Implementation of the Sustainable Cities and Communities Sustainable Development Goal (SDG) in the European Union

Abstract

The study addresses problems related to the implementation of the goal aimed at making cities inclusive, safe, resilient, and sustainable—Sustainable Development Goal 11: Sustainable Cities and Communities—based on the example of the European Union countries. The introduction attempts at defining the concept of sustainable development, with particular emphasis on its complex nature and importance for future generations, including its basic five dimensions. The purpose of the study is to analyze and present the ranking of individual European Union Member States regarding the level of sustainable development measured by the implementation of SDG 11. The conducted research covered mostly the period of 2005–2020 and included 28 countries. Sweden was the country that predominantly took the leading position in terms of the implementation of SDG 11 in the years 2010–2020; other leaders included Ireland, Denmark, Finland, the Czech Republic, Austria, Malta, and Estonia. Romania most often ranked in the last position. Moreover, Finland and Ireland were always ranked among the top 10. The general conclusions allow it to be stated that the situation related to the implementation of SDG 11 in the European space has been gradually improving.

1. Introduction

Sustainable development can be defined, in simplified terms, as a process of change ensuring that the needs of the present generation are met without compromising the development opportunities of future generations, as well as through integrated activities in the area of economic, social, and environmental development. Following this approach, sustainable development refers to three basic dimensions, which can be expanded by the spatial as well as the institutional and political dimensions.

In this context, the overall approach to sustainable development, considering all of its dimensions, is of crucial importance. The aforementioned definition assumes that both the economic and civilization development of the current world population should not be carried out at the cost of reducing non-renewable resources and damaging the environment for the sake of future generations, allowing them prosperous development [1,2,3,4,5,6]. The presented interpretation of sustainable development was popularized based on the report issued by the World Commission on Environment and Development under the title “Our Common Future.” Further dissemination of the discussed idea was continued at the Earth Summit 1992, the effect of which was publishing Agenda 21. Another significant step towards the implementation of the Sustainable Development Goals was the Millennium Declaration published by the United Nations, which specified the Millennium Development Goals. Their realization aimed at satisfying the challenges of the 21st century until 2015. The provisions of the 1992 summit were reestablished in 2002 in Johannesburg, and later at the Rio de Janeiro summit in 2012, also called Rio + 20. Additionally, “The Future We Want” declaration was adopted at this summit. Its participants voiced their readiness to promote the idea of a sustainable future in economic, social, and environmental aspects of life. In 2015, the Millennium Development Goals were followed by the Sustainable Development Goals (SDGs) listed in the Transforming Our World 2030 Agenda for Sustainable Development. This agenda is a plan of action for the entire world focused on eliminating poverty by 2030, ensuring decent living conditions for the global population, and ensuring peace. Sustainable development, or rather the pursuit towards its uncompromised achievement, is, no doubt, one of the essential challenges to be met by the contemporary world [7,8,9,10,11,12,13,14,15,16,17].

Currently, we are on the path towards the achievement of complex goals, and due to the turbulent international environment, accomplishing these ambitious goals by 2030 may turn out to be extremely difficult. This does not mean, however, that we should be satisfied with the results achieved so far; on the contrary, the respective efforts should be focused on the international level in order to keep the promises made to each other. This will be beneficial not only for the planet, the economy, and peace but also for the global political balance.

It is worth emphasizing that the principle of sustainable development, referring to the projects co-financed by European funds, implies that neither social nor economic development should be in conflict with the interests of environmental protection and spatial order. Future actions and programs have to consider the needs of subsequent generations and cannot infringe either the natural or spatial balance. All scheduled activities have to be focused on respecting the requirements of preserving biodiversity, taking a sustainable approach towards using natural resources, restoring and consolidating spatial order, and protecting environmentally precious areas in terms of their integrity and coherence.

The above-mentioned Sustainable Development Goals (SDGs) are monitored using indicators grouped in 17 areas corresponding to the following objectives: 1: No Poverty; 2: Zero Hunger; 3: Good Health and Well-being; 4: Quality Education; 5: Gender Equality; 6: Clean Water and Sanitation; 7: Affordable and Clean Energy; 8: Decent Work and Economic Growth; 9: Industry, Innovation and Infrastructure; 10: Reduced Inequality; 11: Sustainable Cities and Communities; 12: Responsible Consumption and Production; 13: Climate Action; 14: Life Below Water; 15: Life on Land; 16: Peace, Justice and Strong Institutions; and 17: Partnerships to Achieve the Goal.

An assumption can be adopted that the implementation of the Sustainable Development Goals is a natural process for the European Union countries and the next stage in creating a better place to live for future generations. Undoubtedly, an important element of this process is focusing the activities in a coordinated, monitored manner based on the principles, guidelines, and indicators [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34].

The purpose of this study is to analyze and present the position of individual European Union countries in relation to the level of sustainable development measured by the implementation of objective 11: Sustainable Cities and Communities. The research covered mostly the years 2005–2020 or 2010–2020 and included 28 countries, including Great Britain, which left the European Union in 2020.

