risks

Article
The Risks of Smart Cities and the Perspectives of Their
Management Based on Corporate Social Responsibility in the
Interests of Sustainable Development
Irina A. Morozova 1, * and Stanislav S. Yatsechko 2

1 Economics and Management Faculty, Volgograd State Technical University, 400005 Volgograd, Russia
2 Department of Economic Theory, Financial University under the Government of the Russian Federation,
125167 Moscow, Russia; stsyatsechko2018@edu.fa.ru
* Correspondence: morozovaira@vstu.ru

Abstract: Purpose: Bring to light the risks of smart cities and the perspectives of their management.
It has been discovered that smart cities are created and developed under the impact of not only
technological factors but also social factors. The connection between smart cities and quality of life is
systemic (direct and reverse)—the quality of life also specifies the creation and development of smart
cities. The impact of the COVID-19 pandemic on the development of smart cities is almost null (smart
cities do not depend on the implementation of SDG 3). This paper’s originality lies in the description
of a new angle of studying smart cities—from the position of risks, and in the determination of the
current level of these risks and the dynamics of their change during systematisation and description
of the wide international experience of creation and development of smart cities. This paper’s
uniqueness lies in the development of a new approach to managing the creation and development





 of smart cities, which is based on corporate social responsibility, thus specifying and ensuring the
Citation: Morozova, Irina A., and involvement and important role of the subjects of entrepreneurship in this process. It is proved that
Stanislav S. Yatsechko. 2022. The the contribution of smart cities to the implementation of the SDGs is much wider and goes beyond
Risks of Smart Cities and the the limits of SDG 9—it also extends to SDG 1 and SDGs 11–13.
Perspectives of Their Management
Based on Corporate Social Keywords: risks; smart cities; corporate social responsibility; sustainable development; SDGs;
Responsibility in the Interests of risk management
Sustainable Development. Risks 10:
34. https://doi.org/10.3390/ JEL Classification: D81; O14; O18; Q01; R51; M14
risks10020034

Academic Editor: Elena Popkova

Received: 29 December 2021 1. Introduction

Accepted: 19 January 2022
Published: 2 February 2022
A smart city is a progressive model of the technocratic urban environment, a cyber-
physical system at the city level. The transition from traditional cities to smart cities is a
Publisher’s Note: MDPI stays neutral strategic initiative of the modern economic systems, which is joined by the growing number
with regard to jurisdictional claims in of cities around the world. For a national government, the creation of smart cities means
published maps and institutional affil-
support of the national strategy of the digital economy’s development and the transition to
iations.
Industry 4.0, as well as overcoming spatial disproportions (socio-economic inequality) and
well-balanced development of territories within a country.
The creation of smart cities provides for their public authorities’ better transparency
of economic operations and better control, as well as increased control of the city’s econ-
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
omy. The advantages include the overcoming of the shadow economy and prevention of
This article is an open access article
violations of law, as well as the increase in the general level of predictability and safety
distributed under the terms and of the city environment as a socio-economic system. The creation of smart cities leads to
conditions of the Creative Commons a significant increase in the competitiveness of territories—their attractiveness for doing
Attribution (CC BY) license (https:// business, placement of investments, living (residents do not leave the city and demographic
creativecommons.org/licenses/by/ problems are solved), and work (inflow of skilled personnel—labour migrants).
4.0/).

Risks 2022, 10, 34. https://doi.org/10.3390/risks10020034 https://www.mdpi.com/journal/risks

Risks 2022, 10, 34 2 of 15

The key mission of smart cities consists in providing advantages for their residents,
which ensure the increase in quality of life. There’s a whole complex of advantages, from
the simplified procedure of taxation and more targeted and effective fight against poverty
to expanded opportunities of employment and guaranteed protection of labour rights
in the online (more flexible and well-balanced) labour market in smart cities. As for the
contribution of smart cities to the increase in quality of life, it is necessary to note their high
environmental friendliness.
However, despite their described comprehensive advantages, the process of transition
to smart cities is slow, and their number in the world is relatively small. These are mainly
large cities (capitals) of the leading digital economies of the world. This phenomenon is
not explained by the smart cities concept, according to which their creation is specified by
technological factors. That is, according to this concept, if there is the sufficiently developed
telecommunication infrastructure and the national programme of digital modernisation
of the economy, smart cities should be created everywhere—but this does not take place.
Therefore, there are other factors of the creation and development of smart cities (apart
from technological) which are not taken into account in the existing concept.
In addition to the described uncertainty of the reasons (factors) for the creation of
smart cities, the problem is that the international experience of the creation of the first
smart cities shows the contradiction of their consequences. Not all urban communities are
interested in the creation of a smart urban environment, and many of them oppose the
government initiatives of smart city creation. The most general formulation of the drawback
of smart cities is down to violation (erasure) of the limits of privacy and unprotectedness of
personal data.
Living under the constant control of “machine vision”, which is a usual thing in a
smart city, is a serious challenge for culture, social institutes, and human psychology. This
challenge requires serious social adaptation and it is unknown whether everyone would
pass it and whether there are objective limits of social adoption and support for smart cities.
Thus, together with advantages, there are risks of smart cities, without the consideration of
which the smart cities concept is not complete and correct.
This leads to the following research question: What are the risks of smart cities and
perspectives of risk management? To answer this question, using the existing works
(Chiang 2021; Radziejowska and Sobotka 2021; Shimizu et al. 2021), which note that the
creation of smart cities requires a certain social readiness and social support, this paper
suggests the following hypothesis: quality of life is not just a function of smart cities but
also the condition of their creation and development. That is, smart cities, in their turn, are
a function of the quality of life—social factors, which determine the risks of the creation
and development of smart cities.
The purpose of this paper was to specify the risks of smart cities and the perspectives
of risk management. This paper’s originality lies in the description of a new angle of study-
ing smart cities—from the positions of risks, and in the determination of the current level
of these risks and the dynamics of their change during systematisation and description of
the wide international experience of creation and development of smart cities. This paper’s
uniqueness lies in the development of a new approach to managing the creation and devel-
opment of smart cities, which is based on corporate social responsibility, thus specifying
and ensuring the involvement and important role of the subjects of entrepreneurship in
this process.

