Measuring and Assessing the Level of Living Conditions and Quality of Life in Smart Sustainable Cities in Poland—Framework for Evaluation Based on MCDM Methods

Abstract

The increasing degree of urbanization of the world community is creating several multidimensional challenges for modern cities in terms of the need to provide adequate living and working conditions for their residents. An opportunity to ensure optimal conditions and quality of life are smart sustainable cities, which integrate various resources for their sustainable development using modern and smart technological solutions. This paper addresses these issues by presenting the results of a study of the level and quality of living conditions in the 29 largest cities in Poland, an EU member state. This study used 35 indicators characterizing the six main areas of activity of the cities to assess the living conditions and quality of life in these cities. To achieve this purpose, an original research methodology was developed, in which the EDAS and WASPAS methods and the Laplace criterion were applied. The application of a multi-criteria approach to the issue under study made it possible to determine the levels of quality of life and living conditions in the studied cities for each dimension, as well as the final index of this assessment (Smart Sustainable Cities Assessment Scores). On this basis, a ranking of these cities was made. In addition, relationships between living conditions and quality of life and the levels of wealth and population of the cities were also assessed. The results showed a wide variation in the levels of living conditions and quality of life in the cities studied, as well as their independence from geographic location. Cities with higher GDP levels that were investing in innovation and knowledge-based development fared much better.

1. Introduction

Today, more than 56% of the world’s population, or about 4.4 billion people, live in cities [1], which, with the current state and structure of the world economy, are also becoming multidimensional centers responsible for the development of individual countries. In many countries, cities have become centers that determine their development and position in the competitive global market. The United Nations estimates that by 2030, about 60% of the world’s population will live in urban areas, which will be the result of population growth, on one hand, and migration from rural areas, on the other [2]. In Poland, this level has already been reached, as urban residents now account for more than 60% of the entire country’s population [3]. The increase in the population living in urban areas poses several challenges for city authorities, especially in terms of their sustainable development, referring mainly to infrastructure and the environment [4,5]. Indeed, the concept of sustainable development, which is now being implemented to an increasing extent, must also extend to cities, which are becoming huge economic, intellectual, cultural, and social centers. The implementation of the concept of sustainable development in cities should affect the quality of life (QoL) of residents and the efficiency of the cities themselves [6].

The reasons for the dynamic growth of urban residents (urbanization process) are easier access to work, education, and culture; better social and living conditions; and greater opportunities for civilization development. Living in cities, especially large ones, has also for years been synonymous with better social status, convenience, and easy access to the scientific, cultural, and economic achievements of the modern world [7,8]. However, it should be noted that the rapid growth of the urban population and the concentration of economic activity in cities or in their immediate vicinity also generates huge infrastructural, social, often political, cultural, and environmental problems. These problems relate to transportation, education, the integration of different communities, providing access to work, counteracting social inequality, and what is now becoming one of the most significant problems of cities, namely, their impact on the environment [9].

These problems are faced by most cities around the world, although their distribution sometimes varies, even within cities in a given country. This is a result of their location, history, culture, level of social and economic development, public awareness, education, economic potential, and many other factors, including attitudes toward the environment [10].

One trend that is increasingly guiding the development of cities and influencing the QoL and living conditions of residents is the concept of the smart sustainable city (SSC) [11]. This concept implies a high degree of innovation in cities, which increasingly use information and communication technologies (ICT) and other solutions resulting from technological transformation to improve the QoL and the efficiency of operations and urban services, as well as competitiveness, while ensuring that the needs of current and future generations are met with regard to economic, social, environmental, and cultural aspects [12]. Thus, it can be assumed that the SSC concept combines the achievements and elements of the Industry 4.0 idea and the sustainable economy in order to create very convenient and unobtrusive living and working conditions for the environment and residents in modern cities.

This issue is very well understood in the European Union (EU), where it has become one of the priorities of its current and future development. Such an approach coincides with the UN Sustainable Development Goals contained in Agenda 2030 and, in particular, with Goal 11, which concerns sustainable cities and communities. Its goal is to provide cities and human settlements with security, stability, sustainable livelihoods, and favorable conditions for social development [13].

Improving the quality and conditions of life in cities using smart solutions to ensure their sustainability is one of the basic elements of EU policy [14]. The aspiration of cities to be smart and sustainable has been supported for years. This is reflected in many European documents, as well as those of individual countries. This issue is also the subject of research interest, since living conditions in cities translate into the QoL in individual countries, becoming determinants of the overall quality level of this life.

Publications on the subject of smart sustainable cities are dominated primarily by studies of a theoretical and/or polemical nature, which deal, among other things, with combining various aspects in the modeling of sustainable and smart cities and approaches to evaluating these cities [15], including, in particular, the development of guidelines for measuring smart sustainable cities [16,17], smart sustainable urban development, and the effects of combining the issues of smartness and sustainability [18,19,20,21,22,23].

A significant portion of published research work on smart and sustainable cities also focuses on technological solutions and proposals to influence sustainable and smart city development. An analysis of the literature indicates that works are published relatively rarely that contain the results of empirical studies on the actual evaluation of cities in terms of living conditions and QoL in smart sustainable cities. On the other hand, if such results are already published, they usually concern large metropolises located in the so-called “old EU-14” countries [24], Asia [25,26], or the United States [27].

Thus, there is a conspicuous lack of such studies on smaller cities, whose importance for the overall development of the smart sustainable cities concept is also very important. Also, cities of the so-called “new EU-13” countries are omitted, too, and their importance for the EU as a whole is becoming increasingly important. Therefore, considering the importance of the subject of the living conditions and QoL in cities throughout the EU, it becomes reasonable to conduct research and analysis for cities located in developing countries, to which little attention has been paid in the existing literature. It can be assumed that this is related to the lower familiarity, popularity, and attractiveness of cities located in the countries of the “new EU-13” compared to large European metropolises. Therefore, it becomes fully justified to conduct a study of selected cities in Poland to assess them in terms of the achieved level of life quality and living conditions in smart sustainable cities. This research should also show how the concept is implemented in cities in Poland, a large EU country, economically counted among the so-called emerging markets. This research should also impact the presentation and cognition of Polish cities, which, in the future, may be taken into account to a greater extent in international smart and/or sustainable rankings. So far, these rankings include only a handful of cities located in developing countries, which creates an impression of exclusivity reserved only for cities located in highly developed economies and gives a false sense that only large metropolises can be smart and sustainable cities. In addition, it makes sense that the assessment of sustainability and smart growth should include as many cities as possible. Studying and comparing the QoL in these cities will identify the leaders of the changes being made, and this should also mobilize the remaining cities to perform more effectively in this regard. The results should therefore be used for planning and evaluating the effectiveness of temporary measures taken regarding sustainable development for the authorities of individual regions and cities. The developed ranking of cities should be treated as their evaluation, from which the authorities of individual cities should draw conclusions regarding the ongoing processes of globalization and urbanization and their impact on the development of cities [28]. The results of such an assessment also provide opportunities for individual cities to evaluate their actions in the areas (dimensions) studied and compare them with the results of other cities. Such analyses also make it possible to identify areas that need improvement. This is necessary because it will not be possible to achieve the goals of sustainable development in the EU without achieving them in individual countries and cities of those countries.

With regard to the above circumstances, this paper formulates the following research questions:

RQ1: 

What is the general level of living conditions and life quality in cities in Poland from the point of view of their sustainable and smart development?

RQ2: 

What is the level of living conditions and life quality in the various partial dimensions studied (economy, life standard and safety, environment and energy, infrastructure, health, and governance)?

RQ3: 

Is there a relationship between the living conditions and life quality (as measured by the Smart Sustainable Cities Assessment Score—SSCAS) and the level of wealth and number of inhabitants of these cities?

To obtain an answer to the above questions, this article analyzes the 29 largest Polish cities (including 16 provincial capitals) that implement the concept of sustainable and smart development. The assessment of the living conditions and QoL in these cities was based on a set of 35 indicators that characterize the following six dimensions: economy, education and innovation; life standard, culture and safety; health; environment and energy; infrastructure, transport and accessibility; and governance. Since the research problem under consideration is multi-criterion in nature, an approach based on the EDAS and WASPAS methods from the MCDM methods group was used for the evaluation, and the Laplace criterion (for decision-making under uncertainty) was applied. The values of the weights of the indicators adopted for the study were determined based on the CRITIC, standard deviation, and equal weight methods.

The adopted research concept, the developed methodology, and the applied methods made it possible to point out the following elements proving the originality of the conducted research:

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To develop a new research methodology including the use of MCDM methods and decision-making criterion under uncertainty to determine the ranking and level of cities in terms of living conditions and life quality in smart sustainable cities;

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To fill in the research gap in assessing the living conditions and life quality in smart sustainable cities in Poland, a country classified as an emerging market;

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To apply a holistic approach to the assessment of the QoL in smart sustainable cities covering all its important dimensions from the point of view of inhabitants;

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To determine a relationship between the determined standard of living (from the point of view of smart sustainable cities) in the surveyed cities and their wealth and number of inhabitants;

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To make conclusions and recommendations for the formation of policies for the development of smart and sustainable cities in turbulent geopolitical and socioeconomic conditions.

The research conducted and the results obtained should expand the knowledge in the field of research on the living conditions and QoL in smart sustainable cities. At the same time, they can be taken as a basis for further research on this highly topical and important matter. A comparison of the degree of implementation of the concept of sustainable development in EU cities should become a standard, giving the opportunity to monitor their current state and develop effective strategies for this development using smart solutions. Solidarity between countries and cities should also be of great importance, as well as the universally applied principle of good practice, enabling mutual assistance in this challenging process of building smart and sustainable cities. Especially in the case of the EU, which is definitely becoming a world leader in promoting the idea of smart and sustainable development, such actions should become common practice.

2. Literature Studies

The literature review presented here covers selected publications relating to the development of the concepts of smart cities and smart sustainable cities, as well as methods for studying the quality and conditions of life in smart sustainable cities.

2.1. The Concept of Smart and Sustainable Urban Development

As mentioned in the Introduction, today, more than 56% of the world’s population resides in urban areas, which means that many existential, social, economic, political, cultural, and environmental problems accumulate in these areas. Their interdisciplinarity and complexity make it necessary to use the latest technological and organizational solutions to solve them, considering their impact on the environment. Residents have increasing demands on the quality and conditions of urban life, the shaping of urban space, and the socio-economic tasks carried out by cities. Therefore, the process of building smart and sustainable cities should be looked at very broadly, taking into account the diverse opinions of all stakeholders using urban space [29].

Environmental issues are particularly important in this process. It seems that they are now the basis for building modern cities. In turn, their effective implementation must be supported by modern, innovative, and intelligent solutions, improving urban management and the condition, quality, and accessibility of technical infrastructure, especially information and communication technology (ICT) solutions. Without these solutions, the functioning of cities can be very difficult. It is also important that cities focused on sustainable development do not become slaves to new technologies but try to implement them in a balanced and thoughtful manner, adequate for their capabilities [30].

Sustainable urban development can therefore be understood as striving to achieve a state of balance between the development of these areas and the protection of the environment, taking into account economic aspects (income equality), education, employment issues, and security, as well as providing access to basic services, social infrastructure, and transportation [31]. A sustainable city is also one that responds to people’s needs by designing sustainable solutions to mitigate social and economic inequalities [32,33,34]. A fundamental aspect of the whole concept of sustainability is a holistic and multidimensional approach to its implementation. In order to help this process, smart technological solutions and management competencies adequate for the changes being made should be considered.

The smart city concept itself became an area of interest in the late 1990s, when the phrase began to be used to label the development of urban regions in terms of innovation, globalization, and new technologies [35]. However, the very origins of the smart city concept can be traced back to the 1960s [36], and the dynamic development of the concept took place after 2010 due to its support from the EU [37]. In general, the concept, supported by the European Commission, concerns the use of various communication and information technologies to achieve sustainable development in smart cities [38].

