The Exclusiveness of Smart Cities—Myth or Reality? Comparative Analysis of Selected Economic and Demographic Conditions of Polish Cities

Abstract

The Smart City concept is perceived as a method of dynamic development of cities and an opportunity to improve the quality of life of the urban community. Nevertheless, it is not without its disadvantages, among which the possibility of exclusion (economic, social or digital) is most often mentioned. However, the literature on the subject lacks empirical research verifying this allegation. For this reason, the purpose of this article is to conduct a comparative analysis of economic and social conditions in 17 Polish cities, 3 of which are recognized as Smart Cities in international rankings. By analyzing the economic and demographic conditions in the long term, an attempt is made to answer the question of whether Smart Cities offer better living conditions, and if so, how big is the imbalance and the risk of excluding other cities? In the course of the research, the following are taken into account: tax revenue per capita, unemployment rate, population density and level, as well as the share of working and post-working age population. These parameters are analyzed using descriptive statistics and systematized using multi-criteria analysis. The collective comparison of all the surveyed provincial cities shows that the best economic and demographic conditions apply to cities recognized as smart. The average annual rate of changes in tax revenues in the surveyed cities ranges from 5% to almost 8% and is the highest in Warsaw, Kraków and Wrocław. These cities are also characterized by the lowest unemployment rate, ranging from 3% to 4% (in other cities, from 4% to almost 7%). The mentioned cities and Gdańsk are the only ones with a positive rate of population change (from 0.62% to 1.08%). Other studied cities are systematically depopulating (annual rate of change from −0.37% to −7.09%). In Warsaw, Wrocław and Kraków, the share of the working-age population is also decreasing the slowest (the annual rate of change below −1.0%). The cities recognized as smart (Warsaw, Kraków and Wrocław) are matched by Gdańsk and Poznań, which can be considered strong contenders for being smart. Unfortunately, the remaining cities are far from the leaders of the ranking, which may expose them to economic and social exclusion, all the more so that the parameters examined in them are characterized by negative tendencies. It can, therefore, be concluded that striving to be smart can be a cause of increasing the economic and demographic distance. Therefore, it may increase unbalance and generate exclusion in the analyzed areas.

1. Introduction

The Smart City (SC) concept fits perfectly into the progressive digitization of economies and societies [1,2], which makes it positively perceived and desired by both city decision-makers and other local stakeholders. Its implementation undoubtedly also contributes to improving the quality of life of residents [3,4,5]. Thanks to it, the provision of public services can become massive, faster and more effective. This is documented by numerous studies in the field of logistics or IT systems implemented in cities and their surroundings [6,7,8,9].

As a consequence, the literature on the subject is currently dominated by research and publications focused on the advantages and usefulness of Smart Cities. The authors also offer many modern technological solutions focused on the intensive development of smart urban infrastructure [10,11,12,13,14,15]. In addition, in the scientific and practical spheres, there are more and more approaches and methods to assess smart urban structures, including their maturity [16,17,18,19]. Therefore, city decision-makers feel strongly obliged to compete in terms of being smart. This, in turn, can lead to various distortions of the Smart City concept, such as disregarding real local needs in favor of implementing technological innovations that are not always needed, or developing only those SC areas that are assessed in international rankings.

The pursuit of technological innovations and improving the rating may, in turn, lead to neglecting the broadly understood social aspects of the development of Smart Cities. Such an approach then becomes a simple way to generate various types of exclusions, which not only worsen the image of the Smart City concept, but, above all, negatively affect the living conditions of participants in the urban community. The Smart City idea is not only a theoretical scientific entity, but a real model of public management of a modern city [20,21]. This model is currently implemented by most large cities around the world and at the current stage of its implementation is no longer possible.

Therefore, efforts should be made to improve the Smart City model and its sustainable implementation focused on preventing all kinds of exclusions. Work in this area should be preceded by a detailed inventory of possible types of discrimination and an assessment of the scale of their occurrence. Meanwhile, the literature on the Smart City (described in detail in the literature studies) lacks quantitative research that documents the scale of exclusion. Most of the considerations in this area are descriptive and polemical in nature. Moreover, few quantitative studies are conducted mainly in relation to specific social groups (seniors, women, national minorities or people with disabilities). Few publications discuss issues of exclusion from a regional or national perspective. This type of exclusion is important due to the need to ensure sustainable development in all geographical regions, not only those where Smart Cities are being created. Assessment of the scale of exclusion in the meso perspective is the basis for improving local and regional management aimed at practical improvement of the quality of life of the urban community.

For this reason, the aim of the research presented in this article is to conduct a comparative analysis of economic and social conditions in 17 Polish cities, three of which are recognized as Smart Cities in international rankings. By analyzing the economic and demographic conditions in the long term, an attempt is made to answer the question of whether Smart Cities offer better living conditions, and if so, how big is the imbalance and the risk of excluding other cities?

The research was carried out in the Polish economy due to two circumstances. The first results from the distance that separates developing economies from developed economies, which is visible and noticeable also at the level of development of intelligent urban structures. For this reason, research in such a context enables the geographical and economic sustainability of the Smart City concept. The second circumstance is pragmatic and concerns the availability of uniform statistical data on urban development. Additionally, including provincial cities in the sample allows for comparison of different regions of a large European country, which increases the possibility of generalizing the results to other developing economies, including smaller ones.

To achieve this goal, in the further part of the article, literature studies were conducted in the field of exclusion in Smart Cities. Then, the main research intentions were identified, and the selection of the sample, parameters and research methods were explained. The obtained results were presented taking into account the economic and demographic aspects, as well as the synthesis of the research carried out using a multi-criteria analysis. In the final part, a discussion was held in which a polemic with the previous considerations and practical implications for municipal public management were included. The whole considerations were closed with key conclusions and an indication of research limitations and directions for further research.