2. Literature Review
The smart city is a new model of the organisation of the urban economy, which corre-
sponds to the digital technological mode and which emerged and is actively implemented
around the world under the impact of the Fourth Industrial Revolution (Deja et al. 2021;
García-Retuerta et al. 2022; Verma 2022). That is why smart cities are subject to technological
risks, which include the risks of cyber security, risks of failures in the functioning of the
telecommunication infrastructure (e.g., failures in the work of the Internet, the Internet of
Risks 2022, 10, x FOR PEER REVIEW 3 of 16

Risks 2022, 10, 34 technological risks, which include the risks of cyber security, risks of failures in the func‐3 of 15
tioning of the telecommunication infrastructure (e.g., failures in the work of the Internet,
the Internet of Things), risks of anthropogenic disasters, etc. (Chen et al. 2022; Dolla et al.
2022;Things),
Galego and risksPascoal
of anthropogenic
2022; Holladisasters,
et al. 2016; etc. (Chen
Khan et al.
et al. 2022;
2022; Dolla et
Sarwesh andal. Mathew
2022; Galego
2022).and Pascoal 2022; Holla et al. 2016; Khan et al. 2022; Sarwesh and Mathew 2022).
Taking Taking into account
into account the above
the above technological
technological risks, therisks, the authors
authors Ristvej etRistvej et al.
al. (2020) and(2020)
and Żywiołek
Żywiołek and Schiavone and Schiavone (2021) recommend
(2021) recommend creating acreating
smart city a smart
within city
thewithin
“Safety theand“Safety
and Security First” concept. However, a smart city is a specific
Security First” concept. However, a smart city is a specific cyber–physical system, which cyber–physical system,
which
is very is very
different from different
the most from the mostcyber–physical
widespread widespread cyber–physical
systems: smartsystems:companies, smartthe com-
panies,
transition the transition
to which to which
implies the implies the total
total automatisation andautomatisation
autonomy of digital and autonomy
devices (their of digi-
abilitytaltodevices (their ability
work without human to participation)
work without (Inac human and participation)
Oztemel 2022; (Inac andand
Vişan Oztemel
Ioniţă 2022;
2022).Vişan and Ioniţă 2022).
Though Though the creation
the creation of smart
of smart cities
cities (as(as well
well asassmart
smartcompanies)
companies)envisages
envisages the the increase
in‐
in the level of economic processes’ automatisation, humans
crease in the level of economic processes’ automatisation, humans are not replaced with are not replaced with machines,
but remain
machines, in thein
but remain smart city (Leite
the smart 2022; 2022;
city (Leite van der vanWouden
der Wouden 2022).2022).
Due to this,
Due tothe
this,smart
the city
is a complex socio-economic cyber–physical system (Ahmad
smart city is a complex socio‐economic cyber–physical system (Ahmad et al. 2022; Duygan et al. 2022; Duygan et al. 2022;
et al. Rajawat et al. 2022).
2022; Rajawat When When
et al. 2022). studying it, it is necessary
studying to pay special
it, it is necessary attention
to pay special to the social
attention
urban environment (Bokhari and Myeong 2022; Singh
to the social urban environment (Bokhari and Myeong 2022; Singh and Dwivedi 2022). and Dwivedi 2022).
The “human
The “human factor”
factor” in theineconomy
the economy is theisrisk
the factor,
risk factor,
so the sosmart
the smart city should
city should be be
studied from the positions of risk (Cavada 2022; Miah et al. 2022; Zhang 2022). This paperpaper
studied from the positions of risk (Cavada 2022; Miah et al. 2022; Zhang 2022). This
usescurrent
uses the the current
conceptconcept of smart
of smart cities,cities,
which which is demonstrated
is demonstrated in Figure
in Figure 1. 1.

Current concept of smart cities

State regulation

telecommunication
infrastructure,

technological contribution to
factors increase in quality
Smart
Technological Social urban
progress environment

RQ

social
? ?
factors – risks
Figure 1. Causal connections of the creation and development of smart cities in their current concept.
FigureSource:
1. Causal connections of the creation and development of smart cities in their current con‐
authors.
cept. Source: authors.
As shown in Figure 1, the key provisions of the current concept of smart cities are
As shown in Figure 1, the key provisions of the current concept of smart cities are as
as follows:
follows:
Smart cities are a result of technological progress and they are created and developed under the
Smart
impactcities are a result factors
of technological of technological
(Anwar etprogress and they
al. 2021; Huang et are created
al. 2021; and developed
Shahrour and Xie 2021);
under the impact of technological factors (Anwar et al.
The approach to managing the creation and development of smart cities is2021; Huang et based
al. 2021;
on state
Shahrour and Xie 2021);
regulation, aimed at the development of telecommunication infrastructure and regulatory
The approach
support to managing
of smart the creation
cities (Peoples andPtak
et al. 2021; development
2021); of smart cities is based on
state
Smartregulation, aimed
cities improve theatsocial
the development of telecommunication
urban environment, contributing to theinfrastructure and of
increase in quality
regulatory
life of urbansupport
dwellersof (Keawsomnuk
smart cities (Peoples et al. 2021; Ptak 2021);
2021; Rodríguez Bolívar 2021).
According to the current concept, smart cities are the manifestation, and the factors that
specify them are concentrated within SDG 9 “Industry, innovation and infrastructure” (Bibri
2021; Ibrahim et al. 2021; Jackson 2021; Mach et al. 2021; Sharma et al. 2021; Trzeciak 2021).
Risks 2022, 10, 34 4 of 15

