The sustainable transport system and policy design in metropolitan
context: environment facing transport or vice versa?
Faculty of Economics and Administration
Masaryk University
Vilém Pařil
Brno, 2023
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Bibliographic record
Author: Vilém Pařil
Faculty of Economics and Administration
Masaryk University
Department of Economics
Title of Thesis: The sustainable transport system and policy design in metropolitan context:
environment facing transport or vice versa?
Field of Study: Economic Policy
Academic Year: 2022/2023
Number of Pages: 68 pages + 75 pages in annexes
Keywords: metropolisation, environment, transport, policy
Annotation
This habilitation thesis aims to contribute to the topic of sustainable transport planning in an era
of continuing metropolisation of society, focusing on the geographically defined area of Central
Europe, taking into account several dichotomies in the transport planning process, namely
infrastructure and traffic planning, external and internal factors, transport behaviour of
inhabitants and passengers, and transport policy at different levels of the public sector. This
heterogeneity of perspectives on transport planning allows for unravelling the feedback
interactions of the different steps and actors. The thesis focuses successively on metropolisation
and the associated economic or environmental burdens; transport systems interacting between
and within metropolitan areas, including transport externalities; the last part focuses on the
interaction between travel behaviour and transport policy, reflecting the gradual institutional
changes in the integrating European area and the long-term changes in the value frameworks of
society. The methodological approach is based on regional economics, combining methods from
both economic and geographical disciplines, and this way of looking at the topic is one of the
fundamental determinants of this work, which is precisely in the field of transport, environment
or urban planning a necessary part of dealing with complex and mutually interacting system
components.
Acknowledgements
This habilitation thesis would not have been possible without the enormous support of many
people. The main credit for this goes to prof. Milan Viturka, my former Ph.D. supervisor, who
taught me a lot and became a valued co-author. Next, I really appreciate the support of my
colleagues, especially prof. Zdeněk Tomeš, Dominika Tóthová, Hana Fitzová, Richard Kališ,
Aneta Krajíčková. They have created a nice working environment, are always willing to help, and
have become great companions and co-authors. Last but not least, my thanks go to my wife, who
provided me with enough peace and comfort at home, allowing me to deliver this work.
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Table of content
Table of content................................................................................................................................3
Introduction ......................................................................................................................................5
I. The metropolitan process .......................................................................................................12
1. The Metropolisation Processes A Case of Central Europe and the Czech Republic .........12
Background..................................................................................................................12
Data and methods.........................................................................................................13
Results..........................................................................................................................16
2. The cost of suburbanization: spending on environmental protection ................................18
Background..................................................................................................................18
Data and methods.........................................................................................................20
Results..........................................................................................................................22
II. The transport system design and externalities........................................................................25
3. Assessment of the burden on population due to transport-related air pollution: The Czech
core motorway network..............................................................................................................25
Background..................................................................................................................25
Data and methods.........................................................................................................26
Results..........................................................................................................................28
III. The mobility behaviour and policy ....................................................................................32
4. Competition in long-distance transport: Impacts on prices, frequencies, and demand in the
Czech Republic ..........................................................................................................................32
Background..................................................................................................................32
Data and methods.........................................................................................................34
Results..........................................................................................................................35
5. Fare Discounts and Free Fares in Long-distance Public Transport in Central Europe......39
Background..................................................................................................................39
Data and methods.........................................................................................................42
Results..........................................................................................................................42
Conclusion......................................................................................................................................45
Authorship contribution statements ...............................................................................................46
Literature ........................................................................................................................................47
List of tables...................................................................................................................................66
List of figures .................................................................................................................................67
Annexes: Published articles ...........................................................................................................68
A. The Metropolisation Processes: A Case of Central Europe and the Czech Republic (pages 16,
69-84) .............................................................................................................................................68
B. The cost of suburbanization: spending on environmental protection (pages 21, 85-105) ........68
C. Assessment of the burden on population due to transport-related air pollution: The Czech core
motorway network (pages 14, 106-119) ........................................................................................68
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D. Competition in long-distance transport: Impacts on prices, frequencies, and demand in the
Czech Republic (pages 13, 120-132).............................................................................................68
E. Fare Discounts and Free Fares in Long-distance Public Transport in Central Europe (pages 11,
133-143) .........................................................................................................................................68
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Introduction
This habilitation thesis is presented as a collection of already published articles that always
reflect individually defined research contexts and objectives and methods addressing specific
aspects of the author's research focus. This thesis's following chapters present these research
contexts, objectives, methods, and brief individual studies' results. In contrast, space is given here
in the introduction to the thesis to the author's key focus and the way of looking at the work's
subject matter that defines his research activities.
The goal of this work is to define the perspective challenges of economic policy reflecting longterm
transformations of, if possible, economically and environmentally sustainable transportation
systems in the Central European context that reflects the economic development determined by
the process of creating and empowering metropolitan areas and metropolitan axes while
constantly interacting with changes in the differentiated individual value frameworks of end-users
of these systems, which are determined by changes in their personal and social value paradigms.
The purpose and ambition of this work are to contribute in the Central European context to the
mutual reflection of different methodological approaches used in economics and geography
through their collaborative interaction in order to outline the possibilities of the added value of
this way of looking at scientific, economic problems (for inspiration see Krugman 1991). This
approach is reflected in the long-term goal of achieving a holistic approach in economic research
(Sala et al. 2013, Dalton et al. 2015) corresponding with the policy recommendation concept of
Leitbild as desired future scenario (Pearson and Gorman 2010, Albert et al. 2012) and using the
example of metropolisation processes that transform the design of transportation systems
(Rodrigue 2006) not only among metropolitan areas in an increasingly globalised world (Hall et
al. 2006, Dodi 2020) but also within these areas (Clark and Kuijpers-Linde 1994) in the
interaction of metropolitan cores with its hinterland (Paul 2017). An essential aspect of the
metropolisation transformations shaping transport systems is the currently much-debated
environmental impacts of these changes, which are a necessary part of such an approach and are
significantly reflected and discussed in this work.
The key research question and the motivating question for the author is whether the synergy of
innovative development of transportation systems reflecting varying passenger preferences can
be achieved through rational transportation policy while minimising conflicts with the
environment following the principle of minimising negative externalities. The intensity of
internalisation of these external costs into the transport planning process across the entire
spectrum of transport modes and even means of transport is an essential issue of transport policy.
Furthermore, the respect for non-discrimination of any of these multimodal and intramodal
solutions remains an intriguing element. Many of the outcomes of such scientific methodologies
might therefore give an inspirational basis for transportation policy design, which is typically
constrained by the traditional use of cost-benefit analysis, which is challenging to work with
when dealing with broader economic benefits or costs. Thus, this mainly used method needs to be
accompanied by other approaches to deal not only with the efficiency of any transport policy
decision but even with the effectiveness of such a decision (Drucker 1967).
Metropolisation is the process by which a city or metropolitan area becomes a region's dominant
economic and social centre, involving structural, functional, and institutional recomposition and
frequently resulting in increased urbanisation, economic growth, and concentration of wealth and
resources (Gachelin 1992). The concentration of authority, influence, and economic activity
inside a city or metropolitan region characterises this trend, which usually leads to the
marginalisation of smaller cities and rural regions. The analysis of the Northeastern seaboard of
the United States (Gottman 1957, 1961) was one of the first studies on the metropolisation
process, defining the so-called "megalopolis" (originally used in ancient Greek literature) and
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"megalopolitan process" as areas providing several essential economic functions such as
maritime, manufacturing, commercial and financial functions, but also cultural leadership
(Gottman 1957). While Gottmann did not use the term "metropolisation" directly in his work, his
notion of megalopolis is largely regarded as a predecessor of the phenomena. Original studies of
the metropolisation process evolved from geography; thus, these are usually based on the
characteristic of population density studied already earlier in the process of urbanisation, defined
as a population concentration that can proceed in two ways: the multiplication of points of
concentration and the increase in the size of individual concentrations (Tisdale 1941). Although it
originates in more geographical or urban studies, this term has considerable economic
applications. The primary issue shared by both perspectives is the critical role of population
expansion, concentration (Krugman 1993), structure and change in specific regions or countries.
Furthermore, even economic growth models deal with population growth and discuss its role in
economic development (Headey and Hodge 2009, Peterson 2017).
The process of metropolisation coincides with globalisation (Morin and Hanley 2004, Lang and
Török 2017). These two phenomena are interconnected processes with substantial implications
on the global economic, social, cultural, and, importantly, political structures (Audikana and
Kaufmann 2022) and, thus, policy design (Melkonyan et al. 2020, Ingvardson and Nielsen 2019).
The globalisation process refers to the increasing connectivity and interdependence of economies,
institutions, and cultures beyond national borders. This process is encouraged by reasons such as
technology improvements and innovations (Krugman 1979), trade and investment liberalisation,
and the development of novel modes of communication (Moss and Townsend 2000) and
transportation (Rodrigue et al. 2009). The interaction between metropolisation and globalisation
is reciprocal and challenging to solve regarding action and reaction.
Nevertheless, the rise of large urban centres has been a fundamental driver of globalisation, as
cities have become crucial nodes in global commerce, finance, and innovation networks. At the
same time, global interconnectivity even accelerated its nodal roles. Globalisation has altered the
process of metropolisation by bringing not only new opportunities but also problems for
metropolitan areas. The expansion of global supply chains and the increased trade presented by
goods and services (Feenstra 1998), furthermore growing mobility flows of people (Freeman
2006), and flexibility of capital (Obstfeld 1998) have altered how countries, regions and cities
compete and work with one another what resulted in new patterns of economic inequality. This
inequality can occur inside and across cities or regions since certain regions gain more from
global economic integration (global cores) than others, which are increasingly becoming the
peripheries. In economic words, globalisation affects the location of manufacturing and trade
gains, and at high transport costs, all countries have some manufacturing, however when
transport costs fall below a critical value, a global core and periphery system spontaneously form,
and countries on the periphery suffer a decline in real income (Krugman, Venables 1995).
Furthermore, this worldwide process reflects on lower hierarchical structural levels such as
mezzo-regional, regional, micro-regional or local. As a result, metropolitan areas become hubs of
innovation, creativity, and cultural interchange, considerably contributing to national economic
growth and development. However, metropolisation brings new issues, including traffic
congestion, socio-economic inequities, and environmental deterioration, which must be addressed
with public policy, management and planning.
The metropolisation phenomenon can be studied in several aspects regarding policy and
reorganisation of local government (Young 1975); economic production dynamics in
metropolitan areas resulting in a dispersal of productive activity to suburban and nonmetropolitan
areas as one of the dominant processes shaping the contemporary economic landscape (Scott
1982); technological improvements and innovation activities (Krugman 1979, Sharif 2012) or
technological externalities (Fujita el al. 1997). Moreover, Krätke (2007) emphasises the
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increasing role of the economic specialisation of urban agglomerations in the knowledge
economy in the European economic territory. Other studies focus on demographic shifts and their
impacts on housing preferences (Patterson et al. 2014) and public spaces planning (LoukaitouSideris
2016, Loo et al. 2017) or population segregation in urban centres (Logan et al. 2004). The
important role lies in studies on employment activities (McMillen and McDonald 1998)
traditionally relying on agglomeration economies in central cities clustering highly educated
workers with increases in their wage premium (Ehrlich and Overman 2020) but with growing
employment opportunities in various peripheral locations (Hall 1997) which means a significant
role for intra-metropolitan work reconfiguration (Wu and Wang 2021). Further approaches
discuss the role of general residential relocation preferences (Timmersmans 1984) concerning life
cycle changes (Nijkamp et al. 1993, Dökmeci and Berköz 2000) regarding distribution of local
amenities (Polinsky and Shavell, 1976). A growing literature is focused on reshaping land use in
the city and its neighbourhood (Henderson et al. 2021), including its environmental challenges
(Tian et al. 2012). The intersection of the environmental perspective with the perspective of
population mobility coming from economic nature is the most relevant paradigm for this work.
In summary, metropolisation is essential in spreading economic performance and profile since
major metropolitan regions are frequently the engines of economic growth and development but
have different economic backgrounds and varying agglomeration contexts. The concentration of
economic, social, and cultural activity in metropolitan regions generates agglomeration
economies which are the advantages that result from many economic stakeholders such as public
bodies, enterprises, people in business and inhabitants being positioned in the exact location or
very close neighbourhoods. These levels of spatial aggregation, including the regional,
metropolitan, and even neighbourhood scales resulting in agglomeration effects, have been
presented by Rosenthal and Strange (2020). However, these agglomeration economies often
cause long-term economic disparities (Glaeser 2013), negatively affecting those not participating.
The causes lie in different launching situations and city conditions based on varying spatialtemporal
and socio-economic profiles. Thus to formulate reasonable policy, it is necessary to
consider the concept of path dependence to understand the evolution of the economic landscape
and the regional development process in the local context (Greif 1994, Martin and Sunley 2006).
The historical and geographic development and context of individual metropolitan areas and
regions determine not only their current state, involving a particular economic specialisation, how
to achieve a quality of life through shaping the urban landscape and supporting entrepreneurship
in a given area. At the same time, these historical and geographical links also define the interrelations
among and even within metropolitan areas, which can further reinforce their specialised
economic and residential profile. According to Quigley (1998) it is the heterogeneity and the
diversity of cities that is the source of economic growth which means relations among individual
metropolitan areas, at the same time, can be an incentive for the successful development of the
whole metropolitan area through exploiting potential opportunities. Nevertheless, in contrast, in
some situations, they can lead to stagnation or even degradation of the whole metropolitan region
or at least selected parts of metropolitan areas due to the transformation of economic activities
because these transformation processes are not only time-consuming but also administratively,
economically and in final consequence socially and politically demanding.
It is the process of metropolisation in the context of Central Europe that is the focus of the first
chapter 1 of this work, which introduces a typology of Central European metropolises and, at the
same time, provides a practical application of metropolisation processes through the
identification of metropolitan axes in Central Europe (Viturka et al. 2017). The study of these
metropolisation processes based on economic links and reflected in transport and tourism links
has been the subject of other publications focused on both inter-metropolitan relations (Šauer et
al. 2019) and intra-metropolitan links (Viturka and Pařil 2013) through the introduction of a
marginal rate of labour mobility explaining the economic motivation for commuting, reflecting
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the potential increase of wages by reaching the more attractive job in the metropolitan core (Pařil
et al. 2015).
The process of metropolisation of human society and economic activities is both the starting
point and the core context of this work. Thus, in the economic framework defined by
metropolisation trends, it is necessary to observe the long-term effects and changes that it brings.
These effects are the subject of broader literature and research studies, partially already
mentioned. The most critical effect of metropolisation is the formation of the duality of the
metropolitan core or centre and its hinterland, accompanied by one of the key problematic issues:
suburbanisation. Mieszkowski and Mills (1993) consider two classes of theories of
suburbanisation, both of which are important: the first, preferred by urban theorists and
transportation experts, is the so-called natural evolution theory, and the second stress the fiscal
and social problems of central cities. The process of suburbanisation often results in urban sprawl
associated with several particular effects. Urban sprawl is a result of the expansion of the
metropolitan area and its suburbs, which can result in the spread of development into previously
undeveloped areas. This expansion is often driven by factors such as the desire for larger homes,
access to green space, and lower housing costs. However, urban sprawl is related to a range of
challenges and, in final even economic costs including. According to Nechyba and Walsh (2002)
the phenomenon of sprawling cities has created opportunities for significantly higher levels of
housing and land consumption for most households. Nevertheless, these benefits have not come
without associated costs. These are road congestions related to high levels of car pollution, the
loss of open space amenities, and unequal provision of public goods and services across
sprawling metropolitan suburbs that can give rise to residential segregation and areas of poverty.
From an economic point of view, social issues are less visible because their impacts are usually
not immediately apparent. Somewhat more visible are direct economic costs because they can
result in reduced and limited possibilities of public goods management of many different services
in a decentralised city, such as transport accessibility, water supply management or waste
management. Thus, managing still-growing geographical areas on both metropolitan levels
(corresponding institutionally with municipal or regional levels) can immediately lead to
increased infrastructure costs, as more roads, sewers, water, and utility lines need to be built to
accommodate the geographically expanding residential areas. These infrastructure costs are
critical in two specific fields: transportation systems with the goal of accessibility and
environmental goods management systems linked to very fundamental human needs. These two
sections of the public management framework are often associated with large amounts of public
financial expenditure. However, these direct expenses are not solely of an infrastructure nature,
as individuals are more likely to rely on cars in hinterland areas, resulting in increased traffic
congestion, longer commuting times, and higher fuel consumption. These consequences can
result in more extraordinary transportation expenses, even for people and businesses, as well as
increased air pollution, noise, and greenhouse gas emissions. Induced environmental costs can
have diverse nature and correspond with other issues such as resource consumption, water
supply, waste management or land use and habitat fragmentation leading to habitat loss. It is a
problem of direct infrastructural and environmental costs in metropolitan areas that is the
essential subject of the second chapter 2 that shows the differentiating effects on wastewater,
waste management and public greenery management according to the affiliation of metropolitan
core, hinterland or periphery (Pařil et al. 2022a).