The literature on smart cities emphasizes the role of the use of ICT and modern technologies as the key to achieving smart city status [17,39,40], both of which, at the same time, are tools to enable a better QoL for residents and care for the environment [41]. These publications also point out that in addition to ICT, human capital is extremely important in the development of cities aspiring to be smart, as it contributes to economic, social, and environmental sustainability [9]. Thus, it can be assumed that smart cities use the latest technological advances in administration, management, and social activities for the development of a modern economy and mobility and environmental protection, as well as for building a modern and informed society [17].

The concept of smart sustainable cities emerged as a result of the tenuous link between smart and sustainable cities, despite the confirmed role of ICT in supporting cities in their transition to sustainability (especially with regard to the management and planning of urban systems). Nowadays, the concept is gaining increasing importance, being recognized and perceived worldwide as an opportunity for further urban development. The basis for this development is to be modern technologies, especially ICT solutions and increased social and environmental awareness [16,19,42,43].

The term “smart sustainable cities” is therefore a combination of the concepts of “smart cities” and “sustainable cities”, combining various aspects of sustainable development with the classic paradigm of “smart cities” [44,45,46]. At the core of the smart sustainable cities concept is the utilization of the potential and increasingly widespread use of modern ICT in building sustainable urban areas that are environmentally friendly and enable socio-economic development for their residents.

According to the definition proposed by the International Telecommunication Union Focus Group on smart sustainable cities (ITU-T FG-SSC), “A SSC is an innovative city that uses information and communications technology (ICT) and other means of improving the QoL to achieve efficiency in urban operations and services and competitiveness, while ensuring that the economic, social, environmental and cultural needs of current and future generations are met” [47,48]. This definition identifies very well the functions and tasks that a sustainable smart city must fulfill during the current socio-economic transition and ongoing climate policies.

The concept of smart sustainable cities also considers the innovative solutions being developed for sustainable development and relating to the issue of urban design and planning and the synergistic development of various related areas [19,42,49]. This is because it is obvious that for the already mentioned multifaceted nature of this issue, both planning and monitoring the effects of actions taken are fundamental to achieving long-term sustainable development goals.

In these processes, ICTs should be considered tools to solve a number of complex problems and challenges facing cities in order to achieve sustainable development. Mitchell [50] and GeSI [51] found that smart cities can help reduce energy consumption and reduce greenhouse gas and air pollution emissions in cities. In turn, Zanella et al. [52] indicate that smart cities can support the more efficient use of public resources, improving the QoL and services provided while reducing government operating costs. Smart cities also help improve transportation, thereby reducing traffic congestion [53,54,55].

Smart cities can also influence advances in energy production, mobility and transportation, and ICT development, as well as link and create interdisciplinary solutions that improve service processes while reducing energy and greenhouse gas emissions. This broad approach to the problems of the existence of modern cities makes the smart solutions used in them also part of their sustainable development. This creates favorable conditions for improving, in many aspects, the QoL of their current and future residents [56,57,58].

The cited selected studies indicate how extensive and multifaceted, while dynamically developing, the issue of intelligent and sustainable development of cities is. Therefore, it is fully justified to refer to this topic in the context of actual activities undertaken by individual cities. After all, the extensively developed and discussed theory must find its application in practice. Since the EU is now becoming a leader in the implementation of the concept of sustainable development, with Poland being one of the members, it becomes fully justified to conduct research regarding the effectiveness of the implementation of this concept in Polish cities.

2.2. Study of Life Quality and Living Conditions in Smart Sustainable Cities

Issues related to the study of quality and living conditions in smart and sustainable cities are increasingly being addressed by researchers. Since the importance of cities to the economy and the lives of citizens is increasingly important, it is reasonable to conduct research regarding this topic. Various approaches are used to conduct such research, but in most cases, indicator methods are used, providing an opportunity to study various factors affecting this quality. The issue is so complex that it is a multi-criteria problem, the study of which, depending on the approach, utilizes from several to almost 200 indicators [45,59,60]. These indicators characterize the various dimensions of urban life and are selected depending on the purpose of the research.

Meanwhile, the most popular dimensions for assessing QoL include economy, standard of life, governance, infrastructure, mobility, and environment.

In addition to the selection of indicators, a key consideration in assessing living conditions and QoL is the method and methodology of analysis. One of the basic methodological decisions is the question of a subjective or objective account of the quality and conditions of life. Most often, variables of a quantitative nature (objective variables) are used for this assessment, which are then used in analyses conducted by various methods. Giffinger and Gudrun [61] point out that the evaluation of cities in terms of conditions and quality of life in smart and sustainable cities should include such dimensions as economy, people, governance, mobility, environment, and life.

Different research approaches with different methods are used for essential indicator-based ranking studies of life quality in smart sustainable cities. For example, Akande et al. [24] used the PCA method and Hierarchical Clustering. Also, Praharaj et al. [62] used the PCA method to study living conditions in smart sustainable cities. On the other hand, Phillis et al. [25] used the SAFE model. Ozkaya and Erdin [11], on the other hand, used ANP and TOPSIS methods from the MCDM methods group to assess the QoL in smart sustainable cities. Lazaroiu and Roscia [63], in turn, developed a synthetic index to determine a “smart sustainable city index”. Praharaj and Han [64] developed a system for the typology and classification of cities using taxonomic methods based on key performance indicators (KPIs).

Among the best-known ranking studies that assess smart sustainable cities in terms of urban QoL are the Sustainable Cities Index [65], the United Nations’ (UN) City Prosperity Index [66], the Quality of Life Index by City [67], the European Smart Cities [68], the European Green City Index [69], the Cities in Motion Index [70], the City Card Index [28], the Cities of Opportunity index [71], The Global Liveability Index [72], the Global Power City Index [73], the Mercer Quality of Living [74], and the Sustainable Assessment by Fuzzy Evaluation (SAFE) index [75].

Thus, the problem of studying and assessing the quality and conditions of life in smart sustainable cities is complex and requires an appropriate choice of method and indicators for its analysis. At the same time, it is apparent that there is little interest in conducting such an assessment for cities in developing countries, such as Poland. In general, studies of EU cities focus mainly on large agglomerations in the so-called “old EU-14.” The lack of research on smaller cities, especially in countries of the so-called “new EU-13”, justifies taking up the subject of research and filling the research gap.

Therefore, considering the results of the literature review and our own experience, it was assumed that the MCDM methodology and selected indicators characterizing the six main dimensions of smart sustainable cities’ quality would be used to study living conditions in Polish cities. This provides an opportunity for a broad assessment of the surveyed cities, determining their ranking and comparison with other surveyed cities.

3. Materials and Methods

This section presents the research area and the source of data for analysis. It also discusses the indicators adopted for the study and the research methodology developed, along with the methods used to analyze the data.

3.1. Study Area

The research on assessing the QoL in smart sustainable cities was carried out for the 29 largest Polish cities in terms of population (Figure 1), 16 of which serve as provincial cities (regional capitals). The characteristics of the surveyed cities are shown in

Table 1. Characteristics of surveyed cities (own elaboration based on [76]).

City	Inhabitants	Surface, km2	Population Density, Persons/km2
Wrocław	642,687	292.8	2194.8
Bydgoszcz	339,053	176	1926.7
Toruń	196,935	115.7	1701.8
Lublin	336,339	147.5	2280.7
Gorzów Wielkopolski	120,087	85.7	1400.9
Zielona Góra	140,403	278.3	504.6
Łódź	664,071	293.3	2264.5
Kraków	782,137	326.9	2393
Radom	206,946	111.8	1851
Warsaw	1,795,569	517.2	3417.4
Opole	127,077	148.9	853.6
Rzeszów	198,609	129	1539.5
Białystok	295,683	102.1	2895.2
Gdańsk	470,621	262	1796.5
Gdynia	243,918	135.1	1804.9
Bielsko-Biała	168,319	124.5	1351.9
Bytom	161,139	69.4	2320.6
Częstochowa	214,342	159.7	1342.1
Gliwice	175,102	133.9	1307.9
Katowice	286,960	164.6	1743
Ruda Śląska	135,008	77.7	1736.9
Rybnik	135,994	148.4	916.6
Sosnowiec	194,818	91.1	2139.4
Tychy	125,781	81.8	1537.5
Zabrze	168,319	80.4	2101.3
Kielce	191,448	109.7	1746
Olsztyn	169,319	88.3	1922.3
Poznań	529,410	261.9	2021.3
Szczecin	395,513	300.6	1315.7
Total	9,611,607	5014.3	-

. For this study, cities with a population of over 120,000 inhabitants were selected. The surveyed cities are home to more than 9.5 million residents out of a population of 38 million, which is about 25% of Poland’s total population [76].

The selection of these cities for the study is grounded in their roles within Poland’s urban landscape. Warsaw serves as a metropolis, while Kraków, Poznań, Łódź, and Wrocław are cities aspiring to attain such status. Additionally, they represent large- and medium-sized regional centers in the country. Their selection is further justified by their status as the largest cities in Poland, serving as local administrative, scientific, and cultural hubs. These cities were chosen based on their functions, aspirations, and declarations to implement the concept of smart sustainable cities. As such, they are actively addressing challenges related to infrastructure improvement, transportation, environmental pollution reduction, and enhancing residents’ living conditions in alignment with the smart cities concept. Furthermore, these urban centers represent the most densely populated areas within their respective regions.

These cities are the most densely populated areas in the regions where they are located.

3.2. Data

The research on assessing the QoL in cities was conducted based on a set of 35 indicators (

Table 2. Summary of indicators used for the study (own elaboration).

Dimension	Indicator	Designation	Direction of Impact
Economy, education, and innovation	Gross domestic product per capita, PLN	E1	Stimulant
	Urban income per capita, PLN	E2	Stimulant
	Average gross monthly salaries, PLN	E3	Stimulant
	Percentage of population with higher education, %	E4	Stimulant
	Percentage of population with primary and incomplete primary education, %	E5	Destimulant
	Innovative enterprises, %	E6	Stimulant
Life standard, safety, and culture	Average floor area of housing per person, m2	L1	Stimulant
	Number of apartments per 1000 city residents	L2	Stimulant
	Apartments equipped with central heating, %	L3	Stimulant
	Apartments equipped with bathroom and toilet, %	L4	Stimulant
	Number of crimes per 1000 residents	L5	Destimulant
	Traffic accidents per 10 thousand residents	L6	Destimulant
	Public libraries per 10 thousand residents	L7	Stimulant
	Number of residents per 1 cinema seat	L8	Stimulant
	Museums per 10 thousand residents	L9	Stimulant
Health	General hospital beds per 1000 residents	H1	Stimulant
	Doctors per 10 thousand residents	H2	Stimulant
	Nurses and midwives per 10,000 residents	H3	Stimulant
Environment and energy	Index of average exposure to PM2.5 dust	EE1	Destimulant
	Mass of municipal waste generated per capita, kg	EE2	Destimulant
	Municipal wastewater treated per capita, dam3	EE3	Destimulant
	Water consumption from waterworks per capita, m3	EE4	Destimulant
	Total energy consumption per capita, kWh	EE5	Destimulant
	Share of parks, greens and neighbourhood green areas in total area, %	EE6	Stimulant
Infrastructure, transport, andaccessibility	Population with access to and use of the Internet, %	I1	Stimulant
	Roads for bicycles per 100 km2 of the city, km	I2	Stimulant
	Length of bus lanes, km	I3	Stimulant
	Number of hybrid and electric passenger cars, % of all passenger cars	I4	Stimulant
	Number of parking lots in the Park & Ride system (Park & Ride)	I5	Stimulant
Government	Funds from the state budget or other transferred as co-financing of programs and projects implemented with the participation of EU structural and cohesion funds, per capita PLN	G1	Stimulant
	Unemployment rate, %	G2	Destimulant
	Poverty rate, %	G3	Destimulant
	Local development plans—draft plans in total	G5	Stimulant
	Detection rate of perpetrators of stated crimes, %	G6	Stimulant

), characterizing this quality in the following six dimensions (areas): economy, education, and innovation; life standard and safety; health; environment and energy; infrastructure; and governance. To perform the analysis, data from the Local Data Bank [76] were used.