According to the current concept, under the impact of the COVID-19 pandemic, the
additional factor of creation and development of smart cities is healthcare, the high level
of which provides opportunities for financing of smart cities (Czech and Puszer 2021;
Zhang et al. 2021). However, a low level of healthcare and the ensuing serious problems
(such as pandemics) slow down the development of smart cities and hinder the creation of
new smart cities, causing the movement of resources from telecommunication infrastructure
to healthcare and distracting state regulators from the regulatory support of smart cities
(Inshakova et al. 2021; Ngo et al. 2021).
The practical experience contradicts the described current concept of smart cities,
demonstrating their fragmentary character (incompleteness), insufficient precision, and
insufficient correctness. In Russia, a smart city has been created only in Moscow (economic
capital) and St. Petersburg (cultural capital). The decree of the Ministry of Construction,
Housing and Utilities of the Russian Federation (2021) dated 25 December 2020, No. 866/pr
“On adoption of the Concept for the project of digitisation of the city economy ‘Smart city’”
envisages the creation of smart cities all over the country.
According to the materials of the Institute for Statistical Studies and Economics of
Knowledge of the National Research University “Higher School of Economics”, the Ministry
of Digital Development, Communications and Mass Media and Federal State Statistics
Service (Rosstat) (2021), the number of broadband Internet users per 100 people of the
population in the Yamalo-Nenets Autonomous Okrug (130.4), Krasnodar Krai (119.5), the
Republic of Tatarstan (108.6), Nizhny Novgorod Oblast (118.1), and some other regions
and cities is similar to Moscow (127.4) and St. Petersburg (128.0). Therefore, the impact of
technological factors in the above regions and cities is favourable, but smart cities are not
created in them, which is clearly caused by other factors, which are ignored by the current
concept of smart cities.
This is confirmed by the international experience. Thus, the United Arab Emirates
is ranked 4th in the ranking of the World Economic Forum (2021) by the indicator “Legal
framework’s adaptability to digital business models” (72.5 points in 2019), while the ranking
of IMD (2021) contains only two cities of this country, which are not the ranking leaders:
Abu Dhabi (28th) and Dubai (29th).
Similarly, Malaysia is ranked 5th in the ranking of the World Economic Forum (2021)
by the indicator “Legal framework’s adaptability to digital business models” (72.5 points
in 2019), while the ranking of IMD (2021) contains only one city of this country, which
is below the middle part of the ranking (closer to the end): Kuala Lumpur (74th). The
examples of the UAE and Malaysia confirm Russia’s experience and show that the presence
of developed telecommunication infrastructure and regulatory support do not guarantee
the creation and development of smart cities.
This is a sign of a gap in the literature—concerning the uncertainty of risks of creation
and development of smart cities, as well as imperfection of the applied approach to the
management of this process. Another gap exists because the current approach, which is
based on state regulation, does not cover the risks of the creation and development of smart
cities, the management of which requires a new approach.
The evidential base of the impact of the COVID-19 pandemic on the creation and
development of smart cities is not formed, which is another gap in the literature. This
paper fills these gaps through the study of the social factors’ influence on the creation and
development of smart cities, reconsideration of this influence from the positions of risk,
determination of the specifics of risks of the creation and development of smart cities before
(in 2019) and after (2020–2021) the start of the COVID-19 pandemic, and the search for the
perspectives of risk management.
This literature review is followed by the Materials and Methods—with the research
model and description of methods that are used to achieve the set research tasks. Then, the
results of the achievement of each of the set research tasks are provided:
Risks 2022, 10, 34 5 of 15

– Identification of the risks of the creation and development of smart cities through
determining the social factors’ impact on them (results of the achievement of the first
task are provided);
– Determination of the current level of risk of smart cities (results of the achievement of
the second task are provided);
– Study of the change of the risks of smart cities in the dynamics of recent years (2019–
2021) (results of the achievement of the third task are provided);
– Determination of the perspectives and development of recommendations for manag-
ing the discovered risks of the creation and development of smart cities (results of the
achievement of the fourth task are provided).
Then, the Discussion section contains a detailed consideration of the research results
and the evaluation of conclusions from the position of the existing literature. The Conclu-
sion, providing the key implications, limitations, and further research directions sums up
the research.

3. Materials and Methods

The logical structure and tasks of this research are as follows. The first task is to
identify the risk of the creation and development of smart cities (SmC) through specifying
the influence of the social factors (sf) on them. Mathematical tools are used for this; the
research model has the following form:

SmC = F(sf) (1)

To specify the model, a method of regression analysis is used. The reasons for its use
are as follows:
– Regression analysis allows one to find not only the general connection between the
indicators but also the isolated contribution of each separate factor to the development
of smart cities, thus identifying risks (positively influencing factors);
– Regression analysis allows specification of the research model (1) in each time period
in isolation and determination of specific risks. This is especially useful under the
conditions of the COVID-19 pandemic and crisis during the comparison of the pre-
pandemic data of 2019 and the pandemic data of 2020–2021.
The indicator of the level of smart cities’ development is their position in the cor-
responding ranking of IMD (2021): Smart City Rank (SmC). The social factors are the
indicator of the quality of life from the materials of Numbeo (2021):
– Purchasing Power Index (sf1 );
– Safety Index (sf2 );
– Health Care Index (sf3 );
– Cost of Living Index (sf4 );
– Property Price to Income Ratio (sf5 );
– Traffic Commute Time Index (sf6 );
– Pollution Index (sf7 );
– Climate Index (sf8 ).
Since the lower the values of sf1 , sf4 , sf5 , sf6 , sf7 , the better, for them to be considered
the risks of smart cities their connection (regression coefficients) with the resulting variable
must be negative. For the values of other factor variables (sf2 , sf3 , sf8 ), the rule “the higher
the better” applies; for them to be considered the risks of smart cities, their connection
(regression coefficients) with the resulting variable must be positive. The reports of IMD
(2021) are compared to 2019. That is why to study the dynamics of change of the selected
indicators the statistics for 2019–2021 are used. In each period, the data on 84 smart cities are
gathered. The research model (1) is compiled based on a continuous sample (for 2019–2021)
of 252 observations (Table S1).
Risks 2022, 10, 34 6 of 15

The second task is determining the current level of risk of smart cities. For this,
their risk profile is compiled according to the methodology, which implies the following
sequence of actions. In the first step, the indicator’s values in the considered period are
found. In this paper, this is the arithmetic mean of the indicators that characterise the risks
of smart cities in 2021. In the second step, the qualitative treatment of the indicator’s value
is performed. For the indicators of quality of life, which characterise the risks of smart
cities, the following scale is proposed (Table 1). It is compiled given the greatest possible
(200 points) and factually achieved by the leaders of the ranking of Numbeo (2021) values
of these indicators.