The research presented in the article fills the research gap in the Smart City concept in terms of the following:

• Practical diagnosis of the scale of economic and social exclusion in cities in the developing economy;
• Determining the economic and demographic distance between cities recognized as smart and cities not having such a status;
• Assessing the unsustainability of implementing the Smart City concept across the economy;
• Providing knowledge about the economic and demographic aspects of the development of Smart Cities;
• Formulation of practical implications oriented at preventing economic and demographic exclusions identified in the course of research.

2. Literature Overview

As already mentioned, the literature on the subject is dominated by research and considerations aimed at the systematic expansion of the Smart City concept and its implementation in cities around the world [22,23]. Critical and polemical remarks are on the margins of the research mainstream [24,25]. For this reason, the literature review referred to problematic threads, in particular those related to exclusion of various types. Threats and types of exclusion can be divided into two groups. The first one is related to the intensive development of modern technologies. The second concerns the issue of stakeholders and their involvement in the development of Smart City solutions. From this perspective, these issues are described in the next two sections.

Before starting the considerations, it is also worth adding that exclusion is understood in a general (uncontested) sense as the lack of equal access to the opportunities and benefits resulting from the implementation of Smart City solutions. This lack may concern individuals, social groups, but also cities, regions and countries. This creates a broad perspective for analyzing different aspects of exclusion and different reasons for exclusion (technical, social, economic, etc.). In this article, exclusion is examined from a national and regional perspective and concerns demographic and economic issues considered from the perspective of cities.

2.1. Technological Aspects of Exclusion in a Smart City

And so, one of the most serious accusations against Smart Cities is ignoring the opinion of the local community and forceful, sometimes even thoughtless, implementation of modern technologies without the consent, involvement or acceptance of residents. This leads not only to the waste of public funds, but also arouses reluctance to implement the SC concept in practice. Such a threat is highlighted in their research by Al Sharif and Pokharel (2022) [26] or Vidiasova and Cronemberger (2020) [27], who identify non-technical sources of risk generated by SC. Their opinion is also shared by Ahad et al. (2020) [28]. These researchers additionally state that the source of most of the risks is precisely the massive improvement of cities with the use of modern technologies.

Among such risks, there is undoubtedly a threat related to the protection of privacy of residents, widely discussed in the literature on the subject in connection with the use of Big Data and artificial intelligence or the Internet of Things in Smart Cities. In this context, residents are concerned about the possibility of using personal data for commercial purposes [29,30,31] or surveillance of citizens by local or national government, as described by, among others Dirsehan et al. (2022) [32], Amsellem (2021) [33] and Cabalquinto and Hutchins (2020) [34].

The above observations correspond to the results of empirical research conducted by Habib et al. (2020) [35], which show that the level of acceptance of modern technologies in cities is primarily influenced by the security of the services offered, the protection of residents’ privacy and the price determining the availability of these services to the urban community.

In addition to the threats of the loss of data and privacy, there are many other concerns of residents that, due to the lack of bottom-up qualitative research and the glorification of Smart City solutions, are not sufficiently well documented and articulated. As the in-depth analyses of Mutambik (2023) [36] show, the reluctance of the local community is also aroused by the prioritization of corporate interests and the dehumanization of urban space. This is not a negation of the idea of a Smart City, but an attempt to draw attention to its distortions.

Similar observations are made by Shah’s (2023) [37] studies, which draw attention to the need to take into account the opinions and interests of the local community when implementing modern information and communication technologies in cities. The author emphasizes that effective cooperation should be promoted in this respect and selfish business results should be avoided.

Nevertheless, as noted by Nguyen et al. (2021) [38], the inclusion of the local community in the Smart City decision-making process is not a simple task and, in practice, it is more complex than at the level of the scientific discourse on the importance of the quadruple helix.

Technological threats will certainly increase as IT and ICT technologies develop. Nevertheless, experts are able to develop and provide solutions to IT or infrastructure problems. It can therefore be concluded that there are many ways and methods of mitigating technological risk. Social threats, which are described in the next section, are much more difficult to control. Unfortunately, they receive significantly less attention, which may result in their systematic increase.

2.2. Social Aspects of Exclusion in a Smart City

The concept of a Smart City often forgets that the recipients of IT and technological solutions are residents. They should therefore decide on the directions of urban development and management. Their participation should guarantee equal access to all social groups. Nevertheless, in practice, such cooperation raises various problems described below.

While it is worth emphasizing, per Joss et al. (2017) [39], that the very idea of a Smart City has created a new standard of social participation, which is being talked about and written more and more. Residents gained new advocates in the form of promoters of a sustainable approach to SC management. Until the development of this concept, citizens were discussed much less often and in a very limited context.

Smart cities are also accused of fragmentary solutions. This problem is pointed out by Ehwi et al. (2023) [40]. Their research shows that SC projects are most often implemented based on general political guidelines, in response to the needs of entrepreneurs and public service providers. Collective demand is not analyzed and a holistic approach to Smart City management is not applied.

Osowska (2023) [41] rightly notes in this context, that it is difficult to talk about real changes in the city management model. In the practice of Polish cities, the SC concept fits in with the city’s strategy and vision, rather than with the reality of its residents. In addition, smaller cities, which cannot afford to implement image-related technological solutions, are forced to look for other ways to be “smart”, which, on the one hand, causes additional problems, and, on the other hand, can be a very creative and fruitful approach, closer to the local community.

The lack of a holistic approach to the challenges of Smart City projects is also emphasized by Gupta and Hall (2021) [42]. The authors note that the practical implementation of smart urban solutions is associated with many threats that overlap with each other. They can, therefore, also reinforce each other. This suggests the need for a comprehensive approach to risk management in such initiatives.

Thematic imbalance in the implementation of the Smart City concept is also noticed by Clement et al. (2023) [43] in the course of analyzing the strategies of 57 cities in the context of sustainable development goals. Their analysis shows that most cities only focus on 4 priorities: (1) clean and accessible energy; (2) economic growth and decent work; (3) innovation, industry and infrastructure; as well as (4) sustainable cities and communities. City decision-makers, on the other hand, very rarely refer to the following goals: (1) zero hunger, (2) gender equality and (3) life on land, which significantly affect the quality of life of residents.