2. Materials and Methods

The article provides the analysis of the indicators characterizing the area of SDG 11: Sustainable Cities and Communities. This area is characterized by 10 indicators. They are as follows: overcrowding rate by poverty status; population living in households considering that they suffer from noise by poverty status; settlement area per capita; people killed in road accidents; exposure to air pollution by particulate matter; recycling rate of municipal waste; population living in a dwelling with a leaking roof, damp walls, floors, or foundation, or rot in window frames of floor by poverty status; population connected to at least secondary wastewater treatment; share of buses and trains in total passenger transport; and population reporting occurrence of crime, violence, or vandalism in their area by poverty status.

The data for the years 2005–2020 are available for the above-mentioned indicators. In the case of some indicators, there were data gaps for selected years, which was signaled during the analysis of the findings. The source of data for the study was the Eurostat database. The definition of each indicator and its nature, stimulant, or destimulant is presented in

Table 1. Sustainable development indicators covering area 11: Sustainable Cities and Communities selected for analysis.

No.	Indicator	Definition	Indicator Nature
X1	Overcrowding rate by poverty status (%)	The indicator measures the share of people living in overcrowded conditions in the EU. A person is considered to be living in an overcrowded household if the house does not have at least one room for the entire household as well as a room for a couple, for each single person above 18, for a pair of teenagers (12 to 17 years of age) of the same sex, for each teenager of different sex, and for a pair of children (under 12 years of age).	Destimulant
X2	Population living in households considering that they suffer from noise (%)	The indicator measures the proportion of the population that declares that they are affected either by noise from neighbors or from the street. Because the assessment of noise pollution is subjective, it should be noted that the indicator accounts for both the levels of noise pollution as well as people’s standards of the level they consider to be acceptable. Therefore, an increase in the value of the indicator may not necessarily indicate a similar increase in noise-pollution levels but also a decrease in the levels that European citizens are willing to tolerate and vice versa. In fact, there is empirical evidence that perceived environmental quality by individuals is not always consistent with the actual environmental quality assessed using objective indicators, particularly for noise.	Destimulant
X3	Settlement area per capita (square metres per capita)	This indicator captures the amount of settlement area used for buildings, industrial and commercial areas, infrastructure, etc., and includes both sealed and non-sealed surfaces.	Stimulant
X4	People killed in road accidents (%)	The indicator measures the number of fatalities caused by road accidents, including drivers and passengers of motorized vehicles and pedal cycles as well as pedestrians. Persons dying in road accidents up to 30 days after the occurrence of the accident are counted as road-accident fatalities. After these 30 days, the reason for dying might be declared differently. For Member States not using this definition, corrective factors were applied. The average population of the reference year (calculated as the arithmetic mean of the population on 1 January in two consecutive years) is used as denominator (per 100,000 persons).	Destimulant
X5	Exposure to air pollution by particulate matter (%)	The indicator measures the population weighted annual mean concentration of particulate matter at urban background stations in agglomerations. Fine and coarse particulates (PM10), i.e., particulates whose diameters are less than 10 μm, can be carried deep into the lungs, where they can cause inflammation and exacerbate the condition of people suffering from heart and lung diseases. Fine particulates (PM2.5) are those whose diameters are less than 2.5 μm. They are therefore a subset of PM10 particles. Their deleterious health impacts are more serious than those of PM10, as they can be drawn further into the lungs and may be more toxic.	Destimulant
X6	Recycling rate of municipal waste (%)	The indicator measures the tonnage recycled from municipal waste divided by the total municipal waste arising. Recycling includes material recycling, composting, and anaerobic digestion. Municipal waste consists mostly of waste generated by households, but may also include similar wastes generated by small businesses and public institutions and collected by the municipality. This latter part of municipal waste may vary from municipality to municipality and from country to country, depending on the local waste-management system. For areas not covered by a municipal waste-collection scheme the amount of waste generated is estimated. The Member States report each year the amount recycled and the total municipal waste generated to Eurostat. Data collection, validation, and dissemination are performed by the EDC Waste hosted at Eurostat.	Stimulant
X7	Population living in a dwelling with a leaking roof; damp walls, floors, or foundation; or rot in window frames of floor by poverty status (%)	The indicator measures the share of the population experiencing at least one of the following basic deficits in their housing condition: a leaking roof; damp walls, floors, or foundation; or rot in window frames or floor. A breakdown by poverty status is available.	Destimulant
X8	Population connected to at least secondary wastewater treatment (%)	The indicator measures the percentage of the population connected to wastewater-treatment systems with at least secondary treatment. Thereby, wastewater from urban sources or elsewhere is treated by a process generally involving biological treatment with a secondary settlement or other process, resulting in a removal of organic material that reduces the biochemical oxygen demand (BOD) by at least 70% and the chemical oxygen demand (COD) by at least 75%.	Stimulant
X9	Share of buses and trains in total passenger transport (%)	The indicator measures the share of collective transport modes in total inland passenger-transport performance, expressed in passenger-kilometers (pkm). Collective transport modes refers to buses, including coaches, trolley-buses, and trains. Total inland transport includes transport by passenger cars, buses, coaches, and trains. All data are based on movements within national territories, regardless of the nationality of the vehicle. The data-collection methodology is voluntary and not fully harmonized at the EU level. Other collective transport modes, such as tram and metro systems, are also not included due to the lack of harmonized data. For countries where rail-transport statistical legislation does not apply, the totals contain only the share of coaches, buses, and trolley buses.	Stimulant
X10	Population reporting occurrence of crime, violence or vandalism in their area by poverty status (%)	The indicator shows the share of the population that reported that they face the problem of crime, violence, or vandalism in their local area. This describes the situation where the respondent feels crime, violence, or vandalism in the area to be a problem for the household, although this perception is not necessarily based on personal experience.	Destimulant