Table 1. The scale for the qualitative treatment of the value of the indicators of quality of life, which
characterise the risks of smart cities.

Range of Values of the Indicator, which Corresponds to

Type of Indicator the Assessment, Score
Low Value Moderate Value High Value
The lower the indicator’s value,
above 75 50–75 below 50
the better (−)
The higher the indicator’s value,
below 50 50–75 above 75
the better (+)
Source: authors.

In the third step, the significance of the risk is specified. For this, the sum (in absolute
value) of all the regression coefficients, which reflect the specified risks, is calculated. Then,
the percentage ratio of each regression coefficient in isolation to this sum is calculated. If the
percentage ratio is less than 0.20, the significance of the risk is low. If the percentage ratio
is in the range from 0.20 to 0.50, the significance of the risk is medium. If the percentage
ratio exceeds 0.50, the significance of the risk is high. In the last—fifth—step, the level of
risk is found given the qualitative treatment of the values of the indicators of risks and the
significance of risks. To specify the level of risk of smart cities on a scale from one to five,
the following matrix is offered (Table 2).

Table 2. Matrix for specifying the level of risk of smart cities on a scale from one to five.

Value of the Indicator That Characterises the Risk

Significance of Risk
High Value Moderate Value Low Value
Low (below 0.20) low risk (0) acceptable risk (1) moderate risk (2)
Medium (0.20–0.50) acceptable risk (1) high risk (3) very high risk (4)
High (above 0.50) moderate risk (2) very high risk (4) critical risk (5)
Source: authors.

According to Table 2, this paper uses a scale from one to five for the level of risk: the
lower the value, the better.
The third task is studying the change of the risks of smart cities in the dynamics of
recent years (2019–2021). For this, the arithmetic means of all indicators of quality of life are
calculated, and their regressive connection with the Smart City Rank in isolation in each
period is specified. The specifics of each period are determined. Additionally, the dynamics
of the change of the risks of creation and development of smart cities in 2019–2021 are
found with the help of the method of horizontal analysis (calculation of the indicators’
growth). Special attention is paid to the factor of healthcare (Healthcare Index)—its role
and significance as a risk of creating and developing smart cities before (in 2019) and after
the start of the pandemic (in 2020–2021) are specified.
The fourth task is describing the perspectives and developing recommendations for
managing the risks of the creation and development of smart cities.
Risks 2022, 10, 34 7 of 15

4. Results
To achieve the first task of this research, based on the research model (1), the method
of regression analysis is used to find the impact of social factors on smart cities. Thus, the
model of linear regression with multiple regressors (2) is used:

SmC = 105.07 + 0.07 × Sf1 − 0.21 × Sf2 − 0.43 × Sf3 − 0.72 × Sf4 − 0.33 × Sf5 + 0.07 × Sf6 + 0.25 × Sf7 + 0.24 × Sf8 (2)
The lookup values of regression coefficients are found with factor variables sf4 , sf5 , sf8 .
According to the created model (2), the risks of creating and developing smart cities are
as follows:
– Risk of increase in cost of living;
– Risk of increase in property price to income ratio;
– Risk of unfavourable change of the climate.
Thus, an increase in the cost of living by 1 point leads to a decrease in Smart City
Rank by 0.72 positions. An increase in property price to income ratio by 1 point leads to a
decrease in Smart City Rank by 0.33 positions. Aggravation of climate by 1 point leads to a
decrease in Smart City Rank by 0.24 positions. The model is correct at the significance level
of 0.01 (significance F = 2.34 × 10−26 ). The coefficient of multivariable correlation produced
a rather high value, equalling 0.6589. Therefore, the creation and development of smart
cities were 65.89% explained by the impact of social factors.
To achieve the second task of this research, based on Tables 1 and 2, a risk profile of
smart cities in 2021 is compiled (Table 3).

Table 3. The risk profile of smart cities in 2021.

Risks of Creation and Development of Smart Cities

Element of the Risk Profile Risk of Increase in Cost Risk of Increase in Property Risk of Unfavourable
of Living Price to Income Ratio Change of Climate
Indicator of quality of life Cost of Living Index Property Price to Income Ratio Climate Index
Type of indicator * − − +
Arithmetic mean in 2021,
63.95 14.34 80.09
score 1–200
Treatment of value moderate high high
Significance of risk 0.56 (high) 0.26 (medium) 0.19 (low)
Level of risk very high risk (4) acceptable risk (1) low risk (0)
* “−”—the lower the indicator’s value, the better; “+”—the higher the indicator’s value, the better. Source:
calculated and compiled by the authors.