Due to the high degree of technology in Smart Cities, one of the key risks is also the issue of excluding certain social groups from participating in the benefits offered by SC. And so, the research by Shayan and Kim (2023) [44] shows that the elderly and women are most at risk of social exclusion in Smart Cities. These groups may struggle with lack of access or difficulties in using modern urban solutions.

Wang et al. (2021) [45], in the course of research on projects that are a response to the challenges of Smart Cities in the USA, notice that very little attention is paid to the problems of seniors and people with disabilities. Such an approach threatens to exclude these groups from the local community. The authors, therefore, postulate the development of individual projects guaranteeing seniors and people with disabilities full participation in the benefits of the Smart City and the involvement of the government in creating guidelines in this regard for local decision makers.

Research by Mullick and Patnaik (2022) [46] shows that the intensity of external threats may intensify the risk of digital exclusion. The authors examine this phenomenon in the example of the COVID-19 pandemic. The results of their research suggest that the lack of digital competence of part of the local community has reduced the effectiveness of remedial and preventive actions and contributed to deepening the already existing digital divide. These observations illustrate the seriousness of the effects of exclusion, which does not exist only in the imagination of scientists.

According to Cao et al. (2023) [47], the implementation of the Smart City concept may have a negative impact on the labor market, as digitization and new technologies limit employment opportunities. This is particularly painful for people with low professional qualifications, which, in turn, may contribute to the pauperization of certain groups of the urban community. Therefore, according to the authors, a compromise should be sought between smart construction and the protection of urban human capital.

Exclusion from the SC concept can also be discussed in a geographical context. It is difficult to compare cities operating in highly developed economies with cities located in emerging or developing countries [48].

For example, Dixit and Shaw (2023) [49], describing the development of Smart Cities in Nepal, emphasize the low degree of urbanization of this country and the low level of advancement of the implementation of the Smart City concept. Nevertheless, even in such conditions, the greatest emphasis is placed on innovations and new technologies, disregarding social and humanistic aspects.

Similar observations apply to Hindu cities described by Kummitha and Crutzen (2019) [50]. In addition, the authors note that in order to develop local urban initiatives, it is necessary to create an appropriate legal framework and a system of incentives for the creation and implementation of smart urban solutions.

Development differences also arise at the regional and local level. They are also described by Hu et al. (2023) [51] on the example of Chinese cities. The results obtained by these researchers indicate a different degree of advancement and different methods of implementing the SC concept in individual units. At the same time, both large cities—as more advanced—and smaller ones primarily prefer technological innovations, which may intensify the dehumanization of smart urban structures.

The above review shows that social exclusion in the Smart City concept may concern various analytical levels. It may refer to specific social groups (women, seniors, people with disabilities). It may also have a subject context: economic, digital or social. In turn, in geographical terms, this exclusion may also result from differences in the development of Smart Cities in developed and emerging economies.

In this article, exclusion is examined at the meso level. The assessed cities represent the developing Polish economy, but the author’s attention is focused on regional and national analysis. In this way, it is assessed whether differences in the level of advancement of the implementation of the SC concept may deepen the economic and social exclusion of cities. Research at this level and in this context has not been conducted before. Moreover, the analyses also use a demographic perspective that is rarely included in research on SC.

Consolidating the above considerations, it can be also said that, despite the declaration of sustainability of the Smart City concept, it is still dominated by a technical approach, in which the attention of decision-makers and researchers is focused on the design and implementation of information and communication technologies. There is no focus on the social aspects of city life, including a detailed discussion of the risk of exclusion. For these reasons, this article considers issues related to the regional and national level of development of Smart Cities, as well as the economic and demographic consequences of such development. Analyses in this area have not been carried out so far, and the knowledge and empirical conclusions obtained thanks to them will make it possible to fill the research gap related to the assessment of the impact of smart urban solutions on the economy and demography of cities in regional and national terms.

3. Materials and Methods

The research methodology and materials were adapted to the scope of the analyses conducted. As already mentioned, the research covers two key threads—economic and demographic—and will be presented in this manner in the following sections. The economic and demographic indicators enabling comparison of the studied cities are described below, along with the method of their synthesis and the characteristics of the studied cities.

The research focused on the economic and demographic aspects of the functioning of cities because they are much less frequently described in the literature than technological conditions. Meanwhile, the former determine the scope of development of smart urban infrastructure, and the latter are a measurable expression of the attractiveness of the city for the local community. For these reasons, the following parameters were selected for detailed analysis:

• The amount of tax revenue per capita, identifying the level of general wealth and economic independence of the city (total annual revenue from taxes and local fees/number of residents):

$$T{R}_{pc}=\frac{total annual revenue from taxes }{number of residents}$$

(1)

• Unemployment rate, reflecting the attractiveness of the labor market and employment opportunities (number of unemployed/number of residents at working age):

$$U_r=\frac{number of unemployed}{number of residents at working age}[\%]$$

(2)

• Population density, illustrating the attractiveness of the city for the local community (number of residents/city area):

$$P_d=\frac{number of residents }{city area }\left[{\mathrm{people}\mathrm{per}\mathrm{km}}^2\right]$$

(3)

• Population, allows for determining the level of interest of residents in a given location and its changes over time
• The share of the working-age population, affecting the economic development opportunities (working age population/total population):

$$S_{wp}=\frac{working age population }{total population }[\%]$$

(4)

• The share of post-working age population, which allows for determining the extent of senior exclusion and the potential support required by this group of residents (number of post-working age population/total population):

$$S_{pwp}=\frac{number of post-working age population }{total population }[\%]$$

(5)

The above-mentioned indexes (apart from the amount of tax revenue per capita) were analyzed for the years 2003–2022, which allowed for looking at the economic and demographic situation of cities in the long-term perspective and was an additional value of cognitive research. In addition to the raw data, the average annual rate of change and the total change for all parameters studied were analyzed. Raw data was obtained from the Local Data Bank [52] and the Ministry of Finance [53].