The economy, education, and innovation area determines the standard of living of residents and maintains harmony in society. The smart and sustainable economy of cities consists of their economic strength and an educated society, which determines the ability to create innovation. Innovative companies operating in these cities, in turn, are the driving force behind their development by, among other things, offering attractive jobs, paying taxes, and creating GDP. In Poland, classified among the group of developing countries, the development of cities plays a pivotal role in enhancing competitiveness and attracting investment while fostering job creation. A crucial factor influencing this development is the level of education among residents. Highly educated individuals not only establish innovative enterprises but also significantly contribute to the formation of local and national knowledge-based economies.

Life standard and safety is an area that is also very important for building smart sustainable cities. This is because human resources are crucial in this process, for which a smart city should provide a certain standard of living, enable cultural development, and provide a sense of security [77]. The standard of living in this case reflects the living conditions and determines the attractiveness of a place. Culture and education are, of course, related to this area, necessary for economic, cultural, and social development [78]. In turn, a sense of security is one of the basic needs of human beings, enabling them to function normally on a daily basis. Within the context of Polish cities, the dimension of life standards and safety holds significant importance. This dimension encompasses various factors related to housing, notably housing conditions, which play a crucial role in the well-being of the population. Meeting housing needs not only enhances the sense of security and stability but also serves as a determining factor in decisions regarding family planning, particularly the decision to have children. Moreover, access to public services, both economically and geographically, is closely intertwined with housing considerations. Ensuring adequate access to public services is essential for maintaining a high quality of life and fostering sustainable urban development.

The next area of study, i.e., health, covers the general conditions of health care delivery for urban residents. In smart sustainable cities, the provision of adequate health care conditions is fundamental to the life of society, as, due to their characteristics, they often suffer negative health effects caused, for example, by inadequate air quality, housing and transportation conditions, and poor sanitation or waste management, which often translates into the health of residents [79]. For Poland, as for many other countries, the health of the population as an area of assessment is extremely important due to the problem of an aging population and limited access to proper health care. This problem is exacerbated by the insufficient number of medical personnel and hospital beds, and consequently their availability.

Environment and energy, on the other hand, is an area that is essential to human life and is linked to other dimensions. Concern for the state of the environment is now becoming a key responsibility of conscious residents. The whole idea of sustainable development is based on concern for the living conditions of future generations and their social development. Thus, measures taken to protect the environment in cities include rational water consumption, waste reduction, or electricity conservation, among other activities. Such measures represent each resident’s individual contribution to sustainable development. Polish cities often face a number of environmental problems, such as the generation of excessive amounts of waste, poor air quality, or low levels of energy efficiency of urban installations. Therefore, indicators relating to this dimension should be taken into account when evaluating these cities.

Infrastructure is an area that plays an important role in the area of economic and social life and the urban economy in general. Adequate infrastructure is essential to ensure good and inclusive economic, social, and environmental conditions and to improve the QoL through convenient access to the wider sphere of services. Good infrastructure also facilitates opportunities for the development of education and science and promotes the economic well-being of residents. For Polish cities aspiring to be smart sustainable cities, road infrastructure development plays a key role. Due to high air pollution, the development of alternative means of transportation and dedicated infrastructure (e.g., the development of bicycle paths) is important.

Another area contained in the analysis includes processes related to urban governance (government). It is clear that competent and resident-friendly city authorities have a very important influence on the building and development of smart sustainable cities. These authorities should be the initiators and coordinators of changes and solutions beneficial to such development. They should direct and support social initiatives related to building a sustainable and smart urban fabric.

Thus, it can be concluded that the dimensions (areas) adopted for this study characterize the most important issues related to urban life. The indicators that characterize them, in turn, reflect the needs of the various stakeholder groups of the urban fabric, mainly its residents. That is why the diversity of the needs of the various groups formed the basis for the selection and choice of these indicators. The concept of “life quality and living conditions” is understood very differently by individual urban citizens. Workers, teachers, students, retirees, and entrepreneurs have different expectations. Therefore, when selecting indicators, an effort was made to take into account the needs of these groups by relating them to the assumptions of the smart sustainable cities concept, also considering the results of the literature analysis.

A summary of the 35 indicators adopted for the study, broken down by the dimensions they characterize, as well as their designations and the direction of their impact, is presented in Table 2.

The selection of indicators (evaluation criteria) for the study was also determined by the following conditions:

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Their relevance to the objectives of national, regional and urban policies for smart and sustainable urban development;

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The simplicity in their construction and description of the issues studied;

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Their clarity and acceptability of normative interpretation;

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Their reliance on reliable data sources;

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The timeliness, comparability, and availability of data.

Of particular importance is the availability of data, which, in terms of regular collection and release, regarding the level, quality, and conditions of life in Poland are not very extensive.

3.3. Framework of Evaluation Based on MCDM Methods

An evaluation framework was developed to assess the conditions and QoL in the surveyed cities using the MCDM methods and the Laplace criterion for decision-making under uncertainty. The MCDM methods used in the study are EDAS and WASPAS. The EDAS method belongs to the distance-based group of methods, while the WASPAS method is the utility-based approach. In turn, the CRITIC, equal weight method, and standard deviation method were used to determine the weights of the indicators used in the study.

The methods used in the study differ in terms of the algorithm on the basis of which an evaluation index is determined to rank alternatives. The choice of a multi-criteria method depends on various factors such as the nature of the problem under study and the objectives of the research, as well as the preferences and experience of the researcher. MCDM methods, for the same research problem and the same value of index weights, may give different results. The following question then arises: which of these methods is most suitable for solving a specific research problem? The adoption of a suitable method for multi-criteria analysis often becomes a subject of discussion about the correctness and validity of such a choice. The choice of such a method also becomes a multi-criteria problem. To minimize the discussion on this issue, this research proposes using the Laplace criterion to solve this problem.

The research methodology developed using the MCDM and Laplace criterion methods includes the following steps:

(1)

Determine criteria for assessing the conditions and QoL in the studied cities;

(2)

Select methods for evaluating the QoL in smart sustainable cities and methods for determining the weights of the evaluation criteria (indicators);

(3)

Carry out calculations and determine the weights of the criteria (indicators) for each evaluated dimension using the selected methods;

(4)

Apply the Laplace criterion to determine the weights of the evaluation criteria (indicators) used for the study;

(5)

Perform calculations and determine the evaluation indices of each dimension characterizing the QoL in smart sustainable cities for the MCDM methods used;

(6)

Perform the zero unitarization of the evaluation indices obtained for the methods used:

If a higher value of the evaluation index is better in a given MCDM-type method (stimulant), then the following score is used:

$$Assesment Score=\frac{x_{ij}-{minx}_{ij}}{{maxx}_{ij}-{minx}_{ij}}$$

(1)

If a lower value of the evaluation index is better in a given MCDM-type method (destimulant), then the following score is used:

$$Assesment Score=\frac{{{max x}_{ij}-x}_{ij}}{{maxx}_{ij}-{minx}_{ij}}$$

(2)

(7)

Apply the Laplace criterion to determine the final smart sustainable cities assessment index of conditions and QoL in each dimension of the studied cities;

(8)

Determine the level of smart sustainable cities’ living conditions and QoL in the assessed areas in the studied cities according to the algorithm:

Class 1 Very high level

$${AS}_{SSC}\ge \overline{{AS}_{SSC}}+s_{{AS}_{SSC}}$$

(3)

Class 2 High level

$$\overline{{AS}_{SSC}}+s_{{AS}_{SSC}}>{AS}_{SSC}\ge \overline{{AS}_{SSC}}$$

(4)

Class 3 Average level

$$\overline{{AS}_{SSC}}>{AS}_{SSC}\ge \overline{{AS}_{SSC}}-s_{{AS}_{SSC}}$$

(5)

Class 4 Low level

$${AS}_{SSC}<\overline{{AS}_{SSC}}-s_{{AS}_{SSC}}$$

(6)

where ${AS}_{SSC}$ is the assessment index obtained from the MCDM methods and using the Laplace criterion, $\overline{{AS}_{SSC}}$ is the mean value of the ${AS}_{SSC}$ assessment index, and $s_{{AS}_{SSC}}$ is the standard deviation from the mean value of the ${AS}_{SSC}$ assessment index.

(9)

Determine Smart Sustainable Cities Assessment Scores (SSCAS) index values for the surveyed cities:

$$\begin{array}{cc}\hfill \mathit{S}\mathit{m}\mathit{a}\mathit{r}\mathit{t}\mathit{S}\mathit{u}\mathit{s}\mathit{t}\mathit{a}\mathit{i}\mathit{n}\mathit{a}\mathit{b}\mathit{l}\mathit{e}\mathit{C}\mathit{i}\mathit{t}\mathit{i}\mathit{e}\mathit{s}& \mathit{A}\mathit{s}\mathit{s}\mathit{e}\mathit{s}\mathit{m}\mathit{e}\mathit{n}\mathit{t}\mathit{S}\mathit{c}\mathit{o}\mathit{r}\mathit{e}\mathit{s}\hfill \\ & =Economy, Education and Innovation Assessment Scores\hfill \\ & +Life standard and safety Assessment Scores\hfill \\ & +Health Assessment Scores\hfill \\ & +Environment and Energy Assessment Scores\hfill \\ & +Infrastructure Assessment Scores\hfill \\ & +Government Assessment Scores\hfill \end{array}$$

(7)

(10)

Determine the level of living conditions and QoL in the assessed cities (Equations (3)–(6))

The general scheme of the research procedure is shown in Figure 2, while the methodology for using MCDM methods and the Laplace criterion is shown in Figure 3.

3.3.1. The Evaluation Based on Distance from Average Solution (EDAS)

The EDAS method uses an average solution to evaluate alternatives. Positive distance average (PDA) and negative distance average (NDA) are two separate measures used to evaluate alternatives. The best alternative is determined based on these two distances, with the alternative with higher PDA values and, at the same time, lower PDA values being the best [80,81,82].

The algorithm for proceeding with the EDAS method includes the following steps:

(1)

Select alternatives and criteria and create the decision matrix:

$$AV={\left[{AV}_j\right]}_{1\times m}$$

(8)

$${AV}_j=\frac{\sum _{i=1}^n{x}_{ij}}{n}$$

(9)

(2)

Determine positive distance from the average (PDA) and negative distance from the mean average (NDA):

$$PDA={\left[{PDA}_{ij}\right]}_{n\times m}$$

(10)

$$NDA={\left[{NDA}_{ij}\right]}_{n\times m}$$

(11)

where for stimulants, the following equations are used:

$${PDA}_{ij}=\frac{max\left(0,\left(x_{ij}-{AV}_j\right)\right)}{{AV}_j}$$

(12)

$${NDA}_{ij}=\frac{max\left(0,\left({AV}_j-x_{ij}\right)\right)}{{AV}_j}$$

(13)

and for destimulants, the following equations are used:

$${PDA}_{ij}=\frac{max\left(0,\left({AV}_j-x_{ij}\right)\right)}{{AV}_j}$$

(14)

$${NDA}_{ij}=\frac{max\left(0,\left(x_{ij}-{AV}_j\right)\right)}{{AV}_j}$$

(15)

(3)

Calculate the weighted sum of PDA and the weighted sum of NDA for all alternatives:

$${SP}_i=\sum _{j=1}^m{w}_{j}PD{A}_{ij}$$

(16)

$${SN}_i=\sum _{j=1}^m{w}_{j}ND{A}_{ij}$$

(17)

where w_j is the weight of the jth criterion.