The process of metropolisation has a significant impact on all transport systems, as the
concentration of economic, social, and cultural activities in large metropolitan areas leads to
increased demand for transportation infrastructure and services. As metropolitan areas grow and
expand, the transportation systems that serve them must adapt to the population's changing needs.
Metropolisation can have different impacts on both long-distance transport and short-distance
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commuting, as the two types of transportation serve different needs and have different
characteristics.
Due to globalisation trends, an essential part of metropolisation processes at the macro-regional
or international level is the increasing transport interconnectivity of territorially separated
metropolitan areas. In Europe, these trends are also supported by political and economic
integration within the European Union. Regarding long-distance transport, metropolitan areas are
typically connected through various modes of transportation, such as air travel, highways,
railways or high-speed railways and maritime systems, more often for freight transport or tourism
reasons. These long-distance transportation networks facilitate the movement of people, goods,
and information among geographically remoted metropolitan areas and are critical to the
functioning of globalised economies. Air travel is a crucial mode of transportation for connecting
metropolitan areas far from each other. However, this mode is essential even for middle or shortdistance
passenger transport where strong demand for fast mobility is determined by tourism or
business reasons. When studying the nature of air transport in geographically smaller countries
with smaller populations, air transport needs to be placed in a broader international context.
Assessing the role of domestic demand in air transport and, in turn, the impact of the border
effect is crucial. The border effect is a concept described primarily in international trade (Leamer,
Medberry, 1993; McCallum, 1995). Still, it also has important implications for transport
planning, as crossing borders brings a significant drop in demand (Klodt, 2004; Hazledine, 2009)
that must be considered when planning transportation projects. This effect, in turn, creates the
conditions for the emergence of hegemons in air transport at the mezzo-regional level, which
shapes the air transport system in Central Europe, where most countries are smaller and
international transport is thus highly affected (Pařil et al. 2022b).
Despite the irreplaceable place of air transport, the crucial role in the volumes of goods and
passengers transported lies with road transport. The role of car transport is undeniable, both in
transport among metropolises and within metropolitan areas. The road infrastructure is thus the
essential communication and circulatory system of a global metropolitan organism. A
considerable advantage of the road infrastructure is the high hierarchical heterogeneity of the
design of the road network, which from the highest level of motorways through the level of
national and regional roads, is further linked to various types of local roads, which on the other
side of the imaginary scale are terminated by dirt roads used for agricultural or tourist purposes.
However, motorways and national roads are essential in meeting the population's long-distance
transport needs. The planning of motorway construction is topical in those countries where the
motorway network has yet to reach the socially desirable extent. It is a highly complex issue
involving direct and indirect economic costs. The issue of a comprehensive assessment of options
for the planned sections of the motorway network, including its prioritisation, involves assessing
many factors such as relevance, usefulness, integration potential, and economic stimulation of the
action or environmental context (Viturka et al. 2012, Viturka and Pařil 2015).
It is the latter criterion, including the environmental context, which is currently gaining
momentum in the planning of transport systems and has a significant impact on the two modes of
transport mentioned above, namely air and road transport. There are, of course, more reasons for
the growing importance of this criterion. The most fundamental one is the contribution of man
and his activities to climate change. At the same time, however, other negative externalities
causing impacts not only on nature and ecosystems cannot be overlooked. However, due to
metropolisation in suburbanisation processes occurring precisely in transport-accessible locations
(determined by good road infrastructure), transport externalities significantly negatively impact
the population's health. Negative externalities can be divided into two primary groups directly
impacting nature or humans. Among those that directly impact nature, in addition to greenhouse
gas emissions causing climate change accompanied by well-to-tank costs, are the cost of habitat
10
damage, soil and water pollution, land use and forest and agricultural land grabbing or landscape
fragmentation. Costs with a direct impact on human health are accident costs, costs of air
pollution, noise, and congestion costs. According to the Handbook on the external costs of
transport (Van Essen et al. 2019), road transport is responsible for 83% of external transport costs
in EU28. Among these externalities, the most important role is related to accidents (29%) and
congestion (27%), followed by air pollution (14%). While accidents and congestion immediately
affect a person's health or loss of time, air pollution is a long-run impact, not only on people. The
impact on human health must therefore be reflected in research on impacts over the long term.
The impact of road transport on human health through the burden of air pollution, including
consideration of the variability of impacts according to the demographic structure of the
population concerned, is the focus of chapter 3.
Because of the high external costs of road or air transport, the current EU transport policy seeks
to shift part of the transport to more environmentally friendly modes and means of transport or
even to change car transport through regulatory requirements to limit emissions. Public transport
is typically considered a more environmentally friendly mode of transport, and rail transport is
politically preferred for long-distance passenger transport. Of course, even planning railway
infrastructure, especially high-speed railway infrastructure, requires a complex assessment of
these projects (Viturka et al. 2022a), including integration potential (Viturka and Pařil 2020),
regional development context (Pařil and Viturka 2020), commuting behaviour change (Pařil and
Viturka 2023), relevance and usefulness (Viturka et al. 2022b), capacity planning, economic
stimulation (Viturka et al. 2021) and investment or environmental costs. Nevertheless, the latter
is considered more favourable and sustainable than road or air transport. Transport policies,
therefore, seek to motivate people to switch to more environmentally friendly modes of transport,
but this requires knowledge of the transport behaviour of the population in the changing living
conditions of a globalised and metropolitan world. Thus, the nature of end-users mobility
behaviour of transport modes and means is crucial. Among the essential factors influencing
passengers' choices following characteristics are considered significant: the socio-demographic
profile, easier accessibility and the need for transfers (Yen et al. 2018), travel time or speed
(Fröidh, 2008), traffic congestion (Ben-Akiva and Morikawa 2002), comfort (Allard & Moura,
2018), safety (Si et al., 2009), fares and frequency (Paulley et al., 2006), income (Toro-González,
et al., 2020), the opportunity to work (Varghese and Jana, 2018), congestion (Droes and Ritvield,
2015), capacity (Daly et al., 2014) or parking availability (Pagliara et al., 2012). However, in the
European context, even the variety of choices has to be taken into account because there is a
long-term liberalisation process in all modes of transport that creates a more competitive
framework with more opportunities to be used by potential travellers or passengers. The process
of liberalisation in transport is broadly discussed in the literature related to air transport (Button et
al. 2007, Button 2012), bus transport (Beria et al. 2018) or railway transport (Bergantino et al.
2015). Chapter 4 focuses on the issue of passenger behaviour in public transport in an
environment of intermodal competition, taking into account both competition between modes of
transport and competition within these modes.
The study of the transport behaviour of the population is often based on the identification of
elasticities to various factors influencing passenger behaviour, such as the price or frequency or
others mentioned above, as it is widely represented in the literature. From a policy-making
perspective, the application of the results of such studies is limited because the elasticities capture
behavioural changes at the level of minimal percentage changes. Nevertheless, transport policies
can use robust instruments and incentives to motivate passengers to use more environmentally
friendly modes of public transport at the expense of individual car travel, e.g. through robust
discount schemes or even free fares. However, such transport policy instruments operate at a
different level of change, not in the order of percentages but in the order of tens of percentages,
and therefore results at the level of percentage elasticities are of very limited predictive power for
11
these instruments and need to be studied in the context of short and long term changes in the
context of historical data on transport demand reflecting absolute changes in the volumes and
structure but also in the context of overall modal split changes. The introduction of robust
discounting with the aim of redirecting transport demand to more environmentally friendly
transport modes is the subject of chapter 5, which uses the example of Slovakia and Czechia to
present the possibilities of changing the modal split through such robust instruments, as well as
their economic consequences or potential impacts on non-included competing transport modes
(Tomeš et al. 2022).
The design of the transport system in a globalised world creates a system of metropolitan centres,
axes and their hinterland. It reflects the increasing needs of urban life of inhabitants with the
possibility to cover greater geographical distances when travelling for work, leisure, holidays, or
family is thus a very complex issue. However, it is the setting up of the transport system in terms
of not only infrastructure but also technological and regulatory framework (including possible
preferences for selected transport segments or selected passenger segments) that has the potential
not only for ex-post solutions to transport problems arising from a particular spatial economic
situation but is often a stimulus that can be one of the factors determining the further direction of
development or a significant factor in mitigating selected negative impacts.
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I. The metropolitan process
The first part of the habilitation thesis includes two chapters focused on metropolisation process
in Central Europe and its impacts caused by suburbanisation in the metropolitan hinterland
reflected on the environmental, both infrastructural and operating costs for public budgets.
1. The Metropolisation Processes A Case of Central Europe and the
Czech Republic
In Paper A, the metropolisation process in Czechia is presented with an emphasis on the Central
European context. It deals with the strategically important global phenomenon of the metropolis.
We present a theoretically based method for evaluating large cities and examine its effectiveness
using the example of Central Europe. This method is based on three components: population size
(initial assumption), economic profile (linkage to economic performance) general attractiveness
(perception of development potential). The results of evaluating a selected sample of 27
identified metropolitan cities were generalised based on the typologies within the framework of
these three components. Most metropolitan areas are categorised as Type B (established
metropolis), followed by Type C (elementary metropolis), and finally, Type A (dominant
metropolis). Next, we use the example of the Czech Republic to demonstrate a practice-oriented
conceptualisation. A primary focus was on the strength of economic ties between Prague (and her
two other Czech centres) and other central European metropolises.
Background
Metropolisation is one of the most visible manifestations of long-term changes in the scale and
structure of urbanisation occurring in the context of the globalisation process (Hanssens et al.
2012). Generally, it is seen as a higher stage of urbanisation in which the primary focus is no
longer on population concentration but on importance concentration in information-knowledgemanagement.
Its growth promotes the strengthening of ties between metropolises and their
hinterlands and between metropolises (Hampl 1996). The growing relevance of metropolises in
nations' global competitiveness generates a need for theoretical and applied study of this issue.
The basic leitmotiv of the further presented approach of assessing metropolisation processes is
their understanding from the perspective of the post-industrial fading of the horizontal forms of
social organisation and the deepening of vertical forms of this organisation. The position of the
metropolis corresponds with the mentioned information as being an ever more dominant part of
the national and supranational urban systems integrated not only by operational interactions
mediated by the technical, in particular transport infrastructure (Kraft et al. 2014) but also the
creative interactions mediated by the knowledge infrastructure. In this context, it is a specific
development from the monocentric metropolitan agglomerations to polycentric macrosystems
whose manifestation creates supranational axes (Growe, 2012). The described development leads
to significant changes in the localisation of production. Thus, it can be considered a renaissance
of localisation approaches (Brender and Golden 2007). In addition to the theory of localisation,
there are, e.g. the theories of central places, polarised development and cumulative causality, a
deeper analysis of which goes beyond the thematic focus of the paper (McCann 2010). In this
respect, utilising the working theory of integrated and sustainable regional development is
beneficial. This theory (Viturka et al. 2010 and Viturka 2014) stresses that the essence of the
social movement is the hierarchical differentiation of social systems and their integration through
the territorial division of labour and socio-political links. The resulting arrangement ensures the
coherence of the systems, which reflects the balance between the effects of the economic, social
and environmental factors, whose results are then accumulated in the quality of the business and
social environment. The following decisive driving forces of integration are then considered:
labour interactions on a microregional level, production interactions on a mesoregional level,
administrative interactions on a macroregional level and trade interactions on a global level.
Improving the quality of the business and social environments stimulates the development of
13
different activities, positively impacting innovation and living standards. It is exciting that
advances in ICT do not result in the dispersion of information activities (Sassen 1991). This fact
can be attributed to the need for face-to-face interactions, which is somewhat unequivocally
called the "tyranny of proximity" (Duranton 1999; Bourdeau-Lepage 2004). A controversial issue
is a suburbanisation, whose negative impacts are often interpreted in connection with urban
sprawl (European Environment Agency 2006). Suburbanisation is an essential tool for reducing
the differences in the quality of the business environment between the core and the hinterland.
There are even very local issues with potentially significant impacts on the overall attractiveness
of cities, such as brownfield regeneration (Frantál et al. 2015). The crucial question is the
management of metropolitan regions where solutions to multiscale problems require integrative
management (Ianos et al. 2012). The article's main objective is to create a comprehensive method
to evaluate a metropolis focusing on Central Europe to verify the possibilities and benefits of the
method applications, emphasising the availability of the necessary information and comparing the
results. Czechia was chosen for the practically oriented conceptualisation of the research results,
including assessing the potential cooperation and policy recommendation. Overall, there is still
no consensus on the scientific definition of the metropolisation concept (Abrantes et. al. 2005).
Data and methods
The publications The World Factbook (2016), the Encyclopædia Britannica (2016) and
Brockhaus Enzyklopädie (2016) were used as the primary documents for a definition of the
working macroregion of Central Europe. Based on these definitions, Central Europe includes
nine countries: Germany, Poland, Czechia, Hungary, Austria, Switzerland (together with
Liechtenstein), Slovakia and Slovenia, with a current number of about 163 million inhabitants.
The mentioned theoretical ambiguity of the metropolisation concept complicates evaluating the
metropolis position, starting with the definition itself. A systematic method of evaluation of the
metropolis was created with an analysis, preferring theoretically anchored approaches based on
the three components:
1. the population of the metropolis, a sufficient size of which is generally regarded as the
initial assumption for starting the metropolisation process;
2. the economic profile, emphasizing the progressivity of the economic structure evolving
from the representation of knowledge-based industries;
3. the general attractiveness as a reflection of the high business and residential attractivity.
In the case of population size, there is a lack of clear distinction between the metropolises and
other major cities. The size limit of metropolises is influenced by several territorially
differentiated factors, including historical development and the achieved degree of urbanisation.
They can be seen, from the standpoint of administrative (administrative definition), as urban
(continuously built-up areas) and functional (the influential territory of the city). Within this
sequence, when the functional approach most corresponds with the metropolitan concept, an
increase in the population of the respective units happens. In this context, a boundary of 1 million
inhabitants is considered a population limit to be the size of supranationally significant
metropolitan regions. Metropolises of global importance occupy the highest hierarchical level
(Friedmann, Wulff 1976) - in this respect, London and Paris are the essential global metropolises
localised in Europe (Knox et al. 1995). In the case of metropolitan regions of secondary
importance, the commonly used limit is a population of 500 thousand (Brezzi et al., 2012).
During the evaluation, it is necessary to consider the comparability of their territorial limits.
Therefore, the OECD (2015) data were selected as appropriate sources.
The above-mentioned size limit of a metropolis of supranational importance of one million
inhabitants was, rather, for the comparatively international solid position of a series of smaller
metropolises, reduced to a population of 750 thousand. Two capital cities which do not meet even
the reduced limit, Bratislava and Ljubljana were also included. A total of 27 metropolises or
14
metropolitan regions were identified. At this point, it is also appropriate to highlight the problem
of the comparability of statistical data in the metropolitan areas of post-socialist Central Europe
related to the census data versus the annual population register. Steinführer et al. (2010) found
significant statistical variations in the measurements of the population size (influenced by
suburbanisation, intraregional and international labour migration). However, these impacts are
negligible in terms of the results of our research. Specific data about metropolitan areas are based
on the uniform method for defining functional urban areas introduced by the OECD in
collaboration with the EU. Here, it is appropriate to mention some fundamental features (Brezzi
et al. 2012):
- the basic building blocks of regionalisation are units at LAU 2 level,
- the core of the region is determined based on the minimum population size of a
continuously urbanized area,
- in the case of nearby cores, their mutual relations are tested regarding commuting time,
due to the identification of the polycentric structure,
- the hinterland of the core consists of the municipalities from which at least 15% of
workers commute to work.
Chosen metropolises can be in accordance with their basic demographic importance divided into
three size groups: metropolises with a population of more than 2.5 million, metropolises with a
population of 1 to 2.5 million and metropolises with a population of less than 1 million.
Big cities logically have suitable conditions for diversification and, thanks to access to
knowledge, systematic specialisation in sectors with high added value (OECD 2006). Progressive
industry groups taken from the study The Metropolization of the European Urban and Regional
System (Krätke 2006) were used in the primary assessment of the given component. A
progressive economic profile with a rapidly growing share of knowledge-based high-tech sectors
in industry and services is widely regarded as the typical character of a metropolis. Metropolitan
regions are the centres for knowledge-based production chains and innovation-strong production
clusters (Krätke 2006). The concentration of the corresponding global firms has been analysed at
Loughborough University (GaWC 2014). Unfortunately, this database often has gaps for single
EU regions and specific periods. Following the available knowledge, the metropolises can be
grouped into the following groups:
1. Group A: above average proportion of research-intensive high technology industrial
branches (HTI) and research-intensive medium high technology industrial branches (MTI)
and knowledge-intensive technology-related services (TS)
2. Group B: above average proportion of knowledge-intensive market-related enterprise
services (ES) and knowledge-intensive financial services (FS) and knowledge-intensive
services in healthcare, education and the media industry (HEM)
3. Group C: average proportion of knowledge-based industries with a better position of
technology-related branches (HTI + MTI + TS)
4. Group D: average proportion of knowledge-based industries with a better position of
service-related branches (ES + FS + HEM)
5. Group E: below average proportion of research-intensive and knowledge-intensive
branches.
