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):
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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
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McDowell, Graham, James D. Ford (2014) The Socio-ecological Dimensions of Hydrocarbon
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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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27. 26
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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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.