According to the risk profile in Table 3, the risk of an increase in the cost of living is
very high in 2021 (assessed at 4 points from the scale from 1 to 5). The arithmetic mean of
the Cost of Living Index in 2021 is 63.95 points (moderate value, according to Table 1: in
the range from 50 to 75 points). The regression coefficient for this indicator in model (2)
equals 0.72. The sum of all three regression coefficients for the selected indicators equals
1.29 (0.72 + 0.33 + 0.24). This is why the significance of the risk of increase in cost of living
equals 0.72/1.29 = 0.56 (high: above 0.50). According to Table 2, a very high risk (4) occurs
at the crossing point of the moderate value and high significance of the risk.
The risk of an increase in property price to income ratio is acceptable in 2021 (1 point
according to the scale from 1 to 5). The arithmetic mean of the Property Price to Income
Ratio in 2021 equals 14.34 points (high value, according to Table 1: below 50 points). The
regression coefficient for this indicator in model (2) equals 0.33. The sum of all three
regression coefficients for the selected indicators equals 1.29 (0.72 + 0.33 + 0.24). Thus, the
significance of the risk of increase in cost of living equals 0.33/1.29 = 0.26 (medium: in the
 at the crossing point of the moderate value and high significance of the risk.
The risk of an increase in property price to income ratio is acceptable in 2021 (1 point
according to the scale from 1 to 5). The arithmetic mean of the Property Price to Income
Ratio in 2021 equals 14.34 points (high value, according to Table 1: below 50 points). The
Risks 2022, 10, 34
regression coefficient for this indicator in model (2) equals 0.33. The sum of all three re‐
8 of 15
gression coefficients for the selected indicators equals 1.29 (0.72 + 0.33 + 0.24). Thus, the
significance of the risk of increase in cost of living equals 0.33/1.29 = 0.26 (medium: in the
range from 0.20 to 0.50). According to Table 2, acceptable risk (1) occurs at the crossing
rangeof
point from 0.20 to
the high 0.50).
value According
and to Table 2, acceptable
medium significance of the risk.risk (1) occurs at the crossing
point of the high value and medium significance of the risk.
The risk of unfavourable change of the climate is low in 2021 (0 points according to
The risk of unfavourable change of the climate is low in 2021 (0 points according
the scale from 1 to 5). The arithmetic mean of the Climate Index in 2021 equals 80.09 points
to the scale from 1 to 5). The arithmetic mean of the Climate Index in 2021 equals 80.09
(high value, according to Table 1: above 75 points)). The regression coefficient at this in‐
points (high value, according to Table 1: above 75 points)). The regression coefficient at
dicator in model (2) equals 0.24. The sum of all three regression coefficients at the selected
this indicator in model (2) equals 0.24. The sum of all three regression coefficients at the
indicators equals 1.29 (0.72 + 0.33 + 0.24). That is why the significance of the risk of increase
selected indicators equals 1.29 (0.72 + 0.33 + 0.24). That is why the significance of the risk
in cost of living equals 0.24/1.29 = 0.19 (low: below 0.20). According to Table 2, low risk (0)
of increase in cost of living equals 0.24/1.29 = 0.19 (low: below 0.20). According to Table 2,
is at the crossing point of the high value and low significance of the risk.
low risk (0) is at the crossing point of the high value and low significance of the risk.
To achieve the third task of this research, the change in risks of smart cities in the
To achieve the third task of this research, the change in risks of smart cities in the
dynamics of recent years (2019–2021) is specified. For this, the arithmetic means of all in‐
dynamics of recent years (2019–2021) is specified. For this, the arithmetic means of all indi-
dicators of quality of life (value) are calculated, and their regressive connection (signifi‐
cators of quality of life (value) are calculated, and their regressive connection (significance)
cance)
with the with the City
Smart Smart CityisRank
Rank is specified
specified (according
(according to the research
to the research model
model (1)) (1)) in iso‐
in isolation for
lation for each period. The results for each period are shown
each period. The results for each period are shown in Figures 2–4. in Figures 2–4.

2019
90.00 80.50 0.21 0.19 80.09 0.40
80.00 0.20
0.22 67.86
70.00 62.82 0.00
57.78
60.00 53.00
−0.08 -0.20
50.00 −0.26 −0.26 39.77
−0.27 -0.40
40.00
-0.60
30.00
20.00 14.16 -0.80
10.00 −0.95 -1.00
0.00 -1.20
Purchasing Safety Index Health Care Cost of Property Traffic Pollution Climate
Power Index Index Living Price to Commute Index Index
Index Income Time Index
Ratio

Values, points 1-200 Significance

Figure 2. Arithmetic means of the indicators of quality of life and their regressive connection with
Figure 2. Arithmetic means of the indicators of quality of life and their regressive connection with
smart cities
smart cities in
in 2019.
2019. Source:
Source: calculated
calculated and
and built
built by
by the
the authors.
authors.

According to Figure 2, in 2019 (before the COVID-19 pandemic), there existed only
According to Figure 2, in 2019 (before the COVID‐19 pandemic), there existed only
two risks of creation and development of smart cities:
two risks of creation and development of smart cities:
– Risk of increase in cost of living: the value of the Cost of Living Index in 2019 was
 Risk of increase in cost of living: the value of the Cost of Living Index in 2019 was
62.82 points (moderate, according to Table 1). The sum of regression coefficients:
62.82 points (moderate, according to Table 1). The sum of regression coefficients: 0.95
0.95 + 0.26 = 1.21. Significance of the risk: 0.95/1.21 = 0.79 (high, according to Table 2).
+ 0.26 = 1.21. Significance of the risk: 0.95/1.21 = 0.79 (high, according to Table 2).
Level of risk: very high (4), according to Table 2;
Level of risk: very high (4), according to Table 2;
– Risk of increase in property price to income ratio: the value of the Property Price
to Income Ratio in 2019 was 14.16 points (high, according to Table 1). The sum of
regression coefficients: 0.95 + 0.26 = 1.21. Significance of the risk: 0.26/1.21 = 0.21
(medium, according to Table 2). Level of risk: acceptable (1), according to Table 2.
  Risk of increase in property price to income ratio: the value of the Property Price to
Income Ratio in 2019 was 14.16 points (high, according to Table 1). The sum of re‐
Risks 2022, 10, 34 gression coefficients: 0.95 + 0.26 = 1.21. Significance of the risk: 0.26/1.21 = 0.21 9(me‐
of 15
dium, according to Table 2). Level of risk: acceptable (1), according to Table 2.