(4)

Normalize SP and SN values for all alternatives:

$$NS{P}_i=\frac{S{P}_i}{{max}_i\left({SP}_i\right)}$$

(18)

$$NS{N}_i=1-\frac{S{N}_i}{{max}_i\left({SN}_i\right)}$$

(19)

(5)

Determine the Appraisal Score (AS_i) for all alternatives:

$${AS}_i=\frac{1}{2}\left({NSP}_i+{NSN}_i\right), 0\le {AS}_i\ge 1$$

(20)

(6)

Rank the alternatives (cities) based on the AS_i value in the descending direction.

For the EDAS method, the alternative with the largest value of the AS_i index is the best.

3.3.2. The Weighted Aggregates Sum Product Assessment (WASPAS) Method

The Weighted Aggregates Sum Product Assessment (WASPAS) method makes it possible to determine the evaluation of the decision problem under consideration using weighting, summation, and multiplication. This method is a combination of two methods: the Weighted Sum Model (WSM) and the Weighted Product Model (WPM) [83,84]. The algorithm for proceeding with this method is as follows:

(1)

Create a decision matrix:

$$X_{ij}=\left[\begin{array}{ccc}{X}_{11}& \cdots & X_{1n}\\ ⋮& \ddots & ⋮\\ X_{n1}& \cdots & X_{nn}\end{array}\right] for i=1, 2\dots ., m and j=1, 2\dots ., n$$

(21)

(2)

Create a normalized decision matrix:

where for stimulants, the following equation is used:

$$x_{ij}^{*}=\frac{x_{ij}}{\underset{i}{\max }{x}_{ij}}, i=1,\dots , m, j=1,\dots ,n$$

(22)

and for destimulants, the following equation is used:

$$x_{ij}^{*}=\frac{\underset{i}{\min }{x}_{ij}}{x_{ij}}, i=1,\dots , m, j=1,\dots ,n$$

(23)

(3)

Calculate the total relative importance of the ith alternative (WSM approach):

$$Q_i^{\left(1\right)}=\sum _{j=1}^n{x}_{ij}^{*}{w}_j, i=1,\dots , m$$

(24)

(4)

Calculate the total relative importance of the ith alternative (WPM approach):

$$Q_i^{\left(2\right)}={\prod }_{j=1}^n{\left(x_{ij}^{*}\right)}^{w_j}, i=1,\dots , m$$

(25)

(5)

Determine the generalized evaluation criterion (Q) using the weighted total evaluation method:

$$Q_i=⋋\sum _{j=1}^n{x}_{ij}^{*}{w}_j+\left(1-⋋\right)\sum _{j=1}^n{\left(x_{ij}^{*}\right)}^{w_j}, ⋋=0,\dots ..1.$$

(26)

where ⋋ has a value of 0.5.

3.3.3. The Criteria Importance through Intercriteria Correlation (CRITIC) Method

One of the three methods used to determine the weights of evaluation criteria (indicators) is the CRITIC (Criteria Importance Through Intercriteria Correlation) method. This is a method in which correlations between evaluation criteria are determined, and its analytical approach allows the use of all information included in the evaluated criteria. The CRITIC method makes it possible to determine the values of the criteria weights in an objective manner, taking into account the intensity of contrast and conflict contained in the decision problem [85,86,87]. The stages of determining the weights of the evaluation criteria are as follows:

(1)

Create a decision matrix;

(2)

Create a normalized decision matrix:

where for stimulants, the following equation is used:

$$\begin{gathered} X_{ij}^{*}=\frac{X_{ij}-min\left(X_{ij,}i=\mathrm{1,2},\dots .,m\right)}{max\left(X_{ij, }i=\mathrm{1,2},\dots ,m\right)-min\left(X_{ij, }i=\mathrm{1,2},\dots ,m\right)} \\ for i=1, 2\dots ., m and j=1, 2\dots ., n \end{gathered}$$

(27)

and for destimulants, the following equation is used:

$$\begin{gathered} X_{ij}^{*}=\frac{max\left(X_{ij,}i=\mathrm{1,2},\dots .,m\right)-X_{ij}}{max\left(X_{ij, }i=\mathrm{1,2},\dots ,m\right)-min\left(X_{ij, }i=\mathrm{1,2},\dots ,m\right)} \\ for i=1, 2\dots ., m and j=1, 2\dots ., n \end{gathered}$$

(28)

(3)

Determine standard deviation (SD) for the criteria in the normalized decision matrix:

$${SD}_j=\sqrt{\frac{\sum _{i=1}^n\left(x_i-\overline{x}\right)}{n-1}}$$

(29)

(4)

Determine correlations (r_jk) between evaluation criteria in the normalized decision matrix:

$$r_{jk}=\frac{\sum _{i=1}^n\left(x_{ij}-{\overline{x}}_j\right)\left(x_{ik}-{\overline{x}}_k\right)}{\sqrt{\sum _{i=1}^n{\left(x_{ij}-{\overline{x}}_j\right)}^2\sum _{i=1}^n{\left(x_{ik}-{\overline{x}}_k\right)}^2}}$$

(30)

(5)

Determine the weights of the evaluation criteria:

$$w_j=\frac{C_j}{\sum _{i=1}^n{C}_j}{;where: C}_j={SD}_j{\sum }_{i=1}^n\left(1-r_{jk}\right)$$

(31)

where C_j is the amount of information contained in the jth criterion

3.3.4. The Standard Deviation Method

The Standard Deviation method uses a mathematical approach that describes a measure of the variability in the values of the indicators (variables) used in the study. This method is very similar to the Entropy method in that both assign smaller weights to an attribute with similar values of different alternatives [88]. The difference is that the standard deviation analyzes the values of the data, considering another aspect that ensures that the results may vary according to their values [89]. The procedure includes the following steps:

(1)

Create a decision matrix (Equation (21));

(2)

Normalize the decision matrix (Equation (27), Equation (28));

(3)

Determine the standard deviation:

$$SD=\sqrt{\frac{\sum _{i=1}^m{\left(X_{ij}-\overline{X_{ij}}\right)}^2}{m}};i\in \left\{\mathrm{1,2},\dots ,m\right\}, j\in \left(\mathrm{1,2},\dots ,n\right)$$

(32)

(4)

Determine indicator weights:

$$w_j=\frac{{SD}_j}{\sum _{j=1}^n{SD}_j}$$

(33)

3.3.5. The Mean Weighting Method

This method, used to determine the weights of indicators, provides equal values of these weights for each evaluation indicator [89]. This weighting technique is considered the simplest method for determining weights. Its use is recommended in cases where all criteria are equally important to the decision maker, and there is no statistical or empirical evidence to suggest a different strategy:

$$w_j=\frac{1}{\mathrm{t}\mathrm{h}\mathrm{e} \mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{e}\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{c}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}}$$

(34)

4. Results

4.1. Assessing Living Conditions and Life Quality in Polish Cities in the Context of the Smart Sustainable Cities Concept

To answer the first two research questions (RQ1 and RQ2), analyses were conducted to assess the level of quality of life and living conditions in the 29 cities studied.

Based on the research procedure outlined in Section 3, the first step involved determining the values of the weights of the indicators adopted for each dimension (area) characterizing quality and living conditions in these cities (Figure 4).

The results (Figure 4) indicate that, depending on the method used, the same indicator can be characterized by a different weight value. In several cases, the differences are significant (e.g., for indicators E2—Urban income per capita, PLN, and E5—Innovative enterprises, %; L1—Average floor area of housing per person, m2, L5—Number of crimes per 1000 residents, L6—Road fatalities per 100,000 population, and L7—Public libraries per 10,000 population; or G4—Local land use plans). Therefore, the final value of the weights of each indicator for the analyzed dimensions of life quality and living conditions in cities was determined based on the Laplace criterion. According to this criterion, the sum of the values of the indicators’ weights for each dimension was equal to 1.

In the next stage of the research, based on the adopted indicators and their weight values, the values of the Assessment Score for life quality and living conditions in all analyzed assessment dimensions were determined. The Assessment Score for each dimension was determined based on the indices obtained from the EDAS and WASPAS methods after applying zero unitization and based on the Laplace criterion.

Table 3. Assessment indices for economy, education, and innovation obtained from the EDAS and WASPAS methods and assessment index values after applying the Laplace criterion.

	EDAS		WASPAS		EDAS	WASPAS	Evaluation Index Determined Based on the Laplace Criterion	Final Rank
	Assessment Score	Rank	Assessment Score	Rank	Unified Evaluation Value	Unified Evaluation Value
Wrocław	0.714	3	0.840	4	0.714	0.633	0.673	3
Bydgoszcz	0.266	20	0.629	22	0.266	0.151	0.208	21
Toruń	0.287	18	0.635	21	0.287	0.163	0.225	19
Lublin	0.449	10	0.714	10	0.449	0.345	0.397	10
Gorzów Wielkopolski	0.000	29	0.563	29	0.000	0.000	0.000	29
Zielona Góra	0.225	21	0.627	24	0.225	0.145	0.185	23
Łódź	0.389	15	0.670	17	0.389	0.245	0.317	15
Kraków	0.734	2	0.852	2	0.734	0.660	0.697	2
Radom	0.201	22	0.642	20	0.201	0.181	0.191	22
Warszawa	1.000	1	1.000	1	1.000	1.000	1.000	1
Opole	0.422	12	0.709	11	0.422	0.333	0.378	11
Rzeszów	0.524	9	0.764	7	0.524	0.460	0.492	8
Białystok	0.404	14	0.697	12	0.404	0.307	0.356	13
Gdańsk	0.662	5	0.801	5	0.662	0.545	0.603	5
Gdynia	0.609	6	0.771	6	0.609	0.475	0.542	6
Bielsko-Biała	0.417	13	0.694	13	0.417	0.298	0.358	12
Bytom	0.017	28	0.582	28	0.017	0.042	0.029	28
Częstochowa	0.286	19	0.653	19	0.286	0.206	0.246	18
Gliwice	0.524	8	0.724	9	0.524	0.367	0.446	9
Katowice	0.591	7	0.748	8	0.591	0.424	0.507	7
Ruda Śląska	0.035	26	0.594	26	0.035	0.071	0.053	26
Rybnik	0.122	25	0.614	25	0.122	0.117	0.120	25
Sosnowiec	0.181	24	0.628	23	0.181	0.148	0.165	24
Tychy	0.360	16	0.675	16	0.360	0.255	0.307	16
Zabrze	0.025	27	0.588	27	0.025	0.056	0.040	27
Kielce	0.425	11	0.681	15	0.425	0.271	0.348	14
Olsztyn	0.197	23	0.659	18	0.197	0.220	0.209	20
Poznań	0.669	4	0.841	3	0.669	0.635	0.652	4
Szczecin	0.321	17	0.687	14	0.321	0.284	0.302	17

shows, as an example, the detailed calculation results for the economy, education, and industry dimension (calculation results for the other dimensions are included in Appendix A).

The calculations carried out and the designated ranking positions for the economy, education, and industry area by the EDAS and WASPAS methods, for several of the surveyed cities, show noticeable discrepancies. The largest, in terms of the ranking position obtained for the evaluated cities, is 5 (for the city of Olsztyn). Only 13 of the 29 assessed cities obtained the same ranking position in both methods. The Spearman correlation coefficient (

Table 4. Spearman correlation coefficients between the positions of cities obtained in the rankings for the EDAS and WASPAS methods and after applying the Laplace criterion.

