An Evaluation Model for Smart City Performance with Less Than

50,000 Inhabitants: A Greek Case Study

C. Nikoloudis

, E. Strantzali

, T. Tounta

, K. Aravossis

, A. Mavrogiannis

, A. Mytilinaioy

E. Sitzimi

and E. Violeti

Sector of Industrial Management and Operational Research, School of Mechanical Engineering,

National Technical University of Athens, Iroon Polytechniou 9, 15780, Athens, Greece

Municipality of Elefsina, Chatzidaki 41 & Dimitros, 19200, Elefsina, Greece

alemitilineou@yahoo.gr, rsitzimi@mail.com

Keywords: Smart Cities, Smart Economy, Smart Mobility, Smart Governance, Smart Environment, Smart Living,

Smart People, Smart City’s Footprint.

Abstract: New intelligent technologies are seen as a key factor in fighting against climate change and improving the

sustainability in cities. A smart city is a place where services use advanced information and communication

technologies. According to literature, a smart city includes actions in 6 main domains: economy, environment,

governance, living, mobility and people. The aim of the current study is to compose a holistic smart city

ranking model for cities with population less than 50,000 inhabitants, applicable in the context of Greece.

Based on the European guidelines, 25 crucial factors have been determined and 68 indicators have been

adopted for the development of the evaluation model. The case of Municipality of Elefsina is analyzed and

actions to improve its smartness profile are proposed. The proposed model will help cities with similar

characteristics (less than 50.000 inhabitants) evaluate their status in the field of “smart cities” in order to

develop programs and strategies.

1 INTRODUCTION

A city is the centre for all sustainable urban

development strategies. Today, more than half of the

world’s population live in cities, and it is predicted

that by 2050 urban areas will occupy 70% of the

population (Miloševic et al., 2019). Nowadays there

has been observed a shift in a new city pattern based

on smart targets instead of only sustainability goals.

Smart city provides better urban services based on the

use of advanced Information and Communication

Technologies (ICT). Although the dominant part of

the smart cities profile is the infrastructure, the

involvement of people and citizens is, also, crucial

(Shen et. al, 2018).

As the exact definition of a smart city does not

exist, the smart city concept contains several

dimensions: Smart Economy, Smart Mobility, Smart

Environment, Smart People, Smart Living and Smart

Governance. These smart characteristics have been

identified through a literature review: Giffinger and

Hainlmaier, 2010; Lazaroiu and Roscia, 2012; Tahir

and Malek, 2016; Shen et al., 2018; Petrova-

Antonova and Ilieva, 2018; Alibegović and Šagovac,

2015; Miloševic et al., 2019; Akande et al., 2019.

Smart economy is driven by economic

competiveness, entrepreneurship and innovation.

Smart mobility refers to local accessibility, safe

transport systems and availability of ICT (Tahir and

Malek, 2016). The smart environment is related to the

quality of environment, including the attractiveness

of nature, lack of pollution and sustainable resource

management. Smart people refers not only to the level

of education of the citizens but, also, to the key role

of people in developing a smart city. Smart living

includes factors all around quality of life. Smart

governance comprises aspects of political

participation, public services and e-governance.

A smart city is a city well perfoming in these six

smart characteristics (Giffinger et al., 2007). In the

literature, there are a few studies that have proposed

ranking models to examine the performance of a

smart city: Giffinger et al. (2007) ranked 70 European

smart cities by adopting a set of 74 indicators under

the above analysed six dimensions. All the examined

cities had population between 100,000 and 500,000

Nikoloudis, C., Strantzali, E., Tounta, T., Aravossis, K., Mavrogiannis, A., Mytilinaioy, A., Sitzimi, E. and Violeti, E.

An Evaluation Model for Smart City Performance with Less Than 50,000 Inhabitants: A Greek Case Study.

