are similar to a set of
hypotheses, which reflect a lot of uncertainty in terms of components and the future.
According to Bohensky, each scenario contains a great deal of uncertainty and low
controllability. Why are scenarios still needed in economics and in social sciences?
The fundamental reason is that the studied phenomena and processes, such as climate
change and its impacts, are rather complex and the triggering event itself is very com-
plex with a great deal of uncertainty.
The scenario analysis can be used to analyze the future of complicated processes
when traditional mathematical and statistical methods fail. Naturally, it does not
mean that with the help of a scenario analysis the same clear and provable quantitative
conclusions can be reached as with more simple mathematical models, suitable for
describing deterministic situations. Even so, methods need to be applied which can at

117. 116
least provide some guidance about the future of complicated natural correlations even
if they cannot give a clear result.
Scenario analysis as a method
The scenario refers to a future vision that results from logically related assumptions. The
scenarios describe the hypothetical consequences of related events in order to identify
the cause and effect relationships and the decision making situations. Various versions
and options need to be outlined, which represent characteristic development trends
(Hungenberg, 2012).
In the course scenario analysis, alternative future trends need to be described that
lead to future situations. The scenario technique is based on the analysis of extreme
situations. By analyzing the best, worst and expected cases, potential consequences and
their probability may be identified in the form of sensitivity analyses. A scenario is a
possible alternative future vision.
The scenario analysis is based on a descriptive model that describes potential future
events. It may also be used to identify risks of potential future development and to
describe the impacts thereof. Although a scenario analysis cannot be used to project
probability, by considering the consequences, society and politicians can be assisted in
developing their strength and flexibility in adaptation to foreseeable changes.
Steps of a scenario analysis
In general, the scenario analysis is divided into phases in literature. There are two dif-
ferent approaches:
 a forward approach,
 and a backward approach.
The first two steps (task and problem analysis and influence analysis) are the same
in both approaches. It is then followed by the elaboration of the scenarios according to
the two different approaches. Finally, the evaluation and interpretation phases are also
the same in both approaches.
Task and problem analysis
In the course of the task and problem analysis, first the process forming the subject
of the analysis must be defined clearly. In general, it requires a longer iterative process,
because in the course of the definition and description of a complicated process, profes-
sional knowledge, experience and imagination are needed. The Delphoi method may be
an adequate methodology guide in defining the process.
Then key factors (descriptors) are defined, which may influence the analyzed
process and scenarios to be developed. The output of this phase could be a detailed

118. 117
task and problem description and a list of factors. Table 1 contains a list of factors
as an example, which may influence the situation of as particular region in relation
to climate change. Brainstorming or the nominal group technique may be useful
methods when defining the key factors. These creative techniques use associations
to generate ideas that may be suitable for selecting the right key factors after suffi-
cient filtering.
Natural, technological and anthropogenic factors, where the changes (in anthropo-
genic and technology factors) primarily depend on society and economy have a key role
among the key factors.
Influence analysis
In the influence analysis we wish to identify how the various key factors influence
each other. In order to do that, first a network table has to be prepared. The descriptors
are compared in the table. The objective of a direct comparison is to identify the degree
of correlation between the various factors (no influence, average and strong influence).
Apart from that, indirect influence (cause and effect chains) can also be detected by using
e.g., Ishikawa diagram (otherwise known as fishbone diagram). The general structure of
a network table is illustrated below.
Table 1: Influence analysis
Influencing/
Influence
Factor 1 Factor 2 Factor 3 … factor n.
Accumulated
influence
Factor 1 - 0 0 … 0 0
Factor 2 0 - 0 … 0 0
Factor 3 0 0 - … 0 0
… … … … … … …
factor n. 0 0 0 … - 0
Accumulated suscep-
tibility to influence
0 0 0 0 -
Source: Baum et.al, 2007
After developing a network table, the impacts are summarized, the result of which
may be illustrated in an influence matrix.
The output of the influence analysis includes the network table and influence matrix
and an overview of the degree of influence of the various factors. By using those, the
generally large number of influencing factors can be reduced to a manageable quantity,
and only the factors exerting the greatest influence may be selected.

