Gitana Dudzevičiūtė
The General Jonas Žemaitis Military Academy of
Lithuania,
Vilnius Gedinimas Technical university, Lithuania
E-mail: gitana.dudzeviciute@vgtu.lt
Agnė Šimelytė
Vilnius Gediminas Technical University,
Vilniaus kolegija/University of Applied Sciences, Lithuania
E-mail: agne.simelyte@vgtu.lt
Aušra Liučvaitienė
Vilnius Gediminas technical university,
Vilniaus kolegija/University of Applied Sciences, Lithuania
E-mail: ausra.liucvaitiene@vgtu.lt
Submission: 15/04/2017
Revision: 11/05/2017
Accept: 19/05/2017
ABSTRACT
Urbanization and expansion of cities requires new tools to improve the
quality of life of city inhabitants for all areas from mobility to leisure
activities. Thus, technological development and digitalisation have been
introduced into infrastructures such as rails, roads, airports, bridges,
tunnels and communications. Policy of smart cities concept focuses on economy,
people, mobility, governance, environment, and living. Even more, implemented
framework of smart cities stimulates sustainable economic development. Smart
economy is a trigger for innovations and entrepreneurship. Installed measures
of smart mobility reduce traffic jams and optimise transportation systems. This
research attempts to compare largest different cities of Lithuania and Sweden
in the context of smart cities’ concept. Due to the shortage and mismatching
statistical information, the paper is limited with only four following
indicators: smart economy, smart mobility, smart environment, and smart
governance. The analysis of indicators shows that Lithuanian major cities in
all groups of criterion are below average while values of indicators in the
case of Swedish major cities are much higher than average.
Keywords: smart
city; smart economy; smart governance; smart mobility; Sweden; Lithuania
1. INTRODUCTION
Nowadays,
in increasingly interconnected world, urbanization process can raise a variety
of socio – economic, technical and organization problems. The process of
urbanization describes a shift in a population from small rural areas in which
agriculture is the dominant economic activity towards one where the population
is concentrated in urban settlements with industrial and service activities
(MONTGOMERY et al, 2004).
In
2007, for the first time in history, the world’s urban population exceeded the
population living in rural settlements (UNITED NATIONS, 2014). According to the
statistics of United Nations (2014), today over half of the world’s population
lives in urban areas. The population living in cities is expected to grow.
By
2050, around 66 per cent of global population is projected to be urban. It
means that due to the concentration of people in urban areas, the coming
decades will bring further changes which are integrally linked to sustainable
development. On the one hand, with good planning and governance, increasing
urbanization can facilitate socio – economic development.
On
the other hand, unplanned urban growth might threaten sustainable development
when the necessary governmental policies are not implemented (UNITED NATIONS,
2014). The world has known many examples of cities that have grown rapidly
without any kind of planning. The result has been chaotic and detrimental (KIM;
HAN, 2012; MCKINSEY & COMPANY, 2013; NEIROTTI et al, 2014).
As
cities faces the challenges, such as performance, growth, competitiveness and
others, the leaders supposed to be more flexible and forward looking, planning
for growing and changing populations and the impact on different aspects of
city life such as transportation, education, health, pollution and others
(MCKINSEY & COMPANY, 2013).
Many
cities leaders choose to transform cities into “smart cities”. This label
refers to new socio – economic environment in which population, enterprises,
and governments can perform more efficiently (LETAIFA, 2015). However, the
concept of Smart City (SC) is a relatively new. The context of SC concept is
dependent on country, government, IT, communications, natural resources and
other capacities (WEISI; PING, 2014; LETAIFA, 2015).
Many
researchers (HOLLANDS, 2008; CARAGLIU et al, 2009; ALLWINKLE; CRUICKSHANK,
2011; BAKICI, 2012; HIELKEMA; HONGISTO, 2012; VANOLO, 2013; LETAIFA, 2015) have
acknowledged the shortage of consensus on how to define smart cities and common
methodology for assessing them. Due to
the fact that cities vary across size, resources, infrastructure and other
capacities, a need exists for a comprehensive framework that conceptualizes
different components of a smart city, integrates the measures, and explains the
strategic steps to follow (ZYGIARIS, 2012).