DOI: 10.5220/0009327700150021

In Proceedings of the 9th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2020), pages 15-21

ISBN: 978-989-758-418-3

inhabitants and their data have been aggregated and

standardized with z-transformation. Lazaroiu and

Roscia (2012) used z-transformation and fuzzy logic

for evaluating 10 Italian cities, by adopting 18 crucial

indicators. Alibegović and Šagovac (2015)

implement a ranking methodology for Croatian large

cities by using indicators in strategic decision-

making. Shen et al. (2018) developped an evaluation

model of smart city performance specialized for

China. The evaluation process has been carried out by

applying entropy method and the multicriteria

method, TOPSIS. Akande et al. (2019) ranked 28

European capital cities on how smart and sustainable

they are, by using 32 indicators. Their methodology

has been based on hierarchical clustering and

principal component analysis (PCA). Finally,

Miloševic et al. (2019) incorporated 35 key indicators

for the assessment of Serbian smart cities. Their

approach has been based on a hybrid fuzzy

multicriteria decision making model.

In summary, all the above mentioned papers

focused their reseach on metropolises with more than

100,000 inhabitants. Furthermore, their

methodologies are based on multicriteria decision

anlysis. So, it appears that there is no existing study

examining smart city performance for cities with

population less than 50,000 inhabitants. The aim of

this study is to propose a holistic smart city ranking

model, based on multicriteria analysis, for cities with

population less than 50,000 inhabitants and, at the

same time, recommend actions for improving the

smart city performance. The majority of Greek

municipalities cover this feature, as 95% of Greek

municipalities have less than 50,000 inhabitants, and

an evaluation process for smart cities’ profile has not

been carried out in Greek cities until now. A

representative case study has been selected and so the

proposed methodology has been implemented for

Municipality of Elefsina.

The remainder of this paper is structured as

follows: Section 2 presents the methodology of the

study. Section 3 contains the analysis results for the

performance of Municipality of Elefsina including,

also, some improvement actions. The new city’s

profile after the implementation of the proposed

actions is indicated. Finally, Section 4 concludes the

study including, also, future thoughts.

2 RESEARCH METHODOLOGY

The approach adopted in this research comprises of four

steps. Firstly, the selected set of smart city indicators are

presented. Secondly, the evaluation methodology is

described. In the third step, a questionnaire is developed

according to the selected indicators in order to determine

their values and in the fourth step, the classes of a smart

city footprint are presented.

2.1 Smart City Indicators

As smartness of a city is not easily measurable, a

European or International agreement on smart city

indicators does not exist (Lazaroiu and Roscia, 2012).

The overall goal is to improve sustainability with the

help of technology. It should meet the needs of the

population and is composed of several smart

characteristics that interact with each other

(Miloševic et al., 2019).

According to literature each smart characteristic

(Smart Economy, Smart Mobility, Smart

Environment, Smart People, Smart Living and Smart

Governance) is defined by a number of factors.

Furthermore, each factor can be broken into relevant

indicators, which reflect the most important aspects

of every smart characteristic (Giffinger et al., 2007),

(Giffinger and Haindlmaier, 2010). The research

team has identified 36 factors and 136 indicators

through the literature review process.

In this study, the evaluation indicators have been

selected by applying a hybrid research methodology

including literature review and structured interviews.

The significance of each candidate indicator is

examined with the aid of local stakeholders. A

questionnaire has been developed which is addressed

to the municipalities, based on the European

guidelines for smart cities. The selection of the factors

and their indicators has been based on their

applicability in cities with population less than 50,000

inhabitants. In total, 25 crucial factors have been

selected and 68 indicators were elicited (Table A,

Appendix). These factors with their relevant

indicators are based on the European trends for smart

cities and the local needs.

2.2 Evaluation Process

The problem has been modelled using multicriteria

analysis. The aim of multicriteria analysis is to solve

complicated problems taking into consideration all

the criteria that affect the decision process. In the

current study, the criteria are the selected indicators.