The Eurostat Regio database (2011) was selected to assess the economic profile due to the need
to have comprehensive data based on territorial structure (NUTS2 regional level).
Evaluation of general attractiveness is the most complicated matter. Three components are
considered: business attractiveness (BA), residential attractiveness (RA), and innovation potential
(IP). Business attractiveness (BA) occupies a central position in the case of the evaluation of
metropolises' attractiveness. Information from the European Cities Monitor/ECM, presenting the
views of approximately 500 respondents from a range of managers of the world's leading
companies (Cushman & Wakefield, 2011), was used for its evaluation. Within the framework of
15
the Central European macroregion, at the beginning of the current decade, rankings included 13
cities, whose semantic position was assessed primarily based on data for 1990, 2000 and 2010.
This information was accompanied by time-corresponding data obtained from the benchmarking
of large cities created by the HWWI/Berenberg bank, emphasising the results of the aggregate
rating of location advantages (Neumann, 2013) and further from the database of GaWC,
Loughborough University (2014). The researched metropolises can be, based on BA, divided
from a broader perspective into three basic categories:
1. metropolises of global importance – five metropolises,
2. metropolises of European importance – seven metropolises,
3. metropolises of Central European importance – fifteen metropolises (ECM database
includes only Bratislava).
Data from Mercer, managing the most well-known world database, precisely the average order
for freely available surveys of 2011 and 2012 (Mercer, 2012), were used for residential
attractiveness (RA). Additional resources are taken from the webpage Numbeo quality of life by
the city for 2011 and 2014; in the case of Poland, it was necessary to complete it with information
from the database of GaWC (2014) and Bańczyk (2012). The position of a metropolis is rated as
balanced in the case of differences compared to the position according to the BA (+/− 1 to 2
places; above this level, it is an unbalanced position with positive or negative deviations). The
comprehensively conceived information, taken from the world ranking processed by the
2thinkknow Consulting agency since 2007 (Innovation Cities, 2015), was used as a primary
source of information about the IP. An extensive set of used indicators is aggregated here into
three factors: cultural assets, human infrastructure and networked markets - the average order for
2011 and 2014. In some cases, this information has been accompanied by Annoni and Dijkstra
(2013).
The evaluation results of metropolises form a basic framework for the strategically targeted
conceptualisation. In the presented example, we mainly focus on the assessment of the intensity
of the links between Prague, accompanied by two following important centres, Brno and Ostrava
(the functional regions of Brno and Ostrava reached a population size of 643 and 563 thousand in
2012; OECD 2015), and other Central European metropolises and perceptions of their
development potential. The procedure consists of the following steps:
▪ evaluation of the intensity of ties with an emphasis on the identification of development
axes of supranational importance,
▪ synthesis of obtained knowledge in the context of a spatial model of the development of the
Czech economy,
▪ the final conceptualization of the research results with the use of scenarios of regional
development (level NUTS 3).
The evaluation of metropolitan links is based on a gravity model as a standard tool of a qualified
estimate of potential interactions:
ij
jxi
ij
d
MM
G = ,
where Gij = the gravity force acting between the metropolises i and j, Mij = the economic
importance of metropolises and dij = the distance of metropolises. For measuring the importance,
GDP created in metropolitan regions in 2010 were used, and the distance of the metropolis
corresponds to the length of the fastest road connection.1
1
During the evaluation, metropolises were preferred that met the criterion of so-called effective distance, that is, in
the case of road freight transport as the most important means of transport of goods, in accordance with the relevant
EU legislation (regulation of the daily working time of drivers) set at 600 km.
16
Results
The typology method, which sorts examined phenomena according to the similarity of the
selected characteristics, was chosen due to the problems with the comparability of the data to
evaluate the semantic position of the metropolises. We dealt with the degree of similarity in
classifying metropolises within individual components - population size, economic profile and
attractiveness. The metropolises were classified into dominant, established and elementary types
(see Table I.1). The statistical analysis of the results shows that the type classification of the
metropolises has the strongest link to the component "attractiveness", with the correlation
coefficient on a significance level of 0.05 k = 0.85. We also encounter a similar orientation of the
priority links for both remaining components, of which the more powerful dependency shows the
component "size". We extended the statistical analysis about the indicator of GDP per capita,
finding the strongest link to the component "structure" with k = 0.73, which corresponds with the
general premise about the higher added value of the production of knowledge-based industry. In
addition, it is helpful to note that we encounter above-average levels of this component only for
the dominant and established metropolises.
Table I.1: Results on Central European Metropolises
country/area population
GDP
(USD) per
capita
% share
of total
GDP
economic
profile
attractiveness (BA order/
RA/IP variation)
aggregate
group
Czech Republic 10 505 445 23 712 x (//)
Prague 1 868 631 41 543 30,5 Group D 2 (8/▼/●) II
Germany 81 843 743 33 517 x (//)
Berlin 4 386 551 29 971 4,8 Group D 1 (2/▼/●) I
Rhein-Ruhr 7 089 648 36 366 9,4 Group D 1 (5/●/▼) I
Hamburg 2 996 750 44 934 4.9 Group B 2 (7/●/●) I
Munich 2 904 480 51 350 5.3 Group B 1 (3/●/●) I
Frankfurt a. M. 2 525 458 48 802 4.5 Group B 1 (1/▼/▼) I
Stuttgart 1 960 286 42 895 3.1 Group A 2 (11/▲/▲) II
Mannheim 801 951 36 501 1.7 Group A 3 (1/▼/▼) II
Hannover 1 220 106 36 327 1.6 Group A 3 (2/●/●) II
Nuremberg 1 168 145 38 548 1.6 Group A 3 (4/▲/●) II
Bremen 1 026 367 36 431 1.4 Group D 3 (3/●/▼) III
Leipzig 833 828 25 917 0.8 Group D 3 (6/●/▲) III
Dresden 842 159 25 383 0.8 Group C 3 (5/●/▲) III
Poland 38 538 447 17 353 x (//)
Warsaw 3 008 921 37 456 16.9 Group D 2 (10/▼/▼ ) II
Katowice 2 608 651 20 119 8.0 Group E 3 (4/●/●) II
Krakow 1 357 206 19 716 4.0 Group E 3 (3/▲/●) III
Gdansk 1 098 435 20 470 3.4 Group E 3 (5/●/▲) III
Lodz 947 767 19 642 2.8 Group E 3 (6/▲/●) III
Poznan 941 914 27 729 3.9 Group E 3 (1/●/▼) III
Wroclaw 835 403 23 691 3.0 Group E 3 (2/▼/▼) III
Switzerland 7 954 662 39 351 x (//)
Zürich 1 226 332 48 128 19.0 Group B 1 (4/▲/▼) I
Geneva 807 646 40 039 10.3 Group B 2 (6/●/▼) II
Basel 773 332 38 635 9.7 Group A 3 (x/▲/▼) II
Austria 8 443 018 35 400 x (//)
Vienna 2 737 753 40 107 36.3 Group D 2 (9/▲/▲) II
Hungary 9 957 731 16 957 x (//)
Budapest 2 862 326 28 417 47.6 Group D 2 (12/▼/●) II
Slovakia 5 404 322 20 178 x (//)
Bratislava 722 106 45 414 29.7 Group D 3 (x/●/●) III
Slovenia 2 055 496 25 118 x (//)
Ljubljana 576 370 34 870 38.5 Group D 3 (x/▼/▲) III
Source: own research.
17
The results on metropolitan axes show (Figure I.1) the strongest links of Prague to Berlin and
Munich with a value of G = 29 and Vienna with G = 25. From a broader perspective, the
metropolitan axis Prague-Nuremberg-Munich seems to be the most important, with the aggregate
value G given by the sum of P↔N and P↔M = 41, followed by the axis of the Prague-DresdenBerlin
with G = 40. The importance of these axes shows the current shares of the German federal
land concerned with the foreign trade of Czechia with Germany - turnover/import: Bayern
23/29%, Baden-Württemberg 16/20%, Nordrhein-Westfalen 15/14%, Sachsen7/9% and Hessen
6/6% (Statischises Bundesamt, 2012). Brno has the strongest ties to Vienna and Ostrava to
Katowice (G = 11 or 7). From a broader perspective, the metropolitan axis Prague-Brno-Vienna
(from which a metropolitan axis diverges, pointing over Bratislava to Budapest) plays a decisive
role. The metropolitan axis Prague-Munich with the West Bohemia axis Prague-Pilsen, the
metropolitan axis Prague-Berlin with the North Bohemia axis Prague-Ústí n. L. and the
metropolitan axis Prague-Vienna with the Bohemia-Moravia axis Prague-Jihlava-Brno show
strong interactions.
Figure I.1: The metropolitan system of Central Europe from the point of view of Czechia
Source: own research.
Evaluation of the metropolis is an essential basis for creating supranational development
concepts, emphasising the metropolitan networks as one of the building blocks of territorial
integration. The presented analysis of three components covering the main determinants of the
development - the input assumptions (population size), the ability of adaptation (economic
profile) and development potential (attractiveness), can be regarded as an inspirational approach
to assessing metropolisation process, which can be deepened through generalisation and
practically targeted conceptualisation of the results. The suggested approach was demonstrated
with the example of Czechia, using the typology of metropolises as the basic design of a longterm
strategy for their development. It is necessary to note that several other factors influence
metropolises' cooperation. The German metropolises Berlin, Munich and Frankfurt a. M. together
with the Austrian metropolis Vienna have crucial importance from the perspective of the Czech
metropolises Prague and Brno. Strengthening mutual economic and social ties is a crucial
condition for improving the international position of our metropolises which is confirmed by tool
of integrated territorial investments (ITI) to support cities and urban areas approved (European
Commission 2011, Statutory city of Brno 2015).
18
2. The cost of suburbanization: spending on environmental
protection
The subject of Paper B is an analysis of the suburbanisation costs based on municipal expenditure
on protecting the environment in Czechia. The goal is to assess disparities between different
municipalities and evaluate the relevance of these differences to suburbs compared to other areas.
The analysis is based on a methodological framework of CEPA environmental expenditure
corresponding with the Czech public-sector budget financial structure. This study has three
essential areas for Czech municipal expenses: water protection (with emphasis on wastewater
treatment plant and infrastructure), waste management and biodiversity and landscape protection
corresponding with public municipal greenery. We used the Ministry of Finance´s State Treasury
Monitor dataset, providing significantly detailed and precise data on municipal expenses for all
6,255 municipalities in 2010–2015. We compared relevant expenses in Czechia’s OECD
metropolitan and non-metropolitan areas. The results show that municipalities with the most
outstanding water protection expenses per capita are exposed to a suburbanisation burden and are
situated in neighbourhoods of Czech metropolitan centres. Disparities between municipalities
clearly show that less populous municipalities' water protection costs per capita are three times
those in bigger towns. The reason lies in the enormous fixed costs of building and operating the
required infrastructure. On the other hand, the most extraordinary spending on maintaining public
greenery was found in the metropolitan cores, showing more significant demand for public
greenery where there is no open countryside. Regarding waste management, there is no apparent
relationship with localisation in suburban areas.
Background
The issue of suburbanisation is primarily discussed in the research literature concerning such
areas as land use management (Pendall, 1999), urban studies with a focus on urban sprawl
(Ewing, 1997) and transport (Ahlfeldt and Feddersen, 2018). Other studies cover long-term
population patterns and public perceptions of living in the suburbs (Goodling et al., 2015).
Suburbanisation can be defined as "a complex and changing process that results in the creation
of suburbs with suburbs being a form of land use or a form of development that takes place close
to, yet outside of, major cities, and which are substantially influenced materially by the economy
and ways of life of these central urban areas” (Woodbury 1955, p.2). Suburbanisation can be
characterised as when urban populations disperse over a larger area encompassing urban
neighbourhoods (Edmonston, Davies, 1976). Within a broader definition, the literature usually
refers to the following characteristics of suburbanisation, especially in Western countries, namely
the internal decentralisation of the population within agglomerations, the expansion of lowerdensity
housing in close proximity to cities, and the blurring of the boundaries between urban and
rural areas, including sociological changes in the attitudes of such populations (Tammaru, 2001).
In a broader definition, some authors prefer suburbanisation as a situation where the enlargement
of areas surrounding cities is more intensive than the city's growth (Hardi et al., 2020). Ewing
(1997) distinguished between suburbanisation itself and creating urban sprawl. Ewing (1997, p.
108) defines sprawl as "leapfrog or scattered development; commercial strip development; and
large expanses of low-density or single-use development as well as by such indicators as low
accessibility and lack of functional open space". He also identified issues including excessive
public spending, loss of resource lands and a waning sense of community. Pendall (1999) showed
that local governments relying on ad valorem property taxes to fund services and infrastructure
tended to create more sprawl than those relying on a broader tax base. Brueckner (2000)
identified three fundamental forces driving suburbanisation: growing population, rising incomes
and falling commuting costs. Qin (2017) showed through the example of high-speed railway
network development in China how transportation costs affected urban peripheral patterns. Areas
with upgraded railway lines experienced reductions in GDP and GDP per capita following the
upgrade, which was largely driven by a concurrent drop in fixed-asset investment.
19
There is a vast literature on the environmental impacts or consequences of suburbanisation.
Burchell et al. (1998) assessed the cost of urban sprawl in several areas: public and private
investment and operating costs, transportation and travel costs, land and natural habitat
preservation costs, impacts on quality of life, and consequences in social issues related to living
in the suburbs. Adelmann (1998) specified before all environmental impacts of suburbanisation
with loss of farmland (Liang et al., 2015), excessive removal of native vegetation, and as a result,
reduced diversity of species (confirmed by Wang et al., 2017). Associated impacts are an
increased proportion of non-permeable surfaces, increased stormwater runoff and a higher risk of
flooding (Hardi et al., 2020). Johnson (2001) categorised other environmental impacts
corresponding with previous studies with loss of environmentally fragile lands, reduced regional
open space, more significant air pollution, higher energy consumption, and decreased aesthetic
appeal of landscape corresponding with the monotonous (and regionally inappropriate)
residential visual environment. Margules and Meyers (1992) also emphasise ecosystem
fragmentation usually associated with the transport infrastructure, but of course, suburbanisation
is accompanied strongly by transport network development. Novak and Wang (2004) analysed
the impacts of suburban sprawl on Rhode Island's landscape. They found that the land transition
in the study area contributed to the scarification of forest land and, consequently, a declination of
ecological connectivity. Radeloff et al. (2005) confirmed forest fragmentation by urban sprawl,
even in rural areas.
In Czechia, the most suitable case of suburbanisation is the capital Prague and the development
of its neighbouring area. Although Ourednicek (2003) presents the post-1990 development in
Prague as a shining example of suburbanisation tendencies, in the context of the post-2000
development, the 1990s development can be considered as the relatively slow growth of built-up
areas in the Prague hinterland up to 1999. On the contrary, the pervasive development of
suburbanisation in the years 1999-2009, when suburban development was recorded not only in
the closer hinterland of the capital city but also in areas further away from the border (Franke,
2015). The development in these two periods is represented in Figure II.1, representing the
change in population density (in percentage) as an indicator of suburbanisation in two periods
corresponding with the national Census in 1991, 2001 and 2011. These two figures emphasise the
phenomenon of suburbanisation, especially in the OECD-defined metropolitan areas surrounding
their metropolitan cores. The acceleration of the phenomenon after the year 2001 is evident.
Regarding other regional centres in Czechia, suburbanisation tendencies in different periods are
shown, such as in Brno, followed by Plzeň and České Budějovice. These tendencies have not
been statistically demonstrated in Ostrava (Nevedel, Paril, 2014). There is current literature
showing continuing residential suburbanisation process after 2010 in the neighbourhood of both
the capital Prague (Zevl, Ourednicek, 2021) and regional centres: Brno (Stastna et al., 2018),
Olomouc (Biolek et al., 2017), České Budějovice (Kubeš, 2015), Hradec Králové, Liberec and
Ústí nad Labem (Obrebalski, 2017).