In
this context, government supposed to implement policies to ensure sustainable
urbanization which requires that cities generate better employment
opportunities, greater income, and living conditions and welfare; expand the
necessary infrastructure; ensure appropriate access to services; reduce the
number of people at risk of poverty and social exclusion; and preserve the
natural assets (UNITED NATIONS, 2014).
This
research attempts to compare largest different cities of Lithuania and Sweden
in the context of smart cities’ concept. Due to the shortage and mismatching
statistical information, the paper is bounded with only four following
indicators: smart economy, smart mobility, smart environment, and smart
governance. All other factors are not considered here. That is the major
limitation of this paper.
The paper is organized as follows. Section 2 reviews
previous studies on Smart City concept and analyses different approaches and
research methodology. The investigations are summarized and the main insights
are provided. On the basis of theoretical insights and statistics data, section
3 compares the Lithuanian and Swedish cities in the context of smart cities.
Section 4 concludes summarizing the main trends observed.
2. THEORETICAL INSIGHTS REVIEW AND METHODOLOGY
2.1.
Literature
review on Smart City concept
The
concept of smart cities is very close to other similar concepts such as
intelligent and creative cities. The line among these three concepts is very
blurry (HOLLANDS, 2008). Historically, the concept of intelligent city has been
the first. It has referred to top-down approaches with the main focus on technologies
and the strong emphasis on optimization through technology (ZYGIARIS, 2012;
WALRAVENS, 2015). These cities have integrated all conditions of their
infrastructures such as rails, roads, airports, bridges, tunnels and
communications (WALRAVENS, 2015).
The
concept of creative cities highlights the opposite bottom-up approach. Such
kind of cities usually relies on community-based and private sector
initiatives, social entrepreneurship without a focus on coordination and a
long-term vision. The initiatives of creative cities often fail to become
sustainable due to the shortage of resources and formal leadership (HARTLEY et
al, 2012; LETAIFA, 2014).
In
recent years, the concept of smart cities has been quite popular in the policy
arena (LOMBARDI et al, 2012). In the scientific literature, this has been
described from different viewing angles. In smart cities context, the main
focus of Giffinger et al. (2007) is on well performing and a forward-looking
way in economy, people, mobility, governance, environment, and living. Hollands
(2008) noted that smart cities relied on “implementation of information and
communication technology (ICT) infrastructures to support social and urban
growth through improving the economy, citizens’ involvement and governmental efficiency”.
Smart
cities are the result of innovation ecosystem, which involves wide-ranging
social interactions and educated labor force that generates value through
information use (KOMNINOS, 2008; LETAIFA, 2015). According to Caragliu et al. (2009), smart cities are safe, secure,
environmental and efficient urban centres with advanced infrastructures, which
stimulate sustainable economic development.
Dirks
and Keeling (2009) argued that a smart city consists of the urban services, and
residents, transport and communication, business, water and energy supply
systems. Moreover, the concept of smart cities relates to the use of smart
computing technologies in city administration, healthcare, education, public
safety, transportation, and real estate (WASHBURN et al, 2010).
Many
scholars (HOLLANDS, 2008; SHAPIRO, 2008; GIRAD et al, 2009; DEAKIN, 2010;
ALLWINKLE; CRUICKSHANK, 2011; LOMBARDI et al, 2012; BAKICI et al, 2012;
LETAIFA, 2015) have agreed that smart cities are intelligent and creative.
However, they differ from intelligent and creative cities by focusing on
balance of technology, institutions and citizens. The focus is on neither a
bottom-up nor top-down approaches. The concept of smart cities integrates
formal leadership and democratic participation in the IT-based urban ecosystem
(ZYGIARIS, 2012; LETAIFA, 2015).
Despite
smart cities’ focus on the role of IT infrastructure, many studies has also
been carried out on the role of social and human capital and environmental
factors as important drivers of urbanization process (LOMBARDI et al, 2012). It
has been noted, that the term of smart cities has been used in association with
various aspects, such as economy, business, education, government
administration, modern technologies and other aspects referring to life in a
city (GIFFINGER et al, 2007; EZKOWITZ, 2008; CARAGLIU et al, 2009; LOMBARDI et
al, 2012).