Source: authors’ compilation based on Eurostat [35].

. A stimulant is a statistical feature whose increase in value indicates an increase in the level of a complex phenomenon, and a decrease in value indicates a decrease in the level of a complex phenomenon. A destimulant can be defined as a statistical feature whose increase in value leads to a decrease in the value of the dependent variable.

The statistical data, necessary to carry out the research, come from the Eurostat database [35]. The methods of descriptive statistics, dynamics analysis, and the methods of multivariate statistical analysis (the linear-ordering method) were used in the empirical research.

The empirical analysis was carried out in accordance with the following stages of the research procedure [36,37,38,39,40]:

• Spatial and temporal comparative analysis of the indicators covering the area of Sustainable Cities and Communities (X1–X10) using the basic parameters of descriptive statistics and dynamics indexes.
• Selection of final indicators for the construction of a synthetic measure of sustainable development in the area of Sustainable Cities and Communities.
• Normalization of final indicators using a fixed reference point.
• Construction of a synthetic measure of sustainable development in the area of Sustainable Cities and Communities and a linear ordering of the European Union Member States.
• Analysis of the dynamics of changes in the value of synthetic measures of sustainable development in the area of Sustainable Cities and Communities covering the analyzed countries.

Basic statistical parameters were calculated for each of the analyzed indicators, including the minimum value, maximum value, arithmetic mean, and coefficient of variation.

In the process of constructing the aggregate measure, the first stage consisted of identifying partial features describing the complex phenomenon, i.e., the area of Sustainable Cities and Communities. In the next stage, the selected set of partial features, due to the diversity in terms of measurement units, orders of magnitude, and preferences, was brought down to comparability through the application of the zero-unitarization method. In the case of features presenting a stimulating preference function, it was performed using the following formula:

$$z_{ij}=\frac{x_{ij}-mi{n}_{\left(x_{ij}\right)}}{max{\left(x\right)}_{ij}-min{\left(x\right)}_{ij}}$$

In the case of destimulant features, it was necessary to transform them into stimulants. This was completed based on a modified normalization formula for partial features with a destimulating preference function based on zero unitarization using the formula below:

$$z_{ij}=\frac{max{\left(x\right)}_{ij}-x_{ij}}{max{\left(x\right)}_{ij}-min{\left(x\right)}_{ij}}$$

In the subsequent research stage, equal weights were adopted for all partial features. Next, due to the fact that aggregate measures constructed as part of a model approach use different types of distances of individual objects from the pattern object in their formulas, the appropriate distance measure should be selected. The conducted analysis used numerical features; therefore, the Euclidean distance was applied. The following formula was used for this purpose:

$$d_{ik}=\sqrt{{\displaystyle \sum }_{j=1}^m(z_{ij}}-z_{kj}{)}^2$$

While constructing the aggregate measure for numerical features, the synthetic measure of development proposed by Zdzisław Hellwig was used. Calculations were made using the formula below:

$$S_I^{HG}=1-\frac{d_i^{+}}{d_0}$$

where $d_i^{+}$ is the distance of the i-th object from the pattern object presenting the most favorable values of partial features:

$$d_0=\overline{d_0}+2\ast S_0; \overline{d_0}=\frac{1}{n}{\displaystyle \sum }_{i=1}^n{d}_i^{+}; S_0=\sqrt{\frac{1}{n}} {\displaystyle \sum }_{i=1}^n{(d_i^{+}-\overline{d_0})}^2$$

The constructed aggregate measure adopts values in the range (0; 1). The higher the value of the aggregate measure, the higher the level of the complex phenomenon.

To analyze the dynamics of changes in the values of synthetic measures for sustainable development in the area of Sustainable Cities and Communities, individual single-base and chain indexes were used.

3. Results and Discussion

In Section 3.1 the analysis of differences between individual countries was performed separately for each indicator, the basic measures of descriptive statistics were calculated for each indicator, and how the value of each indicator changed over time was determined. In Section 3.2, the analysis of the already aggregate ratio was carried out.

