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Szerkesztő Dr. Kulcsár LászlóUniversity of West Hungary Press

Sopron

SOCIAL AND ECONOMIC IMPACT OF CLIMATE CHANGE IN RURAL HUNGARY:ANALYSIS AND MONITORINGEdited by Dr. László Kulcsár

Social and Economic Impact of Climate Change in Rural Hungary:Analysis and Monitoring

Edited byDr. László Kulcsár

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

“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

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.

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

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

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

Location of the Empirical Studies

Zala County in Hungary

Micro Regions in Zala County

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

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.

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

Diversifi ca 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)

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.

13

References

Baerlocher, Bianca, Paul Burger (2010): Ecological Regimes: Towards a Conceptual Integration

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.

Ford, James, D. Lea Berrang-Ford (eds) (2011): Climate Change Adaptation in Developed Nations.

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:

Residential environment conference, University of Debrecen.

Malcomb, Dylan W, Elizabeth A. Weaver, Amy Richmond Krakowka (2014): Vulnerability mod-

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.

14

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.

Parrotta, John A., Ronald L. Trosper (eds) (2012): Traditional Forest-Related Knowledge. sustaining

Communities, Ecosystems and Biocultural Diversity. Springer.

Ruiz-Mallén, Isabel, Esteve Corbera (2013): Community-Based Conservation and Traditional

Ecological Knowledge: Implications for Social-Ecological Resilience. Ecology and Society 18

(4):12

Trosper, Ronald L, John A. Parrotta (2012): Introduction: The Growing Importance of Traditional

Forest-Related Knowledge. In: Parrotta, John A., Ronald L. Trosper (2012): Traditional Forest-

Related Knowledge. sustaining Communities, Ecosystems and Biocultural Diversity. Springer.

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.

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

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

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) availabilityEvaluation of water storage capacity, and reliability of resourcesAssessment 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 incomeGDP 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 rateExistence of disaster warning systems, and strength of municipal institutions Percentage of people living in informal housingAccess 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 densityLoss of habitatsFlood 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 sourcesDeforestation, desertification and/or soil erosion ratesDegree of land conversion from natural vegetationDeglaciation and risk of glacial lake outburst

Source: Based on Sullivan, C. – Meigh. J. (2005), edited by authors

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/methodsSpatial

domainsTop-down

global→localBottom-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

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

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, f lood) 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

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.

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).

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

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

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.

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

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 arearatio 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 ratioRatio aged below 15 and over 65 years within the total

population, aged 15-64 (%)

Aging ratio and its variationRatio 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 variationVariation 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

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

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.

34

Table 2: Ratio of the suburban population and rural population, %

Micro regionSuburban 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.

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

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.

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

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

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

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 indexdependen-

ce ratiosensitivity 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.

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.

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,

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 regionexposure index

(maximum 8)

sensitivity index

(maximum 8)

lack of adaptation

capacity index

(maximum 8)

Vulnerability index*

(maximum 24)

Hévíz1

no exposure

5

exposure

2

no exposure

8

moderately vulnerable

Keszthely1

no exposure

5

exposure

1

no exposure

7

moderately vulnerable

Lenti7

strong exposure

7

strong exposure

5

exposure

19

strongly vulnerable

Letenye5

exposure

7

strong exposure

7

strong exposure

19

strongly vulnerable

Nagykanizsa2

no exposure

0

no exposure

3

moderate exposure

5

not vulnerable

Pacsa6

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

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.

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környezetegészségügyi vonatkozásai [Environmental Health Aspects of the Health Conditions

of the Hungarian Population]. National Institute of Environmental Health, Budapest.

Pálvölgyi, Tamás – Czira, Tamás – Dobozi, Eszter – Rideg, Adrienn – Schneller, Krisztián (2010):

A kistérségi szintű éghajlat-változási sérülékenység vizsgálat módszere és eredményei [Micro

Region Level Climate Change Vulnerability Analysis. Method and results.]. Climate-21 leaf-

lets, 2010. No. 62. pp. 88-103.

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Change - Income Stability - Progress]. Climate-21 leaflets, No. 59. pp. 32-37

IPCC (2011): SREX Special Report on Managing the Risks of Extreme Events and Disasters to

Advance. Climate Change Adaptation [Field, C.B.,et al eds.) Cambridge Univ. Press. UK

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risk and management of extreme climate events. Summary for decision makers. Budapest

December 2011, Ministry of National Development

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”

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.

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

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.

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

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

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%.

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

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

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

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

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Gálos, Borbála (2014):A9. Klímaszcenáriók időszakai, kiválasztott klímamodellekkel (Climate Scenario Periods with Selected Climate Models]. Manuscript.

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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.

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.pdfDownloaded: 23 July 2014

National Drought Strategy - discussion paperhttp://2010-2014.kormany.hu/download/7/0a/90000/Aszalystrategia.pdfDownloaded: 15 July 2014

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%20Nemzeti%20%C3%89ghajlatv%C3%A1ltoz%C3%A1si%20Strat%C3%A9gia%202014-2025%20kitekint%C3%A9ssel%202050-re%20-%20szakpolitikai%20vitaanyag.pdfDownloaded: 14 February 2014

Status analysis of Zala county regional development concept and programmehttp://www.zalamegye.hu/koncepcio/1Helyzetelemzes.pdfDownloaded: 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

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”

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).

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.

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).

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.

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

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

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.

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.pdfDownloaded: 23 July 2014

National Disaster Risk Assessment, Hungary 2011. http://vmkatig.hu/KEK.pdfDownloaded: 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%20Nemzeti%20%C3%89ghajlatv%C3%A1ltoz%C3%A1si%20Strat%C3%A9gia%202014-2025%20kitekint%C3%A9ssel%202050-re%20-%20szakpolitikai%20vitaanyag.pdfDownloaded: 14 February 2014

Data source:

Szabolcs Czigány’s data supplies 2014.