All factors have their internal impact reclassified to

a common scale so that it is necessary to determine

each criteria’s (indicator’s) relative impact. Weight is

assigned to the criteria-indicators to indicate its relative

importance. Different weights could influence directly

the results and it is necessary to obtain the rationality

SMARTGREENS 2020 - 9th International Conference on Smart Cities and Green ICT Systems

and veracity of criteria-indicators weights (Jia et al.,

1998), (Wang et al., 2009).

The method of equal weights has been adopted in

the proposed methodology. The criteria weight in

equal weights method is defined as:











, 1,2,…, (n: indicators)

(1)

This method is very popular and is applied in

many decision-making problems since Dawes and

Corrigan argued that the obtained results are nearly as

good as those optimal weighting methods (Dawes and

Corrigan, 1974).

All the values of the indicators have been

normalised from 0 to 1, as the standardization of

indicators is required, in order to compare them.

The ranking is obtained through the additive value

model. The formulae describing the additive value

model is the following:



























(2)









∗



 0,









∗



 1,  1,2,…,

(3)





1





(4)





0



1,2,…,

(5)



where g=(g

,…,g

) is the performance of each smart

characteristic based on n indicators, 





∗

 and









∗

 are the least and most preferable levels of

indicator 



, respectively, 











,1,…, are

non-decreasing marginal value functions of the

performances 



,1,…,. 



is the relative weight

of the 



function 







. Thus, for a candidate city

, and 







 represent the multicriteria

vector of performances and the global value of the

alternative solution (in case that there are more than

one city to be compared and evaluated), respectively

(Siskos et al., 2014), (Androulaki and Psarras, 2016),

(Strantzali et al., 2018).

The results have been aggregated on all levels

without further weighting (Giffinger et al., 2007),

(Lazaroiu and Roscia, 2012). The aggregation has

been done additive but divided through the number of

values added.

2.3 Questionnaire

The development of the questionnaire is based on

literature and the special features of Greek cities.

Zong et al. (2019) developed an evaluation indicator

system of green and smart cities studying ten aspects:

resource utilization, environmental governance and

environmental quality, green and smart medical care,

green and smart facilities, network security and

citizens’ experience. A similar questionnaire relative

to the selected 68 indicators has been developed. It is

addressed to the authorities, in order to answer the

questions with their existing actions towards smart

cities, and so the score for each factor and therefore

for each smart characteristic has been calculated.

2.4 The Footprint of a Smart City

The aim of the proposed approach is for each city to be

able to rank itself. The proposed footprint of a smart

city includes 9 classes, from I to H (Figure 2). The

range of scores in the higher classes is smaller than the

range in the lower classes. As a result, the candidate

city is obligated to implement more actions towards

smart cities strategy when it is in the lower classes. The

classification is elicited by aggregating the score from

each separate Smart Characteristic. The result is

aggregated on all levels by using equal weights and the

method of additive value model (Table 1).

3 THE CASE OF MUNICIPALITY

OF ELEFSINA

The municipality of Elefsina is in West Attica, Greece,

situated about 18 km northwest from the centre of

Athens. The municipality Elefsina was formed at the

2011 local government reform by the merger of the

following two former municipalities, that became

municipal units: Elefsina and Magoula. The

municipality has an area of 36.589 km

, the municipal

unit 18.455 km

and a population of 29.902. Elefsina is

a major industrial centre, at least 40% of the industrial

activity of the country is concentrated there, with the

largest oil refinery in Greece. On 11 November 2016

Elefsina was named the European Capital of Culture

for 2021 (Wikipedia).

3.1 Smart City Performance across 6

Different Characteristics

The aim of this step is to record all the actions,

fulfilling the requirements of each indicator, that

Municipality of Elefsina has, already, implemented

towards the smart city concept. The necessary

information has been collected from the developed

questionnaire and the individual interviews, addressed

to the responsible Departments of the Municipality

(Department of revenues, IT Department, Department

An Evaluation Model for Smart City Performance with Less Than 50,000 Inhabitants: A Greek Case Study

of Economics, Department of Transparency

Programming and Department of Environment). All

the answers have been matched with the selected

indicators and their values have been normalized from

0 to 1. The total score for each smart characteristic is

calculated following the additive value model. Based

on these data, the evaluation process has indicated the

following results:

Smart Economy: The indicators in the group of

smart economy measure the performance of

productivity, innovation, entrepreneurship and the

integration with international markets. The total score

in this smart characteristic is 0.224 (Table 1).