3.1. Spatial–Temporal Comparative Analysis of Indicators in the Area of Sustainable Cities and Communities in the European Union in the Years 2005–2020

In the case of the “overcrowding rate by poverty status (%)” indicator, no data were available for Bulgaria in 2005, for Croatia in 2005–2009, for Romania in 2005 and 2006, or for the United Kingdom in 2019–2020. The best situation in the years 2005–2006 and 2008–2012 was recorded in the Netherlands and in 2007 and 2013–2020 in Cyprus. The worst situation was observed in Latvia in the period 2005–2010, followed by Romania. A positive phenomenon was the systematically declining disparity between the analyzed countries, as well as the decreasing maximum value.

For the indicator of “population living in households considering that they suffer from noise,” no data were available for Croatia in the years 2005–2009, Italy and Poland in 2020, or Great Britain in 2019–2020. The analysis of the indicator values for “population living in households considering that they suffer from noise” showed that the best situation was in Sweden (2005–2007), Hungary (2008–2012), and Croatia (2012–2020). The worst situation was recorded in the Netherlands (2005, 2015, 2017), Cyprus (2006, 2007), Romania (2008–2010), and Malta (2011–2014, 2016, 2018–2020). In the years under study, an increase in differences between the analyzed countries was observed, which should be assessed negatively.

For the “settlement area per capita” indicator, the respective data were available only for the years 2009, 2012, 2015, and 2018. The value of the indicator went up in the analyzed years. This increase in 2018 comparing to 2009 amounted to over 11%. The differences between individual countries remained at the same level. The best situation in all the analyzed years was in Finland, whereas the worst was in the Netherlands (2009), followed by Malta.

Another analyzed indicator referred to “people killed in road accidents.” In this case, the data were available till 2019. Only the data for the UK in 2019 were unavailable. The analysis of this indicator value revealed a positive direction of these changes. The average value of this indicator in 2019, as compared to 2005, went down by over 50%. The differences between individual countries also decreased. In the first analyzed years (2005–2008), the worst situation occurred in Lithuania, followed by Romania (2009, 2010, 2012, 2013, 2017–2019). In 2011, the highest value of this indicator was recorded in Poland and in 2014 in Latvia, whereas in 2015 and 2016 it was recorded in Bulgaria. The countries characterized by the best situation included Malta (2005–2009, 2012, 2014–2015), Sweden (2010, 2013, 2016–2017, 2019), and the United Kingdom (2011, 2018).

In the case of the “exposure to air pollution by particulate matter” indicator, there were significant information gaps. Therefore, the period between 2013 and 2019 was analyzed. However, the data for Malta, Hungary (2015, 2016), Slovakia (2013), and Greece (2014) were unavailable. In the years 2013–2019, the average value of the indicator featured a slight decline. The differences between the analyzed countries also declined. Estonia (2016, 2019), Finland (2015, 2017), and Sweden (2013, 2014 and 2018) were characterized by the lowest values of this indicator. The highest values were recorded in Bulgaria (2013–2015, 2019) and Poland (2014, 2016–2018).

Positive changes were observed in relation to the “recycling rate of municipal waste” indicator. Its average value increased in 2020 comparing to 2005 by over 15 percentage points. A positive phenomenon was also manifested by a decline in disparities between individual countries by more than half. Germany was the leader in all analyzed years, where the value of the indicator was at the level of over 60%. The worst situation was recorded in 2005–2009 in Romania and in the following years in Malta.

In the case of the “population living in a dwelling with a leaking roof; damp walls, floors, or foundation; or rot in window frames of floor by poverty status” indicator, no data were available for Croatia for the years 2005–2009, Italy for 2020, or Great Britain for 2019–2020. In the analyzed period, favorable changes were also observed in relation to this indicator. Its average value dropped by approx. 6 percentage points. The differences between individual countries were also slightly smaller. Finland was the leader in all analyzed years. The worst situation occurred in Poland (2005–2007), Hungary (2008), Slovenia (2009–2012), Portugal (2013–2016), and Cyprus (2017–2020).

The analysis of the indicator for “population connected to at least secondary wastewater treatment” was very difficult due to numerous gaps in the available data. The full time series covering the years 2005–2019 was available only for Bulgaria, the Czech Republic, Latvia, Hungary, Malta, Poland, Romania, and Slovenia. Out of these eight countries, the best situation was recorded in the Czech Republic, whereas the worst was in Romania. A positive phenomenon was the increasing average value of the indicator and its decreasing diversification.

In the analyzed period, the average value of the indicator for “share of buses and trains in total passenger transport” decreased by less than four percentage points. At the same time, the differences between individual countries showed a declining tendency. The highest value of this indicator in all analyzed years was achieved in Hungary, whereas the lowest was in Lithuania. The exception was in 2014, when the lowest value was recorded in Portugal.