CARPATCLIM

ENSEMBLES EU– FP6

NYUDUVIZIG

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.

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

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

4.354.34

1.028

0.981.051

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

1.561.74

1.90

1.0351.217

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

2.952.99

1.3931.525

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

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-ficationwomen

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

1.1231.273 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 factor14was 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

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

73

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.

74

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.

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.

76

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)

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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.

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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.

79

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.

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.

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).

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,

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 .

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

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.

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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”

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 3.8oC, while the average temperature of the individual

years is on average 0.74oC different from the average 9.5oC.

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 0.6oC 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)

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The coldest year of the analyzed period in Szombathely was 1940 when the annual

average temperature was only 7.4oC. The second coldest year was 1956, when the annual

average temperature was only 0.3oC higher. In the ranking order of cold years the average

temperature of the third subsequent years was higher than 8oC. 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

11.2oC average temperature. The second hottest year was in that millennium, when the

average temperature in 2008 was only 0.01oC lower than the average recorded for 2000,

followed by 1994 (11.0oC). 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 1oC 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.

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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 39.7oC. Looking at maximums, seven from

the ten “coldest years” were before 1950, with a negative peak of 28.0oC in 1926 (Figure 5.).

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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.

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.

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Volatility of daily minimum temperatures

Analyzing the absolute minimums, it is clear that 1929 was the coldest year when

-29.3oC was measured. On the list of the ten coldest days 1985 represents the last third

of the 20th century with -21.9oC, 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 -5.5oC 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.

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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

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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 23.8oC In 2011 the same figure was “only” 23.5oC, followed by 1911 and 1990 with the

third highest fluctuation (23.4oC). In that respect 1927 and 1982 were the most fortunate

years with only 17.4oC 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

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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.

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.

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.

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 - globaltropical forest cover reductionrangelandsriver and sediment dischargeworld glacier inventoryisotope concentration increase in precipitation

BIOSPHERE Wildlife sampling and monitoringImpact of pesticide residuesLiving marine resources

POLLUTANTS Air quality monitoringwater qualityeutrophication in inland waterfood and animal feed contaminantsionising radiation

CLIMATE climatic variabilityWorld weather watchsolar radiationatmospheric ozoneclimate changeglacier mass balance and fluctuationatmospheric pollutants and effects

OCEANS Pollutants in regional seasOpen ocean watersMarine oil pollutionOcean-bed contaminants

NATURAL DISASTERS Tropical cyclonesTsunamiFloodEarthquakeVolcanic eruption

Source: SCOPE 15, 1980, modified

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 wavesPartly 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.

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

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.

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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 applicableA: applicableNA: non-applicable

Source: IEC/FDIS 2009

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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

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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

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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

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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 ISO 22000, Food safety management systems – Requirements for any organization in the food chain

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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

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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.

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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.

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Strategic organisationaland risk management

aspects

What can happen?How can it happen?

Comparison with criteria

Establishment of risk priorities

Identification of optionsAssessment of options

Elaboration of plansImplementation of the plans

Communication andconsultation

Monitoring andreview

Definition of criteria

Identification of risks

Risk analysis

Proba-bilities

Conse-quences

Risk level

Risk assessment

Acceptablerisk?

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

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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

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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.).

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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

European Commission, 2010: Risk Assessment and Mapping Guidelines for Disaster Management.

Commission Staff Working Paper, Brussels, 21.12.2010. SEC (2010) 1626 fi nal.

Hugenberg, H.: Strategisches Management des Unternehmens. Springer-Gabler Verlag, 2012.

IEC/FDIS 31010 (2009): Risk management – Risk assessment techniques.

SCOPE 15. Whyte, A. V., Burton, I. (ed.):. Environmental Risk Assessment. John Wiley & Sons,

Chicester, New York, Brisbane, Toronto, 1980.

Wanner, R., 2009: Risikomanagement in Projekten. S.93.

Williams, C.A. Jr., Smith, M.L., Young, P.C.: Risk Management and Insurance. McGraw-Hill, Inc.,

1995.

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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

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 inf luence the analyzed

process and scenarios to be developed. The output of this phase could be a detailed

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task and problem description and a list of factors. Table 1 contains a list of factors

as an example, which may inf luence 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/

InfluenceFactor 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 influence0 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.

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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

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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.

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.

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

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

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.

Economy1. 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.

Society1. 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 vulnerability5. Lack of the required knowledge, cultural

capital and information6. 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 capacities7. Response: abandoning agricultur-

al activities8. 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, erosionAppearance of pests and alien species, change in the previous composition of species

Economy1. Devaluation, increasing uncertainty con-

cerning income,2. Difficulties in the business strategy of forest

users and owners.3. Increasing expenses4. Market loss Society1. Decreasing employment, lost income2. Increase in the role of traditional knowledge

and skills

1. Response: Search for new markets2. Response: Change of technology3. 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

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 threatsFloods 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 areas3. Health expenses (population, municipality)4. Construction expenses (population, municipal-

ity)5. Inadequacy and increasing expense associated

with resources received from insurance.Health threats1. Heart and vascular system problems at the vul-

nerable population (old people)2. Increase in the health causes of traffic accidents3. Increase in the number of accidents at work tak-

ing place in the open air4. Increase in the frequency of auxiliary events

(e.g., bathing accidents)

Settlement, commu-nity adaptationMigration of the population1. Creation of the required

institutions and regula-tions

2. Increase in state aidIndividual adaptation1. Adherence to health reg-

ulations2. 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

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.

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 consequencesIncreasing 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 probability40% 25% 25% 10%

Source: edited by the authors

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128

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)