Smart Environment: Indicators in the group of

smart environment addresses the issues related to the

energy saving in public buildings, ecological

awareness, sustainable resource management, air

pollution and attraction of natural conditions.

Municipality of Elefsina has already implement some

actions in this direction and the total score in the field

is 0.171 (Table 1).

Smart Governance: The indicators in the group of

smart governance are associated with transparency in

governance: municipality expenditure, e-government

online availability, political strategies and

perspectives and participation in decision making. In

this field municipality of Elefsina has its higher score,

0.409 (Table 1).

Smart Living: Smart Living improves the quality

of life and it is measured by the following indicators:

educational and cultural facilities, individual safety

and health conditions. The total score in this

Characteristic is 0.268 (Table 1).

Smart Mobility: Smart Mobility indicators refer

to local accessibility, touristic attractivity, availability

of ICT infrastructure, public database and in general

sustainable, innovative and safe transport systems.

Here the score is very low, 0.194 (Table 1).

Smart People: Lifelong learning, level of

qualification and participation in public life are the

indicators that determine the Characteristic of “Smart

People”. The score is, also, high, 0.310 in comparison

to the other fields (Table 1).

Figure 1: Municipality Elefsina’s smart footprint.

Table 1: Weights and scores for Municipality of Elefsina.

Characteristics/

Factors

Weights Scores

I) Smart Economy 0.17 0.224

Innovation 0.25

0.100

Entrepreneurship 0.25

0.094

Productivity 0.25

0.700

Integration with international

markets

0.25

II) Smart Environment 0.17 0.171

Attraction of natural conditions 0.20

Air pollution integrated index 0.20

0.286

Sustainable resource

management

0.20

0.171

Ecological Awareness 0.20

0.400

Energy Saving in Public

Buildings

0.20

III) Smart Governance 0.17 0.409

Participation in decision-

making

0.25

0.710

Political strategies &

perspectives

0.25

0.643

E-Government on-line

availability

0.25

0.285

Municipality expenditure 0.25

IV) Smart Living 0.17 0.268

Cultural facilities 0.25

0.020

Health conditions 0.25

0.550

Individual safety 0.25

Educational facilities 0.25

0.500

V) Smart Mobility 0.17 0.194

Touristic attractivity 0.20

0.429

Local accessibility 0.20

0.066

Availability of ICT

infrastructure

0.20

0.473

Sustainable, innovative an

safe transport systems

0.20

Public Database 0.20

VI) Smart People 0.17 0.310

Participation in public life 0.34

0.600

Level of Qualification 0.34

0.330

Affinity to lifelong learning 0.34

3.2 Overall Performance for

Municipality Elefsina

Figure 2 gives the overall smartness of Municipality

Elefsina for all the Characteristics and Figure 1 shows

its smart footprint. It is classified in level H

(aggregated total score 0.263). Therefore, its overall

smart city performance is poor. The aggregate scores

from all the Characteristics are low, even under 0.5,

with a slight promotion of smart governance and

smart people among the rest ones. The domains of

smart environment and smart mobility have the

SMARTGREENS 2020 - 9th International Conference on Smart Cities and Green ICT Systems

lowest scores. It is obvious that the authorities are

working towards the direction of smart cities, but

more effort is needed. In that direction, a set of

indicative actions will be recommended in order to

improve their smart footprint.

3.3 Recommended Actions for

Improving Smart City

Performance

Transformation of a city into a smart city is a long

process. As appreciated in literature, smart

infrastructure is the key to implement smart city

programs (Shen et al., 2018). Infrastructure facilities

will enable the development of all smart

characteristics: smart economy, smart environment,

smart governance, smart living, smart mobility and

smart people. Actions for improving smart city

performance are recommended in the context of

Municipality of Elefsina. Although the recommended

actions are based on data from Elefsina, their content

could be implemented from any candidate smart city.