119. 118
Source: Baum et.al, 2007
Figure 1: Influence matrix
Extrapolation of trends and definition of scenarios
According to the forward approach, the various development options of each selected
factor need to be defined according to the following question: what outcomes / future
development options can be envisaged for each factor?
The combination of the various factor outcomes will generate the individual scenarios.
As an example, the first outcome of factor No. 1 is combined with the second outcome of
factor No. 3 (e.g., politics will take a good turn, and all important countries will adhere to
the Kyoto Protocol and the local economy will become sustainable by using the available
options of environmental technology. As under certain circumstances not all combina-
tions make sense, or certain combinations exclude each other, or several combinations
may be merged due to similarity or significance, certain alternatives should be combined
or the analysis should be reduced to selected scenarios or sets of alternatives. In order
to work effectively with scenarios, at least 3 and no more than 8 scenarios should be
defined. Generally at least the two extreme scenarios and a few other selected scenarios
should be further analyzed.
By using the correlation analysis, various subsequently occurring events can be
analysed, focusing on their correlations. That is how the previously identified potential
scenarios can be investigated according to plausibility. Those scenarios need to be de-
fined, which illustrate, yet consistent statuses of each factor.
With the backward approach, the scenario refers to two or three alternative future
visions, describing e.g., the worst and best possible status of development and, as the

120. 119
first scenario, the continuation of the current situation. Then those future visions “are
dissolved” in the most important factors. That is the basis on which consideration should
be given to what changes should occur in order to achieve a specific scenario.
The outputs of this phase include the potential outcomes of the various factors/de-
scriptors or their combination/connection into various scenarios. In relation to that the
various scenarios need to be described/formulated in order to make them understandable
and communicable.
Evaluation and interpretation
In this phase, further analyses are conducted on the selected scenarios. The prob-
ability of occurrence of the scenarios is estimated and the opportunities and risks
related to each scenario are compared. In addition, the scenarios are also evaluated
according to their plan and actual situation (the scenario in which we are at present
and direction in which future will develop). Accordingly, organizations can define
measures/action options for each scenario with which they can prepare for their im-
plementation. With the help of the scenario, an organization may review its previous
strategy. If it concludes that the current strategy is unlikely to be successful in any
elaborated scenario, the strategy needs to be revised. Thus, the scenarios may help find
robust strategies for the future.
The output of this phase may be the evaluation and comparison of the selected sce-
narios and derived action options and measures.
Features of the analytic process
A scenario analysis can be conducted in a formal or informal structure. After estab-
lishing the research team and putting in place adequate communication channels, and
wants the relationships and topics of the problem have been identified the nature of the
potential changes should also be defined. In order to do that, the main trends and the
probable time of occurrence of the changes need to be identified, which also requires
imagination about the future.
The following changes may need to be considered:
• changes in the needs of the stakeholders,
• decisions to be made in the future, which may have several potential outcomes,
• external (e.g., technology) changes,
• Changes taking place in the macroeconomic environment (regulations, demo-
graphic changes, etc.).
Some changes will inevitably occur, and others may be uncertain.
An actual change may also be the consequence of a different risk. An example can be
when consumer demand for foodstuffs changes due to climate change. That influences
food exports and the food products that may be produced locally.

121. 120
The local and macro economic factors and trends can be listed and ranked according
to their importance or uncertainty. Special attention must be paid to the most important
and most uncertain factors. The key factors and trends may also be illustrated relative to
each other (in the form of a “map”) in order to highlight the areas from which scenarios
can be developed.
Such scenarios need to be developed which focus on the potential changes of the
parameters. Then a “story” has to be written for each scenario, explaining the path to
be followed from the present until the respective scenario is reached. The stories may
contain life like parts, which increase the value of the scenario.
Then the scenario can be used for testing or evaluating the original issue. The
tests take into account any significant, predicable factor (e.g., use of samples), and
then seeks to identify how “successful” the policy, activity can be in the new scenar-
io, and identifies the “pre-test” results from “what if” questions asked in the model
assumptions.
As the scenarios can only be defined as “segments” of the potential future, it is im-
portant to make sure that they take into account the probability of occurrence of indi-
vidual events (scenarios) i.e., the risk framework system. As an example, when analysing
the worst and estimated scenario, an attempt must be made to classify and express the
probability at which the various scenarios will occur.
We may not be able to find the test matching scenario but the review must be
closed with a clearer and more straightforward result about the existing options and
how to modify the selected development direction or action to reflect the changes in
the indicators.
Strengths and limitations of the scenario analysis
A scenario analysis takes into account several future situations, which may be advanta-
geous compared to traditional approach, which trusts high average and low projection
types. It assumes that when historic data are used, future events will occur as a likely
continuation of historic trends. This is important in situations when there is little knowl-
edge available based on which long-term risks can be defined. However, in such a case,
strength can be coupled with a problem that there is such a great uncertainty about the
future that part of the scenarios cannot be considered real.
The greatest problem of the scenario analysis relates to the availability of data and
the ability of analysts and decision makers to develop real scenarios that can be used to
test potential consequences.
A scenario analysis as a decision making tool may also hide threats when the elabo-
rated scenarios are not sound enough, when the data are based on speculations and when
the realistic result is not recognised as such.

