Socio-Spatial Aspects of Shrinking Municipalities: A Case Study of the Post-Communist Region of North-East Poland

Abstract

Urban shrinkage has become a common feature for a growing number of European cities and urban regions. Cities in Europe have lost populations during the previous few decades, many of them in the post-communist countries. A similar phenomenon has been observed in smaller units: municipalities and villages. Shrinking towns/municipalities/villages grapple with insufficiently used housing infrastructure, a decrease in labor force, investment and in the number of jobs. This analysis examines the socio-spatial factors present in municipalities in the north-east of Poland, which are expected to experience the greatest population decrease by 2030. The study focused mainly on determinants with the greatest impact on the good life standards. It also sought to answer why the population growth forecasts for these units are so unpromising. The findings have shown that the majority of determinants adopted in the conceptual model describing the good life standards are below the reference values. The applied taxonomic measure of good life standards (TMGL) method allowed for identifying five municipality clusters representing “different speeds” at which these forecasts are fulfilled. Two clusters have dominant determinants in five criteria and three clusters, in two criteria adopted in the conceptual model. The findings indicate that approx. 35% of the municipalities under analysis have a chance for stabilization of the population size, provided local stakeholders take some targeted actions.

1. Introduction

Reducing poverty and inequalities and achieving economic welfare are all global challenges in line with the United Nations Sustainable Development Goals 1, 8 and 10 [1,2]. It is very difficult to achieve those goals in spatial entities inhabited by local communities and it requires both the residents and the decision-makers to be involved in the process. Failure to take the relevant actions results primarily in a considerable decrease in population size at some locations despite the global forecasts indicating a steady population increase until the year 2100 [3,4,5,6]. Globally, the largest population increase is forecast for Asia and Africa. However, the increase is inversely proportional to the level of development of a specific continent, country or region. Depopulation results in urban shrinkage [7,8], a phenomenon noticeable on the micro-scale, even leading to the disappearance of some villages [9]. The definitions of shrinkage have seen a shift in the literature since the latter half of the 20th century [10]. It applied mainly to critical demographic issues, such as a low fertility rate and an increase in the elderly population [11,12]. Only recently has the discourse focused on “the challenges” before urban shrinkage in terms of policy [13] and planning [14,15,16]. According to the definition, urban shrinkage/shrinking cities is an empirical phenomenon resulting from the specific interplay of different macro-processes at the local scale [7,17,18,19]. A population decrease there is larger than would result from the trends observed in the country/region/town. Other theoretical explanations of urban shrinkage have also been offered. Possible causes include depreciation of the national infrastructure (i.e., motorways) and suburbanization [20]. Pallagst [21] also suggests that the shrinkage is a response to deindustrialization since jobs are transferred from the centers to cheaper suburban areas. Other factors include (a) current urban development model—urbanization is a cyclical process and the town downfall will ultimately result in an increased growth rate [22]; (b) urban monostructures focused on one branch of economic growth are susceptible to rapid slumps [22]; (c) state-owned companies in the post-communist countries of Eastern Europe did not survive privatization, which resulted in shutting down factories and mass unemployment [22]; (d) Intelligent downfall, “planning less means fewer people, fewer buildings, less land development”: urban development is oriented towards improving the life quality of the current residents without taking into account their needs, thereby pushing more people out of the town center [20].

The term urban shrinkage/shrinking cities is therefore used to stress that fact that it is a multidimensional process with multidimensional consequences; it has an economic, demographic, geographic, social and physical dimension [19], which is evolving as a result of global and local reality. Much research has been conducted on metropolises/cities [23,24,25,26]. The phenomenon is also noticeable in smaller units, such as municipalities, small towns and villages [9]. According to historical sources, the process of their shrinkage has been observed since antiquity. The most common factors which make its intensity increase include emigration, economic crises, a deteriorating socio-economic status of the society [9], environment pollution, a peripheral location and economic marginalization, as well as epidemics [27,28].

Depopulation is an unfavorable phenomenon. It affects the aging of the society, leads to an unfavorable economic structure, depreciation of real estate, landless cultivation or abandonment of agricultural areas, in addition to a lack of inheritance of the cultural landscape. Such locations are also becoming unattractive for entrepreneurs due to the unfavorable age structure of the population. As a consequence, this leads to the deepening of infrastructure collapse and further depopulation [29,30,31,32]. This paper attempts to determine the main causes of shrinking municipalities (SM) in Poland. Poland has been a member of the European Union for 16 years and membership has had a substantial impact on its social, economic and infrastructural development. The question is: why are population-related forecasts so adverse at some locations despite the large financial investments in the region development? The study authors put forward two research questions: what is the level of socio-spatial determinants in the municipalities threatened with SM relative to the reference ones? Can the SMs under analysis be grouped in terms of how close they are to achieving the good life standards?

This paper is organized in the following manner: The introduction is followed by a presentation of the Polish perspective of rural and urban-and-rural municipality shrinkage, a description of information sources together with the study object, primary data used in the study and its methodology. These parts are followed by a presentation of the research results and discussion. The paper also includes graphics to support the phenomenon analysis. The final part presents the conclusions from the study findings.