For the “population reporting occurrence of crime, violence, or vandalism in their area by poverty status” indicator, no data were available for Croatia in the years 2005–2009, Romania in 2005–2006, Great Britain in 2019–2020, or Poland and Italy in 2020. Inconclusive changes were observed in relation to this indicator. In the analyzed period, its average value went down, whereas the differences between individual countries went up. The best situation among the analyzed countries was observed in Greece (2005), Lithuania (2006–2009), and Croatia (2010–2020). The worst was recorded in Great Britain (2005–2006, 2018), Latvia (2008), and Bulgaria (2007, 2009–2017, 2019).

3.2. The Level of Sustainable Development in the Area of Sustainable Cities and Communities of the European Union Countries in Dynamic Terms

Figure 1 shows values of the coefficients of variation for 8 indicators in the area of Sustainable Cities and Communities in the years 2005–2020, and Figure 2 presents their average values. Due to numerous data gaps, it was impossible to calculate the values for indicators X3 and X8. The greatest differentiation, remaining practically at the same level throughout the analyzed period, was observed in the case of X1: overcrowding rate by poverty status (%). Significant differences, even though showing a decreasing trend, were also observed in relation to indicator X6: recycling rate of municipal waste (%). The average value of the coefficient of variation in the years 2005–2020 for these indicators was at the level of 84.0% and 52.5%, respectively. The analyzed EU Member States were characterized by the smallest average differences in terms of X2: population living in households considering that they suffer from noise and X9: share of buses and trains in total passenger transport. However, in the case of the first indicator, the disparities showed an increasing tendency, whereas regarding the second one, there was a noticeable tendency towards a further decrease in the disproportions between individual countries.

In the years 2005–2020, a growing tendency was observed in the differences between the analyzed countries regarding X2: population living in households considering that they suffer from noise and X10: population reporting occurrence of crime, violence, or vandalism in their area by poverty status.

Finally, seven indicators were used to construct the aggregate indicator in the area of Sustainable Cities and Communities: X1: overcrowding rate by poverty status (%); X2: population living in households considering that they suffer from noise; X4: people killed in road accidents; X6: recycling rate of municipal waste; X7: population living in a dwelling with a leaking roof, damp walls, floors, or foundation, or rot in window frames of floor by poverty status; X9: share of buses and trains in total passenger transport; and X10: population reporting occurrence of crime, violence, or vandalism in their area by poverty status. The time range of the study, due to data gaps, was in this case limited to the years 2010–2020.

Finally, the indicators selected for the analysis formed the basis for the construction of Hellwig’s aggregate measure. The values of the descriptive parameters of Hellwig’s aggregate measure in the years 2010–2020 are presented in

Table 2. Descriptive parameters of Hellwig’s aggregate measure in the years 2010–2020.

Statistics	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Min	0.22241	0.27086	0.30515	0.30695	0.34036	0.35083	0.35891	0.37674	0.37381	0.49038	0.50167
Max	0.66669	0.65461	0.66482	0.67816	0.67466	0.67561	0.68299	0.67808	0.70644	0.75635	0.74862
Range	0.44428	0.38374	0.35967	0.37120	0.33429	0.32478	0.32408	0.30133	0.33263	0.26596	0.24694
Median	0.47035	0.50087	0.50438	0.51988	0.50808	0.53896	0.55296	0.56771	0.58456	0.62178	0.62701
Arithmetic mean	0.46287	0.48501	0.50470	0.50917	0.52192	0.53678	0.54887	0.55824	0.55678	0.62642	0.62710
First quartile	0.36769	0.38980	0.41489	0.41759	0.45140	0.47279	0.49241	0.48469	0.47362	0.58837	0.58210
Third quartile	0.54470	0.55801	0.57932	0.58757	0.59338	0.61434	0.62025	0.63046	0.63425	0.68108	0.6735
Standard deviation	0.12096	0.10708	0.10126	0.09842	0.09538	0.09089	0.08771	0.08796	0.09381	0.06192	0.0604
Coefficient of variation	26.1%	22.1%	20.1%	19.3%	18.3%	16.9%	16.0%	15.8%	16.8%	9.9%	9.6%
Skewness	−0.15995	−0.32062	−0.08852	−0.20363	0.2015	0.0649	0.0527	−0.13911	−0.3812	0.2792	0.0174

Source: authors’ compilation.

. The analysis of the values below allowed a conclusion to be formulated about the decreasing disparities between individual countries in the subsequent years. The level of implementation of the concept of sustainable development in the area of Sustainable Cities and Communities also increased, as evidenced by the growing value of the arithmetic mean and the median.

A comprehensive assessment of the differentiation of sustainable development in the area of Sustainable Cities and Communities in the European Union countries requires analyzing both the level of development and the dynamics of its changes. Changes in the dynamics in the area of Sustainable Cities and Communities in the years 2010–2020 in the European Union countries were predominantly favorable. It is noteworthy that only in three countries (Denmark, Austria, and Sweden) was a downward trend observed. In the others, the tendency was an increasing one.

Table 3. Values of single-base individual indexes of the aggregate measure (2010 base year).