122. 121
Potential scenarios concerning climate change
in the agriculture and forestry sector
Macroeconomic and political approach
In that approach, based on Bohensky et al. (2011) the basic value of the scenario is
reflected in the market-economy-competition based theory of social-economic develop-
ment and in its contrary, development towards well-being driven society and economy.
In other words, at one end point of the value set, traditional market development is
given the green light and consumption has central significance, while ecological and
environmental difficulties are pushed into the background. At the other end of the scale
development, driven by market conditions faces strong environmental problems and so-
cial resistance, while ecological threats become visible and near. On coordinate therefore
is the dimension of a rural economy and society, and the other coordinate is a dimension,
typical for the whole economy and society of the country. In both dimensions, there is
a scale of the described basic value. The scenario shows the development expected in
each combination and how the experienced/likely climate change will affect the rural
areas and population in the various types of development-visions.
Source: edited by the authors
Figure 1. Development visions based on different basic
values: Main pillars of the scenario analysis

123. 122
One scenario recalls the situation of classic market capitalism to the general public. The
situation here is similar to the relationship between developing and developed countries.
To a certain extent “island type” Modernization hubs are created as described by Korten.
Social-economic equalization is still missing and regional social-economic disparities do
not decrease. There is another scenario, the contrary of the former one but, as we will see,
its implementation is hindered by cultural, political and financial impediments.
Special trends in the development of the country and rural areas
The individual scenarios clearly show that we cannot avoid the processes of macro
level development of economy and society and we must take into account positive and
negative aspects of each scenario, yet the probability of the specified scenarios is different.
Earlier we saw that macro social and political relations are not always favorable to
economic and social actors facing the problem of climate change. Certain support is still
available on the production side of agriculture and forestry management, but it should
also be said that support and amounts received from insurance do not sufficiently en-
courage either an increase in the adaptation ability, or the mitigation of negative effects.
A further extremely important problem is to enforce social justice in the local and
central measures that mitigate the negative impacts of the risks of the climate change.
Brisley et al (2012) highlight that the enforcement of social justice assumes the identifi-
cation of vulnerable social groups and preference to information and advisory services
to them besides financial support. In the scenario analysis, we tried to make sure that
those aspects were given the required significance.
Below we shall present the potential social-economic consequences of climatic effects
primarily in agriculture and forestry management, as well as the environment of the
settlements which are likely to prevail in the country and in the rural areas in the second
half of the 21st century (Gálos 2014).
It is clear from the described key factors that state intervention cannot be avoided in the
case of vulnerable groups of farmers. It is also clear that the agricultural and forestry man-
agement scenarios relating to climate change go beyond the analyzed sectors. We strongly
argue that within the analyzed economic sectors the negative consequences of climate
change cannot be remedied in the long term. The scenarios go beyond those boundaries.
Some of the negative impacts of climate change directly affect the analyzed sector of
the economy, while others exert a primarily negative impact on the total local community
or on certain demographic groups thereof. The scenario analysis must take into account
those discrepancies even if the impacts cannot be clearly separated from each other. E.g.,
due to a sudden great volume of rainfall and soil erosion, there may be groundwater or local
floods (Judit Vancsó Mrs. Papp, Csilla Obádovics, Mónika Hoschek 2014), which do not
simply affect agricultural producers, but the total population of the region, especially the
disadvantaged groups of the population, even though they are not significant actors in the
particular economic sector. Reisinger et al. (2011) pointed out that local governments have