Spending on the environment is the cornerstone of its protection and a widely debated issue,
focusing primarily on corporate or business spending (Vargas-Vargas et al., 2010) and national
spending. Less explored is public expenditure at a regional or municipal level. There are many
self-governing entities within one national economy – in the case of Czechia, 14 regions and
6,255 municipalities. The management of these entities causes the duplication of some economic
activities and involves additional transactional costs (Pannell et al., 2013). This paper examines
how the system of municipal environmental accounting is set up (Hajek, 2003) and its relation to
environmental protection (Soukopova and Bakos, 2013) with an emphasis on expenditure
(Heideri, 2012) and the possibilities of its evaluation (Sarra et al. 2017). It is possible to use
financial indicators and their geographical nature to evaluate the effectiveness and to outline
other relevant relationships (Hajkowicz et al., 2005). We see a particular gap regarding our
20
specific research focus on municipal environmental expenses concerning population changes in
suburban areas. It is partly discussed in the Czech context with results provided by Maštálka and
Valíková (2014). They showed in the Pardubice metropolitan area that there is no statistically
significant relationship between population increase in suburbs and the increase of total
municipal operating profit/result expected by municipalities supporting the development of
residential areas. In Spain, Hortas‐Rico (2014) analysed the relationship between urban sprawl
and local budget using data from 4,000 Spanish municipalities from 1994 to 2005, concluding
that sprawl considerably increases demand for new infrastructure. Gielen et al. (2021) calculated
the effect of urban sprawl on the expenditure on public municipal services for 542 municipalities
in Valencia, showing highly cost-sensitive items to urban sprawl areas: waste management, water
supply and distribution, road cleaning or public lighting. We focused on the costs of
suburbanisation involved in local government expenditure directly aimed at environmental
protection. Evaluating the impact of suburbanisation on municipal budgets is relatively rare, even
though such an approach can be found as far back as the mid-twentieth century (Hawley, 1951).
Our approach compared spending by municipalities influenced by suburbanisation with those that
are not. This approach united assessments from temporal, spatial and financial perspectives in
correspondence with the European Union's emphasis on Territorial Impact Assessment
(Camagni, 2009; EU Committee of the Regions, 2015; Nosek, 2017) instruments and allowed the
economic valuation of long-term socio-geographic patterns. Thus, our research question
corresponds with the local costs to develop and manage new residential areas in the
municipalities in the vicinity of metropolitan cores compared to other municipalities.
Representatives often disregard these costs with the prospect of higher municipal tax revenues
that result from a higher municipal population. From our point of view, the direct environmental
costs (shown directly in municipal accounting) of such development are not insignificant, but
they can occur with some delay.
Fig. II.1. Change in population density in the period 1991 to 2001 (left) and 2001 to 2011
(right) (in %, CZSO, 1991, 2001, 2011)
Source: Census 1991, Census 2001, Census 2011, own elaboration
Data and methods
Environmental expenditures aim to prevent or eliminate environmental damage (Soukopová,
2011). Environmental-economic accounting (EEA) provides a conceptual framework for
integrating environmental statistics and their relationship to finance. The UN Committee of
Experts on Environmental-Economic Accounting (UNICEEA) oversees the System of
Environmental-Economic Accounting (SEEA, United Nations Statistics Division, 2016). The
introduction of the SEEA to the EU member states in 1993 has meant that the above-mentioned
internationally comparable indicators have already been developed (CEPA, SERIEE, 1994, the
Classification of Environmental Protection Facilities; CEPF, Eurostat, 2002a, 2002b;
Classification of Resource Use and Management Activities and expenditure CRUMA, SERIEE,
2002; Falticeli and Ardi, 2007).
21
Table II.1. Structure of environmental protection expenditure
Category Paragraph Description
Water protection
2321
2322
2329
2331
2333
Drainage and treatment of sewage, sludge
Prevention of water pollution
Drainage and wastewater treatment (not elsewhere classified).
Modifications of water management of watercourses (reconstructions etc.)
Adjustment of small watercourses
Air protection
2115
2542
3711
3712
3713
3714
3715
3716
3719
Warming and energy-saving programmes
Meteorology
Removal of solid emissions
Gaseous emissions
Changes in heating technology
Measures to reduce greenhouse gas production
Changes in production technologies to eliminate emissions
Monitoring of air protection
Other air protection activities
Waste
management
2122
3721
3722
3723
3724
3725
3726
3727
3728
3729
Collection and processing of secondary raw materials
Collection and transport of hazardous waste
Collection and transport of municipal waste
Collection and transport of other wastes
Use and disposal of hazardous waste
Use and disposal of municipal waste
Use and disposal of other waste
Prevention of waste generation
Waste management monitoring
Other waste management
Soil and
groundwater
protection
2342
2541
3731
3732
3733
3734
3739
Anti-rooting protection
Geology
Soil and groundwater protection against polluting infiltrations
Soil decontamination and groundwater purification
Soil and groundwater monitoring
Prevention and remediation of salinisation
Other protection of soil and groundwater
Protection of
biodiversity and
landscape
1037
2334
3741
3742
3743
3744
3745
3749
Complex socio-economic functions of forests
Revitalisation of river systems
Protection of species and habitats
Protected parts of nature
Recultivation of land as a result of mining activities
Anti-erosion, anti-avalanche and fire protection
Caring for the appearance of villages and public greenery
Other activities for nature and landscape conservation
Reduction of
physical effects
factors
3751
3753
3759
3771
3772
3773
3779
Design and application of anti-noise devices
Monitoring to detect noise and vibration levels
Other noise and vibration control activities
Anti-radon measures
Radioactive waste
Monitoring to detect the level of radiation
Other radiation protection activities
Environmental
protection
administration
3761
3762
3769
Central Government Administration in Environmental Protection
Other organisations of state administration in environmental protection
Other ecology management
Other ecological
activities
3780
3791
3792
3793
3799
Environmental research
International cooperation on the environment
Ecological education
Environmental programmes in transport
Ecological issues and programmes
Source: Decree 323/2002 of Ministry of Finance (2017a).
Our research approach corresponds with the most commonly used classification of environmental
protection expenditure from CEPA classification (CEPA, SERIEE, Eurostat 1994) regarding
Czech specification by types of expenditure created by Soukopová and Bakoš (2013). Reflection
of the environmental accounting system in the Czech public accounting system is shown in Table
II.1. The dataset used in our study from the State Treasury Monitor of the Ministry of Finance in
the Czech Republic (2017b) is based on this accounting scheme. It is our study's critical financial
22
data source. The data presented in Table II.1 covering 2010 to 2015 includes 6,581,924 financial
transactions related to self-government expenses and earnings in the Czech 6,255 municipalities.
According to the economic importance of the municipal self-government agenda, three principal
areas are considered: water protection, waste management and the protection of biodiversity and
the landscape. The relevance of these areas is shown in Fig. II.2, which sets out the structure of
municipal environmental expenditure. We used the OECD metropolisation database (OECD,
2020) to define three municipal categories: OECD functional urban cores (comprising 15 cities or
towns); OECD functional urban hinterland areas (2,480 FUAs); and others (3,760 NON-FUAs).
This categorisation allows for identifying the cost differences and answers whether it is more
costly for a municipality to be near a metropolitan centre.
Fig. II.2. Share of expenditure on the environment 2010–2015 (%).
Source: Ministry of Finance (2017b).
Results
In the following part, we consequently provide results on three key areas where municipalities
spend their expenses: water protection, waste management and biodiversity and landscape
protection corresponding with municipal public greenery. Water management is becoming a
strategic issue for most countries due to resource depletion and continuing metropolisation that
creates suburban zones and affects water quality (Yang et al., 2013). Fig. II.3 shows the average
municipal operating and investment expenditure on water protection. The black hatched areas at
the location of large cities (OECD metropolitan core centres) and their surroundings indicate
values up to CZK 250 per inhabitant for operating expenditure and up to 1,250 CZK per
inhabitant for investment, the former including around Prague and Brno but not Ostrava. Overall
results show that municipalities in functional urban areas (FUAs), on average, pay up to 17%
more for water protection than municipalities not situated in FUAs. On the other hand,
metropolitan centres pay only 19% as much as NON-FUA municipalities, which means there are
large economies of scale. The highest additional costs for suburban water protection are paid near
Olomouc (76% more), Prague (50%) and Brno (47%).
Fig. II.3. Yearly arithmetic average of operating (left) and investment (right) expenditures per
capita on water protection by municipalities in Czechia in 2010 to 2015
Source: Eurostat (2020), CZSO (2020), Ministry of Finance (2017b), own elaboration.
41,33%
0,03%
0,65%0,25%
30,47%
27,28%
Water protection
Reduction of physical effects factors (noise,
radiation etc.)
Air pollution
Soil and groundwater protection
Waste management
23
The available data show that the expenditure on water protection is inversely related to the
municipality's population – the smaller the population, the greater the average per capita
expenditure. There is an uneven distribution of municipal size categories, and most municipalities
in Czechia are small, with 300 to 1,000 inhabitants. Fig. II.4 shows the average results for all
municipalities by size category in 2010–2015, with the link between the municipality's
population and the average expenditure per capita. There is a step-change at the 5,000-inhabitant
level. Large municipalities spend less per capita on water protection on average due to the fixed
costs of constructing wastewater treatment plants; for large municipalities, economies of scale are
confirmed.
Fig. II.4. The average expenditure per capita on water protection according to municipality
population category (EUR).
Source: Ministry of Finance (2017b), CZSO (2020), own elaboration.
The next case is biodiversity protection. FUA core areas and municipalities in urbanised areas
spend considerable amounts on public green maintenance to improve people's quality of life and
on bio-corridor systems that serve as the green infrastructure (Peimer et al., 2017; GrodzinskaJurczak
and Cent, 2011). Fig. II.5 shows many areas with a zero or very small average amount of
operating and investment expenditure on biodiversity and landscape protection, indicating that
investment in this category is only a high priority for FUA cores. The overall results on
protecting biodiversity and landscape indicate the significance of this environmental component
as municipalities in FUAs show 30% more costs, but this significance seems to be restricted to
urban centres, which spend 20 times more than non-urban areas.
Fig. II.5. Yearly average operating (left) and investment (right) expenditure per hectare on
protecting biodiversity and landscape by municipalities (2010 to 2015)
Source: Eurostat (2020), CZSO (2020), Ministry of Finance (2017b), own elaboration.
Our results regarding expenditure on waste management show that, unlike the previous case
study on water protection, this service is provided at a lower cost in functional urban areas
(FUAs) compared to NON-FUAs. FUAs provide this service at 1.87% less costly compared to
the nationwide municipality average, while for NON-FUAs, it is 1.32% more expensive. The
difference is low. The highest costs compared to the municipality average can be found in the
0,00 5,00 10,00 15,00 20,00 25,00 30,00 35,00 40,00 45,00 50,00 55,00 60,00
less than 300
301 - 1 000
1 001 - 5 000
5 001 - 10 000
10 001 - 50 000
50 001 - 100 000
100 001 and more
24
following FUA areas: Most (64%), then Zlín (16%) and Chomutov (15%). In Prague, costs are
higher but only by about 7%. FUA centres again confirm the importance of economies of scale in
providing waste services – they reach an average level of 78% compared to the nationwide
municipality average.
Analysis revealed that from 2010–2015, Czech municipalities allocated most environmental
spending to water protection, waste management and biodiversity and landscape protection. In
contrast, the protection of soil and groundwater, air quality protection and the reduction of
physical factors were only marginal categories. The annual total municipal spending oscillated
around 18 billion CZK for operating expenditures and 16 billion CZK for investment. Total
municipal operating expenditure on environmental protection exceeded the investment amount;
the only exception was in 2015, when the investment was higher by about CZK 200 million due
to the final draw-down of finance from European structural funds for 2007–2014. Analysis of this
expenditure reveals that most of it target drainage and sewage treatment. When constructing
wastewater treatment plants, fixed costs are similar for all municipalities, so they are
proportionately cheaper for large cities than for less populous municipalities. A point of stepchange
was found with municipalities with less than 5,000 inhabitants, which showed
significantly higher average expenditure. The environmental expenditure for the protection of
biodiversity and landscape, meaning care of the area's appearance and public greenery, showed
the most significant expenditure in functional urban cores. A different situation was found in the
case of waste management. Here, the main expenditure is on waste collection and disposal, which
is directly proportional to the amount of waste produced, which depends on the number of
residents. The average per capita expenditure on waste management in the municipalities of
Czechia was approximately the same for all size categories. The cost of waste management
showed a growing trend, which can be expected with increasing consumption and thus increasing
waste.
The paper shows a fundamental approach to comparing a large number of municipality budgets
and provides an essential evaluation framework that reveals it is possible to increase the quality
of municipality financial management not only given "efficiency as doing things right") but also
in the view of "effectiveness as doing the right thing" (Drucker's 1967). Our results provide
evidence that suburbanisation leads to higher direct environmental costs and burdens municipal
budgets in relevant areas over the long term. Public sector representative often disregards this
effect. Our findings also show potential in the public sector budgetary framework to find a more
effective system of financing environmental services on the municipality level. Simply, general
budgetary rules are not respecting significant municipal disparities corresponding with local
conditions and population categories. Our results raise some follow-up questions for further
research. One of these areas is, of course, analysing an even more extended period of at least one
decade between two population Censuses, 2011 and 2021, as a critical research enlargement is
also to enrich the analysis of more factors leading to higher costs, such as altitude or slope of the
terrain or other relevant costs that correspond with the approach of Guilen et al. (2021).
25
II. The transport system design and externalities
The second part of the habilitation thesis focuses on transportation system design with emphasis
on road transport on inter-metropolitan level of connectivity, with particular emphasis on the
environmental externalities of transport caused by air pollution.
3. Assessment of the burden on population due to transport-related
air pollution: The Czech core motorway network
The need for human mobility is responsible for many negative environmental impacts, including
human health. Negative externalities of transport are one of the crucial issues in environmental
discussion and policy. Paper C aims to assess transport externalities related to air pollution from
particulate matter (PM10) using the example of the Czech highway D1 in 2007–2016. The
geographical assessment method distinguishes varying elevations in which different buffer zones
are identified to assess PM10 concentration changes according to distance from the road. An
econometric analysis then discusses the resulting relation between PM10 and highway proximity.
In the final step, we assess the size and demographic structure of the population affected by the
highway PM10 pollution, and we compare several evaluation methods to assess related
morbidity. The results show falling levels of PM10 pollution, not only with increasing distance
but also in intertemporal comparison, with concentrations lower by 2μg.m3 in the 2012–2016
period than in 2007–2011 despite increasing road traffic on the highway, which means both a
very significant reduction in the number of cases and economic value.
Background
The context of assessing the external effects of transport is primarily related to construction and
maintenance. The most crucial benefits concern the time saved and improvements in the overall
level of accessibility (Martens and Di Commo, 2017; Forslund and Johansson, 1995). Costbenefit
analysis (CBA) is considered to be one of the best possible methods for transport project
assessment, and it generally provides significant results for differentiated transport solutions.
CBA is used differently according to country and policy-making decision processes (Hansson
and Nerhagen, 2019). Recently, the emphasis on long-term strategic planning in the EU was
enriched by a territorial impact assessment (TIA; 2020), which makes it possible to measure the
regional impacts of strategic planning. However, Czechia still needs to introduce this instrument
as an integral part of the general planning process. Instead, it is used only for separate
assessments of regional development, regardless of whether it involves environmental or
transport policy. The application of CBA is a critical way that can provide better results in longterm
transport project assessment because it provides a way to integrate CBA with a TIA (2020)
approach, emphasising dynamic and long-term data (Schade and Rothengatter 2003). This
approach is used to assess specific problems linked to road transport and human health (de
Campos et al., 2019), such as noise (Serrano-Hernández et al. 2017; Hammer et al. 2014) and air
pollution (Watkiss et al. 2001; Rotaris et al. 2010, Le Boennec 2017, Cavallaro et al. 2018). The
research focus is usually in the field on air transport emissions (Monks et al., 2009), but for
human health, the concentration of pollutants in the environment is crucial.