As part of the synthesis of the results, a multi-criteria analysis was used. Multi-criteria analysis allows combining the analyzed criteria within a single objective function, which in this article is the ranking of socio-economic conditions in the studied cities. This is completed according to the following formula:

$$F_w={{\displaystyle \sum }}_{i=1}^m{w}_i\times f_i\left(x\right)$$

(6)

where:

$w_iϵ\left[0;1\right] and{\displaystyle \sum }_{i=1}^m{w}_i=1$—the weight of each criterion;

f_i(x)—functions describing each criterion.

As part of multi-criteria analysis, the analyzed variables are divided into stimulants, destimulants and nominants. In the process of variables normalization, the method of zero unitization was used, which is characterized by assuming a fixed point of reference, which is the stretch of a given normalized variable. The article assumes that each of the analyzed criteria has the same weight of 1.

The method used is clear and legible and, above all, allows for the comparison of various criteria and obtaining a total, easily interpretable assessment. It is also often used to compare Smart Cities [54,55,56]. In such comparisons, similarly to the article, several indicators are used, which are then aggregated into a one-dimensional assessment. This method is used to create rankings or assess maturity [57,58,59]. The criteria used in this article are not holistic and the assessment is not intended to resemble another city ranking [60]. The article uses a multi-criteria analysis to identify the scope of possible exclusion of cities from the concept of being smart in two important social aspects: economic and demographic. This approach distinguishes this research from others, including those cited above.

Figure 1 shows the research stages.

In the course of the analysis presented on Figure 1, answers to the following detailed research questions were sought:

• What is the level of economic unbalance between the examined cities and can it—now and in the future—become a source of exclusion at the local and individual level?
• Are cities recognized as smart perceived as more attractive to residents in the context of changes in demographic parameters and what consequences may this have for the development of urban space at the regional and national level?

As already mentioned, the research covered 17 largest Polish cities, 16 of which are the province capitals (Figure 2). In this way, the scope of the research covered the entire country and allowed for the identification of economic and demographic conditions for the development of cities in various geographical regions.

Among the examined cities, there are 3 that have, so far, been included in international rankings of Smart Cities. These are Warsaw, Wrocław and Kraków. These cities, formally recognized as smart, constituted a control group, against which other units were compared, while trying to answer the question posed in the title of the article: The exclusiveness of Smart Cities—myth or reality?

The cities identified in the research sample as smart were selected on the basis of two popular and recognizable indices:

• IESE Cities in Motion Index, which was developed by the Spanish Business School University of Navarra. It takes into account 101 indicators in the following categories: Human Capital, Social Cohesion, Economy, Governance, The Environment, Environment, Mobility and Transportation, Urban Planning, International Projection and Technology [61].
• Smart City Index, which was developed by the Swiss Business School in Lausanne (IMD) together with the Singapore University of Technology and Design. Cities are rated on a scale from A (best grade) to D, and the ranking includes two main categories: infrastructure and technology divided into health and safety, transport, work and study, and city management [62].

In the first ranking, the assessment is more complex and quantitative. The second ranking is based on qualitative data. Nevertheless, the assessed areas are similar and are based on a holistic view of Smart Cities. Warsaw and Wrocław were included in the IESE Cities in Motion Index. Kraków was included in the Smart City Index.

The above rankings were selected for research due to 4 circumstances:

• They are holistic and include technological, economic, environmental and social aspects.
• They are developed by scientists and therefore based on the knowledge and experience of experts.
• They are widely recognizable.
• They include Polish cities, which guaranteed the selection of the control group.

The methodology for assessing the scale of possible exclusions adopted in the article is simple, transparent and universal. Thanks to this, it can be used continuously to monitor risk in the studied cities. It can also be adapted for the needs of international analyses. Finally, it can be used as a tool supporting social risk management by city authorities.

The conditions for research replicability are given below:

3.

collecting statistical data (usually publicly available) on: (a) the city’s per capita tax revenues, (b) population density and (c) the number of inhabitants, as well as (d) the unemployment rate and (e) the share of people of working and post-working age in the urban population total;

4.

calculation of indicators regarding total changes and the annual rate of change over time for the above-mentioned variables;

5.

using multi-criteria analysis (quite simple in its essence) to create rankings and benchmarking of cities in space and time.

4. Results

The analysis of the results was carried out taking into account three threads. The first concerns economic aspects. The second covers demographic aspects. As part of the third, the presented results were synthesized in the form of a multi-criteria analysis, which also enabled the hierarchization of the surveyed cities.

4.1. Analysis of the Economic Conditions of the Functioning of Cities

As part of the detailed economic analysis of the examined cities, two indexes were taken into account: (1) the level of tax revenue reflecting the extent of wealth and financial independence, and (2) the unemployment rate describing the condition of the labor market and employment opportunities of residents. The results are presented in

Table 1. Tax revenue index: mean value [in PLN], average annual rate of change [in %] and total change in 2013–2022 in the surveyed cities.

Specification	Index Value	Average Annual Rate of Change	Total Change
Białystok	1587.40	5.39%	34.97%
Gdańsk	2348.80	6.29%	40.19%
Gorzów Wlk.	1656.90	5.70%	35.72%
Katowice	2477.90	5.23%	37.13%
Kielce	1730.40	4.61%	31.13%
Kraków	2242.30	6.18%	41.78%
Lublin	1804.70	5.52%	35.15%
Łódź	1838.60	5.48%	35.82%
Olsztyn	1780.90	5.72%	37.65%
Opole	2104.60	6.13%	39.66%
Poznań	2502.70	6.18%	39.95%
Rzeszów	1867.10	5.60%	36.08%
Szczecin	1726.40	5.14%	34.21%
Toruń	1790.60	5.12%	31.71%
Warsaw	3405.10	5.41%	40.81%
Wrocław	2336.90	6.73%	42.69%
Zielona Góra	2044.30	4.73%	24.58%
Poland	1681.30	5.39%	34.97%

—cities recognized as Smart Cities. Source: own compilation based on the Ministry of Finance data.

and

Table 2. Unemployment rate: mean value [in %], average annual rate of change [in %] and total change in 2003–2022 in the surveyed cities.