2020
90.00 80.09 0.60
80.00 0.31 0.40
70.28 0.18 67.86 0.15
70.00 0.37
62.21 0.20
60.00 57.50 53.39
0.00
50.00 0.03 -0.10−0.10
40.10 -0.20
40.00
−0.60 -0.40
30.00
20.00 14.38 -0.60
10.00 -0.80
−0.87
0.00 -1.00
Purchasing Safety Index Health Care Cost of Property Traffic Pollution Climate
Power Index Index Living Price to Commute Index Index
Index Income Time Index
Ratio

Values, points 1-200 Significance

3. Arithmetic
Figure 3.
Figure means of
Arithmetic means of the
the indicators
indicators of
of quality
quality of
of life
life and
and their
their regressive
regressive connection
connection with
with
smart cities in 2020. Source: calculated and built by the authors.
smart cities in 2020. Source: calculated and built by the authors.

Based on Figure 3, in 2020 (amid the COVID-19 pandemic), there existed four risks of
Based on Figure 3, in 2020 (amid the COVID‐19 pandemic), there existed four risks
the creation and development of smart cities:
of the creation and development of smart cities:
– Risk of safety: the value of the Safety Index in 2020 was 57.50 points (moderate, ac-
 Risk of safety: the value of the Safety Index in 2020 was 57.50 points (moderate, ac‐
cording to Table 1). The sum of regression coefficients: 0.18 + 0.60 + 0.87 + 0.37 = 2.02.
cording to Table 1). The sum of regression coefficients: 0.18 + 0.60 + 0.87 + 0.37 = 2.02.
Significance of the risk: 0.18/2.02 = 0.09 (low, according to Table 2). Level of risk:
Significance of the risk: 0.18/2.02 = 0.09 (low, according to Table 2). Level of risk: ac‐
acceptable (1), according to Table 2;
ceptable (1), according to Table 2;
– Risk of increase in cost of living: the value of the Cost of Living Index in 2020 was
 Risk of increase in cost of living: the value of the Cost of Living Index in 2020 was
62.12 points (moderate, according to Table 1). The sum of regression coefficients: 0.18
62.12 points (moderate, according to Table 1). The sum of regression coefficients: 0.18
+ 0.60 + 0.87 + 0.37 = 2.02. Significance of the risk: 0.60/2.02=0.30 (medium, according
+ 0.60 + 0.87 + 0.37 = 2.02. Significance of the risk: 0.60/2.02=0.30 (medium, according
to Table 2). Level of risk: high (3), according to Table 2;
to Table 2). Level of risk: high (3), according to Table 2;
– Risk of increase in property price to income ratio: the value of the Property Price
 Risk of increase
to Income Ratioininproperty
2020 was price to income
14.38 ratio: the
points (high, value oftothe
according Property
Table Price
1). The to
sum
Income Ratio in 2020 was 14.38 points (high, according to Table 1). The
of regression coefficients: 0.18 + 0.60 + 0.87 + 0.37 = 2.02. Significance of the risk:sum of re‐
gression coefficients:
0.87/2.02=0.43 (medium,0.18 + 0.60 to
according + Table
0.87 +2).0.37
Level= of
2.02.
risk:Significance of according
acceptable (1), the risk:
0.87/2.02=0.43
to Table 2; (medium, according to Table 2). Level of risk: acceptable (1), according
– to Table
Risk 2;
of unfavourable change of climate: the value of the Climate Index in 2020
 Risk of unfavourable
was 80.09 points (high, change of climate:
according the value
to Table 1). Theof sum
the Climate Index in
of regression 2020 was
coefficients:
80.09 points (high, according to Table 1). The sum of regression coefficients:
0.18 + 0.60 + 0.87 + 0.37 = 2.02. Significance of the risk: 0.37/2.02 = 0.18 (medium, 0.18 +
0.60 + 0.87 + 0.37 = 2.02. Significance of the risk: 0.37/2.02 = 0.18
according to Table 2). Level of risk: acceptable (1), according to Table 2.(medium, according
to Table 2). Level of risk: acceptable (1), according to Table 2.
Risks 2022, 10, x FOR PEER REVIEW 10 of 16
Risks 2022, 10, 34 10 of 15

2021
90.00 0.22 80.09 0.60
80.00 0.40
68.17 0.47
70.00 63.65 63.95 0.19 -0.10 0.20
57.12
60.00 0.02 53.12 0.00
50.00 -0.20
39.85
40.00 -0.40
−0.49
30.00 −0.69 -0.60
20.00 14.34 -0.80
10.00 -1.00
−1.04
0.00 -1.20
Purchasing Safety Index Health Care Cost of Property Traffic Pollution Climate
Power Index Index Living Price to Commute Index Index
Index Income Time Index
Ratio