According to the World Health Organization (WHO, 2000), common pollutants from transport
include nitrogen dioxide, ozone, other photochemical oxidants, particulate matter, and sulfur
dioxide. In our paper, we focus on PM10, which is considered one of the primary pollutants
(Maibach et al., 2008), usually bounding polycyclic aromatic hydrocarbons (PAHs) (Yin and Xu,
2018) and suggested as a suitable pollution indicator by Künzli et al. (2000). Based on the WHO
(2000) recommendation, we did not set any lower limit for PM10 to indicate the level of
pollution that is detrimental to health. Adverse effects on health have been observed at levels not
far from natural background concentration values of about 6 μg/m3 (Correia et al., 2013). Many
epidemiological studies have demonstrated negative health consequences from excessive PM10
26
in the child and adult populations (Kirshnan et al., 2019; Sánchez et al., 2019; Gouveia et al.,
2018; Künzli et al., 2001; Abbey et al., 1999; Englert, 1999). The most frequent impacts of PM10
are related to cardiovascular, respiratory, cancer, and cerebrovascular effects, which are
manifested in increased morbidity and mortality. A long-term concentration of particulate matter
is associated with natural-cause mortality (Beelen et al., 2014), especially for cardiovascular
disease mortality or morbidity (Kirrane et al., 2019; Dabass, 2018; Haikerwal et al., 2015; Mills
et al., 2009; Metzger et al., 2004). PM10 also has respiratory health effects that can lead to
increased mortality and morbidity (Kim et al., 2018; Mathew et al., 2015; Gehring, 2013; Hoek et
al., 2012; Zanobetti et al., 2009; Ostro et al., 2005). A relationship has also been shown between
exposure to PM10 and cancer, primarily lung cancer (Dimitriou and Kassomenos, 2018;
Raaschou-Nielsen et al., 2013; Pope et al., 2002; Nyberg et al., 2000) and cerebrovascular disease
(Wettstein et al., 2018; Staffogia et al., 2014; Zhang et al., 2011; Torén et al., 2007). Meisner et
al. (2015) assessed the magnitude of health impacts and economic costs of fine particulate matter
pollution in Macedonia using Disability-Adjusted Life Years (DALYs). Martinez et al. (2018)
obtained particulate matter concentration data from air quality monitoring stations in the Skopje
metropolitan area, applying relevant concentration-response functions and calculating the burden
of disease and societal mortality cost attributable to particulate matter. Künzli et al. (2000) and
Seethaler et al. (2003). estimated the impact of outdoor traffic-related air pollution on public
health in Austria, France, and Switzerland, using attributable cases of morbidity and mortality.
The often used is the health impact assessments (HIA) method in the area of traffic air pollution
(Khreis et al. 2018, Tobollik et al. 2016, Chartasa and Gibson 2015, Boldo et al. 2006).
Korzhenevych et al. (2014) consider the following two studies on the assessment of the external
costs of transport to be essential at the European level: HEATCO – Developing Harmonised
European Approaches for Transport Costing and Project Assessment (Bickel et al., 2006) and
CAFÉ CBA – Clean Air for Europe Cost-Benefit Analysis (Hurley et al., 2005), which were
evaluated in the HEIMTSA project – Health and Environment Integrated Methodology and
Toolbox for Scenario Assessment (Friedrich et al., 2011). Both studies use the impact pathway
approach, which was developed under the External Cost of Energy project (ExternE), with its
own “ExternE Methodology” calculating external environmental costs (Bickel and Friedrich,
2005). The impact pathway analysis identified the most significant impacts of emissions, their
quantifiability and the monetary valuation of costs. The main emphasis in this field of research is
focused on the emission measuring approach, while our study brings a concentration-based
approach. The research objective is to assess the effect of road transport on air pollution
considering accurate spatiotemporal characteristics with impacts on population morbidity
regarding the demographical structure and, finally, bringing monetised values determined by
PM10 concentration changes.
Data and methods
The research approach lies in assessing ten years of the Czech core highway D1 and its trafficrelated
air pollution, as indicated by PM10, emphasising long-term trends. Using the example of
Czech core highway D1, this paper seeks to establish the long-term influence of road traffic on
health precisely. The average daily intensity of vehicles on the D1 highway is around 38,000 cars
per day, with certain parts exceeding 100,000 vehicles per day (RSD, 2016). The first
methodological point outlines the specific geographical route of Czechia's most significant
highway, the D1, connecting three main urban areas: Prague, Brno, and Ostrava (RSD, 2010).
The D1 highway is an incomplete road project, with one part currently under construction
between Říkovice and Lipník nad Bečvou (a segment of approximately 25 km is routed on firstclass
road number 47 which was included in the study). We defined four distance zones to
compare variations in PM10 concentrations based on distance from the D1 highway. The first
zone was designated as the 100-meter distance zone (intersected area). It is based on a significant
reduction in PM10 exposure at a distance of 100 m (Karner et al., 2010). The second distance
zone is 250 m (neighbouring area) based on Yazdi et al. (2015) precise analysis of distance from
27
highway exposure depending on wind speed and rain circumstances, demonstrating that highspeed
wind (velocity >10 m/s) might raise PM10 concentration to a distance of roughly 250 m,
after which it begins to drop. The third distance zone of 500 m (closer burdened area) is used
under the European Commission (2019) approach in the development of a methodology to assess
populations exposed to high levels of noise and air pollution close to major transport
infrastructure (Ritchie et al., 2006). The last category defines the average pollution level in the
appropriate geographical location with a 2,500-meter distance zone (broader areas). The
following methodological step uses a static approach to determine the relevant impacted
population (Mommens et al., 2019). We performed this identification based on the 2011
Population and Housing Census (CSO, 2011), which provided the most thorough information
based on population in basic units of settlement (22,505 units in Czechia) according to the census
registry's cadastral regions (CSO, 2018). Then we intersected the D1 motorway-defined zones
with the residential cadastral areas of individual basic settlement units to identify the affected
population, including the demographic structure. In contrast, the dynamic approach reveals that
commuting during the day varies but is primarily directed towards larger cities in their proximity
(Census, 2011). As a result, our final estimate of long-term exposure may be slightly
underestimated.
The critical methodological step lies in the pollutant concentration analysis based on the
historical data on long-term air pollution focusing on PM10 (CHMI, 2018) from 2007 to 2016.
These datasets provide long-term averages of pollutant concentrations in the grid network.
Datasets are based mainly on the Air Quality Monitoring System (CHMI, 2019). These data are
processed in three dispersion models, one Gauss model SYMOS (CHMI, 2017, defined in Decree
No 330/2012 of the Ministry of Environment) and two Euler method-based models following the
dispersion approach according to the methodology of Kuenen et al. (2014). These three
dispersion models are used in the Czech national REZZO pollutant categorisation system. The
outcomes include a countrywide grid network for PM10, PM2.5, benzopyrene, nitrogen oxides,
ground-level ozone, benzene, heavy metals, and sulfur dioxide. We isolated PM10 concentrations
and overlapped the Czech grid network with appropriate distance zones and residential areas. The
following numbers of concerned basic settlement units were obtained for distance zones: the 100meter
zone contained 820 residential areas, the 250-meter zone included 1,011 locations, the 500meter
zone included 1,293 areas, and the 2,500-meter zone included 2,640 areas.
In the next step, we investigated whether roads influenced PM10 air pollution concentrations,
principally using the indicator of transport intensity (Transport Census, 2010 and 2016). We
intersected 22,505 basic settlement units in Czechia with the Transport Census based on 54,944
traffic counting sections and 80,151 pollution monitoring polygons. The final detailed dataset
concerning traffic, air pollution, and population provides 271,882 local observations. To explain
the significance of the model, we ran a multivariate regression analysis where the explained
variable is an average annual concentration of PM10 particles from 2012 – 2016 (PM10_rp).
Other factors, including traffic intensity, population, population density, and average altitude,
were explanatory variables (US EPA, 1978; Hoek et al., 2008). The power of influence of the
non-standardised regression coefficients is estimated by controlling the effect of other
independent variables in the model (Mareš et al., 2015). We can formally write the multiple
regression model as:
𝑌 = 𝑏0 + 𝑏1 𝑋1 + 𝑏2 𝑋2 + 𝑏3 𝑋3 + ⋯ + 𝑏 𝑛 𝑋 𝑛 + 𝜀 𝑛 (1)
where 𝑌is a dependent variable, 𝑏0 is a constant, 𝑏1, 𝑏2, 𝑏3 are regression coefficients, 𝑋1, 𝑋2, 𝑋3
are independent variables, and 𝜀 is random error.
Parameters are estimated by the ordinary least squares method:
28
𝑚𝑖𝑛 ∑(𝑌𝑖 − 𝛽0 − 𝛽1 𝑋𝑖1 − . . . − 𝛽 𝑛 𝑋𝑖𝑛)2
(2)
This method aims to find those parameters 𝛽 (estimates for 𝑏) for which the error term is
minimized:
𝛽̂ = min
𝛽
∑ (𝑦𝑖 − 𝛽0 − 𝛽𝑋𝑖)2𝑛
𝑖=1 = min
𝛽
∑ 𝜀𝑖
2𝑛
𝑖=1 (3)
Finally, we determine significant PM10 health consequences based on population groups and
distance zones. We derived the essential consequences on the long-term health impact of PM10
from three European studies using the exposure-response function (ERF cases / (year · person ·
μg / m3)]. We used their monetary valuation per case or day based on individual health effects
and risk groups to determine the health impacts on the population living near the D1 motorway,
focusing on acute and chronic morbidity. These studies are the following: EEA 2013 (European
Environment Agency) - Road user charges for heavy goods vehicles (HGV) - EEA Technical
Report 1/2013 (Andersen, 2013); HEIMTSA (Health and Environment Integrated Methodology
and Toolbox for Scenario Assessment) - EU Sixth Framework Program, runtime 2007–2011
(Friedrich, 2011); HEATCO – Developing harmonized European approaches for transport
costing and project assessment, 2006 (Bickel et al., 2006). An overview is given in Table III.1
(Korzhenevych et al., 2014).
Table III.1: Description of priority health effects
Health effect Notes
Chronic bronchitis change in new persistent cases per year per 10 μg/m3
PM10
Respiratory hospital admissions change in attributable emergency admissions per 10 μg/m3
PM10
Cardiac hospital admissions change in attributable emergency admissions per 10 μg/m3
PM10
Restricted activity days (RAD)
change in RADs per year per 10 μg/m3
PM2.5 amongst the working-age
population
Respiratory medication use among
adults change in the probability of daily bronchodilator usage per 10 μg/m3
PM10
Respiratory medication use among
children change in the probability of daily bronchodilator usage per 10 μg/m3
PM10
Lower respiratory symptoms (LRS)
increase in the average daily occurrence of LRS (including cough) per 10
μg/m3
PM10
Bronchodilator use change in the probability of daily bronchodilator usage per 1 μg/m3
PM10
Source: Andersen, 2013; Friedrich, 2011; Korzhenevych et al., 2014; own elaboration
The following formula (Bickel and Friedrich, 2005) is used to calculate the increase in the impact
of air pollution from traffic on the population:
∆𝐼 = ∑ 𝑠𝑖 ∆𝑐𝑖 (4)
I is case per year per average person. The ci is PM10 concentration, and si is the slope of ERF.
We converted this result into Czech prices for 2017 expressed in euro (based on the ExternE
methodology). We considered inflation with the EU harmonized consumer price index (Eurostat,
2018) and the exchange rate based on the PPP-adjusted exchange rate (OECD, 2018).
Results
Figure III.1 shows all the basic settlement units affected by the motorway infrastructure close to
the intersection (zones 100/250/500/2,500 m) with the motorway network. In individual risk
areas, there are residential zones of about 6,000 (100 m zone), 14,000 (250 m zone), and 31,000
(500 m zone) residents affected by increased air pollution due to the motorway. The results show
a slight improvement in air quality in terms of PM10. This improvement amounts to about 2
29
μg/m3 over ten years. The improvement is reflected in Table III.2, which shows the difference in
the degree of pollution between the individual distance zones (100/250/500/2,500 m) and the
different comparison periods. Long-term improvements are seen for all distances (with an overall
improvement of 7.56% on average). The improvement when comparing 2007–2011 and 2010–
2014 is, on average, 0.71%. However, when comparing the 2012–2016 period and 2010–2014,
there is an average improvement of 6.89%. In the same period, the Traffic Censuses for 2010 and
2016 show the average traffic intensity increased from 33,544 to 38,025 cars a day, an increase of
13.36%.
Figure III.1: Basic settlement units according to the distance from the motorway network and
PM10 pollution in 2007–2011 vs 2012–20116
Source: own elaboration
Table III.2. The concentration of PM10 (μg.m3) in the vicinity of the D1 motorway and its
decrease (in %)
Period Zone Difference with zone 2,500 m
100 m 250 m 500 m 2,500 m 100 m 250 m 500 m
2007–2011 27.105 27.050 26.799 26.033 1.072 1.017 0.766
2010–2014 26.862 26.712 26.667 25.978 0.884 0.734 0.689
2012–2016 25.097 24.972 24.624 24.203 0.894 0.769 0.421
Period differences 100 m 250 m 500 m 2,500 m Average
2010–2014 vs 2007–2011 0.90% 1.25% 0.49% 0.21% 0.71%
2012–2016 vs 2010–2014 6.57% 6.51% 7.66% 6.83% 6.89%
2012–2016 vs 2007–2011 7.41% 7.68% 8.11% 7.03% 7.56%
Source: own elaboration
Considering only PM10 concentration and traffic intensity, the observed improvement
achieves a pollutant decrease weighted by traffic intensity of 18.95%2
. However, more factors
influence pollutant concentration, so we cannot attribute this improvement only to transport.
We ran a multivariate linear regression analysis to determine the impact on the level of PM10
air pollution with these variables: transport intensity (trans_int), the population in the vicinity
of road infrastructure (popul_num), population density in the area of 500 m (popul_dens), and
average altitude (alt_avg). To fulfil the assumption of normality, variables were
logarithmised. The results show that a set of estimated independent variables explains 53.1%
of the variance of the dependent variable. According to the F Test in one-way analysis of
variance, we can reject the null hypothesis about the insignificance of the model. In other
words, the model including these variables is useful. According to the model results (Table
III.3) the average value of air pollution by PM10 particles should be 26.654 μg/m3
. Coefficient
B represents the influence of the independent variable on the dependent variable. We see a
2
According to the 2010 level of pollution and pollutant concentration, the expected pollutant concentration with
the 2016 traffic intensity level corresponds to 30,379. But the real concentration is 24,624, which means an
18.95% improvement.
30
positive relationship between the PM10 concentration and transport intensity and population
density and a negative relationship between the PM10 concentration and average altitude and
population. According to the standardised beta coefficient, average altitude is the most critical
indicator influencing PM10 concentration. 53.1% of the variance in the average PM10
concentration near road infrastructure in Czech data is explained by studied variables.
Table III.3: Model Results
Unstandardized
Coefficients
Standardized
Coefficients
95 % Confidence
Interval for B
B Std.
Error
Beta t Sig. Lower
Bound
Upper
Bound
Constant 26.654 0.063 420.395 0.000 26.530 26.778
Trans_int_ln 0.518 0.007 0.115 78.102 0.000 0.505 0.531
Popul_num_ln -0.050 0.002 -0.034 -22.715 0.000 -0.054 -0.045
Popul_dens_ln 0.401 0.004 0.171 108.512 0.000 0.394 0.408
Alt_avg -0.022 0.000 -0,617 -427.798 0.000 -0.022 -0.022
Source: own elaboration
Figure III.2 declares that the decrease in concentration has a significant effect on the reduction
of cases of chronic bronchitis. From the results, we can deduce that reducing PM10 in the air
by 1 μg/m3 will reduce cases or days of all health endpoints and monetary costs by 4%. Even
though we observed long-term improvements in air pollution, the resulting condition still
carries high economic value. The results of our study show that the PM10 concentration
during the reference period, comparing the average of 2007 – 2011 and 2012 – 2016, has
decreased on average by 2 μg/m3
. This positive change brought a reduction in the number of
cases and days of health endpoints and associated monetary values. A relatively small change
in PM10 concentration significantly changes the number of cases and days in most health
endpoints and monetary values, decreasing the financial burden between EUR 485,056 and
842,891 (in Czech prices of 2017) per year. Thus, monetised costs related to chronic
bronchitis mean a monetary decrease in the range of EUR 33,400 to 85,000 a year in the zone
up to 100 m, EUR 73,600 to 182,000 and in the zone up to 250 m, and EUR 160,400 to
202,000 in the zone up to 500 m.
Figure III.2: 1 μg/m3 improvement change of PM10 concentration in the number of cases of
chronic bronchitis and associated monetary valuation in Czech prices of 2017 in euro
Source: own elaboration
Künzli et al. (2000) used a similar approach to estimating the air pollution health impacts in
three European countries. They modelled population exposure for each square kilometre with
31
traffic-related fraction separation and concluded that the public-health consequences are
considerable. Ostro and Chestnut (1998) showed that decreased PM10 concentration has
substantial health benefits. Danielis and Chiabai (1998) estimated the cost of air pollution
imposed by various types of vehicles in urban areas in Italy through increased premature
mortality from exposure to suspended particulate matter derived from the exposed population
multiplied by the statistical value of life. Using the unit values of various health endpoints of
morbidity and mortality, Guo et al. (2010) computed the economic costs of transport-related
air pollution in Beijing. Similarly, using high-density PM10 and population data, Hou et al.