Moreover,
in order to assess performance of smart cities, the framework has been proposed
by Lombardi et al. (2012). This
framework has focused on the measurement of different aspects and linking these
to the main dimensions of a smart city. These aspects have included as follows:
smart economy, smart people, smart living, smart mobility, smart environment
and smart governance (GIFFINGER et al, 2007; GIRARD et al, 2009; NEIROTTI et
al, 2014; LETAIFA, 2015) (Table 1).
Table1: Dimensions and indicators of smart cities
Smart cities dimensions |
Main indicators |
Smart economy |
Public
expenditure on research and development, innovations and entrepreneurship,
public expenditure on education, gross domestic product per capita, debt of
municipal authority per resident, unemployment rate, employment rate in high
tech and creative industries, annual household income, energy intensity,
renewable energy, financial intermediation, culture and entertainment
industry, hotels and restaurants. |
Smart people |
Percentage of
population aged 15-64 with secondary level education, percentage of
population aged 15-64 with higher education, percentage of population working
in education sector, city representatives per resident, foreign language
skills, level of computers skills, patent applications per inhabitant,
participation in life-long learning. |
Smart living |
Health care
expenditure, tourists overnights stays, museum visits, cinema and theatre
attendance, percentage of people undertaking industry-based training, number
of enterprises adopting ISO 14000 standards. |
Smart mobility |
City
logistics, info mobility, people mobility.
|
Smart environment |
Annual energy
consumption, total CO2 emissions, efficient use of electricity,
annual water consumption, efficient use of water, area in green space,
greenhouse gas emission intensity of energy consumption, population exposure
to air pollution, percentage of population engaged in environmental activity,
percentage of citizens travelling to work on public transport, percentage of
total energy derived from renewable resources. |
Smart governance |
E- Government
usage by citizens (percentage of individuals who have used the Internet for
interaction with public authorities in the last 3 months, E-democracy (usage
of innovative ICT to support ballots, green and fair-trade public
procurement), percentage of households with Internet access at home,
transparency enabling citizens to access official
documents in a simple way and to take part in the decision processes. |
Source: GIFFINGER et
al. (2007); GIRARD et al. (2009); TOPPETA (2010); LOMBARDI et al. (2012);
NEIROTTI et al. (2014); LETAIFA (2015).
Smart economy. Smart economy fosters innovations and
entrepreneurship process. According to Bruneckiene & Sinkiene (2014),
“smart economy remains one of the key drivers of the smart city and one of the
smart city indicators, because the city, characterized by high economic
competitiveness, is assigned to smart cities”. Smart economy involves
innovation activity and mutual cooperation of enterprises, research
institutions and the citizens in order to develop and promote innovation
through these networks (BAKICI et al, 2013). Smart economy is a growing and
sustainable economy (CARAGLIU et al, 2009).
Smart
people.
Smart people are the result of ethnic and social diversity, creativity, and
engagement. Cities may offer programs and services to inhabitants in order to
raise social capital and qualification (LETAIFA, 2015).
Smart
living
involves improving life quality in terms of services, enhancing health care,
cultural facilities, attractiveness for tourists, promoting social cohesion,
and safety (TOPPETA, 2010;
LETAIFA, 2015).
Smart
mobility relates with urban planning which enables to achieve smart mobility.
Urban planning focuses on collective modes of transportation through the
extensive use of information and communications technologies (TOPPETA, 2010;
LOMBARDI et al, 2012; LETAIFA, 2015).
Smart
environment. This dimension involves the indicators, such as energy consumption, and
population exposure to
air pollution, population engaged in environmental activity, energy derived
from renewable resources and the use of innovative
technologies, which enhance the natural environment.
Smart
governance includes e-services and social media in order to
enhance citizens’ empowerment and involvement in public management and transparent decision-making processes
leading to smart governance (NEIROTTI et al, 2014).
It should be noted that in scientific literature, all
the identified dimensions of smart cities are treated as equivalent; however
Chourabi et al. (2012) point out that “separate smart city components in
different periods and under certain conditions have a different impact on both
the rest of the components and the smart city initiative itself” (BRUNECKIENE;
SINKIENE, 2014).
To sum up, scientific literature review
has revealed that there
is no agreement on the exact definition of a smart city. However, a number of
the main indicators describing smart cities’ performance have been identified.
2.2.
Methodology
and data
In
order to measure the level of the smartness of cities, the comparative analysis
of the major Swedish (Stockholm, Gothenburg, and Malmö) and Lithuanian cities
(Vilnius, Kaunas) has been performed.