	EDAS	WASPAS	Laplace Criterion
EDAS	1.000	0.971	0.992
WASPAS	0.971	1.000	0.987
Laplace criterion	0.992	0.987	1.000

Notes: Values in red indicate statistically significant results.

) between the ranking positions obtained by the EDAS and WASPAS methods is 0.971. The application of the Laplace criterion, which made it possible to unify the ranking positions on the basis of the MCDM methods, improved the convergence of the results. The value of the correlation coefficient between the rankings obtained with the Laplace criterion and the EDAS method was 0.992, and for the WASPAS method, the value was 0.987 (Table 4). The occurrence of differences in the positions in the rankings obtained depending on the method used confirms the validity of the Laplace criterion.

When analyzing the results, the best situation in the analyzed area is in Warsaw, and the worst is in Gorzow Wielkopolski. A very good one is also found in Krakow, Wroclaw, Poznan, and Gdańsk. The aforementioned cities are among the largest in Poland and are perceived as very attractive places to conduct business, which translates into high GDP per capita values, as well as high incomes and average monthly salaries, on a national scale (not only against the assessed cities). They are also major academic centers, attracting students who often stay in these cities after completing their education and obtaining a college degree. This provides them with high percentages of people with higher education and low percentages with only primary education. Among other things, the level of education translates into a high level of innovation in enterprises operating in these cities.

Apart from Gorzów Wielkopolski, a difficult situation is also found in Zabrze, Bytom, and Ruda Śląska. These are cities which are characterized by a difficult economic situation, low income, a high percentage of people with primary and incomplete primary education, and a low number of people with higher education.

Appendix A (

Table A1. Assessment indices for life standard and safety obtained from EDAS and WASPAS methods and assessment index values after applying Laplace criterion.

	EDAS		WASPAS		EDAS	WASPAS	Evaluation Index Determined Based on the Laplace Criterion	Final Rank
	Assessment Score	Rank	Assessment Score	Rank	Unified Evaluation Value	Unified Evaluation Value
Wrocław	0.363	23	1.080	3	0.368	0.797	0.583	10
Bydgoszcz	0.217	26	0.919	18	0.137	0.361	0.249	25
Toruń	0.509	13	1.155	1	0.600	1.000	0.800	1
Lublin	0.648	6	0.949	13	0.821	0.444	0.633	8
Gorzów Wielkopolski	0.194	27	0.829	24	0.101	0.119	0.110	29
Zielona Góra	0.610	7	0.791	28	0.761	0.016	0.388	20
Łódź	0.558	9	0.804	26	0.678	0.051	0.365	21
Kraków	0.760	1	0.998	9	1.000	0.575	0.787	2
Radom	0.525	11	0.937	15	0.626	0.409	0.518	14
Warszawa	0.657	5	1.026	6	0.836	0.650	0.743	4
Opole	0.579	8	0.896	20	0.713	0.301	0.507	16
Rzeszów	0.483	16	0.970	11	0.559	0.499	0.529	13
Białystok	0.486	15	1.072	4	0.565	0.774	0.669	7
Gdańsk	0.669	4	1.050	5	0.855	0.715	0.785	3
Gdynia	0.710	3	0.807	25	0.921	0.060	0.490	17
Bielsko-Biała	0.421	20	1.013	7	0.461	0.616	0.538	12
Bytom	0.145	28	0.884	21	0.022	0.267	0.144	27
Częstochowa	0.379	22	0.853	22	0.394	0.182	0.288	23
Gliwice	0.526	10	0.935	16	0.628	0.405	0.516	15
Katowice	0.131	29	0.991	10	0.000	0.557	0.279	24
Ruda Śląska	0.263	25	0.797	27	0.211	0.031	0.121	28
Rybnik	0.428	19	0.902	19	0.472	0.315	0.394	18
Sosnowiec	0.340	24	0.785	29	0.332	0.000	0.166	26
Tychy	0.755	2	0.942	14	0.991	0.422	0.707	5
Zabrze	0.381	21	0.929	17	0.397	0.389	0.393	19
Kielce	0.502	14	1.002	8	0.590	0.585	0.588	9
Olsztyn	0.515	12	0.962	12	0.611	0.478	0.545	11
Poznań	0.482	17	1.087	2	0.557	0.816	0.687	6
Szczecin	0.43	18	0.849	23	0.472	0.172	0.322	22

,

Table A2. Assessment indices for health obtained from EDAS and WASPAS methods and assessment index values after applying Laplace criterion.

	EDAS		WASPAS		EDAS	WASPAS	Evaluation Index Determined Based on the Laplace Criterion	Final Rank
	Assessment Score	Rank	Assessment Score	Rank	Unified Evaluation Value	Unified Evaluation Value
Wrocław	0.555	13	0.577	13	0.555	0.436	0.495	13
Bydgoszcz	0.618	10	0.637	10	0.618	0.519	0.568	10
Toruń	0.196	25	0.362	25	0.196	0.144	0.170	21
Lublin	0.918	2	0.920	1	0.918	0.902	0.910	2
Gorzów Wielkopolski	0.177	26	0.350	26	0.177	0.128	0.152	22
Zielona Góra	0.263	5	0.398	11	0.263	0.194	0.228	20
Łódź	0.623	11	0.640	9	0.623	0.522	0.572	9
Kraków	0.609	16	0.626	14	0.609	0.503	0.556	12
Radom	0.294	1	0.412	4	0.294	0.212	0.253	19
Warszawa	0.632	17	0.647	17	0.632	0.531	0.582	8
Opole	0.644	4	0.661	5	0.644	0.551	0.597	6
Rzeszów	0.787	3	0.798	2	0.787	0.737	0.762	3
Białystok	0.633	6	0.650	6	0.633	0.536	0.585	7
Gdańsk	0.381	23	0.469	21	0.381	0.289	0.335	16
Gdynia	0.128	24	0.313	23	0.128	0.078	0.103	27
Bielsko-Biała	0.351	19	0.444	19	0.351	0.256	0.303	17
Bytom	0.158	21	0.342	22	0.158	0.117	0.137	23
Częstochowa	0.332	20	0.433	20	0.332	0.241	0.287	18
Gliwice	0.143	18	0.333	18	0.143	0.105	0.124	24
Katowice	1.000	7	0.992	3	1.000	1.000	1.000	1
Ruda Śląska	0.011	28	0.259	28	0.011	0.004	0.007	28
Rybnik	0.124	22	0.318	24	0.124	0.085	0.104	26
Sosnowiec	0.127	29	0.324	29	0.127	0.093	0.110	25
Tychy	0.000	27	0.256	27	0.000	0.000	0.000	29
Zabrze	0.392	14	0.466	15	0.392	0.286	0.339	15
Kielce	0.699	12	0.711	8	0.699	0.619	0.659	4
Olsztyn	0.612	9	0.633	12	0.612	0.512	0.562	11
Poznań	0.656	8	0.673	7	0.656	0.567	0.611	5
Szczecin	0.466	15	0.519	16	0.466	0.358	0.412	14

,

Table A3. Assessment indices for environment and energy obtained from EDAS and WASPAS methods and assessment index values after applying Laplace criterion.

	EDAS		WASPAS		EDAS	WASPAS	Evaluation Index Determined Based on the Laplace Criterion	Final Rank
	Assessment Score	Rank	Assessment Score	Rank	Unified Evaluation Value	Unified Evaluation Value
Wrocław	0.277	22	0.869	27	0.227	0.106	0.167	26
Bydgoszcz	0.597	5	1.080	5	0.611	0.249	0.430	6
Toruń	0.569	7	1.164	2	0.578	0.305	0.441	5
Lublin	0.598	4	0.988	11	0.612	0.187	0.399	9
Gorzów Wielkopolski	0.699	2	1.116	1	0.734	0.273	0.503	3
Zielona Góra	0.148	26	2.193	14	0.073	1.000	0.536	1
Łódź	0.456	15	0.904	21	0.443	0.130	0.286	14
Kraków	0.152	25	0.819	29	0.078	0.072	0.075	27
Radom	0.562	8	1.112	6	0.569	0.270	0.420	7
Warszawa	0.373	18	0.848	26	0.343	0.092	0.217	21
Opole	0.114	28	1.248	25	0.032	0.362	0.197	23
Rzeszów	0.921	1	0.795	15	1.000	0.056	0.528	2
Białystok	0.650	3	1.066	3	0.674	0.239	0.457	4
Gdańsk	0.476	12	1.146	9	0.466	0.293	0.380	10
Gdynia	0.403	16	1.366	7	0.378	0.441	0.410	8
Bielsko-Biała	0.147	27	1.267	23	0.072	0.375	0.223	20
Bytom	0.552	9	0.961	12	0.557	0.168	0.362	11
Częstochowa	0.313	21	1.123	19	0.271	0.278	0.275	16
Gliwice	0.249	23	1.100	20	0.194	0.262	0.228	19
Katowice	0.159	24	0.712	28	0.086	0.000	0.043	29
Ruda Śląska	0.465	13	0.816	13	0.453	0.070	0.262	17
Rybnik	0.087	29	0.873	17	0.000	0.109	0.054	28
Sosnowiec	0.461	14	0.770	16	0.448	0.039	0.244	18
Tychy	0.370	19	0.737	22	0.339	0.017	0.178	24
Zabrze	0.548	10	0.853	8	0.552	0.095	0.323	13
Kielce	0.399	17	0.800	18	0.374	0.059	0.216	22
Olsztyn	0.595	6	0.871	4	0.609	0.107	0.358	12
Poznań	0.356	20	0.743	24	0.322	0.021	0.172	25
Szczecin	0.531	11	0.745	10	0.532	0.022	0.277	15

,

Table A4. Assessment indices for infrastructure, transport, and accessibility obtained from EDAS and WASPAS methods and assessment index values after applying Laplace criterion.

	EDAS		WASPAS		EDAS	WASPAS	Evaluation Index Determined Based on the Laplace Criterion	Final Rank
	Assessment Score	Rank	Assessment Score	Rank	Unified Evaluation Value	Unified Evaluation Value
Wrocław	0.991	1	0.665	1	1.000	1.000	1.000	1
Bydgoszcz	0.289	17	0.175	22	0.292	0.099	0.195	20
Toruń	0.364	13	0.320	9	0.367	0.365	0.366	12
Lublin	0.456	8	0.340	7	0.460	0.402	0.431	7
Gorzów Wielkopolski	0.235	21	0.246	15	0.237	0.229	0.233	18
Zielona Góra	0.189	23	0.184	20	0.191	0.115	0.153	22
Łódź	0.292	16	0.186	19	0.294	0.118	0.206	19
Kraków	0.539	4	0.378	5	0.544	0.471	0.507	4
Radom	0.261	18	0.261	14	0.264	0.257	0.261	16
Warszawa	0.812	2	0.572	2	0.819	0.829	0.824	2
Opole	0.309	15	0.294	12	0.312	0.318	0.315	13
Rzeszów	0.339	14	0.232	17	0.342	0.204	0.273	15
Białystok	0.400	11	0.325	8	0.404	0.375	0.390	10
Gdańsk	0.519	5	0.362	6	0.524	0.443	0.483	6
Gdynia	0.624	3	0.428	3	0.630	0.563	0.597	3
Bielsko-Biała	0.035	25	0.127	28	0.035	0.009	0.022	26
Bytom	0.029	27	0.127	27	0.029	0.009	0.019	27
Częstochowa	0.256	19	0.237	16	0.258	0.213	0.235	17
Gliwice	0.052	24	0.138	24	0.053	0.031	0.042	24
Katowice	0.385	12	0.310	11	0.388	0.347	0.367	11
Ruda Śląska	0.033	26	0.130	25	0.033	0.016	0.024	25
Rybnik	0.026	28	0.127	26	0.027	0.010	0.018	28
Sosnowiec	0.197	22	0.154	23	0.199	0.060	0.130	23
Tychy	0.255	20	0.183	21	0.257	0.114	0.186	21
Zabrze	0.000	29	0.121	29	0.000	0.000	0.000	29
Kielce	0.481	7	0.293	13	0.485	0.316	0.401	8
Olsztyn	0.441	9	0.197	18	0.445	0.139	0.292	14
Poznań	0.519	6	0.379	4	0.523	0.474	0.499	5
Szczecin	0.433	10	0.311	10	0.437	0.349	0.393	9

and

Table A5. Assessment indices for government obtained from EDAS and WASPAS methods and assessment index values after applying Laplace criterion.