Specification	Index Value	Average Annual Rate of Change	Total Change
Białystok	6.10	−16.07%	−384.00%
Gdańsk	3.74	−14.74%	−320.00%
Gorzów Wlk.	5.02	−18.96%	−563.16%
Katowice	4.05	−14.76%	−321.05%
Kielce	7.87	−10.61%	−174.42%
Kraków	3.73	−9.27%	−140.00%
Lublin	6.39	−6.84%	−89.13%
Łódź	7.35	−11.88%	−212.20%
Olsztyn	4.58	−15.73%	−366.67%
Opole	5.13	−10.29%	−165.63%
Poznań	2.90	−16.71%	−418.18%
Rzeszów	6.70	−6.12%	−76.60%
Szczecin	5.28	−15.30%	−345.83%
Toruń	5.39	−13.04%	−251.72%
Warsaw	3.50	−12.02%	−216.67%
Wrocław	3.88	−17.15%	−443.75%
Zielona Góra	5.21	−16.29%	−395.45%
Poland	18.94	−1.92%	−19.02%

—cities recognized as Smart Cities. Source: own compilation based on the Ministry of Finance data.

, respectively.

Data on the tax revenue index (Table 1) indicate a large variation in the financial independence of the surveyed cities. The lowest level of this parameter was recorded in Białystok (PLN 1587.40) and the highest—more than twice as high—in Warsaw (PLN 3405.10). Apart from the capital of Poland, none of the surveyed cities achieved tax revenues per capita exceeding PLN 3000. The limit of PLN 2000 was exceeded by Gdańsk, Katowice, Kraków, Opole, Poznań, Wrocław and Zielona Góra. In 9 out of 17 surveyed cities, tax revenues were in the range of PLN 1500–2000, which significantly distances them from the communes at the top of the list and limits their spending possibilities, including the development of smart urban solutions. It can, therefore, be concluded that these cities are characterized by a lower potential in terms of creating a Smart City. Nevertheless, it should be noted that almost all surveyed cities had tax revenues higher than the national average of PLN 1681.30. The exceptions in this respect were Białystok and Gorzów Wielkopolski.

Cities recognized as Smart Cities (Warsaw, Kraków, Wrocław) very quickly improved their (already very good) financial situation, as evidenced by the highest level of changes in the tax revenue index in the analyzed period and the high average annual rate of change in this parameter. Wealth also grew rapidly in Gdańsk, Opole and Poznań. Unfortunately, the situation of the cities with the weakest financial condition improved the slowest (Białystok, Kielce, Toruń, Szczecin, Zielona Góra), which deepened the economic imbalance in the sample of provincial cities under study. The average annual growth rate of the tax revenue per capita index in these cities was lower than the average for Poland.

The second economic parameter defining the economic situation of the examined cities is the average unemployment rate calculated in the long-term perspective covering 2003–2022 and presented in Table 2.

The first important observation is that in all the surveyed cities the unemployment rate is definitely lower than the average for Poland, which illustrates a much better situation of urban labor markets. Moreover, in the cities throughout the analyzed period, a significant reduction in the number of unemployed was recorded, which allowed for finally stabilizing the unemployment rate at a very low, creeping level, not constituting a serious economic threat. The lowest level of unemployment was in Poznań, Gdańsk, Warsaw, Wrocław and Kraków. Cities recognized as Smart Cities are, therefore, the most attractive places to work, and finding employment in them is not a problem. The worst situation was in Łódź, Kielce, Lublin, Rzeszów and Białystok. It is worth noting, however, that both the average rate of change and the change in the total unemployment rate indicate a quick and significant improvement in the conditions of the labor market in these cities, which means that they keep up with the leaders of the ranking, and residents can also feel safe in these cities in terms of employment.

Summarizing the results of the economic analysis, it should be stated that, in terms of high tax revenue per capita and low unemployment rate in the study group, the cities recognized as Smart Cities, i.e., Warsaw, Wrocław and Kraków, stood out. Good results were also obtained by Poznań and Gdańsk, which can be considered as promising candidates for being smart.

In turn, the worst financial situation characterized less industrialized cities, located in agricultural areas, mainly in eastern Poland. These were: Białystok, Gorzów Wielkopolski, Łódź, Lublin and Rzeszów.

4.2. Analysis of the Demographic Conditions of the Functioning of Cities

In the second stage of the research, the focus was on demographic indexes reflecting the attractiveness of the city and its further development potential. In this respect, the following were analyzed: population density (

Table 3. Population density: mean value [in people/km²], average annual rate of change [in %] and total change in the years 2003–2022 in the surveyed cities.

Specification	Index Value	Average Annual Rate of Change	Total Change
Białystok	2221.43	−2.41%	−24.53%
Gdańsk	1771.68	0.43%	3.82%
Gorzów Wlk.	1442.83	−0.81%	−7.64%
Katowice	1843.27	−1.56%	−15.16%
Kielce	1822.31	−1.52%	−14.76%
Kraków	2346.87	0.65%	5.67%
Lublin	2340.68	−0.81%	−7.61%
Łódź	2447.77	−1.81%	−17.87%
Olsztyn	1966.06	−0.37%	−3.28%
Opole	1156.81	−4.94%	−57.81%
Poznań	2106.65	−0.68%	−6.30%
Rzeszów	1869.64	−7.09%	−93.83%
Szczecin	1349.41	−0.61%	−5.67%
Toruń	1760.48	−0.72%	−6.74%
Warsaw	3375.50	1.08%	9.21%
Wrocław	2188.84	0.62%	5.43%
Zielona Góra	1418.61	−14.43%	−306.75%
Poland	122.40	−0.12%	−1.08%

—cities recognized as Smart Cities. Source: own compilation based on the Ministry of Finance data.