Countries	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Austria	100.0%	105.5%	106.3%	110.0%	111.0%	109.6%	112.3%	113.3%	118.5%	90.9%	94.1%
Belgium	100.0%	99.4%	107.4%	102.2%	106.7%	104.6%	111.3%	114.5%	113.4%	104.5%	107.1%
Bulgaria	100.0%	111.1%	115.8%	120.5%	111.1%	114.5%	117.4%	122.9%	128.0%	193.8%	197.4%
Croatia	100.0%	106.2%	114.4%	119.9%	129.8%	125.7%	134.9%	137.2%	139.8%	185.6%	185.0%
Cyprus	100.0%	97.3%	110.5%	111.1%	136.5%	135.8%	143.1%	133.4%	128.5%	203.1%	193.5%
Czechia	100.0%	103.0%	112.2%	115.1%	117.7%	120.2%	128.0%	133.0%	130.4%	133.8%	133.5%
Denmark	100.0%	96.7%	104.4%	107.1%	107.4%	107.9%	104.3%	105.0%	104.8%	94.8%	92.5%
Estonia	100.0%	114.9%	114.3%	119.4%	133.6%	142.8%	140.6%	146.7%	142.9%	163.4%	169.6%
Finland	100.0%	100.1%	100.3%	100.1%	101.8%	105.4%	111.4%	108.2%	108.0%	109.9%	111.5%
France	100.0%	102.6%	105.6%	111.8%	112.9%	113.4%	110.8%	115.7%	112.2%	121.7%	118.2%
Germany	100.0%	98.7%	100.0%	99.1%	100.2%	99.7%	100.9%	99.9%	97.2%	90.2%	92.3%
Greece	100.0%	102.0%	108.5%	115.8%	131.5%	136.1%	135.2%	136.7%	140.6%	202.2%	202.4%
Hungary	100.0%	109.5%	110.7%	107.5%	108.8%	114.9%	117.8%	121.5%	142.7%	139.5%	143.9%
Ireland	100.0%	101.6%	101.3%	98.5%	101.8%	101.5%	102.5%	104.8%	105.3%	109.2%	104.4%
Italy	100.0%	101.9%	110.8%	108.6%	103.7%	101.6%	113.4%	122.3%	128.1%	118.1%	111.1%
Latvia	100.0%	142.6%	158.0%	178.9%	159.0%	172.2%	182.2%	187.2%	179.7%	231.3%	235.5%
Lithuania	100.0%	138.5%	138.3%	143.3%	154.4%	161.0%	180.6%	175.7%	185.7%	209.0%	221.5%
Luxembourg	100.0%	102.9%	96.9%	92.1%	92.7%	94.9%	97.5%	98.6%	98.3%	102.0%	98.0%
Malta	100.0%	96.4%	97.6%	90.6%	93.1%	106.7%	102.1%	106.0%	97.4%	164.6%	162.9%
Netherlands	100.0%	98.5%	96,9%	99.3%	98.3%	99.1%	99.8%	103.0%	95.9%	104.4%	105.2%
Poland	100.0%	94.9%	105.3%	111.1%	122.8%	130.4%	133.2%	135.4%	135.1%	154.3%	151.9%
Portugal	100.0%	105.0%	112.2%	101.9%	102.9%	114.2%	115.0%	118.9%	115.8%	184.9%	185.3%
Romania	100.0%	121.8%	137.2%	138.0%	154.4%	158.1%	161.4%	171.1%	168.1%	279.9%	283.0%
Slovakia	100.0%	107.0%	109.1%	111.3%	111.6%	119.0%	129.1%	136.2%	145.1%	134.6%	136.6%
Slovenia	100.0%	113.3%	127.2%	132.5%	130.4%	143.0%	146.1%	153.5%	153.9%	142.5%	142.2%
Spain	100.0%	111.3%	117.7%	109.6%	110.2%	115.0%	115.4%	114.6%	108.7%	126.7%	122.5%
Sweden	100.0%	97.9%	99.7%	101.7%	101.2%	101.2%	95.6%	95.8%	93.2%	93.6%	100.3%
United Kingdom	100.0%	104.8%	108.5%	114.5%	115.0%	117.5%	114.6%	108.1%	97.1%	126.4%	124.9%

Source: authors’ compilation.

and

Table 4. Values of individual chain indexes of the aggregate measure.