	EDAS		WASPAS		EDAS	WASPAS	Evaluation Index Determined Based on the Laplace Criterion	Final Rank
	Assessment Score	Rank	Assessment Score	Rank	Unified Evaluation Value	Unified Evaluation Value
Wrocław	0.502	7	0.464	6	0.552	0.660	0.606	6
Bydgoszcz	0.901	1	0.564	1	1.000	1.000	1.000	1
Toruń	0.474	8	0.405	14	0.521	0.460	0.490	11
Lublin	0.356	20	0.358	23	0.389	0.302	0.345	21
Gorzów Wielkopolski	0.441	10	0.423	10	0.484	0.521	0.503	10
Zielona Góra	0.511	5	0.421	11	0.563	0.516	0.540	8
Łódź	0.459	9	0.391	18	0.505	0.412	0.458	16
Kraków	0.312	25	0.359	21	0.340	0.307	0.323	24
Radom	0.010	29	0.269	29	0.000	0.000	0.000	29
Warszawa	0.703	2	0.536	2	0.778	0.904	0.841	2
Opole	0.397	16	0.415	13	0.435	0.495	0.465	14
Rzeszów	0.424	13	0.393	17	0.465	0.420	0.442	18
Białystok	0.224	28	0.322	27	0.241	0.180	0.210	28
Gdańsk	0.513	4	0.478	3	0.564	0.707	0.636	4
Gdynia	0.394	18	0.430	9	0.431	0.544	0.487	12
Bielsko-Biała	0.428	12	0.436	8	0.470	0.566	0.518	9
Bytom	0.228	27	0.343	25	0.245	0.250	0.248	26
Częstochowa	0.368	19	0.373	20	0.402	0.352	0.377	20
Gliwice	0.327	24	0.359	22	0.357	0.305	0.331	23
Katowice	0.409	15	0.456	7	0.448	0.632	0.540	7
Ruda Śląska	0.355	21	0.340	26	0.388	0.242	0.315	25
Rybnik	0.430	11	0.404	15	0.472	0.456	0.464	15
Sosnowiec	0.345	22	0.383	19	0.376	0.387	0.382	19
Tychy	0.410	14	0.421	12	0.449	0.516	0.483	13
Zabrze	0.339	23	0.357	24	0.369	0.298	0.334	22
Kielce	0.241	26	0.317	28	0.260	0.162	0.211	27
Olsztyn	0.397	17	0.403	16	0.435	0.454	0.444	17
Poznań	0.511	6	0.466	5	0.562	0.666	0.614	5
Szczecin	0.585	3	0.476	4	0.646	0.702	0.674	3

) includes detailed results of calculations involving the determination of evaluation indices (using EDAS and WASPAS methods) for the other areas characterizing the QoL in smart sustainable cities, along with the ranking of cities (created on the basis of the values of these indices), as well as the values of the indices after applying the zero unitization procedure and the cities’ position in the ranking after applying the Laplace criterion.

Table 5. Assessment scores of cities for the studied areas.

Cities	Economy, Education, and Innovation		Life Standard and Safety		Health		Environment and Energy		Infrastructure		Government
	AS	R	AS	R	AS	R	AS	R	AS	R	AS	R
Wrocław	0.673	3	0.583	10	0.495	13	0.240	21	0.508	13	0.606	6
Bydgoszcz	0.208	21	0.249	25	0.568	10	0.689	4	0.588	9	1.000	1
Toruń	0.225	19	0.787	2	0.170	21	0.653	6	0.161	25	0.490	11
Lublin	0.397	10	0.633	8	0.910	2	0.642	7	0.936	1	0.345	21
Gorzów Wielkopolski	0.000	29	0.110	29	0.152	22	0.850	2	0.131	26	0.503	10
Zielona Góra	0.185	23	0.388	20	0.228	20	0.093	24	0.628	7	0.540	8
Łódź	0.317	15	0.365	21	0.572	9	0.428	15	0.588	10	0.458	16
Kraków	0.697	2	0.800	1	0.556	12	0.089	25	0.419	16	0.323	24
Radom	0.191	22	0.518	14	0.253	19	0.638	8	0.861	2	0.000	29
Warszawa	1.000	1	0.743	4	0.582	8	0.352	17	0.392	17	0.841	2
Opole	0.378	11	0.507	16	0.597	6	0.029	29	0.743	4	0.465	14
Rzeszów	0.492	8	0.529	13	0.762	3	1.000	1	0.794	3	0.442	18
Białystok	0.356	13	0.669	7	0.585	7	0.741	3	0.637	6	0.210	28
Gdańsk	0.603	5	0.785	3	0.335	16	0.542	12	0.207	21	0.636	4
Gdynia	0.542	6	0.490	17	0.103	27	0.452	14	0.175	24	0.487	12
Bielsko-Biała	0.358	12	0.538	12	0.303	17	0.083	26	0.273	19	0.518	9
Bytom	0.029	28	0.144	27	0.137	23	0.622	10	0.203	22	0.248	26
Częstochowa	0.246	18	0.288	23	0.287	18	0.273	20	0.231	20	0.377	20
Gliwice	0.446	9	0.516	15	0.124	24	0.221	23	0.334	18	0.331	23
Katowice	0.507	7	0.279	24	1.000	1	0.043	27	0.724	5	0.540	7
Ruda Śląska	0.053	26	0.121	28	0.007	28	0.452	13	0.028	28	0.315	25
Rybnik	0.120	25	0.394	18	0.104	26	0.035	28	0.199	23	0.464	15
Sosnowiec	0.165	24	0.166	26	0.110	25	0.411	16	0.000	29	0.382	19
Tychy	0.307	16	0.707	5	0.000	29	0.283	19	0.110	27	0.483	13
Zabrze	0.040	27	0.393	19	0.339	15	0.589	11	0.438	14	0.334	22
Kielce	0.348	14	0.588	9	0.659	4	0.298	18	0.580	11	0.211	27
Olsztyn	0.209	20	0.545	11	0.562	11	0.623	9	0.575	12	0.444	17
Poznań	0.652	4	0.687	6	0.611	5	0.236	22	0.625	8	0.614	5
Szczecin	0.302	17	0.322	22	0.412	14	0.653	5	0.420	15	0.674	3

Notes: AS—Assessment Score; R—Rank.

, on the other hand, shows the evaluation indices for each dimension (after applying zero unitization and the Laplace criterion). Based on the value of this index, the ranking of cities in terms of QoL and living conditions in each studied dimension was determined.

The results indicate that the cities surveyed are characterized by significant variation in the life quality and conditions of life. Among the evaluated cities, there is not one that achieves the best evaluation result in at least two of the analyzed areas. This is because each area of assessment of life quality and living conditions in the surveyed cities has a different leader.

In the evaluated areas affecting the QoL in the city, the best performers are the previously mentioned Warsaw (economy, education, and industry), Krakow (standard of life, culture, and safety), Katowice (health), Rzeszów (environment and energy), Lublin (infrastructure), and Bydgoszcz (government).

Kraków turned out to be the best city to live in in terms of the standard of life, culture, and safety area. It was rated highly in terms of living standards (some of the highest values for the level of housing availability and usable floor space of apartments per capita, as well as access to technical and sanitary facilities in collective housing), safety (one of the lowest crime and traffic accident rates), and access to cultural attractions (cinemas, museums, and public libraries). In Kraków, the average usable space per capita is 31.5 m², while the national average is 29.2 m². The best performer in this respect is Poznań, where this area is 33.9 m², and the worst is Zabrze (only 25.9 m²). The availability of apartments in Kraków is also very high, as there are nearly 530 apartments per 1000 residents. The best situation is in Warsaw, with 570 apartments per 1000 residents. This city can be considered very safe as the number of crimes per 1000 residents is less than 24, while in Katowice, it is more than 70. The best situation is in Olsztyn (19 crimes per 1000 residents). It is worth mentioning that this city, however, performs badly in the assessment of the environment (27th position in the ranking).

In terms of health care (health area), the best rating was given to Katowice, which is characterized, versus the assessed cities, by the highest number of nurses and midwives per 10,000 residents—nearly 308, while the average for the analyzed cities is 149. Katowice ranks second in terms of the number of doctors per 10,000 residents—157.6 (in Lublin this indicator is 158.1). Also, in terms of the number of available beds per 10,000 residents, Katowice ranks third (117). Only Rzeszów and Lublin are better, with 120 and 119 beds per 10,000 residents, respectively. The worst health care situation is in Tychy, which has the lowest number of nurses per 10,000 residents (69.4), and the number of doctors is also among the lowest in the country—40.7 (only Ruda Śląska is worse, with 35.1 per 1000 residents).

Another very important area included in the analysis was environmental protection. In this area, the best rating was given to Rzeszów, a city located in the southeast of Poland. It has the best air quality among the surveyed cities. Also, energy efficiency, characterized by total energy consumption per capita, is among the lowest in the country. In Rzeszow, it is only 710 kWh, while in Warsaw, it is 1017.9 kWh. The best performer in this regard is Białystok, where this consumption is only 651.1 kWh. In terms of environmental protection, green areas are also very important. They provide ventilation for the city, improve acoustics, provide transportation corridors for migratory birds, retain rainwater, and reduce air temperature. In Rzeszów, these areas account for 13.3% of the city’s area—the best result among the analyzed cities, which is more than double that of the runner-up in this regard, Warsaw (with a share of 6.5%). Rzeszów fares slightly worse in terms of drinking water consumption. Its per capita consumption is relatively high, i.e., 39.4 m³, while in Zabrze, the consumption is only 29 m³ per capita.

The application of smart solutions in a sustainable city requires adequate infrastructure in the form of, among other things, Internet access, bicycle paths, bus lanes, and so on. In terms of such infrastructure, Wrocław achieved the best results. More than 97% of that city’s residents have access to the Internet, which is a very important factor in building a smart city. Similar very good results in this regard are also achieved by Warsaw, Lublin, Radom, Gdańsk, and Gdynia. The total length of bicycle paths in Wrocław per 100 km² of the city is 98 km, while the leader—Warsaw—has 137 km. The length of bus lanes, on which buses and trolleybuses and electric and hydrogen vehicles can run, and which improve city traffic on public roads, is 29 km in Wrocław. It is slightly better in Łódz and Kraków (31.8 and 30.4 km, respectively) and the best in Warsaw (68 km). On the other hand, Wrocław is the leader in terms of the number of Park and Ride parking lots (it has 34 of them), while Warsaw has only 16. Parking lots of this type are supposed to encourage people not to drive their cars into city centers but rather to leave them on the outskirts and use public transportation or, for example, bike sharing. The worst situation in terms of the “infrastructure” area is in Zabrze, which has the shortest length of bicycle paths per 1 km² of the city, 35 km, and has no bus lanes or Park and Ride parking. Bielsko-Biała, Bytom, Ruda Śląska, Rybnik, and Tychy (all cities located in the Silesia region) also do not have bus lanes. When analyzing this area, it is worth mentioning that Park and Ride parking lots are at an early stage of construction in Poland and many cities do not have them (including Bydgoszcz, Zielona Góra, Łódź, Rzeszów, Bielsko-Biała, Bytom, Gliwice, Ruda Śląska, Rybnik, Sosnowiec, and Olsztyn); yet, efforts are being made to build them.