Values, points 1-200 Significance

Figure 4. Arithmetic means of the indicators of quality of life and their regressive connection with
Figure 4. Arithmetic means of the indicators of quality of life and their regressive connection with
smart
smart cities
cities in
in 2021.
2021. Source: calculated and
Source: calculated and built
built by
by the
the authors.
authors.
According to Figure 4, in 2021 (amid the COVID-19 pandemic), there existed three
According to Figure 4, in 2021 (amid the COVID‐19 pandemic), there existed three
risks of the creation and development of smart cities:
risks of the creation and development of smart cities:
– Risk of increase in commute: the value of the Traffic Commute Time Index in 2021
 Risk of increase in commute: the value of the Traffic Commute Time Index in 2021
was 63.95 points (moderate, according to Table 1). The sum of regression coefficients:
was 63.95 points (moderate, according to Table 1). The sum of regression coefficients:
0.69 + 0.10 + 0.47 = 1.26. Significance of the risk: 0.69/1.26 = 0.55 (high, according to
Table+2).
0.69 0.10 + 0.47
Level = 1.26.
of risk: Significance
very of the risk:
high (4), according to 0.69/1.26
Table 2; = 0.55 (high, according to
– Table
Risk of increase in cost of living: the value of theTable
2). Level of risk: very high (4), according to Cost 2;of Living Index in 2021 was
 Risk of increase in cost of living: the
63.95 points (moderate, according to Table 1). The value of the Cost
sum of of
Living Index in
regression 2021 was
coefficients:
63.95 points (moderate, according to Table 1). The sum of regression
0.69 + 0.10 + 0.47 = 1.26. Significance of the risk: 0.10/1.26 = 0.08 (low, according coefficients: 0.69
to
+ 0.10 + 0.47 = 1.26. Significance of the risk: 0.10/1.26
Table 2). Level of risk: acceptable (1), according to Table 2; = 0.08 (low, according to Table
– 2).
RiskLevel of risk: acceptable
of unfavourable change (1),ofaccording
climate: the to Table
value2; of the Climate Index in 2021 was
 Risk
80.09 points (high, according to Table 1). The sum of of
of unfavourable change of climate: the value the Climate
regression Index in0.69
coefficients: 2021+ was
0.10
80.09 points (high, according to Table 1). The sum of regression
+ 0.47 = 1.26. Significance of the risk: 0.47/1.26 = 0.37 (medium, according to Table coefficients: 0.692).
+
0.10 + 0.47 = 1.26. Significance of the risk:
Level of risk: acceptable (1), according to Table 2. 0.47/1.26 = 0.37 (medium, according to Table
2). Level of risk: acceptable (1), according to Table 2.
Using model (2), the dynamics of change of the risks of creation and development of
smartUsing
cities model (2), theare
in 2019–2021 dynamics
specified of (Table
change4).of the risks of creation and development of
smartAccording
cities in 2019–2021
to Table 4,are thespecified (Table 4).
risk of increase in cost of living reduced in 2020 (compared
to 2019) but grew in 2021 (compared to 2020). The risk of increase in property price to
Table 4. ratio
income Dynamics of change
increased of thebut
in 2020, risks of creation
then reducedand development
in 2021. The risk ofof
smart cities in 2019–2021.
unfavourable change
of climate remained stable during the whole period, from 2019 to 2021.
Risks of Creation and Development of Smart Cities
To achieve the fourth task of this research, the perspectives are specified and recommen-
Risk of In‐ Risk Of Increase In Risk of Unfa‐
Characteristics
dations are developed of the Risk
for managing the described risks of the creation and development of
crease in Cost Property Price To vourable Change
smart cities—they are based on corporate social responsibility. For managing the risk of an
of Living Income Ratio of Climate
increase in the cost of living, it is recommended to refuse the increase in commodity prices
by local companies. Unlike Cost of Living Property Price
the measure of state regulation, which supposes to In‐ the establish-
Indicator of quality of life Climate Index
Index come Ratio
ment of price limits, corporate social responsibility allows preservation of the effectiveness
and naturalTypecharacter
of indicatorof the market mechanism, − being more‐ universal (applicable + to all
markets and market segments). in 2019 62.82 14.16 80.09
Arithmetic mean,
in 2020 62.21 14.38 80.06
score 1–200
in 2021 63.95 14.34 80.09
Risks 2022, 10, 34 11 of 15

Table 4. Dynamics of change of the risks of creation and development of smart cities in 2019–2021.

Risks of Creation and Development of Smart Cities

Characteristics of the Risk Risk Of Increase In

Risk of Increase in Risk of Unfavourable
Property Price To
Cost of Living Change of Climate
Income Ratio
Property Price to
Indicator of quality of life Cost of Living Index Climate Index
Income Ratio
Type of indicator − - +
in 2019 62.82 14.16 80.09
Arithmetic mean,
score 1–200 in 2020 62.21 14.38 80.06
in 2021 63.95 14.34 80.09
in 2020 compared
−0.97 1.55 0.00
to 2019
Growth, %
in 2021 compared
2.80 −0.28 0.00
to 2020

Treatment of growth in 2020 compared

Reduction of risk Growth of risk Risk did not change
from the positions to 2019
of risk in 2021 compared
Growth of risk Reduction of risk Risk did not change
to 2020
“−”—the lower the indicator’s value, the better; “+”—the higher the indicator’s value, the better. Source:
calculated and compiled by the authors.

To manage the risk of an increase in property price to income ratio, it is recommended

to provide employees with corporate real estate or easy accommodation rental plans. Unlike
the measure of state regulation, which supposes the establishment of price limits, corporate
social responsibility allows preservation of the effectiveness and natural character of the
mechanism of competition in the real estate market and is more targeted—oriented towards
residents of the city.
To manage the risk of unfavourable change of climate, it is recommended to implement
the corporate programmes of the fight against climate change based on green investments.
Unlike the measure of state regulation, which supposes environmental standardisation and
norming, corporate social responsibility allows preservation of the flexibility of companies
and is more effective, because it is aimed not at the formal observation of requirements but
the specific result.

5. Discussion
This paper contributes to the literature by specifying the provisions of the concept
of smart cities (Table 5). The method of regression analysis is used to create a model of
multiple linear regression, which specifies the research model and quantitatively describes
the social factors’ impact on the creation and development of smart cities. The model
allowed for precise identification of the risks of the creation and development of smart
cities. A risk profile of smart cities in 2021 is compiled. It showed strong differences in the
current level of various risks of smart cities and demonstrated the moderate general level
of risk.
The change of the risks of smart cities in the dynamics of recent years is studied
with the help of the calculated arithmetic means of the indicators of quality of life and
their regression connections with smart cities in 2019–2021. The obtained results demon-
strated relative stability of the risks of smart cities. Based on the systematisation of risks
and their dynamics in recent years, the perspectives are determined and recommenda-
tions for managing the discovered risks of the creation and development of smart cities
are developed.
Risks 2022, 10, 34 12 of 15

Table 5. Comparative analysis of the existing provisions and specified (in this paper) provisions of
the smart cities concept.