124. 123
an outstanding role in creating awareness of the risk factors that result from the vulner-
ability caused by climate change, and in the assessment and elimination of the risks. The
required rules must be adopted accordingly, and the necessary interventions must be made.
Table 2. Key factors in the relationship of agriculture and
forestry management, as well as climate change
Problems
reflected in the
key factors
Social-economic determination
of sensitivity/vulnerability
Adaptation/Response/
Strategy
Agriculture:
Increase in the
frequency of ex-
treme weather
conditions.
Increase in the
frequency of
unfavorable
natural condi-
tions (drought
or sudden pre-
cipitation, ma-
jor temperature
fluctuation).
Increasing
volatility in
agricultural
production.
Economy
1. Decrease on the economic performance and
income generating capacity of agricultural
producers, more uncertainty and deterio-
rating market position.
2. Increasing regional disparities.
3. Missing capacities to prevent vulnerability
and disadvantages.
4. Inadequacy and increasing expense asso-
ciated with resources received from insur-
ance.
Society
1. Increase in the sensitivity of families signif-
icantly exposed to agriculture.
2. Increasing sensitivity of poorer and more
disadvantaged families engaged in agricul-
ture, deterioration in their position.
3. Increasing social differences.
4. Lack of financing for measures that reduce
sensitivity and vulnerability
5. Lack of the required knowledge, cultural
capital and information
6. Low level of adaptation capacity
7. Low level of cooperation, solidarity and in-
terest enforcement (social capital)
8. Unfavorable demographic composition of
the population (low qualifications, aging)
1. Response: Increasing diversifica-
tion in the economy,
2. Response: Profile change in agri-
culture,
3. response: Reduction of agricultur-
al activities,
4. Response: Creation and devel-
opment of local institutions,
strengthening cooperation
5. Effective and intensive advisory
services
6. Increase in planning capacities
7. Response: abandoning agricultur-
al activities
8. Response: Migration of the family
engaged in agriculture or certain
parts of the family
9. Increase in state aid that depends
on vulnerability
Forestry:
Unusual weath-
er and variable
weather condi-
tions. Drought,
sudden rainfall,
erosion
Appearance of
pests and alien
species, change
in the previous
composition
of species
Economy
1. Devaluation, increasing uncertainty con-
cerning income,
2. Difficulties in the business strategy of forest
users and owners.
3. Increasing expenses
4. Market loss
Society
1. Decreasing employment, lost income
2. Increase in the role of traditional knowledge
and skills
1. Response: Search for new markets
2. Response: Change of technology
3. Increase in the planning capacity,
supply of information, advisory
activities
4. Increase in the role of traditional
knowledge, greater cooperation
5. Response: Strengthening the di-
versification of the economy
6. State aid depending on vulnera-
bility
7. Response: Abandoning forestry
Source: edited by the authors

125. 124
From a different aspect, climate changes, and the large and increasing intensity of
temperature volatility triggers not only economic but also significant health problems,
generating difficulties to certain social groups.
The risks affecting settlements may continue to exist for a long time, especially in
disadvantaged and highly exposed regions and settlements. In these areas, sensitivity
is rather large, not only in community areas (settlement protection infrastructure), but
also in health conditions.
Table 3: Key factors in terms of climatic changes affecting
the local community and population
Problems in
the scenario
Social-economic determination
of sensitivity/vulnerability
Adaptation/Response/
Strategy
Increase in the
frequency of
extreme weather
conditions. Increase
in the frequency
in unfavorable
natural conditions
(drought or sudden
extensive precipi-
tation, floods and
inland water, major
temperature fluc-
tuation, volatility).
Settlement, regional, community threats
Floods and inland water
1. Costs local floods threatening urban and subur-
ban areas of settlements (defence, etc.),
2. Household and municipality costs of reaching
flooded areas
3. Health expenses (population, municipality)
4. Construction expenses (population, municipal-
ity)
5. Inadequacy and increasing expense associated
with resources received from insurance.
Health threats
1. Heart and vascular system problems at the vul-
nerable population (old people)
2. Increase in the health causes of traffic accidents
3. Increase in the number of accidents at work tak-
ing place in the open air
4. Increase in the frequency of auxiliary events
(e.g., bathing accidents)
Settlement, commu-
nity adaptation
Migration of the population
1. Creation of the required
institutions and regula-
tions
2. Increase in state aid
Individual adaptation
1. Adherence to health reg-
ulations
2. Prevention of traffic con-
duct errors (drinking liq-
uids, rest, etc.)
3. Adherence to rules per-
taining to (illegal) bathing
Source: Edited by the author
The results of the referred studies confirm the conclusion that the perception of
climate change and adaptation categories are present differently among the agricul-
tural population, backing up the similarly differentiated decisions in state and local
intervention which take into account the social and cultural disparities of the different
territories.
The potential scenarios relating to the social-economic impact of climate change af-
fecting the agricultural and forestry management sector rely a great deal on the results
of our analyses. Based on the results those key factors can be created that may serve as
the basis of likely scenarios. The key factors listed below also indicate that state inter-
vention in the interest of vulnerable economic groups cannot be avoided. It is also clear
that the agricultural and forestry management scenarios relating to climate change go