), number of population (

Table 4. Population: average annual rate of change [in %] and total change in 2003–2022 in the surveyed cities.

Specification	Average Annual Rate of Change	Total Change
Białystok	−2.40%	−24.39%
Gdańsk	0.58%	5.09%
Gorzów Wlk.	−0.73%	−6.83%
Katowice	−1.54%	−15.03%
Kielce	−1.48%	−14.40%
Kraków	0.65%	5.65%
Lublin	−0.84%	−7.86%
Łódź	−1.84%	−18.24%
Olsztyn	−0.25%	−2.29%
Opole	−0.21%	−1.93%
Poznań	−0.64%	−5.96%
Rzeszów	2.35%	18.85%
Szczecin	−0.59%	−5.51%
Toruń	−0.77%	−7.16%
Warsaw	1.10%	9.35%
Wrocław	0.60%	5.26%
Zielona Góra	1.85%	15.19%
Poland	0.05%	0.45%

—cities recognized as Smart Cities. Source: own compilation based on the Ministry of Finance data.

) and the share of working age (

Table 5. Share of working age population: mean value [in %], average annual rate of change [in %] and total change in 2003–2022 in the surveyed cities.

Specification	Index Value	Average Annual Rate of Change	Total Change
Białystok	63.87	−0.99%	−9.41%
Gdańsk	62.67	−1.04%	−9.85%
Gorzów Wlk.	63.36	−1.67%	−16.35%
Katowice	62.51	−1.07%	−10.20%
Kielce	62.66	−1.88%	−18.62%
Kraków	63.53	−0.87%	−8.16%
Lublin	63.20	−1.54%	−14.95%
Łódź	61.56	−1.65%	−16.13%
Olsztyn	64.29	−1.59%	−15.56%
Opole	63.82	−1.67%	−16.32%
Poznań	63.41	−1.13%	−10.76%
Rzeszów	64.12	−1.30%	−12.50%
Szczecin	63.06	−1.44%	−13.92%
Toruń	63.73	−1.55%	−15.12%
Warsaw	61.99	−0.97%	−9.20%
Wrocław	63.66	−0.95%	−9.02%
Zielona Góra	63.16	−1.68%	−16.49%
Poland	62.51	−0.76%	−7.16%

—cities recognized as Smart Cities. Source: own compilation based on the Ministry of Finance data.

) and post-working age population (

Table 6. Share of the post-working age population: mean value [in %], average annual rate of change [in %] and total change in 2003–2022 in the surveyed cities.

Specification	Index Value	Average Annual Rate of Change	Total Change
Białystok	19.39	5.70%	39.26%
Gdańsk	20.71	3.79%	28.45%
Gorzów Wlk.	19.23	7.69%	48.66%
Katowice	22.29	4.87%	34.83%
Kielce	21.22	7.09%	46.01%
Kraków	20.45	3.25%	25.00%
Lublin	19.99	5.90%	40.32%
Łódź	23.98	4.51%	32.75%
Olsztyn	18.57	6.85%	44.90%
Opole	20.61	6.15%	41.57%
Poznań	20.76	4.27%	31.38%
Rzeszów	17.70	5.19%	36.57%
Szczecin	20.96	5.12%	36.19%
Toruń	19.34	6.75%	44.44%
Warsaw	21.87	1.55%	12.95%
Wrocław	20.96	3.02%	23.48%
Zielona Góra	19.71	5.76%	39.59%
Poland	18.94	−1.92%	−19.02%

—cities recognized as Smart Cities. Source: own compilation based on the Ministry of Finance data.

).

Population density is quite diverse and ranges from 1156 people per km² in Opole to 3375 people per km² in Warsaw. In almost all the surveyed cities—as in Poland—the population density is systematically decreasing. The exceptions in this regard are cities recognized as smart, i.e., Warsaw, Wrocław and Kraków, as well as Gdańsk, aspiring to obtain the smart status. The rate of population density decrease is the most intense in Białystok, Opole, Rzeszów and Zielona Góra.

The above observations correspond to changes in the total number of residents (Table 4). Smart cities—contrary to the nationwide trend and declining natural increase—are systematically populated. In other cities, the number of residents is decreasing, including the fastest rate in the economically least developed regions. Such a tendency clearly indicates the high attractiveness of Smart Cities; however, it may also become the cause of increasing the distance between Polish cities and provinces.

In the last research step, reference was made to the share of the working age population (Table 5) and the post-working age population (Table 6). It is worth mentioning that several negative demographic phenomena are currently overlapping and intensifying in Poland. Population growth is declining, and the population is aging faster and faster. This poses a threat to the development of the labor market and the social security system. It is, therefore, worth looking at these phenomena from the urban perspective.

Thus, Table 5 shows that in all the surveyed cities the share of the working age population was higher than the national average. Differences between individual cities are small and do not exceed 2%, which proves the strength and stability of nationwide tendencies. The highest percentage of professionally active people was in Olsztyn and Rzeszów. The lowest was in Łódź and Warsaw.

It is worth noting, however, that both the total change and the rate of decline in the share of working age population were the lowest in cities considered smart. These cities are, therefore, the least exposed to the lack of workers. On the other hand, the labor force was declining the fastest in Gorzów Wielkopolski, Opole and Zielona Góra—places that are economically less attractive.

The share of the post-working age population (Table 6) was higher than the national average in almost all the surveyed cities. The only exception in this regard was Olsztyn. Such a tendency means that now, and in the future, there will be more and more seniors in the examined cities, for whom it is also an attractive place to live.

The growth rate of the share of post-working age population was the highest in Gorzów Wielkopolski, Kielce, Olsztyn and Toruń, and the lowest in Kraków, Warsaw, Wrocław and Gdańsk. In this aspect, cities recognized as smart once again stood out.