Examining the field of smart environment, leak

detectors for water saving are suggested to be installed

in residential and commercial buildings and other

public areas. Smart meters and sensors could be used

in all public buildings in order to collect the real-time

data about energy consumption. These data could be

further used for the proper energy management in

buildings, by analyzing people’ consumption behavior.

The obtained data could be incorporated in authorities’

policies in order to guide citizens, and especially

students, towards energy saving life style. Alongside

the improvement of energy efficiency of at least part of

existing public buildings is of key importance. Smart

street lighting will, also, help energy management and

will improve the city’ profile both in smart

environment and smart governance.

Smart waste management should be adopted by

using smart refuse bins with filling sensors.

Furthermore, contributory recycling in combination

with smart refuse bins and smart applications for the

citizens could enhance the ecological awareness of

inhabitants in a more efficient and effective way of

waste management. All these actions will contribute to

the performance improvement of smart environment,

smart governance and smart living, collectively.

Applications for smart devices with useful

information on points of interest according to the

user’s location will facilitate inhabitant’s life. It

could, also, provide the opportunity of emergency

alert in case it is needed. This way the authorities will

strengthen the characteristics of smart governance

and smart living.

For the domain of smart transportation, smart bus

stops should be implemented. Smart bus stops will

provide information on bus routes combined with

smart parking and rent bicycles. This action will, also,

improve the performance of smart economy, as it

reduces the time wasted on transportation and

increase productive time.

The development of a smart business gate which

will include all the local companies is very crucial for

the smart economy. There will be two benefits: the

inhabitant will be informed for each company’s

profile and the companies for available funding,

national and European.

As Elefsina has been named the European Capital

of Culture for 2021, some smart actions towards the

field of culture will enhance its profile. Photorealistic

visualization for historic buildings and important

historic events will make citizens and tourists

understand historical aspects that lost over time but

remain important and necessary for today. At the same

time, organized points for virtual reality tours could

serve except from tourists, education in schools.

Finally, policy instruments should be introduced in

order to encourage the promotion of smart city

practices.

It is obvious that most of the above-mentioned

actions will contribute to job creation, reducing the

local unemployment rate, significantly, which is,

also, a key indicator in smart economy.

Figure 2: The overall performance of Municipality of

Elefsina.

3.3.1 The New Smart “Footprint” of

Municipality of Elefsina

The smart footprint of Municipality of Elefsina has

been calculated again, by assuming that all the above

recommended actions have been implemented. The

An Evaluation Model for Smart City Performance with Less Than 50,000 Inhabitants: A Greek Case Study

new overall score is now, 0.469 (Figure 2) and

Municipality of Elefsina is categorised in class G (one

class above the previous one). Almost all the smart

characteristics have increased their performance, and

especially, smart economy, smart environment, smart

governance and smart mobility. Particular emphasis

has been given on actions concerned smart

environment and smart mobility as they were the

characteristics with lower rating. The benefit is that

one single action influences at the same time more

than one smart characteristic. There are of course a lot

of actions that could improve the performance of a

smart city but here the most common and most

important are recommended.

4 CONCLUSIONS

Cities are viewed as a part of the solution to many of

today’s economic social and environmental problems

(Akande et al., 2019). The smart city represents the

future challenge. An effective holistic evaluation

model on the performance of a smart city is of utmost

importance. Unlike previous studies, this study

attempts to evaluate small smart cities in the context of

Greece. In this article, a smart city ranking model has

been proposed for cities with less than 50,000

inhabitants, including 25 factors and 68 indicators, and

the case study concerned a Greek city, Municipality of

Elefsina. The selected indicators fall into the most

crucial axes for the evaluation of a small smart city.