126. 125
beyond the analyzed sectors. We strongly argue that within the analyzed economic
sectors the negative consequences of climate change cannot be remedied in the long
term. Consequently, the scenarios go beyond those boundaries and take into account
the significant regional differences in vulnerability and exposure to climate change.
The health threats may be interpreted at individual and family level and relate to the
size of the territory and sensitivity of the social groups. The high level of vulnerability is
associated with the social-economic disadvantages and their continuation in the longer
term. On the basis of natural scientific studies (Gálos 2014) and due to the increasing
severe climatic effects, the social-economic disadvantages are likely to remain in the
second half of the century.
Summary
The scenarios of climatic impacts, affecting agriculture and forest management and
settlements can be interpreted correctly by taking into account the hierarchy of the
scenarios. The first interpretation framework includes the previously mentioned four
development scenario (A, B, C, D), each of which has a different outcome reflecting the
consequences of climatic impacts related to agriculture and forest management and
regional, community and health factors.
Scenario “D” is the ideal solution for society and the economy (see Table 4.). Social
justice and positive discrimination are extremely important in those local and
central measures that will mitigate the negative impacts of the risks of the climate
change. The enforcement of social justice assumes the identification of vulnerable
social groups and preference to information and advisory services to them besides
financial support.
As stressed above, each scenario is a vision, the future occurrence of which is a hy-
pothesis. The scenarios intend to present strongly different situations in order to enable
decision makers interpreting the phenomena to face the estimated consequences of their
decisions.
It is clear from the above that in terms of the development of the economy and society,
the social-economic consequences of climatic effects will be most relevant for our topic.
The consequences are strongly culture dependent (Jankó 2014, Kulcsár 2014), the natural
scientific results could be the antecedent starting points of the consequences, which are
also uncertain in their effect and time and spatial prospects. As we saw before, even
despite the uncertainty, certain alternatives can be outlined with different probability.
Scenario “A”, which illustrates a market centered situation, seems the most likely. The
biggest threat associated with it is that climatic effects will increase regional differences
and social-economic disparities.

127. 126
Table 4. Summary of the social-economic consequences
of climatic impacts - scenario table
Possible directions in social-economic future of national and rural regions (2015-2050)
Scenario “A”
dominance of mar-
ket conditions
“worst case”
Scenario “B”
abandoned local
endeavors
Scenario “C”
low fund ac-
cessing ability
Scenario “D”
Positive discriminance
The unfavorable social-eco-
nomic position is counter-
balanced by the state
“best case”
Climatic impacts
The exposure of sensi-
tive and vulnerable social
groups and regions will
depend on their market
position and competitive-
ness
The state will not
support enough local
endeavors to reduce
vulnerability and ex-
posure, and therefore
most of them will re-
main unsuccessful
The central endeav-
ors and support will
not be useful due to
lack of local recep-
tive skills and lack of
preparation
Reduction in the exposure of
sensitive and vulnerable social
groups and regions
Visions for social-economic consequences
Increasing disparities, dif-
ferentiation of regions,
more concentrated eco-
nomic advantages and
disadvantages, abandon-
ing the sector, migration.
Increasing disadvantages
in settlements. Low level
of state intervention
Lack and low amount
of central support,
slowly disappearing
local initiatives and
their inefficiency.
Funding and informa-
tion deficit of the rel-
evant social groups
Small isolated groups
cannot use central
funding effectively.
The attraction of
support is rather low
in the social groups
and regions, there is
a shortage of funding
and a disadvantaged
situation, with con-
tinued exposure and
migration
Major support to the most
vulnerable groups in the ad-
aptation process, in the reduc-
tion of disadvantages and in
diversification, in social and
health services, preparation of
settlements and their commu-
nities for reducing the disad-
vantages, professional advice,
information and preparation.
Funding is available and can be
used, increasing abilities.
Envisaged probability
40% 25% 25% 10%
Source: edited by the authors
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Contributors
Editor
László Kulcsár PhD, Professor, Institute for Innovative Strategies,
University of West Hungary Faculty of Economics
(kulcsar.laszlo@ktk.nyme.hu)
Authors
Mónika Hoschek PhD, Associate Professor, Institute of Economics and
Methodology, University of West Hungary Faculty of Economics
(hmonika@ktk.nyme.hu)
László Kulcsár PhD, Professor, Institute for Innovative Strategies,
University of West Hungary Faculty of Economics
(kulcsar.laszlo@ktk.nyme.hu)
Csilla Obádovics PhD, Associate Professor, Institute for Innovative
Strategies, University of West Hungary Faculty of Economics
(obadovics.csilla@ktk.nyme.hu)
Csaba Székely DsC, Professor, Institute for Innovative Strategies,
University of West Hungary Faculty of Economics
(szekely@ktk.nyme.hu)
Judit Vancsó Ms Papp PhD, Lecturer, Institute for International and
Regional Economy, University of West Hungary Faculty of Economics
(vancsojudit@gmail.com)