To sum up the results, it should be stated that the lowest values of Smart Sustainable Assessment Score in the assessed areas were obtained by Gorzów Wielkopolski (economy, education, and industry; standard of life and safety), Tychy (health), Opole (environment and energy), Sosnowiec (infrastructure), and Radom (government).

Based on the Assessment Score values determined (partial for each area) for the areas that characterize the life quality and living conditions in smart sustainable cities, in accordance with the methodology discussed in Section 3, the levels that these cities have in the studied areas were determined (

Table 6. Level of life quality in cities for the analyzed evaluation dimensions.

Dimension	Level
	Level I—Very High	Level II—High	Level III—Average	Level IV—Low
Economy, education, and innovation	Warszawa, Kraków, Poznań, Wrocław, Gdańsk	Gdynia, Lublin, Opole, Rzeszów, Białystok, Bielsko-Biała, Gliwice, Katowice, Szczecin	Bydgoszcz, Toruń, Zielona Góra, Łódź, Radom, Częstochowa, Rybnik, Sosnowiec, Tychy, Kielce, Olsztyn	Gorzów Wielkopolski, Bytom, Zabrze, Ruda Śląska
Life standard and safety	Toruń, Kraków, Warszawa, Gdańsk, Tychy Poznań,	Wrocław, Lublin, Radom, Opole Rzeszów Białystok Gdynia Bielsko-Biała, Gliwice Kielce Olsztyn	Zielona Góra, Łódź, Częstochowa, Katowice Rybnik, Zabrze Szczecin	Bydgoszcz, Gorzów Wielkopolski, Bytom, Ruda Śląska Sosnowiec
Health	Lublin, Opole Rzeszów	Wrocław Bydgoszcz, Zielona Góra Łódź, Radom, Białystok, Katowice, Zabrze Kielce Olsztyn Poznań Szczecin	Toruń, Kraków, Warszawa, Gdynia Bielsko-Biała Bytom Częstochowa Gliwice, Rybnik	Gorzów Wielkopolski, Gdańsk, Ruda Śląska, Sosnowiec Tychy
Environment and energy	Rzeszów, Białystok, Gorzów Wielkopolski, Bydgoszcz	Łódź, Radom, Lublin, Toruń, Gdańsk, Gdynia, Bytom, Ruda Śląska, Zabrze, Olsztyn, Szczecin	Wrocław, Warsaw, Częstochowa Gliwice Sosnowiec Tychy Kielce Poznań	Zielona Góra, Kraków, Opole, Bielsko-Biała, Katowice, Rybnik,
Infrastructure, transport, and accessibility	Gdynia, Warszawa, Wrocław	Poznań, Szczecin, Kielce, Katowice, Białystok, Gdańsk, Opole, Kraków, Toruń, Lublin	Olsztyn, Sosnowiec, Tychy, Częstochowa, Rzeszów, Radom, Gorzów Wielkopolski, Zielona Góra, Łódź, Bydgoszcz	Zabrze, Ruda Śląska, Rybnik, Gliwice, Bielsko-Biała, Bytom
Government	Bydgoszcz, Warszawa, Szczecin	Wrocław, Toruń, Gorzów Wielkopolski, Zielona Góra Łódź, Opole, Gdańsk, Gdynia, Bielsko-Biała, Katowice, Rybnik, Tychy, Poznań	Lublin, Kraków, Rzeszów, Częstochowa, Gliwice, Ruda Śląska, Zabrze, Sosnowiec, Olsztyn	Radom, Białystok, Bytom, Kielce

).

The results show that the same cities, depending on the areas of life quality, show varying levels. It is noticeable that some of them are characterized by high levels of life quality in more than one area. Only Warsaw (economy, education, and innovation; life standard and safety; infrastructure; and government) is characterized by high levels in four (out of six) areas.

By contrast, a high level in two areas was found in five cities, namely, Kraków and Gdańsk (economy, education, and innovation; life standard and safety), Wrocław (economy, education, and innovation; infrastructure), Poznań (economy, education, and innovation; life standard and safety), and Rzeszów (health; environment and energy).

In turn, low levels of life quality and living conditions, in more than one area, were shown for several cities. The record holders in this regard turned out to be two cities, Ruda Śląska and Bytom, which have the lowest level in as many as four areas. Ruda Śląska achieved low levels in economy, education, and innovation, life standard and safety, health, and infrastructure, while Bytom achieved low levels in economy, education, and innovation, life standard and safety, infrastructure, and government.

On the other hand, Gorzów Wielkopolski scored low in three areas (economy, education, and innovation; life standard and safety; health), Sosnowiec in two areas (life standard and safety; health), Rybnik in two areas (environment and energy; Infrastructure), and Zabrze in two areas (economy, education, and innovation; Infrastructure). Thus, among the cities achieving low levels of life quality and living conditions in the areas studied, cities located in the Silesia province dominate. This province does not include only the aforementioned Gorzów Wielkopolski or Kraków (low level in environment and energy); it also includes Bydgoszcz (low level in life standard and safety), Gdańsk (health), Radom and Białystok, and Kielce (low level in government).

In the next stage of the research, the final value of the Smart Sustainable Cities Assessment Score (SSCAS) index was determined, which comprehensively determines the QoL in the studied cities. Based on this value, a ranking of cities was created (Figure 5), and the overall level of QoL in these cities was identified (

Table 7. Overall assessment of the level of QoL in the surveyed cities in terms of Smart Sustainable Cities Assessment Score index values.

Level
Level I—high	Level II—Average	Level III—Average Low	Level IV—Low
Wrocław, Lublin, Warszawa, Rzeszów, Poznań	Bydgoszcz, Toruń, Kraków, Opole, Rzeszów, Białystok, Gdynia, Katowice Kielce, Olsztyn, Szczecin	Gorzów Wielkopolski, Zielona Góra, Łódź, Radom, Bielsko-Biała, Częstochowa, Gliwice, Tychy	Bytom Ruda, Śląska, Rybnik, Sosnowiec, Zabrze

).

The maximum value of the Smart Sustainable Cities Assessment Score (SSCAS) index for all six areas of a city’s life quality can be 6. In the conducted research, Warsaw obtained the highest value of this index, and it was 4.21, which is 70% of the maximum value that could be achieved. On the other hand, the worst value was obtained by Ruda Śląska, 0.78, which is only 13% of the maximum value that the surveyed city could receive.

When summing up the aggregate evaluations of the surveyed cities, which consist of partial evaluations of each area related to life quality and living conditions, it can be said that the leading cities include Warsaw, Wroclaw, Poznań, Gdańsk, and Lublin. Zabrze, Sosnowiec, Rybnik, Bytom, and Ruda Śląska, on the other hand, fare the worst in this assessment.

The leaders of this ranking are cities that are regional capitals (provincial cities). They are relatively affluent cities, which means that they ranked better than the other cities in many of the areas studied. The high ranking and high level of QoL in these cities is due to a number of factors. Due to their better financing, many initiatives and projects aimed at improving the QoL have been implemented in these cities for years. It is also easier to obtain EU funds for their development, and this results in better conditions for the development of higher education, thus attracting investors.

Among the cities characterized by a low QoL are mainly those located in the Silesia province, where heavy industry related to mining and metallurgy was thriving years ago. Technological transformation has adversely affected the situation of cities in this region. The legacy of the industrial era adversely affects the standard of living in these cities, worsening their development prospects. At a poor level is the state of the environment, the education of the population, the state of health care, and the level of safety. In addition, unfavorable is the existence of a relatively low level of income in most cities in this province, perhaps except for Katowice and Gliwice, compared to other cities. Cities such as Zabrze, Bytom, and Ruda Śląska focus primarily on more basic development issues. They are definitely cities that require large investments and new development strategies based on sustainable and smart growth. The difficult situation of the cities located in Silesia is also confirmed by the Human Development Index (HDI) [90], according to which the Silesia region is one of the most unfavorable in terms of comfort of life and has high prospects for improving its quality in the EU.

4.2. Evaluating the Relationship between Living Conditions and Life Quality in Poland’s Smart Sustainable Cities and Their Wealth and Population

In the final stage, an analysis was performed to determine a relationship between the value of GDP per capita of the studied cities and their population, as well as the values of the assessment of the various areas studied and the values of the SSCAS index (RQ3). For this purpose, the values of Spearman’s correlation coefficients between these values were calculated (

Table 8. Spearman’s correlation coefficient values.

Study Area and GDP Per Capita	Spearman Coefficient Value (for p = 0.05)	Study Area and Population	Spearman Coefficient Value (for p = 0.05)
Economy, education, and innovation	0.86	Economy, education, and innovation	0.76
Life standard and safety	0.44	Life standard and safety	0.39
Health	0.24	Health	0.33
Environment and energy	−0.39	Environment and energy	−0.25
Infrastructure, transport, and accessibility	0.69	Infrastructure	0.66
Government	0.53	Government	0.44
Smart Sustainable Cities Assessment Score	0.7	Smart Sustainable Cities Assessment Score	0.67

Notes: Values in red indicate statistically significant results.

), including their relationships (Figure 6).

The calculations presented here indicate that there is a positive relationship between GDP per capita and the QoL in a city as characterized by the Smart Sustainable Cities Assessment Score (SSCAS) value. This relationship is strong at the assumed significance level of p = 0.05. The presence of a positive relationship was also found for some areas of this score, i.e., economy, education, and innovation; life standard, culture, and safety; infrastructure, transport, and accessibility; and government. More affluent cities fare better in the ranking of life quality and living conditions, as is evident in Figure 6, since they have a greater ability to set and achieve development goals related to ecology and smart solutions than less affluent cities. These, in turn, focus first on providing for the basic needs of residents, so they invest less in smart and green solutions. However, the presence of a negative relationship between GDP per capita and the environment and energy assessment area is worrisome, indicating an unsatisfactory approach to the greening and energy transition.

The analysis of the relationship between the Smart Sustainable Cities Assessment Score and the city’s population showed a statistically strong relationship. The relationship between the number of residents and the area of economy, education, and innovation was also of similar strength. On the other hand, a slightly weaker relationship exists between the number of residents and life standard, culture, and safety and government. However, there is no such relationship between the number of residents and health.

Thus, the results obtained do not fully confirm the findings presented in the work of Serbanica and Constantin [84], where the authors conclude that the quality and conditions of life in cities do not depend on the number of residents.

In general, it can be concluded that both the value of GDP per capita and the number of residents constitute parameters that affect the living conditions and life quality in the studied cities in Poland in areas related to smart sustainable cities. This is because the resources in the form of GDP per capita and residents make it easier to implement the vision of a smarter and more sustainable city, which of course affects the quality and conditions of life.

5. Discussion

This research, which aimed to determine the level of living conditions and QoL in 29 Polish cities in terms of their smart and sustainable development, involved 6 assessment areas and 35 indicators characterizing them. The assessment areas included the following: economy, education, and innovation; life standard, culture, and safety; health; environment and energy; infrastructure, transport, and accessibility; and government. The use of a new integrated assessment approach for the study, based on the MCDM methods and the Laplace criterion, made it possible to determine an overall assessment of living conditions and QoL in the surveyed cities (SSCAS index) and sub-assessments for each area included in the study.

The results show that the living conditions and life quality in the surveyed cities aspiring to be smart sustainable cities are at a very different level. There is spatial variation not only in overall living conditions and QoL but also in terms of individual areas that affect them. This variation shows that there is no region of the country in which one of the levels of living conditions and life quality dominates. It follows that the previously functioning view regarding the division of Poland into two areas (eastern—less socio-economically developed—as so-called Poland B; and western—more developed—as so-called Poland A [91]) is practically non-existent.