Criterion of Comparison Existing Provisions Specified Provisions

also social factors: cost of
only technological
Factors of creation and living, property price to
(telecommunication
development of smart cities income ratio, favourability of
infrastructure) factors
climate
also the following risks: risk
of increase in cost of living;
Consequences of creation and risk of increase in property
only advantages
development of smart cities price to income ratio; risk of
unfavourable change of
climate.
systemic (direct and reverse)
The connection between smart only direct connection: smart connection—the quality of life
cities and quality of life cities raise the quality of life also defines the creation and
development of smart cities
Approach to managing the suggests risk management
ignores risks and is based on
creation and development of and is based on corporate
state regulation
smart cities social responsibility
Impact of the COVID-19 clear and negative almost zero
pandemic on the development (smart cities depend on the (smart cities do not depend on
of smart cities implementation of SDG 3) the achievement of SDG 3)
Contribution of smart cities to
also SDG 1, SDG 11, SDG 12
the implementation of the only SDG 9
and SDG 13
SDGs
Source: authors.

According to Table 5, due to the above obtained results, this paper specifies all key
provisions of the existing concept of smart cities:
– Unlike (Anwar et al. 2021; Huang et al. 2021; Shahrour and Xie 2021), the obtained
results demonstrate that smart cities are created and developed according to the
impact of not only technological factors but also social factors: cost of living, property
price to income ratio, and favourability of climate;
– Unlike (Peoples et al. 2021; Ptak 2021), this paper proposes a new approach to man-
aging the creation and development of smart cities, which offers risk management
and is based on corporate social responsibility—it overcomes the limitations of the
existing approach (which is based on state regulation);
– Unlike (Keawsomnuk 2021; Rodríguez Bolívar 2021), this paper showed
that smart cities not only create advantages (improve the social urban environment)
but also cause risks: risk of increase in cost of living; risk of increase in property price
to income ratio; risk of unfavourable change of climate. The connection between smart
cities and quality of life is not just direct (smart cities raise the quality of life) but
also systemic (direct and reverse)—the quality of life also defines the creation and
development of smart cities.
Unlike in previous works (Czech and Puszer 2021; Inshakova et al. 2021; Ngo et al.
2021; Zhang et al. 2021), it was discovered that the impact of the COVID-19 pandemic on the
development of smart cities is almost zero (smart cities do not depend on the achievement
of SDG 3). Unlike in previous studies (Bibri 2021; Ibrahim et al. 2021; Jackson 2021; Mach
et al. 2021; Sharma et al. 2021; Trzeciak 2021), it was proved that the contribution of smart
cities to the implementation of the SDGs is wider and goes beyond the limits of SDG 9
“Industry, innovation and infrastructure”—it also extends to SDG 1 (No poverty), SDG 11
(Sustainable cities and communities), SDG 12 (Responsible production and consumption),
and SDG 13 (Climate action).
Risks 2022, 10, 34 13 of 15

6. Conclusions
As a result of the performed study, it is possible to make the following conclusions.
This paper answered the set research question and proved the proposed hypothesis. Three
social risks of creation and development of smart cities were discovered: risk of increase in
cost of living (high, assessed at 4 points in 2021); risk of increase in property price to income
ratio (acceptable, assessed at 1 point in 2021); risk of unfavourable change of climate (low,
assessed at 0 points in 2021).
This paper established the absence of the influence of the healthcare factor on smart
cities before the COVID-19 pandemic (in 2019) and during the pandemic (in 2020–2021).
Before the pandemic (2019), the risk of an increase in the cost of living was assessed as
very high (4). In 2020, it dropped down to high (3); and in 2021, it became acceptable (1).
Therefore, amid the COVID-19 pandemic, the risk of the creation and development of smart
cities was reduced.
The perspectives of managing the risks of smart cities are connected to the support
of the SDGs by local businesses. The proposed recommendations are based on corpo-
rate social responsibility in support of SDG 1, SDG 11, SDG 12, and SDG 13. Risks and
risk management are of a financial nature: inflation, fight against poverty, and green
investments.
The theoretical value of the results is as follows: specification of the factors of creation
and development of smart cities, description of the risks of this process, and reconsideration
of its essence from the positions of sustainable development. The practical value of the
results is as follows: the new approach to managing the creation and development of smart
cities is more flexible and effective—it allows the involvement of local companies in the
management process through their corporate social responsibility. The new approach is
not a replacement but an addition, which fills the gaps of the existing approach (which is
based on state regulation).
Nevertheless, the obtained results are limited by the fact that they showed the vari-
ability of the spectre (list) of the risks of smart cities. Thus, two risks existed in 2019: the
risk of increase in cost of living and risk of increase in property price to income ratio. In
2020, there existed four risks: risk of safety, risk of increase in cost of living, risk of increase
in property price to income ratio, and risk of unfavourable change of climate. In 2021, there
were three risks: risk of increase in commute time, risk of increase in cost of living, and risk
of unfavourable change of climate.
The analysed international experience of 2019–2021 allows the expectation of a further
change of the spectre of the risks of smart cities, which requires further research. The
authors’ recommendations on risk management need certain corrections in the course of
the change of the spectre of risks in each future period. Future studies should be devoted
to finding the spectre of smart cities’ risks in future periods and, according to change in
this spectre, to the correction of recommendations on smart cities risk management.

Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/risks10020034/s1.
Author Contributions: Conceptualization, I.A.M.; Investigation, S.S.Y.; Methodology, S.S.Y.; Writing—
original draft, I.A.M. and S.S.Y.; Writing—review & editing, I.A.M. All authors have read and agreed
to the published version of the manuscript.
Funding: The reported study was funded by RFBR according to the research project No. 20-010-00072
“Formation of creative centers for spatial development as a mechanism for improving the quality of
life of the population of rural areas”.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments: The reported study was funded by RFBR according to the research project No.
20-010-00072 “Formation of creative centers for spatial development as a mechanism for improving
the quality of life of the population of rural areas”.
Risks 2022, 10, 34 14 of 15

Conflicts of Interest: The authors declare no conflict of interest.

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