Smart
economy, smart environment, smart mobility, and smart governance have been
selected for the analysis. For obtaining detailed and more precisely results, each
of these criterion groups is divided in the sub-criteria (Table 2).
Table 2: Dimensions of Smart City
Smart cities
dimensions |
Main indicators |
Smart economy |
Real GDP per capita, unemployment
rate, annual household income, number of hotels and restaurants, percentage
of population working in education sector, level of computers skills, health
care expenditure. |
Smart mobility |
City logistics, info mobility,
people mobility |
Smart environment |
CO2, percentage of
citizens travelling to work on public transport |
Smart governance |
Percentage of households with
Internet access at home |
Source: compiled by the authors
The
selection of indicators is limited by the availability, quality and the volume
of statistical information. After the analysis on available information in the
official statistics databases, mismatching data needed for assessing the smart
cities of Sweden and Lithuania has been noticed (Table 3).
Table
3: Selected indicators for comparative analysis of Swedish and Lithuanian
cities
Smart cities
dimensions |
Main indicators |
Sweden |
Lithuania |
Smart economy |
GDP per capita, unemployment
rate, annual household income, number of hotels and restaurants, percentage
of population employed in education sector, level of computers skills,
expenditure on health care. |
GDP per capita, unemployment
rate, annual household income, percentage of population employed in education
sector. |
GDP per capita, unemployment
rate, number of hotels and restaurants. |
Smart mobility |
City
logistics, info mobility, people mobility |
People’s
mobility |
City
logistics, people’s mobility |
Smart environment |
CO2, percentage of
citizens travelling to work by public transportation |
CO2 Areas covered by forests or green
zones |
CO2, percentage of
citizens travelling to work by public transportation Areas covered by forests or green
zones |
Smart governance |
Percentage of
households with Internet access at home |
Percentage of
households with Internet access at home |
Percentage of
households with Internet access at home |
Source: compiled by the
authors
Thus,
such information limitations reduce the scope of criterion groups, which defines
the smart city. Even more, statistical information of separate criterion is
available within the different period of time. The Report on Smart Cities (2014)
(EUROPEAN MEDIUM-SIZED CITIES..., 2014; LARGER EUROPEAN CITIES..., 2015), covers
profiles and information about European small and medium size cities (Jönköping
and Eskilstuna of Sweden and Kaunas of Lithuania). Meanwhile, the largest
cities of European countries (Stockholm, Gothenburg, and Malmö of Sweden; and
Vilnius and Kaunas of Lithuania) are included in 2015 report.
To
sum up, the shortage, quality and volume of data, result the differences in
various studies on smart cities. Thus, this circumstance implies that the
improvement of data collection and information system might ensure the
continuity of smart city assessment.
The
aim of analysis is to compare the smartness of Swedish and Lithuanian cities
(Stockholm, Gothenburg, and Malmö of Sweden; and Vilnius and Kaunas of
Lithuania). Data from Statistics Lithuania, PLEEC project of 2013-2015 and
European City Model of 2013-2015, compiled by the TUWIEN research group has
been used for the research. The summarised profiles of 70 medium-sized cities,
the assessment of which is based on 81 indicators, are available in the reports
of the PLEEC project (PLEEC PROJECT..., 2016). The profiles of the 90 larger
cities (71 medium-sized cities were involved in the research of 2013, while 77
medium-sized cities were involved in the research of 2014, respectively) are
available in the reports (LARGER EUROPEAN CITIES, 2015). These researches form
an all-embracing viewpoint towards the profile of Smart Cities.
For
comparing development of major Swedish and Lithuanian cities, the statistical
data from OECD Statistics Database and Statistics Lithuania has been used. The
designation and comparison of the development trends of Stockholm, Gothenburg,
and Malmö of Sweden; and Vilnius and Kaunas of Lithuania is carried out
according to the four criteria: smart economy, smart mobility, smart
environment, and smart governance. Real GDP per capita and unemployment define
smart economy, people mobility is used to assess smart mobility, amount of CO2,
areas covered by forests or green areas describe smart environment; while smart
governance is defined as a percentage of households with Internet access at
home.