4.3. Synthetic Assessment of Economic and Demographic Conditions

In the last stage of the research, the results were systematized using a multi-criteria analysis. The following conditions were taken into account in the assessment:

• Tax revenue index as an economic development stimulant conducive to the expansion of urban technical infrastructure;
• Unemployment rate as an economic destimulant reducing the attractiveness of the city and complicating its economic development;
• Average annual rate of population density as a demographic stimulant testifying to the attractiveness of the city for residents;
• The pace of changes in the share of the working age population as a demographic stimulant for the development of the city;
• The pace of changes in the share of the post-working age population as a destimulant causing employment difficulties.

The results of the analysis are included in

Table 7. Ratings of cities in individual areas in the multi-criteria analysis.

Specification	Tax Revenue Index	Unemployment Rate	Average Annual Rate of Change in Population	Share of Working Age Population	Share of the Post-Working Age Population
Białystok	0.00	0.36	0.77	0.88	0.32
Gdańsk	0.42	0.83	0.96	0.83	0.64
Gorzów Wlk.	0.04	0.57	0.88	0.21	0.00
Katowice	0.49	0.77	0.83	0.80	0.46
Kielce	0.08	0.00	0.83	0.00	0.10
Kraków	0.36	0.83	0.97	1.00	0.72
Lublin	0.12	0.30	0.88	0.34	0.29
Łódź	0.14	0.10	0.81	0.23	0.52
Olsztyn	0.11	0.66	0.91	0.29	0.14
Opole	0.28	0.55	0.61	0.21	0.25
Poznań	0.50	1.00	0.89	0.74	0.56
Rzeszów	0.15	0.24	0.47	0.57	0.41
Szczecin	0.08	0.52	0.89	0.44	0.42
Toruń	0.11	0.50	0.88	0.33	0.15
Warsaw	1.00	0.88	1.00	0.90	1.00
Wrocław	0.41	0.80	0.97	0.92	0.76
Zielona Góra	0.25	0.54	0.00	0.20	0.31

—cities recognized as Smart Cities. Source: own compilation based on the Ministry of Finance data.

and collectively presented in Figure 3.

Warsaw was ranked first with a rating close to the ideal value of 1.00. Kraków and Wrocław are slightly further down the ranking. These cities lost the most to the leader in the category of tax revenue per capita, although in this respect it is undoubtedly very difficult to compete with the capital of the country. A rating exceeding 0.7 was also given to Gdańsk and Poznań, which allows us to conclude that these cities are strong contenders for the smart title. In both of these cities, the lower score is due to the lower level of tax revenues and a fairly high share of post-working age population, which is not necessarily a drawback. A skillful approach to shaping the quality of life of seniors may become a distinguishing feature for these cities. It will also contribute to their better balance.

The worst scores in the ranking were given to Zielona Góra and Kielce. These are cities located in agricultural regions of Poland. Kielce has the lowest level of tax income per capita and the lowest share of post-working age population in the surveyed group. It also shows a high level of average unemployment. On the other hand, in Zielona Góra, the number of residents decreases the fastest. The city also has a relatively low level of tax revenues and a low share of the working age population.

It is also worth noting that most of the cities in the surveyed group (except Katowice) are far from the cities recognized as smart. The identified difference will be difficult to eliminate due to the observed development trends. It can, therefore, be assumed that the “smart” will become increasingly “smarter” and the scope of urban imbalance will increase.

5. Discussion

The article covers two thematic axes: economic and demographic. These are important and often underestimated aspects of Smart City development. Without their analysis and strengthening, the Smart City concept will not be sustainable because it will focus on technological and civilizational development. Bearing in mind that the Smart City concept serves to improve the quality of life, when selecting individual indicators, economic and social well-being were taken into account, which is ensured by city income, a well-functioning labor market and sustainable demographic development of the local community.

The obtained conclusions indicate, above all, the attractiveness of Smart Cities for current and potential residents. These cities are in a definitely more favorable economic situation than units without the opinion (status) of smart. The better economic condition of Smart Cities most often results from greater industrialization and, consequently, greater attractiveness for entrepreneurs and investors. This, in turn, results in better employment conditions and more qualified human resources. The above-mentioned elements reinforce and drive each other, which consistently and cyclically increases the distance between Smart Cities and “non-smart” ones.

Despite the negative demographic trends at the national level, Smart Cities manage to increase the number of residents, which means that the local community sees the real benefits of being smart. It can, therefore, be concluded that the quality of life in Smart Cities is better, which is largely due to their very good economic situation, including a better developed labor market. This confirms the conceptual assumptions of the SC idea described by Ligarski and Owczarek (2023) [3]; Addas (2023) [4]; Chang and Smith (2023) [5]; Wolniak and Jonek-Kowalska (2022) [6].

Unfortunately, the above observations also prove the exclusiveness of Smart Cities and the fragmentation of implementation of Smart City solutions, which confirms the conclusions obtained by Ehwi et al. (2023) [40]; Osowska (2023) [41] or Gupta and Hall (2021) [42]. At the same time, this exclusiveness is present both at the level of the cities themselves and at the level of the country, where there is no coherent strategy for the development of smart urban structures available to each individual.

However, the authors’ findings also highlight a significant issue with the implementation of Smart City solutions. The research results did not confirm the negative impact of the implementation of smart urban solutions on the labor market previously described by Cao et al. (2023) [47]. In Polish Smart Cities, the unemployment rate was lower than in the other surveyed units. It seems, therefore, that the labor market in the analyzed period was more intensively influenced by the general economic situation in Poland and the reduction of the labor force related to the aging of the population. The observed contradiction results from the systematic improvement of employment conditions on the Polish labor market throughout the country in the period analyzed in the article. This means that national economic conditions and general development trends dominate the local and regional situation. It is worth adding, however, that in Smart Cities the situation on the labor market is changing faster and more intensively than in other units.