Counties	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Austria	-	105.5%	100.8%	103.5%	100.9%	98.8%	102.5%	100.9%	104.6%	76.7%	103.6%
Belgium	-	99.4%	108.1%	95.1%	104.4%	98.1%	106.4%	102.9%	99.0%	92.2%	102.5%
Bulgaria	-	111.1%	104.3%	104.1%	92.1%	103.1%	102.6%	104.7%	104.1%	151.4%	101.9%
Croatia	-	106.2%	107.7%	104.8%	108.2%	96.9%	107.3%	101.7%	101.8%	132.8%	99.7%
Cyprus	-	97.3%	113.5%	100.6%	122.8%	99.5%	105.3%	93.2%	96.3%	158.1%	95.3%
Czechia	-	103.0%	108.9%	102.6%	102.3%	102.1%	106.5%	103.8%	98.1%	102.6%	99.8%
Denmark	-	96.7%	108.0%	102.6%	100.3%	100.4%	96.7%	100.7%	99.8%	90.4%	97.6%
Estonia	-	114.9%	99.4%	104.5%	111.9%	106.8%	98.5%	104.4%	97.4%	114.3%	103.8%
Finland	-	100.1%	100.2%	99.8%	101.6%	103.6%	105.7%	97.1%	99.7%	101.8%	101.4%
France	-	102.6%	102.9%	105.8%	101.0%	100.5%	97.7%	104.4%	97.0%	108.5%	97.1%
Germany	-	98.7%	101.3%	99.1%	101.2%	99.4%	101.2%	99.0%	97.3%	92.8%	102.3%
Greece	-	102.0%	106.4%	106.7%	113.6%	103.5%	99.3%	101.2%	102.8%	143.8%	100.1%
Hungary	-	109.5%	101.1%	97.1%	101.2%	105.6%	102.5%	103.2%	117.4%	97.8%	103.1%
Ireland	-	101.6%	99.8%	97.2%	103.4%	99.7%	101.0%	102.2%	100.5%	103.7%	95.5%
Italy	-	101.9%	108.7%	98.0%	95.5%	98.0%	111.6%	107.9%	104.8%	92.2%	94.0%
Latvia	-	142.6%	110.8%	113.2%	88.9%	108.3%	105.8%	102.8%	96.0%	128.7%	101.8%
Lithuania	-	138.5%	99.8%	103.7%	107.7%	104.3%	112.2%	97.3%	105.7%	112.6%	106.0%
Luxembourg	-	102.9%	94.2%	95.1%	100.6%	102.4%	102.7%	101.1%	99.7%	103.8%	96.1%
Malta	-	96.4%	101.3%	92.8%	102.7%	114.7%	95.7%	103.7%	92.0%	169.0%	98.9%
Netherlands	-	98.5%	98.4%	102.5%	98.9%	100.8%	100.7%	103.3%	93.0%	108.9%	100.8%
Poland	-	94.9%	110.9%	105.5%	110.6%	106.2%	102.1%	101.7%	99.8%	114.2%	98.4%
Portugal	-	105.0%	106.9%	90.8%	100.9%	111.0%	100.7%	103.3%	97.4%	159.7%	100.2%
Romania	-	121.8%	112.7%	100.6%	111.9%	102.4%	102.1%	106.0%	98.2%	166.5%	101.1%
Slovakia	-	107.0%	101.9%	102.1%	100.3%	106.6%	108.5%	105.5%	106.5%	92.8%	101.5%
Slovenia	-	113.3%	112.2%	104.2%	98.4%	109.7%	102.2%	105.0%	100.3%	92.5%	99.8%
Spain	-	111.3%	105.7%	93.2%	100.5%	104.3%	100.4%	99.3%	94.9%	116.5%	96.7%
Sweden	-	97.9%	101.9%	102.0%	99.5%	100.0%	94.5%	100.2%	97.3%	100.4%	107.2%
United Kingdom	-	104.8%	103.5%	105.5%	100.5%	102.1%	97.5%	94.4%	89.8%	130.2%	98.8%

Source: authors’ compilation.

present the values of individual single-base and chain indexes for the aggregate measure. When analyzing the values of single-base indexes, it was noticeable that in 16 countries the situation regarding the studied phenomenon in subsequent years as compared to 2010 was better. This is evidenced by the index value at the level of over 100%. In other countries, these changes were inconclusive.

In turn, the analysis of the value of chain indexes calculated for the aggregate measure of sustainable development in the area of Sustainable Cities and Communities showed that none of the analyzed countries recorded an annual, undisturbed relative increase in the level of sustainable development in the study area. In each of them, in the years 2010–2020, a decrease in the level of sustainable development in a given year compared to the previous year was observed. Such a situation took place most often, and as many as six times in Germany. It should also be emphasized that in four countries (Bulgaria, Greece, Romania, and Slovakia) a decline was recorded only once.

Table 5. Ranking of the European Union countries in terms of Sustainable Cities and Communities in the years 2010–2020.