3. THE INTERPRETATION OF COMPARATIVE ANALYSIS
The
rapid process of urbanization led to expansion of major cities. Almost 52 % of
the world population lives in cities. In 2014, 41% of the population of the
European Union also lived in cities. During the same year, 35% of Swedes and
42% of Lithuanians were the residents of cities. These differences are the
result of the change of the overall population in those countries during the
period of 2010-2016 (Figure 1).
The
tendency shows that number of inhabitants has increased in all main cities,
except Kaunas, during 2010-2016. Accordingly, the area occupied by these cities
has not changed during the period of the analysis (Stockholm – 7106,87 km2;
Gothenburg – 3850,19 km2; Malmö – 868,65 km2; Vilnius -
401 km2; Kaunas – 157 km2).
Figure
1: The change of the population in the major Lithuanian and Swedish cities
during the period of 2010 – 2016, in millions
Source: (OECD statistics and Statistics Lithuania)
The largest
cities attract more educated, innovative, and competitive employees. During the
period of 2010-2014, the general trend of employed people continued to grow –
an average of 4% in Sweden and an average of 6% in Lithuania, respectively. The
changes in tendencies are similar in the major cities. The biggest companies
are concentrated in the major cities. Thus, the demand and supply of labour is
increasing, which attracts more investments and increases the number of the
innovations in research and development (Figure 2).
Figure
2: Real GDP per capita in USD
Source: (OECD statistics and Statistics Lithuania)
The
obvious differences in real GDP per capita result in the possibilities to use
the growth of economy and its potential in the various sectors of activity.
According to analysis, the growth of the Lithuanian economy is one of the most
rapid in the European Union. However, compared with Swedish GDP growth in 2016,
a decreasing trend is visible.
The
level of digitization of a country and cities is influenced by the possibilities
to use information technologies. One of the main objectives is the
dissemination and availability of information to cities inhabitants and
possibilities to apply smart technologies in a general system of a city. The
wireless Internet hotspots, high-speed Internet, and a general development of an
infrastructure (an establishment of a smart house, e-ticket, e-baking,
e-government, etc.) would allow implementing these objectives. This analysis employs
indicators, which are most commonly used with the aim to measure an effect of
digitization in terms of a smart city. Thus, a computer use, an Internet
access, and application of information technologies in general have been taken
in account (Table 4).
Table
4: The percentage of households owning personal computer and using Internet in largest
Lithuanian cities
Indicator / year |
2010 |
2012 |
2014 |
2016 |
Computer |
71.2 |
72.1 |
73.9 |
78.4 |
Internet access |
71.5 |
71.1 |
73.3 |
79.0 |
Broadband internet access |
69.0 |
70.6 |
72.7 |
78.7 |
Source: (Statistics Lithuania)
Analysed
data revealed significant differences among age groups in interest and
application of IT. Most users of such technologies are inhabitants at age of
16-24 and represent more than 90% of all IT users. It might be assumed that senior
people find to use the information technologies too difficult. Thus, it
complicates the evaluation of possibilities in using smart devices and their
actual benefits.
The possibilities
to use smart technologies cover various activities. Still, effect achieved
(lower taxes, time saved, resources saved, etc.) remains the most significant
aspect while assessing their benefits. Most of the major cities are forced to
solve problems of adaptability to senior residents needs by ensuring possibilities
to use public spaces, transport, and buildings under conditions of limited
mobility (movement disability, driving license restrictions, lack of parking
spots, promotion of bicycles, etc.). The differences in Lithuania and Sweden
are presented in table 5.
Table
5: The use of the means of transport in the capital cities of Lithuania and
Sweden in the year 2015. Presented as a percentage.
Indicators |
Lithuania |
Sweden |
Car use in European
cities |
50 |
9 |
Public transport use
in European cities |
45 |
65 |
Cycling in European
cities |
3 |
18 |
Source: The State of the European Cities 2016
The
presented data suggests, that Lithuanians use public transport services less
often than the Swedes do. The notion, that 71% of Lithuanians and 80% of Swedes
are satisfied with the public transport services, reflects the popularity of
such services. During period of 2010-2015, in Lithuanian major cities number of
inhabitants using the city transport has increased substantially: by 13% in
Vilnius and by 11% in Kaunas, respectively.