(2016) assessed the effect of PM10 on human health in Beijing from 2008 to 2012. They
estimated the average economic cost of five years inside the most urbanised ring road was
USD 4.55 billion. Mueller et al. (2017) emphasised health costs of exposure caused by
transport could be avoided with exposure recommendations. Regarding the research limits, it
is necessary to consider the quality of secondary data used for both air pollution and traffic
intensity. We used the most precise territorial units statistically monitored in Czechia (basic
settlement units). Population data are based on the 2011 Census (CSO, 2011). A further
limitation comes from the actual daily mobility and temporality because inhabitants may
commute to work in another settlement unit and not be exposed to the highest emissions in
their place of residence. A related limitation is the accuracy of the extrapolation air pollution
model, as the measuring station is not always located near the road. Regarding air pollution
assessment regarding human health, the results of our study are limited by several factors. We
took into account the linear exposure-response function, resulting in equally significant gains
in the number of cases of health endpoints, even though a non-linear relationship is observed
in reality (e.g., Liu et al., 2014), which may also vary due to air pollution mix, climate, and
the health of the population (Samoli et al., 2006). Exposure-response functions were then
taken from particular epidemiological studies (recommended projects HEATCO, EEA, and
HEIMTSA), which do not reflect the detailed specific socio-demographic characteristics of
the population in the area, but only relevant target age groups.
Although there is no safe, acceptable threshold of PM10, neither is there any generalised
approach to PM10 limitation worldwide; there are different recommendations and regulatory
frameworks. However, results show that decreases in PM10 concentration lead to lower
morbidity rates and decreases in relevant health symptoms with significant annual cost
savings in healthcare systems. Since 2005, the WHO has recommended a limit of 20 μg/m3
annual mean, and 50 μg/m3 24-hour mean (WHO, 2006), while European Air Quality
Standards are even stricter and define the limit as 40 µg/m3 per year (EU, 2008). The US
EPA cancelled the annual limit established in 1987 at 50 µg/m3 in 2006, leaving only a daily
limit (US EPA, 2020). A very similar situation is in Japan (MOE, 2020). This study found
that high PM10 concentrations along the D1 highway substantially influence morbidity in the
affected population, implying a significant cost burden. The existence of highways with
traffic intensity from 30,000 to almost 100,000 cars a day near residential areas decreases air
quality concerning PM10 in the range of 0.421 to 1.072 μg/m3 compared with the outer zone
of 2,500 m. The abovementioned impact is relevant for at least 30,868 inhabitants living less
than 500 m from the D1 highway. A long-term comparison of 2007–2011 and 2012–2016
shows a decrease between 7.41% and 8.11%. Policy recommendations can be aimed at longterm
reductions in air pollution exposure through various tools, such as incorporating health
information into the impact assessment of infrastructure projects (Seethaler et al. (2003) or
applying the polluter-pays principle by internalising the externalities. Furthermore, the
COVID-19 crisis showed through mobility changes the total potential of transport
improvements relevant to air quality (decreases of PM10, PM2.5, and nitrogen oxide, which
should be studied further (Collivignarelli et al. 2020).
32
III. The mobility behaviour and policy
The last part of the habilitation thesis is based on assessing mobility behaviour in passenger
transport concerning individual preference variability and its reflection in intermodal choice
(with emphasis on the shift to rail). Finally, this part finishes with the transport policy design
assessment presented by robust discount schemes for particular modes of transport.
4. Competition in long-distance transport: Impacts on prices,
frequencies, and demand in the Czech Republic
Paper D assesses different entry regulations' effects on company conduct and mobility
behaviour. The paper reflects three railway markets with significantly other entry policies
using data on prices and frequencies and a survey conducted to obtain revealed preferences.
The study employs data from the three main routes in Czechia. The two open-access markets
tended to provide significantly higher connection frequencies than the line with regulated
entry did. Surprisingly, low price variation across the rail and bus markets suggests low
monopoly power for the monopolised incumbent and its uniform price strategy across markets
with different entry regulations. Conversely, high price sensitivity among travellers confirms
the substance of intramodal competition.
Background
Open access competition on the railway is gradually becoming more widespread, especially
after the Fourth Railway Package of the European railway reforms (European Commission,
2016), mainly in Central Europe. The market structure is slowly changing, as is passenger
behaviour. This ongoing process of deregulation and market restructuring offers a unique
opportunity to compare markets with different levels of transformation. However, the railway
industry has several specifics. Therefore, it is necessary to be careful about the impacts of
competitors on the market and demand. First, intramodal and intermodal competition plays a
vital role in transport behaviour. Therefore, the regulated entry in the case of one
transportation mode can be partially offset by deregulation in other modes, which is often the
case for intercity bus and railroad competition. Second, vertical integration of railways and
high fixed costs can make entries socially undesirable. Third, from the traveller's perspective,
rail services will always remain heterogeneous due to such factors as the importance of
departure times. Therefore, some non-zero market power always exists and may be
challenging to regulate if desired. For these reasons, open access for railroads remains the
subject of ongoing discussion. The Czech transportation market provides a specific
opportunity for cross-section comparison due to the variability of competition across different
routes. The markets to compare include the following long-distance transport routes. The
Prague–Ostrava market has been a competitive open access line since 2011, including three
train providers (Czech Railways, RegioJet since 2011, and LeoExpress since 2013). The
Prague–Brno market is a case of intermodal competition. It has represented a mixed market
since 2016, with the incumbent providing public service obligation (PSO) services and open
access competitor RegioJet operating at its own risk (and also providing bus services via the
parallel motorway in competition mainly with FlixBus). The last market Brno–Ostrava, was
operated as a PSO by a state-owned company (Czech Railways, in December 2019, the
incumbent Czech Railways was replaced on this line by RegioJet). This paper aims to analyse
the effects of different types of railway competition on firms' conduct and travellers'
behaviour using price and frequency information together with elasticity analysis.
Several studies have analysed free entries on railroads and their effect on the given market.
Almost all of these studies have confirmed a positive effect from competition on prices,
33
higher service quality and product differentiation – Cascetta & Coppola (2013), Bergantino et
al. (2015), Beria et al. (2016), and Desmaris & Croccolo (2018) for Italy; Tomeš et al. (2016)
for Czechia; Kvizda & Solnička (2019) for Slovakia; Tomeš & Jandová (2018) for Czechia,
Slovakia, and Austria; and Vigren (2017) for Sweden. However, competition is not the only
factor determining prices, but also demand, capacity or willingness to pay (Beria & Bertolin,
2019). Competition also contributed to increased ridership (Fröidh & Nelldal, 2015).
Accusations of unfair practices were not rare: price war in Czechia (Tomeš et al., 2016),
Slovakia (Kvizda & Solnička, 2019), and Austria (Tomeš & Jandová, 2018) or political action
in Poland (Król et al., 2018). On the other hand, Bergantino et al. (2015) do not find evidence
of predatory pricing by the incumbent, and Desmaris & Croccolo (2018) show that there is no
blatant evidence of anti-competitive behaviour against the new operator in Italy. However,
lower prices meant slowly growing revenues, which can cause problems with profitability.
Pressure on infrastructure capacity and the coexistence of open access and PSOs are other
significant problems (Tomeš & Jandová, 2018). These case studies' findings align with the
modelled situation for a duopoly market in Broman & Eliasson (2019), finding equilibrium
with one dominant firm holding more than two-thirds of the market. Such asymmetry stems
from the natural differentiation of companies through the heterogeneity of departure times.
However, such an outcome is still preferable concerning overall welfare compared to a profitmaximising
monopoly. On the other hand, Wheat et al. (2018) found cost disadvantages for
firms operating in open-access markets, which stemmed from both comparable costs for
franchised operators and the loss of the advantage to profit from increasing returns to scale
common to monopolised markets and partially confirmed in a market with three competitors
(Tomeš et al., 2016). Fares (Finez, 2014; Paulley et al., 2006), travel time or speed (Behrens
& Pels, 2012; Fröidh, 2008), comfort (Fröidh & Byström, 2013, Allard & Moura, 2018),
safety (Si et al., 2009), frequency (Raturi & Verma, 2019, Paulley et al., 2006), income (ToroGonzález,
et al., 2020), the opportunity to work (Varghese & Jana, 2018), congestion (Droes
& Ritvield, 2015), capacity (Daly et al., 2014), and station location and parking availability
(Eagling & Ryley, 2015; Pagliara et al., 2012) have been among the most important factors
that influence passengers' choices. Yen et al. (2018) emphasised trip and socio-demographic
characteristics, frequency, transfer need, and easier accessibility. Ben-Akiva & Morikawa
(2002) discussed traffic congestion and parking space shortages. Attitudes and perceptions
have also affected how individuals choose between different transport modes (BahamondeBirke
et al., 2014). Rail and bus intermodal competition led to price decrease on routes with
intermodal competition compared to monopolistic routes (Gremm, 2018). In Aarhaug et al.
(2018), competition from low-cost air carriers was significant for long-distance coach lines,
whereas improved road infrastructure and rail services led to increased competition from
private cars and rail for shorter coach lines. Beria et al. (2018) showed that intermodal
competition matters. The bus routes overlapping with rail PSO are priced less, but
interestingly this happens also for high-speed lines in Italy, which means that the two markets
are not independent. In France, the level of intermodal long-distance passenger competition
among coaches, BlaBlaCar, highspeed rail, and low-cost airlines is high (Crozet & Guihéry,
2018). New deregulated bus services represent only about 2% of long-distance transport, but
intramodal competition is solid. Burgdorf et al. (2018) analyse long-distance bus services in
Germany and show that price, speed, reliability, convenience, and luggage carriage are the
most critical determinants of modal choice.
Most papers analysed the effects using individual case studies of a single route or by
analysing partial characteristics of the examined markets. Paper contributes to the existing
empirical literature on competition and regulatory effects in long-distance passenger transport
by comprehensively analysing the effects of different regulatory regimes and competition,
34
with the regulatory framework as an essential element. Therefore the research question is to
determine what effects bring open access to railways and how different institutional
frameworks influence the behaviour of competitors and travellers from intermodal and
intramodal points of view.
Data and methods
We carried out a survey collecting data on passengers' mobility choices. We gathered data on
prices and frequencies, and departure times for three different markets with different
regulatory regimes to analyse the firm's conduct. Then we carried out elasticity analysis using
discrete choice models. Furthermore, we used mobile operator data to verify our survey
sample composition. The first step was based on frequency and price data collection for the
relevant transport markets during the same period from 9 November to 22 November 2019.
We collected standard ticket purchases without any special tariffs or discounts. The data set
consists of more than 12,000 bus and train connections and ticket prices on the relevant routes
provided by Czech Railways, RegioJet, LeoExpress, and FlixBus. We merged these data with
the mobility survey to adjust for possible changes from special tariffs because the Czech
Ministry of Transport guaranteed a level of senior and student discounts on public transport
tickets of 75%. The mobility survey aimed to identify the factors determining the use of a
particular transport service by inspecting passengers' preferences. The survey results served as
feedback on the opinions, attitudes, and reasons why passengers "choose" or "do not choose"
a specific mode of transport or company. The survey was conducted via systematic sampling,
arranging the study population under selected routes and modes during October and
November 2019. Interviewers collected data via face-to-face paper and pen interviews
localised in trains and buses or at train stations, bus stops, or motorway rest areas. In the case
of bus and train passengers, there were two phases of field data collection. In the first phase,
the form and content of the questionnaire were verified by a pilot survey of fifty passengers.
In the second phase, the survey was conducted using an optimised questionnaire. In the case
of modal choice focused on car passengers, there were also two phases, but with a different
designs. The data collection was followed by verification with 10% of car respondents.
Randomly selected respondents were queried through telephone inquiries and e-mail
correspondence based on screening questions to select respondents who had been on a car
journey on a relevant route (Prague–Brno, Brno–Ostrava, or Prague–Ostrava) within the
previous 14 days. Our research comprises data for three train operators and two bus operators.
The original sample achieved 1,887 respondents, but the final sample with complete answers
reached 1,521 responses; thus, the error rate was less than 20% of the sample.
We used a standardised methodology of discrete choice models (Greene, 2009; Tomeš &
Fitzová, 2019) concerning different numbers of alternatives. All of our models were based on
logistic regressions: Brno–Ostrava (BRQ–OSR; binary logistic regression), Prague–Ostrava
(PRG–OSR; McFadden's conditional logistic regression), Prague–Brno (PRG–BRQ; nested
multinomial logistic regression). The monopolised line Brno–Ostrava had the simplest model.
The single alternative train (the choice) was predicted relative to individual car transportation,
which was the unchosen alternative. Therefore, the model is closed in the sense of travelling,
and we did not consider outside alternatives, comprising people currently not travelling at all.
The Prague–Ostrava market was modelled with McFadden's conditional logistic regression
(McFadden, 1973). The three alternatives (trains) compete within the line; therefore, a binary
choice is no longer relevant. There are two options to model such a market. The more
common option is multinomial logistic regression, which is focused on the individual unit and
uses only the individual's characteristics to explain a choice. The less common is conditional
logistic regression (Hoffman & Duncan, 1988). The second option has two independent
35
variables: alternative-specific (varying across and within cases, e.g. price) and case-specific
(constant within cases, e.g. travel purpose). Our explanatory variables included both types and
so we used the conditional option. The last market, Brno-Prague, according to the well-known
problem of the independence of irrelevant alternatives (McFadden, 1974), can be solved by
grouping similar alternatives into groups or nests. There are two bus alternatives and two train
alternatives. This methodological approach was standardised based on the available literature,
such as Koppelman & Bhat (2006) and applications in Forinash & Koppelman (1993) and
Polydoropoulou & Ben-Akiva (2001). Finally, the elasticities were calculated the same way
across different models. First, the original fitted values and adjusted fitted values were
calculated. In the case of price elasticities, all observations were adjusted by increasing price
and frequency by 1%. The individual elasticity for the given mode and company was then
calculated by subtracting the original and adjusted fitted value. The market elasticities for
transport mode were calculated as the average across individual elasticities. In our research,
the elasticity analysis represents a means of identifying the relationship between the level of
competition on the markets and behaviour using revealed preferences obtained through a
survey of all analysed markets. Due to the different specifications of the models, the
predictors differed slightly. We generally controlled for mode and company-specific
variables, including ticket price and frequency. Elasticities were calculated concerning these
variables. The model specification for the last market did not enable frequency use. The
existing alternative to Czech Railways between Brno and Ostrava was only car transport,
which is inconsistent with any frequency information. Second, the socio-demographic
characteristics were specific to individuals and did not vary across modes or companies. The
variables of travel purpose and passenger travel frequency were used in all three models. In
addition, the Prague–Brno model used the highest completed education to better distinguish
among the available options. Last, the need to change was captured as a dummy variable at
the departure, the origin, or both. Detailed variable descriptions are in Table IV.1.
Table IV.1: Variable description
Individual
specific
Alternative
specific Variable description
Ln_price Yes Yes Log of price in EUR, adjusted for discounts
Ln_frequency Yes Yes Log of number of connections per day
Old_X_public Yes Yes
Interaction variable 1 for people born before 1977 and using
Czech Railways at the same time, otherwise 0.
One_day_travel Yes No 1 if travel duration < one day, otherwise 0
Origin_change Yes No
1 if origin municipality for traveller is not equal to resident
municipality for traveller, otherwise 0
Destination_cha
nge Yes No
1 if destination municipality for traveller is not equal to resident
municipality for traveller, otherwise 0
Change Yes No
1 if destination or origin municipality for traveller is not equal to
resident municipality for traveller, otherwise 0
Weekend Yes No 1 if day of travel is Saturday or Sunday, otherwise 0
Travel purpose
fixed effects Yes No
A) Business trip; B) travel to work; C) study; D) family, friends;
E) tourism – 1 day; F) tourism – overnight; G) private affairs; H)
other
Education fixed
effects Yes No
Elementary, secondary without qualifications, secondary with
qualifications, tertiary
Travel frequency
fixed effects Yes No
4+ times per week, 2–3 times per week, once per week, 1–3 times
per month, 2–10 times per year, once per year
Results
Regarding prices, the route from Prague to Ostrava shows the most significant price volatility.
This is an important finding because this passenger transport market is the only fully open
36
Czech train market. The price strategy of LeoExpress was the most flexible, and prices varied
between 1 and 7 euro cents per km, while those of the incumbent and RegioJet were mostly
3.5–4 euro cents per km. The lowest volatility was observed on the Prague–Brno route for
trains and RegioJet buses, especially compared to the more flexible FlixBus. Operators
offered different types of services. Czech Railways' fast trains offered second, first, and
business classes. RegioJet offered four services: Low cost, Standard, Relax, and Business.
LeoExpress provided Economy, Business, and scarce Premium tickets, thus excluded from
the sample. The inclusion of tariffs, discounts, and classes had a substantial impact on the
minimum and first quartile prices, which were much lower than standard tickets, possibly due
to the composition of the population sample. LeoExpress prices showed the narrowest
interquartile range and the highest final ticket price per kilometre, as students and seniors
represented a minimal share of the passengers. In summary, almost one-third of passengers
took advantage of 75% discounts. In addition to prices, the frequency of connections is
another essential variable influenced by the level of intramodal and intermodal competition
and the openness of the railway markets. On average, there was a train or bus connection
every 20 minutes between Brno and Prague and every 32 minutes between Prague and
Ostrava but only every 74 minutes between Brno and Ostrava.