The study indicates that the best results in terms of living conditions and life quality were achieved by the largest cities, namely, Warsaw, Wrocław, Gdańsk, and Poznań. Their high position is mainly due to their personal resources (mainly educated people) and economic resources, which results in the high availability of well-paid jobs (low level of unemployment), as well as great opportunities in implementing smart infrastructure solutions and taking care of the environment. An important role is also played by the level of safety, cultural offerings, development of transport infrastructure, and environmental awareness of residents. When the basic needs of residents are provided for, cultural and environmental issues become increasingly important to them [92].

An important factor influencing the effectiveness of building smart sustainable cities is the wealth of cities (in terms of GDP per capita and own income). It causes more companies and their headquarters to establish in these cities, which in turn has a very positive impact on the local labor market, further strengthening the position of these cities. With this, of course, also comes a high level of education and culture, which encourages the next generation of young and creative people to live there. In this process, it is also very important for local authorities to be able to take advantage of the potential and possibilities of such a self-reinforcing process toward introducing smart solutions and taking care of the environment.

Thus, the wealth of cities (characterized by the value of GDP per capita) has a significant impact on their development and opportunities to implement new innovative solutions. As this study shows, the living conditions and life quality in cities are related to their wealth and the number of inhabitants (Table 8, Figure 6). The results, in this case, are in line with the findings of other studies, which indicate that the size of the city is a statistically significant factor affecting the living conditions and life quality [93,94]. There are also studies that show different results [95], such as those by Weziak-Białowolska [96] or Serbanica and Constantin [97].

The relationship established in the study between the level of economic development measured by GDP per capita and the living conditions and life quality in the city indicated a positive impact. However, this is not always the case, as evidenced by the results of studies for cities located in China [98]. Thus, in different countries, the impact can be very different. In Poland, the result obtained is quite unambiguous (Table 8).

From the point of view of residents, the important factors of an SSC are adequate living standards, a sense of security, and cultural development/entertainment, as well as its environment, i.e., the natural environment, the state of infrastructure, including transportation, and the availability of attractive work (low unemployment). This ensures good conditions for living, education, and work, which consequently also influences decisions on, for example, the location of business activities or new investments. A high QoL makes cities more attractive to investors, which, in turn, will translate into their faster economic development in the future [99].

According to the study, the best cities in terms of living standards, culture, and safety are Toruń, Kraków, Warsaw, Gdańsk, Tychy, and Poznań. In general, most of these cities are characterized by very good housing conditions, both in terms of floor space per capita, which exceeds the national average (28.2 m²), and housing equipped with technical and sanitary facilities (central heating, bathroom, and toilet). It is also worth noting issues related to security and access to the labor market. The cities mentioned are characterized by low levels of unemployment, which also improves safety [100,101].

An important area under study, especially due to Poland’s aging population, is health care (health) issues. It is closely related to the humanistic and social aspects of life quality and living conditions [102,103]. Polish cities must create such conditions in which each resident feels safe and important, as it is in an SSC. This will be possible if cities also provide residents with an adequate number of highly qualified medical personnel and hospital beds. Unfortunately, in Poland, as shown by the results, cities, due to insufficient outlays allocated to health care, are struggling to provide a sufficient number of hospital beds, and in the case of even an adequate number, the problem is the provision of medical staff. As [98] points out, future Polish cities—as well as the Polish economy—will face a shortage of qualified nurses (needed to care for seniors) and a shrinking number of hospital beds as a result of an aging population. This problem is evident in all highly developed countries of the world [104].

The results show that the best situation related to the health area currently exists in Lublin and Radom, i.e., in cities with the highest number of doctors and nurses per capita. National and local authorities must pay attention to this problem, which can be crucial for the development strategies of cities and entire regions. Such measures should be taken as soon as possible, as the process of preparing competent medical personnel and adequate hospital infrastructure takes time and considerable financial resources.

Another aspect that affects living conditions and life quality in smart sustainable cities for current and future residents (and generations) is the issue of environmental protection and energy conservation. Increasing urbanization has a tremendous impact on the environment, causing, among other things, an increase in municipal waste generation, water consumption, or atmospheric pollution. This, in turn, has further consequences in the form of carbon dioxide emissions and water and soil pollution [105,106,107,108,109]. A significant problem occurring in Polish cities is air pollution, which affects the health and life expectancy of their residents. The worst situation in this regard invariably prevails in cities located in the Silesian province (Rybnik, Bytom, Gliwice, Katowice, Ruda Śląska, Sosnowiec, Tychy, Zabrze, Bielsko-Biała, and Częstochowa) and in Krakow. These cities have been ranked for years in international rankings of cities with the worst air quality [110]. Despite the measures taken to improve this situation, the results are not very optimistic. A good example of decisive action is in Kraków, which, in 2019, introduced restrictive legal and environmental regulations to improve air quality in the form of, for example, a ban on the use of solid fuels for heating purposes. As studies [111] indicate, the effects of these regulations were already visible by 2021. In the Silesian agglomeration, poor air quality is mainly influenced by individual households, as a result of the use of coal-fired boilers and/or stoves with low energy efficiency that do not meet most emission standards and the combustion of low-quality fuels in them. Cities in the Silesian province, wishing to attract investors and young educated people, must solve this problem as soon as possible.

By contrast, the best environmental conditions are found in Rzeszów, Białystok, Gorzów Wielkopolski, and Zielona Góra. These are cities located in vastly different parts of Poland, in areas where there is no past history of coal and metallurgy. Rzeszów is located in the southeast of the country, Bialystok is in the northeast, and Gorzów Wielkopolski and Zielona Gora are in the western part of the country (Figure 1). All these cities have one thing in common—they have no developed industry, which has a significant impact on the environment and energy consumption.

When assessing smart and sustainable urban development, infrastructure, transport, and accessibility issues cannot be overlooked. A well-prepared system of bicycle paths, bus lanes, public transportation, Park and Ride parking, and the operation of low- and zero-emission passenger cars is a key factor in the development of smart and sustainable mobility. All these factors must be at the right level to make mobility issues socially and environmentally friendly. The results in this area indicate that the most modern and sustainable transportation infrastructure is found in Wrocław, Warsaw, and Gdynia. In these cities, road infrastructure is being developed to improve traffic (bus lanes) and provide alternatives to cars (bicycle lanes). In these cities, priority is given to the development of bicycle paths as part of city strategies and ongoing civic projects. As indicated by the experience of European cities [112], which have strategies for the development of smart cities, the development of a network of bicycle paths positively influences the QoL of residents and reduces air pollution [113]. Wrocław, Warsaw, and Gdynia are also cities where the population is affluent and aware enough to increasingly use low- and zero-emission cars, including in public transportation.

The availability of a network of bicycle paths is also essential for the implementation of advanced smart mobility solutions, such as bike sharing or applications that monitor the condition of tires on vehicles. The bikeway network must be dense enough to provide a convenient alternative to traditional modes of transportation.

To sum up the presented discussion, referring only to the most important results obtained from the conducted research, the implementation of the smart sustainable cities concept should definitely influence the development of modern cities that are friendly to their residents [114].

In the case of Europe, and therefore Poland, QoL correlates strongly with goals relating to economy and infrastructure, strong urban and regional institutions, gender equality, and health, as well as Goal 11 of the UN Agenda 2030 (make cities and human settlements safe, stable, sustainable, and inclusive) dedicated to cities. Throughout this process of building smart and sustainable cities, a very thoughtful and balanced approach is also necessary, as already mentioned, so as not to lose the essence of this concept based on the harmonious development of all areas of urban life for the benefit of present and future generations [115,116].

6. Conclusions

The concept of smart sustainable cities has gained great popularity in countries around the world, including Poland. Its implementation is expected to influence the smart and sustainable development of cities, which are to become friendly places for residents to live and work. However, the accomplishment of the goals of this concept requires a comprehensive development strategy supported by the authorities of countries, regions, and cities themselves. Indeed, it is difficult to imagine the implementation of 17 goals of the 2030 Agenda adopted in 2015 by the United Nations for sustainable development and the related 169 tasks without taking into account the sustainable development of cities, where some 55% of the world’s population already lives. Cities account for about 85% of the global GDP while emitting about 75% of greenhouse gases, making them centers of economic and social development, on one hand, and a huge threat to the environment, on the other. It is also clear that the process of urbanization will continue in the years to come, which should even force decisive action to create cities that use the latest technological advances and are environmentally friendly. Without such implementations, the economic, environmental, and social problems of ever-expanding cities cannot be solved.

This issue concerns practically all cities in the world, including the smaller (in relation to large agglomerations) ones in the countries of the so-called “new EU-13”. This was the subject of the research presented in this paper, which involved an assessment of the living conditions and life quality in 29 Polish cities aspiring to be smart sustainable cities. This research was carried out on the basis of the developed research methodology, which used a set of selected indicators characterizing the most important areas of life in cities. This research used an integrated approach based on the MCDM methods and the Laplace criterion for decision-making under uncertainty.

The results showed large spatial variation in the level of living conditions and QoL. Although the area of the country where cities with a high level of conditions and QoL are located cannot be clearly identified, an area was identified where there is a preponderance of cities included in the low average levels of this development. Cities located in the south of Poland—the Silesia province—performed the worst in this research. The culprit of this finding is undoubtedly the economic and social history of the entire region, whose existence for many decades was based on mining and metallurgy. Despite the changes introduced, the region has not yet fully managed to modernize, although its potential for modern development is very high.

When taking the value of GDP per capita as an indicator of a city’s wealth, it has been shown that richer cities perform significantly better in terms of creating friendly living conditions for residents. This is confirmed by the results of the correlation between the position of cities in the designated ranking and their wealth and number of residents.

In summary, the study conducted and the results obtained have expanded knowledge on issues related to living conditions and QoL in Poland’s largest cities. They also showed that it is crucial for their further development to rely on the concept of sustainable development supported by innovative and smart technical and organizational solutions.

The ranking of cities in terms of living conditions and QoL presented in the paper should be used by national, regional, and local government authorities of these cities, as well as other stakeholders (e.g., NGOs), to objectively diagnose the current state of affairs and build modern and sustainable development strategies. This is important because in the free market of the EU, Polish citizens also have opportunities to live in cities of other countries. Competition in the market for highly skilled workers, students, and other groups of residents means that these smaller cities must also take these conditions into account in their policies. Thus, the results also provide an opportunity to identify areas that are at a lower level compared to other cities and indicate the strengths of implementing the smart sustainable cities concept.

When referring to future research, it would therefore also be advisable to compare the results obtained with the results of a survey of cities in other EU countries. Such a comparative analysis would make it possible to identify the strengths and weaknesses of these cities, as well as potential threats and opportunities for their further development. At the same time, despite the aforementioned competition, it would also enable cooperation in selected areas, which should also be a crucial part of building smart sustainable cities.

Like any research, the study presented in this paper also has limitations. Undoubtedly, this is related to the selection of indicators that characterize the areas studied and the research methods used. In these matters, as also indicated in the literature review, there are different approaches in operation, depending on the investigators. In the present study, an important factor affecting the selection of indicators was their availability. In Poland, there is not yet a dedicated database that aggregates more detailed indicators characterizing the living conditions and QoL in cities, beyond those used in the study. Above all, there is a lack of data that can be applied in even greater detail to assess smart city development. Another problem is the number of cities that provide the data necessary for such analyses. There are more than 800 cities in Poland, of which about 700 are small cities with a population of up to 20,000. Research for this group, with a greater availability of data, would also be very interesting. It would therefore be fully justified in the near future to create a database for Polish and European small, medium, and large cities regarding actions taken in their sustainable and smart development.