For
reducing mobility problems in Vilnius, a traffic flow monitoring and regulation
system has been installed, which enabled to renovate and combine all the
traffic lights of the city into one-traffic management centre. After installation
of the system, the average length of a trip has decreased, despite the fact
that, over the last decade, the number of vehicles in Vilnius has increased by
40%. In Vilnius public transport network has become smarter as well. The
electronic ticket-card has been introduced, buses and trolleybuses routes have
been re-planned more effectively, and new rapid bus routes have been
introduced.
Taking
the aforementioned data into account, it might be stated that the volume of CO2
emissions polluting the environment in Lithuania has not contributed much to
fulfilling the requirements related to preserving the nature. In 2010, the green
gas emissions amounted to more than 63 tonnes, while in 2015 the CO2
emissions amounted to more than 65 tonnes, respectively. In Sweden, the volume
of the CO2 emissions was reduced from 7,94 tonnes in 2000 to 7
tonnes in 2008.
Increasing
the number of city residents results in reducing the green zones. The growing
need for the residential or office buildings and the scale of constructions are
the alternatives to these areas (Figure 3).
Figure 3: The areas of the major cities of Lithuania covered
by forests in 2010-2015, in percentage
Source: (Statistics Lithuania)
It is
noticed that size of green zones in Lithuanian cities have not changed much or
remained stable during the analysed period. This may be due to fact that
construction of residential and industrial buildings is expanding to territories
outside the city. When analysing the area of the green zones of Sweden per
capita, a decreasing trend of has been observed, which is adequate to growth of
population (Figure 4).
Figure 4: The area of the green zones in the major
cities of Sweden in 2010-2014, in m2 per capita
Source: (OECD statistics)
Growing
number of city inhabitants, better interconnections and communications result a
development of smart technologies to the territories outside a city. Thus, it
might be assumed that these changes are related to the changes in labour and
real estate markets.
Furthermore,
the impact of digitalisation, in the concept of smart city, is measured as
quality and possibilities to use e-services on governmental and municipality
levels. In Vilnius, the institutions providing the first level and the second
level services via the Internet have taken a dominant position during a period
of analysis. This might be explained due to increase the scope of services
provided via the Internet on third-fifth level. Even more, it is encouraged by
increasing number of households, which owned personal computers and has the Internet
access in major Lithuanian cities. Vilnius has employed the means of smart
governance. The city website has been designed, which provides possibility for
residents of the city to communicate with the politicians, to express their
opinion in polls, and to submit electronic petitions, other proposals.
The
mentioned changes in a public sector required additional investments, which
allowed ensuring the smoothness of digitization process and actual benefits for
the city residents that use these services. In 2015, the recent investments
represented 50% in Sweden, 30% in Lithuania, and 42% in the 28 member states of
the European Union, respectively. However, in Lithuania inward FDI are even
below the average investment amounts in the European Union. An efficiency of
public administration reflects on current situation (Efficiency of public
administration): 68% in Sweden and 44% in Lithuania, respectively. A similar
trend remained while analysing the quality of life in these countries.
According to the data of OECD, in 2015, 97% of Swedes and 75% of Lithuanians,
respectively, were satisfied about their quality of life (Life satisfaction in
European cities).
When
analysing the collected data on the cities, which participated in the PLEC
project and the data of the reports of the TUWIEN research group, two major
cities of Lithuania (Kaunas and Vilnius) and three major cities of Sweden
(Stockholm, Gothenburg, and Malmö) are highlighted. The results presented in
figure 5 indicate summarised meanings of respective indicators,
which define the smart economy, smart mobility, smart environment, and smart
governance in analysed cities.
Figure 5: The comparison of the smartness indicators of
Kaunas, Vilnius, Stockholm, Gothenburg, and Malmö in 2015
Source: compiled by the authors according to the data
the PLEEC project and the TUWIEN report
The graph
indicates that results in groups of smart economy, smart governance, and smart
environment of Vilnius and Kaunas are similar. In smart mobility group, results
of Vilnius and Kaunas are differed, since Vilnius is the only city of
Lithuania, which has a positive city population size growth indicator.
Accordingly, results of Swedish cities are similar in the groups of smart
mobility, and smart environment. The smart economy and smart governance results
of Stockholm differed from the results of the other analysed cities in Sweden. Comparison
of Gothenburg and Malmö reveals that indicators of these cities are similar in
the context of smart city.