Table IV.2 Estimation results
Variable Company/mode
BRQ–
PRG
PRG–
OSR
OSR–
BRQ
Ln_price −1.575** (0.73) −0.565*** (0.212) −1.749*** (0.435)
Ln_frequency 0.35 (0.38) 1.176*** (0.278)
Old_X_public 0.607*** (0.203)
One_day_travel Bus −0.704*** (0.165)
Origin_change Bus −0.299* (0.166)
Constant FlixBus bus −1.952** (0.85)
Constant RegioJet bus −1.851** (0.836)
Constant RegioJet train 0.249 (0.463)
Tau Bus 0.983 (0.778)
Tau Train 0.603** (0.239)
Origin_change
LeoExpress
Train
−0.275 (0.368)
Destination_change
LeoExpress
Train
−2.965*** (0.684)
Weekend
LeoExpress
Train
−0.435 (0.329)
Constant
LeoExpress
Train
0.718 (0.485)
Origin_change RegioJet Train 0.403 (0.376)
Destination_change RegioJet Train −0.833* (0.427)
Weekend RegioJet Train −0.084 (0.262)
Constant RegioJet Train 0.005 (0.426)
Change CD Train 1.419*** (0.519)
Constant CD Train 1.895** (0.953)
Observations 3,036 1,584 205
Note: standard errors in parentheses; asterisks (***, **, and *) correspond to the
significance level (1%, 5%, and 10%, respectively).
Table IV.2 presents estimations for the final models. The Brno–Prague market model contains
both mode-specific and company-specific variables. The variables are interpreted in
comparison to a benchmark, which is the omitted option in all three cases. For example, the
37
statistically significant variable One_day_travel refers to trips shorter than one full day. These
consumers had lower utility for using the bus than the train. The constants were significantly
lower for both bus options compared to Czech Railways trains. In the case of RegioJet trains,
the constant utility was higher but not statistically significant. The constant utility could, with
some caution, be interpreted as unobserved comfort. Lastly, the parameter Tau in the case of
the nested version of the discrete choice model, captures the dissimilarity between options
(companies) in the specified groups (modes). Tau is always lower than one, although for the
bus mode, it was close to one (however, statistically insignificant), which can be interpreted
as showing a high dissimilarity between bus alternatives. Model for Prague–Ostrava market
shows that in both cases, the constant was not significantly higher in comparison to the stateowned
provider. Except for the variable Destination_change, all of the other predictors were
not significantly different from zero. For both private companies, consumers had significantly
lower utility when there was a change in destination. Model for Ostrava–Brno market does
not show the binary variables for travel purposes and travel frequency. Contrary to
expectations, the need to change at the destination or origin positively affected the traveller's
utility, which the poor alternatives can explain. Thus, the time schedules of regional trains are
smoothly integrated with the schedule of fast trains.
The coefficients for prices and frequencies from Table IV.2 provide little explanatory value
due to unobserved and individual-specific utility. Therefore, the market demand elasticities
for travel alternatives were estimated. Table IV.3 gives the calculated elasticities for price.
Three interesting results are observed. First, intermodal competition seems to have had a
stronger effect on the elasticity of demand than another intramodal competitor did. Second,
the results do not provide any sign of brand loyalty on the part of consumers. Third, the
market power of the incumbent within the Brno–Ostrava connection seems to have been low
regarding price. Even though elasticity analysis is not stand-alone proof of any of these
conclusions and requires separate investigation, the direct- and cross-price elasticities are
essential indicators of the findings. Regarding elasticities concerning frequency, percentual
changes of connections by a 1% increase for FlixBus connections on the Prague–Brno route
would increase the company's market share by 0.25%. Such an increase would reduce the
market shares of RegioJet buses by 0.10%, RegioJet trains by 0.07%, and Czech Railways by
0.09%. In this case, the elasticities for the Prague–Brno market are not statistically different
from zero. The elasticities for the Brno–Ostrava market were not estimated due to the model
specification. In general, the lower the frequency was, the higher the elasticity of change was.
Therefore, even though we cannot estimate elasticity for frequency for the Brno–Ostrava line,
the expectation would be findings of a highly elastic market.
Table IV.3 Elasticities with respect to price (frequency)
Company/Route FlixBus RegioJet bus RegioJet train Czech Railways
BRQ–PRG
FlixBus −1.12 (0.25) 0.47 (−0.10) 0.32 (−0.07) 0.30 (−0.07)
RegioJet bus 0.44 (−0.10) −1.15 (0.26) 0.30 (−0.07) 0.28 (−0.06)
RegioJet train 0.29 (−0.07) 0.29 (−0.07) −1.68 (0.37) 0.74 (−0.16)
Czech Railways 0.39 (−0.09) 0.39 (−0.09) 1.05 (−0.24) −1.33 (0.30)
PRG–OSR
Czech Railways −0.28 (0.58) 0.20 (−0.42) 0.22 (−0.46)
LeoExpress train 0.10 (−0.20) −0.40 (0.84) 0.11 (−0.24)
RegioJet train 0.18 (−0.38) 0.20 (−0.42) −0.33 (0.69)
OSR–BRQ
Czech Railways −0.38
38
The results for prices and frequencies for different entry setups provide a surprising
contradiction. First, we observe little variability in price across markets with significantly
different market structures, but the frequencies vary significantly across markets. There is
almost no geographic price discrimination from the incumbent Czech Railways (expecting the
uniform price strategy). Together with conditions based on PSOs, this leaves very little space
for price manoeuvres by the incumbent on the monopolised Brno–Ostrava market. Therefore,
we did not observe higher prices (on average) within the monopolised market compared to
more competitive routes. However, the connection frequency is a different story. There was
significant variation in the number of connections across markets, which aligns with previous
findings of equilibria with one dominant firm (most likely the incumbent) and smaller
entrants. Not only was the number of connections per day higher in the case of open access
routes, but also, more of the day was served. This suggests that players tended to compete for
both peak and off-peak times. The elasticity analysis of market demand both confirmed
previous findings and opened new questions. First, the lack of differences in price elasticities
between the monopolised market and the market with three railway companies confirmed the
results of the price analysis itself (little price discrimination and overall low market power on
the monopolised Brno–Ostrava market). However, there was a striking difference in price
elasticities between the Prague–Brno and Prague–Ostrava markets. Three possible reasons are
discussed here. First, the price war on the Prague–Brno line occurred before the survey, which
may have contributed to travellers' price sensitivity. Second, the intense competition on this
route was even intensified by equally tough competition between bus alternatives. Finally, on
average, there were 82 connections between Brno and Prague during a regular workday. Thus,
a specific connection has minimal market power, generally due to departure time
differentiation. Therefore, market demand was highly elastic to price even for negligible
changes. The market structure enabled us to test for consumers' brand loyalty. However, we
did not find any tendency to prefer a brand alternative to a mode alternative from a
competitor.
We have comprehensively analysed the effects of different entry regulations on competition
and travellers' decision processes with the example of three different Czech lines, which
differed significantly in railway entry policies and market structure. Moreover, intramodal
competition with bus alternatives further intensified the intense railway competition in the
Prague–Brno market. Our findings align with the existing literature describing the positive
effect of railway competition on consumers. However, we could not find significant price
level differences across markets with varying entry regulations. We believe that this is an
effect of the low monopoly power of the incumbent Czech Railways. It seems that the
incumbent's pricing policy does not distinguish between routes. Given the estimated price
elasticity, the policy is not profit-maximising. The pricing strategy is based on what is fair
rather than what is profitable, making it seem unacceptable to discriminate by the route. On
the other hand, we identified a clear relationship between entry setup and increased
connection frequency. Both findings are further supported by estimated market demand and
firm-specific elasticities. In the case of price sensitivity, there seems to have been a significant
effect from the intramodal competition. Furthermore, intense competition increased the
number of connections per day significantly. In summary, the paper contributes to the existing
empirical literature on the competition and regulatory effects in long-distance passenger
transportation. The paper has the potential to provide new arguments for ongoing policy
discussions on trade-offs between open access regimes and more traditional regulation on
railways. Moreover, by collecting a vast amount of supplementary data on prices and
frequencies, together with conducted surveys, we fulfil the existing gap in the academic
literature on comparisons of markets under different regulatory regimes.
39
5. Fare Discounts and Free Fares in Long-distance Public
Transport in Central Europe
Free fare transport schemes have been increasingly used in many cities. They are utilised to
stimulate public transport market share and promote transport equity and justice. These
policies have been applied in two countries in Central Europe on the national level. The
authorities in Slovakia and Czechia have introduced generous fare discount policies for longdistance
transport which is the crucial subject of Paper E. Slovakia has launched free rail fares
for children, students, and pensioners since November 2014. Czechia has introduced 75%
discounts for the same target groups but for both trains and buses from September 2018.
These schemes are unique in their broad coverage and application to long-distance transport.
These policies were motivated by social, transport, and political factors, but the social goals
dominated. This article aims to review ridership and the development of modal shares due to
these policies. The results show that policies significantly increased ridership and the modal
share of railways. The mobility of the targeted groups was significantly affected, and the
share of young and elderly riders increased. However, the policies were costly and had some
undesirable side effects that could have been prevented by better policy design.
Background
Policymakers are trying to diminish car usage and promote public transport through various
measures to struggle with congestion and environmental issues. The much-debated factors in
promoting public transport usage are fare discounts and free-fare schemes. Slovakia and
Czechia recently introduced ambitious fare policies in long-distance public transport in 2014
and 2018. Thus, it is now possible to analyse what effect these measures have had on the
market and determine whether they meet their goals. These policies were not designed only to
reach higher public transport usage. Their main aim was to improve mobility for younger and
elderly people and achieve higher equity and justice in access to transport services. These
policies are costly, and there has been an ongoing debate about their effectiveness. We aimed
to assess the consequences of these policies. Our analysis focused on: transport volumes,
modal shares, changes in mobility among different groups, and the total cost. Our paper
contributes to the existing literature by comparing two wide-scale policies' impacts. Due to
their recent implementation, they are not systematically captured in the academic literature
and are unique in their nationwide coverage.
Fare reduction policies aim to make transport cheaper, improve its affordability, and stimulate
ridership. However, the crucial issue is the price elasticity of demand in long-distance travel.
Based on the existing evidence, short-run elasticity is relatively low, in the range of 0.2–0.4
(Baum 1973, Scheiner – Starling 1974, Litman, 2004, Paulley et al. 2006, Oum et al. 1990,
Ivaldi – Seabright 2003). Long-run elasticity is higher, usually in the range of 0.6–0.9 (Litman
2004, Paulley et al. 2006, Ivaldi – Seabright 2003). Recent estimates of price elasticity from
the rail market in Czechia were identified in the range of 0.6–1.7 (Fitzová et al. 2021).
However, elasticities are of limited value when it comes to radical changes, and existing
research has also suggested that price is not the most critical factor determining transport
ridership. The most significant factors are service quality, time, route, and status attributes.
Switching car users has been particularly problematic, as it has been argued that negative
fares would have to be introduced to motivate car users to change (Baum 1973). The general
conclusion is that forcing significant ridership changes through fare declines is difficult and
costly, and especially car users are hard to persuade (Wardman et al. 2018, Fearnley et al.
2017). However, these studies did not distinguish the price elasticity of younger and elderly
people. There are cases where fares in public transport were abolished; for example,
40
Studenmund – Connor (1982), who described the result of a free-fare experiment in Trenton,
New Jersey (US), where off-peak bus fares were eliminated. The authors claimed that net
ridership went up by 15% (off-peak ridership by 45%). De Witte et al. (2006) analysed the
impact of free fares in Brussels on the included and non-included populations. They
concluded that residential determinants were more important than fares. Van Goeverden et al.
(2006) focused on motives for introducing free fare by presenting four urban case studies
from Belgium and the Netherlands. The free-fare experiment in Tallinn showed that general
ridership went up by 14%, specific groups of youth by 21%, and elderly by 19% (Cats et al.
2017). Tomanek (2017), Štraub (2019) and Štraub – Jaroš (2019) reviewed the introduction of
free-fare schemes in municipalities in Poland and analysed the four key areas that
municipalities try to influence through free fares. There has been a program of free fare for
old-age pensioners in the UK that is well established and covers both rail and bus travel
modes (Kębłowski 2019, Fearnley 2006). An inspiring case is Luxembourg, where a free-fare
system was introduced nationwide, including metro, rail, and buses (Carr – Hesse 2020).
Some attempts have been made to conceptualise these findings from case studies dedicated to
free-fare systems. Perone (2002) analysed the advantages and disadvantages of free fares in
three areas: costs and impacts on transit service and quality of service distinguishing
temporary and permanent systems. She concluded that a free-fare policy could be
recommended for smaller systems, but whether it could be recommended for larger systems is
questionable. Storchmann (2003) pointed out that the reasons for introducing free-fare
schemes (in Germany) were mainly environmental to change the modal shift. Kębłowski
(2019) analysed the broader consequences of free-fare public transport, distinguishing partial
and complete free-fare systems, including economic, sustainability, and politically
transformative aspects. He concluded that it could not be analysed as a sole transport
instrument. Baum (1973) argued that launching free fares has two goals: to overcome income
inequality and relieve traffic congestion. However, the diversion factor from cars is usually
only 15–20%, and it seems that the more effective method to reach the stated goals is to
improve the quality of public transport. Scheiner – Starling (1974) analysed the political
economy of free-fare transport, arguing that four issues are critical: demand elasticity and
responsiveness, the costs and financial sources, the benefits, and the political feasibility.
Fearnley (2013) analysed the impact of free-fare policies on modal shares regarding other
policy goals (economic, political, and environmental). He argued that although these policies
seem attractive, their goal achievement rate is poor and comes at high costs. The effects on
car ridership are marginal and typically offset by a few years of growth. Successful free-fare
traffic schemes are those that concentrate only on public transport ridership growth. Other
goals are best achieved with targeted measures. Thus, the critical parameter is how free fares
and discounts have contributed to transport equity and justice. Church et al. (2000)
distinguished seven social exclusion factors related to transport: physical, geographical,
services, economic, time-based, based on fear, and based on space management. There is
significant research on the issue of transport poverty (see Banister 2018, Mattioli 2016), but it
tends to concentrate on short-distance travel and long-distance travel is significantly less
covered. Existing research has concentrated on mobility differences for different classes (Cass
et al. 2005) and their environmental impacts (Ivanova – Wood 2020). The temporality issue is
also crucial (Moyano – Dobruszkes 2017). Free fare schemes' distributional and equity
impacts are an evolving issue and deserve further investigation. The application of free fare
schemes to long-distance transport nationwide is unique and has not been applied (except for
Luxembourg). We aimed to analyse the impact of this unique system on ridership and modal
shares.
41
Launching these nationawide systems started in Slovakia that became, on 17 November 2014
(symbolically International Students’ Day and Struggle for Freedom and Democracy Day), a
pioneer in providing free transport for selected population groups on a national scale, but only
for rail and only for public-service obligations (PSOs; mostly the incumbent operator ZSSK
with the single exception of RegioJet on the Bratislava–Komárno line). The launch of freefare
rail discount scheme (Table V.1) was presented as a fulfilling political strategy for Prime
Minister Robert Fico’s Implementation of Financial, Economic and Social Measures in Rail
Passenger Transport (Government of the Slovak Republic 2014a).
Table V.1. Rail Discount Scheme in Slovakia
Group
Original tariff
(before 17 November 2014)
Discounted tariff
(after 17 November 2014)
Child/student
0–5 years 50% discount (ticket needed) 100% discount (no ticket needed)
6–14 years 50% discount (ticket needed) 100% discount (ticket needed)
Student
15–26 years 50 % discount (student ID needed) 100% discount (student ID needed)
Elderly 70+ years;
around 80% discount according to train and
class (or €0.15 for each 50 km)
62+ years
100% discount (senior ID needed)
Data sources: Government of the Slovak Republic (2014a); ZSSK (2014); ZSSK (2021).
Note: Discounts valid only on ZSSK trains for 2nd class tariffs (not valid on IC trains, nor RegioJet or Leo
Express trains).
In March 2018, the Czech government approved a proposal to introduce 75% fare discounts
on buses and 2nd class trains for specific groups (Government of the Czech Republic 2018).
Table V.2 reflects the system of discounts eligible for the elderly, children and students under
26. Before the start of the discount policy, children and students already had discounts limited
to journeys between their residences and the location of their school. Newly they received a
discount for any long-distance route at any time of the year. The state also continues to order
a 100% discount on fares for children under 6.