The results of the authors’ research also provide new knowledge about changes in the structure of the population of working and post-working age in Polish cities. The analyses carried out show that a characteristic feature of all Polish cities is a systematic decrease in the share of people in working age in favor of an increase in the share of people in post-working age. This means a growing number of seniors, i.e., a group at risk of digital and social exclusion, and in developing economies also economic exclusion [44,45]. The aging of urban communities is a challenge for city authorities due to the lack of a systematic and effective approach to managing diversity in Smart Cities, which is highlighted in the literature.

Meanwhile, ignoring a minority at risk of exclusion may have catastrophic consequences for their living conditions in the face of growing external threats, as described by Mullick and Patnaik (2022) [46] on the example of the Covid-19 pandemic. This is important, because Poland is currently affected by the effects of the armed conflict between Russia and Ukraine and is located in the immediate geographical vicinity of this conflict.

Another interesting conclusion relates to the regional level of unsustainability of Smart Cities. The conducted analyses also show that Smart Cities are more and more distant from “non-smart” cities, which in practice are less economically developed and less wealthy. It is a source of regional and national exclusion and instability. It may lead to systematic pauperization of smaller cities and intensification of urbanization processes in Smart Cities. This phenomenon has already been partially described and identified by Hu et al. (2023) [51], as well as Jonek-Kowalska and Wolniak (2021) [48].

Taking into account the above conclusions, the article fills the research gap regarding the scale of economic and demographic exclusion related to the implementation of the Smart City concept on a regional basis. Summarizing, the greatest threats are related to the depopulation of non-smart cities and the uncontrolled growth of Smart Cities. Another problem is the intensive increase in the economic prosperity of Smart Cities and the impoverishment of non-smart cities. These are key and real dimensions of the observed exclusion.

The above observations allow the formulation of the following recommendations to reduce the economic and social exclusiveness of the cities studied:

• Diagnosing the economic and social level of development of cities in terms of leveling economic and social disproportions;
• Developing a national framework for the development of Smart Cities aimed at the balanced development of urban structures;
• Inclusion in city strategies of activities preventing social exclusion and issues related to managing diversity;
• Creating a system of incentives to share knowledge and experience in the field of designing and implementing smart urban structures;
• Identifying the needs of cities at risk of economic exclusion and the expectations of local communities living in them.

The above recommendations constitute an important and difficult challenge due to several circumstances. Firstly, they require in-depth research and diagnostic analyses, as well as the interest of city authorities in dealing with complex issues. Secondly, their implementation is determined by many external factors that are difficult to influence and change in the short term (for example, the population’s fertility rate or retirement age). Thirdly, reducing the distance between the studied cities requires some cities to give up being the best at all costs, and other cities to implement the strategy of being smart more intensively. This is a problem of the mentality of the city authorities and a matter of active support from the state authorities.

6. Conclusions

On the basis of the tests performed, the following conclusions were drawn:

• The economic situation of the 17 analyzed cities is very diverse; the highest and fastest growing tax revenues per capita are characteristic of cities recognized as Smart Cities (Warsaw, Wrocław, Kraków); at the end of the list there are cities located in less economically developed regions of Poland, such as Białystok, Gorzów Wielkopolski, Kielce or Olsztyn;
• Unemployment in the analyzed period was systematically decreasing in all the examined cities; however, the lowest unemployment rate was characteristic of cities recognized as Smart Cities (Warsaw, Wrocław, Kraków); the highest level of unemployment was in most of the cities where tax revenues were low, namely in Białystok, Gorzów Wielkopolski, Łódź, Lublin, Rzeszów (however, the situation in these cities improved dramatically, which proves that the economic distance to the leaders was effectively reduced);
• Among the cities that economically follow the leaders, Poznań and Gdańsk—promising contenders for being smart—stand out;
• The number and density of population in almost all surveyed cities—as in the whole of Poland—are systematically decreasing; the exceptions in this respect are Warsaw, Wrocław, Kraków and Gdańsk, where the population is increasing, which confirms the attractiveness of smart urban structures;
• The share of the working age population in all cities is systematically decreasing; however, the rate of decline and the overall change are the smallest in cities recognized as smart, which at least partly protects the labor market from the lack of human resources;
• The share of post-working age population in all cities is systematically growing (which indicates the growing importance of the senior group of stakeholders), but the rate of this increase is the lowest in cities recognized as smart.

Moreover, it follows from the collective comparison of all the surveyed provincial cities that the best economic and demographic conditions apply to cities recognized as smart. They are matched by Gdańsk and Poznań, which can be considered strong contenders for being smart. Unfortunately, the remaining cities are far from the leaders of the ranking, which may expose them to economic and social exclusion, all the more so that the parameters examined in them are characterized by negative tendencies. It can therefore, be concluded that striving to be smart can be a cause of increasing the economic and demographic distance. Therefore, it may increase unbalance and generate exclusion.

Cities recognized as smart (Warsaw, Wrocław, Kraków) have been distinguished in two international and recognizable SC rankings (IESE Cities in Motion Index; Smart City Index). They are therefore Poland’s global showcase. It is difficult to blame them for creating exclusion. For this reason, they can also be treated as leaders in the development of urban space and best practices. The transfer of knowledge and experience and cooperation of these cities with entities aspiring to be smart could also constitute important support in the process of achieving regional sustainability.

The main limitation of the presented considerations is the narrowing of the research area to Polish cities. Nevertheless, both the presented methodology and the obtained results can be used to assess economic and financial conditions also in other economies, including, above all, emerging and developing ones. In addition, the analysis uses simple indexes and not very complex research methods, which do not necessarily follow current research trends. However, such an approach makes it possible to understand and repeat research in the economic sphere, which is particularly important in the case of public management, because it provides practical knowledge that can be used by city decision-makers and stakeholders.

Directions for further research may be related to the comparative analysis of the economic and demographic situation of cities in Poland and abroad. They may also include an assessment of the effects of social and economic exclusion from the perspective of the local community. They should also include the identification of actual and potential ways to prevent exclusion in Smart Cities.