After
comparing the results of smartness indicators in Lithuania and Sweden, it might
be stated, that the results of Lithuanian major cities are lower than the
average, while the results of Swedish major cities are much higher than the
average. However, it is noticed that the analogical results of the smartness are
in the capital cities of these two countries. The changes in trends of their
results are similar. The values are much higher than those of other cities in
the context of smart city. It might be explained due to the fact there is a
higher concentration of companies and labour force, possibilities to attract
investments, a concentration of authorities, and positive trend in population
growth.
Taking
the general trends of urbanisation and digitalisation into account, the
projects on implementing smart city concept supposed to be improved or
implemented faster (to expand the system of smart governance, to upgrade the
m.Ticket and m.Parking applications, to renovate the public transport fleet,
etc. in Vilnius). However, it might be admitted that implementing the concept
of smart city would not ensure a positive effect in itself. In order to solve
these problems, it is necessary to use modern information technologies and
employ smart solutions for improving quality of life of city residents.
4. CONCLUSIONS
The
review of various studies reveals that there is no single accepted model of a
smart city used in practice. Even more, it remains debatable the level of
smartness in every criterion (smart economy, smart mobility, smart governance,
smart people, smart living, and smart environment). The definition of each
group of indicators and even the number of indicators, in the context of smart
city, has not been provided in scientific literature yet.
In
most cases, only the available results of the rankings of cities are used in
the comparative analysis and the rankings of cities and countries in the lists
are statistically compared as well. The indicators of chosen cities of Sweden
and Lithuania are different: the values of the indicators of Stockholm,
Gothenburg, and Malmö are positive in all of the groups, while opposite results
are obtained in the case of the Lithuanian cities. The least differences in values
are identified while analysing the indicators of the cities in each
country. Varying levels of the
development of the countries, growth rate of economy, possibilities to use
modern technologies, and conditions for innovations and investments might be
described as main causes.
Due
to the shortage and availability of data, the comparison has been limited. It
has been noticed that in Lithuania the data needed for evaluation of smart
city, is not stored and even not collected annually in centralised manner.
Thus, in order to assess the improvement of city smartness or country
smartness, the databases supposed to be improved and new indicators supposed to
be introduced.
REFERENCES
ALLWINKLE,
S.; CRUICKSHANK, P. (2011) Creating smarter
cities: an overview. Journal of Urban
Technology, v.18, n. 2, p. 1–16.
BAKICI, T.;
ALMIRALL, E.; WAREHAM, J. (2012) A smart city
initiative: the case of Barcelona. Journal
of the Knowledge Economy v. 4, n. 2, p. 135–148.
BRUNECKIENE, J.;
SINKIENE, J. (2014). Critical analysis of approaches to smart economy. 8 th International Scientific
Conference “Business and Management 2014” May 15–16, 2014, Vilnius,
Lithuania: 886– 894. http://dx.doi.org/10.3846/bm.2014.106.
CARAGLIU, A.; DEL BO,
C.; NIJKAMP, P. (2009) Smart cities in Europe.
Vrije Universiteit. Faculty of Economics and Business Administration. Available
on Internet: https://ideas. repec.org/p/vua/wpaper/2009–48.html.
CHOURABI, H.; NAM, T.;
WALKER, S.; GIL-GARCIA, J. R.; MELLOULI, S.; NAHON, K.; PARDO, A. T.; SCHOLL,
H. J. (2012) Understanding smart cities: an integrative framework. 45th Hawaii International Conference on
System Sciences, p.p 2289–2297.
DIRKS, S.; KEELING, M.
(2009) A vision of smarter cities: how
cities can lead the way into a prosperous and sustainable future. NY: IBM
Global Business Services.
EZKOWITZ, H. (2008) The triple helix: university, industry and
government. Routledge, London.
EUROPEAN
SMART CITIES (2014) European
medium-sized cities. Available:
http://www.smart-cities.eu/index.php?cid=3&ver=3
EUROPEAN
SMART CITIES (2015) Lager European
cities. Available:
http://www.smart-cities.eu/index.php?cid=6&ver=4&city=174
GIFFINGER,
R.; FERTNER, C.; KRAMAR, H.; KALASEK, R.; PICHLER-MILANOVIC, N.; MEIJERS, E.