Table V.2 Rail Discount Scheme in Czechia
Group
Original tariff
(before 1 September 2018)
Discounted tariff
(after 1 September 2018)
Child/student
0–5 years 0–5 years 100% discount (maximum of 2
free-fare children)
100% discount if accompanied by a person
at least 10 years old (maximum of 2 freefare
children); 75% discount otherwise
6–14 years 50% discount (student ID card)
62.5% discount specific route (student ID
card)
75% discount (student ID not needed – no
confirmation of age)
Student
15–26 years 40% discount (ID card) 75% discount (ISIC or student ID needed)
Elderly
65+ years 50% discount (ID needed; up to 2011)
25% discount since 2012 (open-access lines
excluded)
75% discount (ID needed)
Data sources: Government of Czechia (2018); ČD (2018, 2021).
Note: Discounts are valid since 1 September 2018 for all public transport, buses, and trains at 2nd class tariffs
(2nd class for ČD; low-cost, standard, and relax for RegioJet; and economy for Leo Express).
42
Data and methods
We first analyse the development of total ridership using passenger-km and changes in
railway modal share on the passenger market. We further analyse the composition of
travellers regarding age and fare type used, i.e. children and students, pensioners, and
standard-fare adults, based on detailed data from both national rail operators. Finally, we
assess the fiscal consequences, focusing on how revenues from fares, PSO compensation, and
compensation for fare discounts have developed. Finally, we discuss future sustainability and
compare the two countries approaches. To identify the long-term impacts of discounts on the
transport market, we use standard data from Eurostat (2021). As our primary data on ridership
and finances, we work with both Czech Railways (České dráhy, a.s.; ČD) and the Railway
Company of Slovakia (Železničná spoločnosť Slovensko, a. s.; ZSSK) company yearbooks
(ČD 2010–2019; ZSSK 2010–2019) including profit and loss statements. We identify the
yearly amount of PSOs and other compensation from the Ministry of Transport.
Consequently, we use Finstat's (2021) data in Slovakia to analyse the financial impacts on the
bus market. We checked the discount systems on their websites (ČD 2021, ZSSK 2021) and
official government documents and press releases where the changes were defined when
discounts were launched (Government of the Czech Republic 2018; Government of the
Slovak Republic 2014a, 2014b; ZSSK 2014; ČD 2018). We used official government press
releases, press conferences, and news in traditional media to analyse the social and political
context. We use official Czech Transport Yearbooks (Transport Yearbooks 2010–2019) to
identify regional differences in ridership.
Results
The results show that the total growth in ridership in the passenger rail market during the
entire period of interest was much higher in Slovakia and Czechia than in the EU-28. The
Czech transport market grew from 2009 to 2018 at an average rate of 5.2%. Slovak market
growth was slightly higher at 5.9%, while EU-28 growth was only 1.6%. In particular, a sharp
jump in Slovakia appeared in 2015 when the free-fare policy was introduced. The modal share
of passenger rail transport in the EU-28 was nearly the same during 2010–2018 (around 7.5–
8%). However, the development in both Czechia and Slovakia is different. Two reasons are
identifiable – first, the entries of new competitors (in 2011 on the Prague–Ostrava line and in
2016 on the Prague–Brno line in Czechia; in 2012 on the Prague–Ostrava–Žilina–Košice line
in Slovakia); second, the introduction of fare discounts. However, the exact shares of these
two factors are not easy to distinguish. There is head-on competition on two main routes –
with the two operators Czech Railways and RegioJet competing on the Prague–Brno line
(Tomeš – Fitzová 2019) and even three operators (Czech Railways, RegioJet, and Leo Express)
operating on the Prague–Ostrava line (Tomeš – Jandová 2018). The impact on the rail
share of free fares in Slovakia is noticeable (from 7.3 to 9.3%, accompanied by a car share
drop of almost 2%). The changes in modal shares in passenger railway transport and the
number of passengers are positive in both countries, which implies that a similar goal may be
achieved in different ways.
Regarding ridership, the total number in Slovakia increased in the launch year of 2014 by
nearly 7%, followed by more than 21% in 2015, 15% in 2016, and 10% in 2017. The senior
passenger group in Slovakia grew by about 349% from 2013 to 2019, of which the initial
change from 2013 to 2015 covered 248%. Student travel rose from 2013 to 2019 by 126%,
while the initial change was almost 88%. In Czechia, the total ridership grew, but Czech
Railway's market share decreased because demand was partly diverted to other new operators.
The last year of 2019 (the first complete year with the implemented discounts) had a growth
rate of 1.62%. The number of students and children was decreasing by 1.11% per year before
43
2018. This trend changed in 2018 when the number grew by 13% and then even by 44% in
2019, so the total increase from 2017 to 2019 was 63%. Before the discounts were
implemented, the number of seniors was decreasing by 9.48% per year, but this number
increased by almost 16% in 2018 and by more than 24% in 2019, which means the overall
increase from 2017 to 2019 was 44%.
However, it is also necessary to consider the financial costs of those approaches as they
differed significantly. The results show that PSO and free-fare compensation from the
Ministry of Transport to the Railway Company of Slovakia varied throughout the period.
While the sum of both (PSO and discount) compensations stagnated or slightly decreased on
average by 1.5% during 2010–2014, it increased more than four times during 2015–2019. The
change in 2015 is worth mentioning. Revenues from domestic and foreign passenger tickets
decreased by EUR 19.2 million (meaning 22%; in domestic transport, 27%), compensated for
by an increase in PSO and other compensation by EUR 13.5 million. Comparing the last year
without discounts (2013) with the last year of the relevant period (2019) reveals a decrease in
revenue from both domestic passengers and passengers abroad (EUR 1.4 million)
accompanied by a substantial increase in PSO and discount compensation (reaching EUR 66.1
million). Figure V.1 captures the significant increase in passengers and compensation and a
sharp decrease in total revenues in the first two years after introducing the free-fare scheme,
followed by a slight increase in the next three years. In 2019 the revenues are still below the
level of the year 2014. The critical point is the overall financial impacts on the bus sector in
Slovakia, represented by the 15 members of the Slovak Bus Association. In 2013, total
revenues were EUR 141 million (FinStat 2021), but these bus companies' "revenues from
sales of own products and services" gradually decreased to EUR 119 million in 2019 (a
decrease of more than 15%).
Figure V.1 Financial impacts and rail passengers in Slovakia (index; 2010=100)
Data source: ZSSK (2010–2019), Eurostat (2021)
Note: Adjusted for inflation using the Harmonised Index of Consumer Prices.
The experience is very recent in Czechia, so it is impossible to compare long-run effects over
a five-year horizon as in Slovakia. Figure V.2 shows the overall impacts on PSO and other
discount compensation, revenues, and passengers in the period indexed to 2010. We can only
observe the immediate effects as the only year with 75% discounts is 2019. The compensation
for discounts grew more sharply than it did in Slovakia, and revenues from passengers abroad
increased in 2019. The public compensation for discounts during 2010–2017 was, on average,
over EUR 1 million per year. In 2018, which includes four months of effective fare discounts,
there was a sharp increase in public compensation to more than EUR 28 million, then in 2019,
an increase to EUR 90 million. At the same time, PSO compensation was stagnating, so the
75
95
115
135
155
175
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
new fare scheme
Slovakia (free fare)
compensation for PSO and discounts revenues passengers
44
fiscal effect of the new discounts is visible. Furthermore, compensation for Czech Railways is
strictly related to the number of student and senior tickets sold. There is no compensation for
losses from previous years, as in the Slovak case. A common feature for both cases is
immediately falling revenues from domestic transport; however, accompanied by increasing
revenues from international transport in Czechia. The difference between Slovakia and
Czechia lies in the sharp compensation increase in Czechia, where the original level was
almost zero.
Figure V.2. Financial impacts and rail passengers in Czechia (index; 2010 = 100)
Data source: ČD (2010–2019), Eurostat (2021)
Note: Adjusted for inflation using the Harmonised Index of Consumer Prices.
Introducing free fares in Slovakia and fare discounts in Czechia has made public transport in
both countries more attractive and more used. On the other hand, Czech and Slovak fare
levels are lower than those in Western Europe, even when corrected for lower economic
levels and purchasing powers. Both countries' low absolute fare levels diminished the
potential of free fares or fare discounts. Nevertheless, the existing monetary costs may still
have been high for some population groups. In this respect, the free-fare policy in Slovakia
may have had better starting conditions because the fare decrease was higher, and purchasing
power was lower. Therefore, out-of-pocket outlays for public-transport fares constituted a
higher proportion of disposable income.
In addition to the differences in the total fare discounts (100% vs. 75%), the crucial difference
lies in coverage. Slovakia included only trains, not buses, significantly affecting the entire
transport market. The bus market was hit hard with an outflow of passengers, a worsening
financial situation, and reduced long-distance services (zeleznicne.info 2017). The preference
for rail over a bus in Slovakia had some unintended consequences helping marginalised
groups with access to the rail network. However, it has worsened the accessibility for people
reliant on bus transport. The policy design in Czechia seems to be more sophisticated.
However, both designs did little to differentiate between peak and off-peak travel and had no
stimulation for travel from or to disadvantaged regions. Thus, the potential of these policies to
mitigate inequalities in access to transport services was not fully utilised. The fiscal costs of
these policies were significant; however, they are manageable in the context of the total
subsidies for rail/public transport. The policies successfully increased total ridership,
especially ridership among the targeted groups of elderly and young people. However,
whether subsidising fares is the best way to help them with their mobility is still an open
question. In conclusion, the policies aimed at supporting public transport only and did almost
nothing to accompany this measure to decrease the attractiveness of individual car transport.
90
95
100
105
110
115
120
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
new fare scheme
Czechia (75% discount)
compensation for PSO and discounts revenues passengers
45
Conclusion
In conclusion, this habilitation thesis seeks to contribute to sustainable transport planning in
the context of metropolisation tendencies manifested globally, macro-regionally, regionally
and locally. At these different levels, metropolisation raises different challenges in terms of
research niches and policy implications. The need for transport is one of the basic needs of the
human population, which is at the same time one of the necessary but not sufficient conditions
for economic development at the global, international or regional level. However, the way of
meeting this need is changing along with the changes in society as a whole, whose
preferences are influenced by urban lifestyle trends with the possibility of cross-border
mobility for work and leisure, but all this in the knowledge of climate change and the overall
impact of transport on the living environment of people spending most of their lives in
metropolitan centres or their hinterland. Therefore, the development of sustainable transport
systems raises challenges in the field of research on transport between metropolises and in the
connectivity of the metropolis with its hinterland, as well as in their interconnectivity.
However, in order to make policy recommendations, it is necessary to take into account not
only the planning of infrastructure with very long-term implications for the future
development of transport systems but also how these systems are operated or regulated, which
must take into account the preferences and behaviour of residents and passengers.
This habilitation thesis focuses on the interrelationship of the formation of an internationally
integrated economic space and their mutual influence on the design of transport systems
taking into account infrastructure and traffic, with a focus on the economic assessment of the
environmental impacts of these processes and capturing changes in passenger preferences
shaping the demand for specific modes and means of transport. Such a focus is always central
to successful policy formulation, which may, however, operate concerning the nature of
supply and demand ex-post and ex-ante. The following research challenges occur from work
presented in this thesis regarding potential future challenges. In particular, the identification
of long-term changes in transport demand from the perspective of longitudinal studies and
focusing on changes in people's preferences over their individual life cycle to evaluate the
significant factors that influence their mobility and residential behaviour. Because residential
behaviour is the determinant of commuting behaviour, i.e. the precondition for regular
commuting demand, which creates significant transport demands, especially in terms of
spatial distribution and temporality, on the other side of this process is the stage of regulatory
or supportive public policy instruments and the evaluation not only of their immediate
response but also of their long-term impact, including the inclusion of long-term and broader
economic costs that may not be obvious at first sight when implementing a given instrument,
at all levels of the urban system, i.e. international, taking into account the border effect in
transport, national, targeting the interconnectivity of the most important economic areas, and
local, responding primarily to the distribution of the population in the metropolitan corehinterland
context.
46
Authorship contribution statements
Here relevant author contribution statements according to relevant papers are listed.
A. The Metropolisation Processes: A Case of Central Europe and the Czech Republic
List of authors: Milan VITURKA, Vilém PAŘIL, Petr TONEV, Petr ŠAŠINKA a Josef
KUNC.
Author´s contribution share: 20 % (corresponding author).
Author´s relevant contribution is: Resources; Data curation; Investigation; Formal analysis;
Writing – original draft; Writing – review & editing.
B. The cost of suburbanization: spending on environmental protection
List of authors: Vilém PAŘIL, Barbora ONDRŮŠKOVÁ, Aneta KRAJÍČKOVÁ a Petra
ZELENÁKOVÁ.
Author´s contribution share: 45 %.
Author´s relevant contribution is: Term; Conceptualization; Funding acquisition; Project
administration; Supervision; Methodology; Resources; Data curation; Investigation; Formal
analysis; Validation; Visualization; Writing – original draft; Writing – review & editing.
C. Assessment of the burden on population due to transport-related air pollution: The
Czech core motorway network
List of authors: Vilém PAŘIL a Dominika TÓTHOVÁ.
Author´s contribution share: 50 %.
Author´s relevant contribution is: Term; Conceptualization; Funding acquisition; Project
administration; Methodology; Resources; Data curation; Investigation; Validation;
Visualization; Writing – original draft; Writing – review & editing.
D. Competition in long-distance transport: Impacts on prices, frequencies, and
demand in the Czech Republic
List of authors: Hana FITZOVÁ, Richard KALIŠ, Vilém PAŘIL a Marek KASA.
Author´s contribution share: 33 %.
Author´s relevant contribution is: Term; Conceptualization; Funding acquisition; Project
administration; Supervision; Resources; Data curation; Investigation; Validation;
Visualization; Writing – original draft; Writing – review & editing.
E. Fare Discounts and Free Fares in Long-distance Public Transport in Central
Europe
List of authors: Zdeněk TOMEŠ, Hana FITZOVÁ, Vilém PAŘIL, Václav REDERER,
Zuzana KORDOVÁ a Marek KASA
Author´s contribution share: 32,6 %.
Author´s relevant contribution is: Conceptualization; Funding acquisition; Data curation;
Formal analysis; Investigation; Visualization; Writing – original draft; Writing – review &
editing.
47
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List of tables
Table I.1: Results on Central European Metropolises.............................................................. 16
Table II.1. Structure of environmental protection expenditure................................................ 21
Table III.1: Description of priority health effects .................................................................... 28
Table III.2. The concentration of PM10 (μg.m3
) in the vicinity of the D1 motorway and its
decrease (in %)......................................................................................................................... 29
Table III.3: Model Results ....................................................................................................... 30
Table IV.1: Variable description.............................................................................................. 35
Table IV.2 Estimation results................................................................................................... 36
Table IV.3 Elasticities with respect to price (frequency)......................................................... 37
Table V.1. Rail Discount Scheme in Slovakia......................................................................... 41
Table V.2 Rail Discount Scheme in Czechia ........................................................................... 41
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List of figures
Figure I.1: The metropolitan system of Central Europe from the point of view of Czechia ... 17
Fig. II.1. Change in population density in the period 1991 to 2001 (left) and 2001 to 2011
(right) (in %, CZSO, 1991, 2001, 2011) .................................................................................. 20
Fig. II.2. Share of expenditure on the environment 2010–2015 (%). ...................................... 22
Fig. II.3. Yearly arithmetic average of operating (left) and investment (right) expenditures per
capita on water protection by municipalities in Czechia in 2010 to 2015 ............................... 22
Fig. II.4. The average expenditure per capita on water protection according to municipality
population category (EUR). ..................................................................................................... 23
Fig. II.5. Yearly average operating (left) and investment (right) expenditure per hectare on
protecting biodiversity and landscape by municipalities (2010 to 2015)................................. 23
Figure III.1: Basic settlement units according to the distance from the motorway network and
PM10 pollution in 2007–2011 vs 2012–20116 ......................................................................... 29
Figure III.2: 1 μg/m3
improvement change of PM10 concentration in the number of cases of
chronic bronchitis and associated monetary valuation in Czech prices of 2017 in euro ......... 30
Figure V.1 Financial impacts and rail passengers in Slovakia (index; 2010=100).................. 43
Figure V.2. Financial impacts and rail passengers in Czechia (index; 2010 = 100)................ 44
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Annexes: Published articles
A. The Metropolisation Processes: A Case of Central Europe and
the Czech Republic (pages 16, 69-84)
B. The cost of suburbanization: spending on environmental
protection (pages 21, 85-105)
C. Assessment of the burden on population due to transportrelated
air pollution: The Czech core motorway network (pages
14, 106-119)
D. Competition in long-distance transport: Impacts on prices,
frequencies, and demand in the Czech Republic (pages 13, 120-
132)
E. Fare Discounts and Free Fares in Long-distance Public
Transport in Central Europe (pages 11, 133-143)