Countries	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Estonia	18	14	15	13	9	6	7	6	8	2	1
Malta	15	18	19	22	24	19	23	21	24	1	2
Portugal	21	21	21	26	26	25	26	26	26	5	3
Finland	4	4	6	5	5	5	1	4	4	9	4
Greece	24	25	26	24	23	23	24	24	21	7	5
Czechia	12	11	9	8	6	8	5	1	3	6	6
Cyprus	22	27	24	25	20	21	21	23	23	3	7
Ireland	2	1	3	4	4	3	3	3	2	4	8
Sweden	1	2	1	1	1	2	6	7	9	13	9
Spain	9	7	5	7	8	7	8	9	15	8	10
Hungary	16	15	16	18	19	17	18	15	7	12	11
Croatia	23	23	23	21	21	24	22	22	20	10	12
Lithuania	26	20	22	23	22	22	19	20	17	17	13
Romania	28	28	28	28	27	27	28	27	28	14	14
France	10	9	10	6	7	9	10	11	13	11	15
Slovakia	17	17	18	17	16	15	11	10	6	18	16
Poland	19	22	20	19	18	16	17	16	16	15	17
United Kingdom	13	13	13	10	11	11	14	19	22	16	18
Bulgaria	25	26	27	27	28	28	27	28	27	20	19
Belgium	7	8	7	9	10	10	9	8	10	22	20
Luxembourg	6	5	8	11	12	12	12	14	14	19	21
Denmark	3	6	2	2	2	1	4	5	5	21	22
Latvia	27	24	25	20	25	26	25	25	25	24	23
Austria	5	3	4	3	3	4	2	2	1	27	24
Slovenia	20	19	17	16	15	13	13	12	12	25	25
Netherlands	11	12	14	15	14	18	20	18	19	26	26
Italy	14	16	12	14	17	20	16	13	11	23	27
Germany	8	10	11	12	13	14	15	17	18	28	28

Source: authors’ compilation.

presents the ranking information in terms of Sustainable Cities and Communities in the years 2010–2020. During the 11 analyzed years, Sweden was the leader four times. Seven countries took the lead once. The last position in the ranking was occupied by Romania six times. The results were sorted for 2020, where Estonia was the leader.

The ranking covering the area of Sustainable Cities and Communities showed that only two countries (Finland and Ireland) managed to remain among the top 10 in each of the analyzed years.

4. Conclusions

To sum up, it should be noted that despite periodic difficulties, the situation in terms of the level of sustainable development measured by the implementation of SDG 11: Sustainable Cities and Communities gradually improved.

Based on the conducted research, several comments and recommendations can be formulated. In general, in the years 2010–2020, Sweden was the leader regarding the implementation of SDG 11 (Table 5). It ranked first five times. Apart from that, the leading positions were taken by Ireland, Denmark, Finland, the Czech Republic, Austria, Malta, and Estonia. The last position was most often occupied by Romania and Bulgaria.

When analyzing the results of SDG 11, it should be borne in mind that today’s cities represent the centers of culture and science, industry and productivity, and social development, and new ideas are born there. When a city flourishes, people benefit from social and economic development. In other words, creativity in business is born in urban spaces and affects the quality of life of their users.

The number of people living in urban areas is projected to increase to the level of 5 billion by 2030. Therefore, effective urban planning and management practices need to be implemented today in order to meet the challenges of expanding urbanization.

It is imperative for public management to follow modern strategies and draw on the best practices, as it does have a great impact on urban development. On the other hand, if the socio-economic growth of a given country is hampered, poor quality of public management is frequently identified as one of the main reasons underlying this phenomenon.

In the context of the comprehensive implementation of the concept of sustainable development, an answer should be provided to the following question: How can we strive for successful development of cities and job creation without the overexploitation of land and the depletion of resources? At the same time, it refers to the fundamental principles of sustainable development, i.e., meeting one’s own needs without compromising the development opportunities of future generations.

Other urban challenges include overcrowding, insufficient funding for basic services, lack of adequate housing, and deteriorating infrastructure. In addition, there are problems related to the economic slowdown, high inflation, expensive housing loans, difficulties in accessing raw materials and energy, and armed conflicts.

Urbanization forces actions to be taken to ensure safe waste disposal and management in cities. Urban development is a desirable process; however, at the same time, it is necessary to improve the efficiency of using the available resources, make efforts to reduce pollution, and counteract poverty. Cities of the future should offer their users equal opportunities for development and access to basic services, energy, housing, and transport, as well as ensure security.

Among other activities, attention should be paid to counteracting overcrowding through effective housing management and spatial-development plans. Urban noise, which has a negative impact on the quality of life experienced by the residents, should be reduced, e.g., by installing acoustic screens in sensitive places. For urbanized areas, road safety and technical infrastructure at a high level will always be important. Moreover, there is a certain relationship between these elements, i.e., the better the infrastructure, the fewer accidents in road traffic.

Yet another area requiring action is air pollution, which is one of the biggest traditional problems of modern cities, apart from transport congestion. The remedy for the aforementioned congestion is taking advantage of public transport instead of using private means of transport. Even significant financial resources at the disposal of city authorities do not always solve problems, e.g., congestion, due to the fact that space is inherently limited and has to be used in a rational manner.

The analyses, which were conducted within the framework of the study and covered the European Union countries, can also be used in other geographical areas. Sustainable development is a phenomenon that should be monitored at different levels of management and in various geographical areas. The availability of data may be the only limitation. For example, research covering Africa has to be based on the data available in the World Bank.