(2007) Smart cities – ranking of
European medium-sized cities (report), Vienna
University of Technology. Available on Internet:
http://www.smart-cities.eu/download/smart_cities_final_report.pdf.
GIRARD, F. L.;
LOMBARDI, P.; NIJKAMP, P. (2009) Creative urban design and development. International Journal of Services
Technology and Management, v. 13, n. 2-3, p. 111–115.
HARTLEY, J.; POTTS, J.;
MACDONALD, T. (2012) Creative city índex. Cultural
Science Journal, v. 5, n. 1, p. 33-45.
HIELKEMA, H.; HONGISTO,
P. (2012) Developing the Helsinki smart city: The role of competitions for open
data applications. Journal of the
Knowledge Economy, v. 4, n. 2, p. 190–204.
HOLLANDS,
R. (2008) Will the real smart city please
stand up? City, v. 12, n. 3, p. 303–320.
KIM, H. M.; HAN, S. S. (2012)
City profile: Seoul. Cities, v. 29,
n. 2, p. 142–154.
KOMNINOS, N. (2008) Intelligent cities and globalisation of
innovation networks. London and New York: Routledge.
LAZAROIU,
G.C.; ROSCIA, M. (2012) Definition methodology forthesmart cities model. Energy, n. 47, p. 326-332.
LETAIFA, B. S. (2014)
The uneasy transition from supply chains to ecosystems. Management Decision, v. 52, n. 2, p. 278–295.
LETAIFA, B. S.
(2015) How to strategize smart cities:
revealing the smart model. Journal of Business Research, v. 68, n. 7, p. 1414-1419.
LOMBARDI, P.; GIORDANO,
S.; FAROUH, H.; YOUSEF, W. (2012) Modelling the smart city performance. The European Journal of Social Science
Research, v. 25, n. 2, p. 137-149.
MCKINSEY
& COMPANY (2013) How to make a city
great. Available on Internet: http://www.mckinsey.com/insights/urbanization/how_to_make_a_city_great
MONTGOMERY, R. M.;
STREN, R.; COHEN, B.; REED, H. E. (2004) Cities
Transformed: Demographic Change and its Implications in the Developing World.
London: Earthscan.
NEIROTTI, P.; DE MARCO, A.; CAGLIANO, A. C.; MANGANO, G.; SCORRANO, F.
(2014) Current trends in smart city initiatives: some stylised facts. Cities, v. 38, p. 25–36.
PLEEC. (2016) Factsheet:
summary of results. Available:
http://www.pleecproject.eu/downloads/Reports/pleec_factsheet_summary_of_results-final.pdf
SHAPIRO, J.M. (2008)
Smart cities: quality of life, productivity, and the growth effects of human
capital. The review of economics and
statistics, v. 88, n. 2, p. 324-335.
THE
STATE OF EUROPEAN CITIES. (2016) Cities
leading the way to a better future. European Commission, p. 216.
TOPPETA, D.
(2010) The smart city vision: how
innovation and ICT can build smart, “liveable”, sustainable cities.
Available on Internet:
http://www.inta-aivn.org/images/cc/Urbanism/background%20documents/Toppeta_Report_005_2010.pdf.
UNITED NATIONS. (2014) World Urbanization Prospects. New York: Department of Economic and
Social Affairs.
VANOLO, A.
(2013) Smart mentality: the smart city as
disciplinary strategy. Urban Studies, v.
51, n. 5, p. 883–898.
WALRAVENS,
N. (2015) Mobile
city applications for Brussels citizens: smart city trends, challenges and a
reality check. Telematics and
Informatics, v. 32, p. 282-299.
WASHBURN, D.; SINDHU,
U.; BALAOURAS, S.; DINES, R.A.; HAYES, N.M.; NELSON, L.E. (2010) Helping CIOs understand “smart city”
initiatives: defining the smart city, its drivers, and the role of the CIO. Cambridge,
MA: Forrester Research, Inc.
WEISI, F.;
PING, P. (2014) A discussion on smart
city management based on meta-synthesis method. Management Science and
Engineering, v. 8, n. 1, p. 68–72.
ZYGIARIS, S. (2012)
Smart city reference model: Assisting planners to conceptualize the building of
smart city innovation ecosystems. Journal
of the Knowledge Economy, v. 4, n. 2, p. 217–231.