The multicriteria method, Additive Value Model,

and the method of equal weights have been selected for

the evaluation process. The combination of these two

methods simplified and summarized a complex

concept into a manageable form. The smart footprint

of a city is introduced as a result of the evaluation

process.

Although it seems that Municipality of Elefsina has

already taken small steps towards the smart cities, its

overall score is very poor. It is remarkable its low score

on smart environment, as the development of actions

for improving the local environmental conditions

should be a prime objective of the authorities.

A set of the most important actions, customized

for its needs, have been recommended. The proposed

actions are able to improve the smart city

performance and the new evaluation process after

their implementation has shown that the new score is

markedly higher than the initial score in almost all the

smart characteristics. The proposed evaluation

mechanism should be applied alongside the actions in

order to record in real-time the progress of smart city.

The contribution of the research is indicated by two

axes: the proposed evaluation methodology for small

smart cities and the implemented case study for a

Greek city. Future research could focus on testing the

methodology in more than one case studies, its holistic

application will be improved. The presented model

could be further enhanced with the evaluation of more

Greek cities and the ranking of their results using

multicriteria analysis. Furthermore, the comparison

with other cities will enable the share of experience and

effective actions could be formulated for the

development of smart city in the whole country.

ACKNOWLEDGEMENTS

This study is part of a program agreement between

Municipality of Elefsina and National Technical

University of Athens, entitled “Investigate strategies

for the transition of a local authority to a smart city

community by implementing new systems of

innovation, entrepreneurship and technology. Pilot

application in the Municipality of Elefsina with

examination of the interaction with the contribution

of National Technical University of Athens”.

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APPENDIX

The proposed model includes 25 crucial factors and

68 relative indicators, shown in Table 2:

Table 2: The selected factors and their indicators.

Factors Indicators

I) Smart Economy

Innovation

Public Expenditure on R&D

Funded projects

Entrepreneurship

New businesses registered

Promotion of digital adoption

Entrepreneurship Programs

Productivity Unemployment rate

Integration with international

markets

Research grants funded by

international projects

II) Smart Environment

Attraction of natural

conditions

Green space

Air pollution integrated index

emissions

Air Pollutants

Sustainable resource

management

Waste separation and disposal

Annual thermal energy

consumption

Street lighting

Electricity consumption

Renewable resources

Intelligent management of waste

and recycling products

Smart resource management

Ecological Awareness Ecological consciousness

Energy Saving in Public

Buildings

Public Schools

Town hall and office buildings

Museums / Theatres

Sports Facilities

Library

III) Smart Governance

Participation in decision-

making

City representatives per inhabitant

Political activity of inhabitants

Share of female city representatives

Political strategies &

perspectives

Communication of economic and

community development to the

outside world

Strategies for economic & social

development

E-Government on-line

availability

Employment services

Online Payments

Social services

Public cultural and sporting

activities

Services for disabled people

Safeguard system

Public Health

Urban management

Public security

E-commerce

Municipality expenditure Βridging the digital divide

IV) Smart Living

Cultural facilities

Theatres/Cinemas

Culturally active citizens

Technologies for cultural facilities

Museums and historic monuments

Public Libraries

Health conditions

Public care facilities

Doctors

Individual safety

Safety at playgrounds

Safety at sport facilities

Safety at parks

Safety at pools and beaches

Safety at public buildings

Educational facilities

Public lessons

Quality of educational system

V) Smart Mobility

Touristic attractivity Municipality's site

Local accessibility

Availability of public transport

Quality of public transport

Cycle paths

Availability of ICT

infrastructure

Internet facilities

Wireless networks

Sustainable, innovative and

safe transport systems

Green mobility share

Use of economical cars

Public Database

Urban infrastructure database

Urban economy and society

database

VI) Smart People

Participation in public life Voters

Level of Qualification

Computer skills

Foreign language lessons

After school study

Affinity to lifelong learning Book loans

An Evaluation Model for Smart City Performance with Less Than 50,000 Inhabitants: A Greek Case Study