Shrinking Municipality—Polish Perspective

It has been noted that the population decrease in some locations in Poland is much higher than the average. The population is projected to decrease in 1665 out of 2478 municipalities in Poland by 2030, with the decrease exceeding 5% in 1007 municipalities and 10% in 322 [33]. The majority of the municipalities with the largest forecast population decrease are situated in the east of Poland (the so-called “eastern wall”). These municipalities are situated mainly in the Podlaskie Voivodship (44% of the municipalities in the voivodship), Warmińsko-Mazurskie Voivodship, in the southern part of the Lubelskie Voivodship, close to the Russian border, in the eastern part of West Pomerania and in the mountainous area in the south-east of Poland (see Figure 1). According to forecasts, a good demographic situation is enjoyed by municipalities situated in the following Voivodships: Małopolskie, Pomorskie, Wielkopolskie and the central part of Mazowieckie. The regions (municipalities) with the largest forecast population increase are situated in the immediate neighborhood of the largest urban centers. The increase results from so-called “urban sprawl” [34]. It is a consequence of the attractiveness of large urban centers as labor markets.

Studies of locations with a high level of depopulation should focus on the life quality standards [35,36,37], as their unsatisfactory level often makes residents emigrate. Life quality is defined as the extent to which life meets the good life standards [38]. Evaluation of the place where one lives (home, work, leisure, health, entertainment, etc.) is the central element of the objective (spatial) dimension of life quality. Good life standards change from one epoch to another, they depend on the social group, level of wealth, the needs of a group under study as well as historical and cultural factors. Young people, “singles”, families with children, elderly people—they all perceive good life standards differently. There are also noticeable differences between the needs of village and small-town residents and those of large cities [2]. Young people value lively and vibrant places with good transport facilities, short commuting times to work, school or places of entertainment [39]. Families with children search for places not only situated close to their place of work, but which are also safe, with access to schools, kindergartens, playgrounds, shops and health clinics [40]. Elderly people expect to find places with good access to services, including public health clinics [41]. The needs of all these groups are different.

These analyses focus on the extent to which municipalities fail to meet the good life standards. For people of working age, this failure often provides a stimulus for looking for other, satisfactory and friendly places to live, which meet their expectations. The level of good life standards is different in each country/region. It will be different in Africa than in Europe and different in the UK than in Poland. It will also vary depending on the level of financial resources. Poland’s accession to the EU and a stream of funds aimed at providing equal opportunities to the backward areas made the differences present two decades ago gradually disappear. This study examined municipalities in danger of the greatest depopulation based on a multi-criteria conceptual model (see Figure 2), which takes into account the following criteria: location, employment, security, living condition, health, social assistance, entertainment, level of development of technical infrastructure and effectiveness of local government.

Each criterion of the conceptual model is represented by several determinants. The objects under study were compared to the reference object, which covered the mean level of factors prevalent in Poland. The choice of criteria was based on the needs of people of working age and the choice of determinants—on the availability of up-to-date data.

2. Materials and Methods

2.1. Materials

The study covered two voivodships in the north-east of Poland (see Figure 3). They are part of the so-called “eastern wall” (Warmia and Mazury and Podlaskie Voivodships). In total, 65 rural and urban-and-rural municipalities in danger of depopulation (over 10%) were analyzed. These included 21 municipalities in the Warmia and Mazury Voivodship and 44 in the Podlaskie Voivodship.

The Warmińsko-Mazurskie Voivodeship covers an area of 24,173 km² (7.7% of the area of Poland) and it has a population of 1.42 million. It is called the “Land of a Thousand Lakes”, as there are over three thousand lakes. About 31% of its area is covered by forests. The GDP per capita accounts for 71.7% of the national average, which ranks the region as one of the poorest regions in Poland. The expenses of 11.2% of the Warmińsko-Mazurskie Voivodship residents are below the extreme poverty level (i.e., below the minimum subsistence level). The Podlaskie Voivodeship covers an area of 20,187 km² (6.5% of the area of Poland) and it has a population of 1.2 million. It is the coldest place in Poland, as it is affected by strong currents of cold continental air and the annual average temperature is below 7 °C. As in the previous voivodship, the GDP per capita in this region accounts for 71.7% of the national average and the expenses of 11.0% of its residents are below the extreme poverty level.

This study used primary data obtained from such sources as the Statistics Poland [43], Head Office of Geodesy and Cartography [44], Institute of Soil Science and Plant Cultivation [45], Headquarters of the State Fire Service [46], Warsaw Police Headquarters [47], data gathered in situ [48,49,50] and indices calculated from primary data according to Formula (1). They are presented in

Table 1. Data used in the study.

Symbol	Description	Unit	Reference Value	Mean	Min	Max
P1	Index of agricultural production space (soil quality to agriculture development)	index	66.6	59.67	43.7	84.2
P2	Area	km2	-	-	7118	24,294
P3	Distance to the nearest town at a higher level (district/voivodship town)	km	-	25.14	7	64
P4	Travel time to the nearest town at a higher level (district/voivodship town)	minute	30	24.74	7	55
P5	Number of bus/train lines		10	6.21	0	24
P6	Forest cover	%	30.8	13.87	9.3	71.7
S7	Population size	cases	-	4578	1484	11,827
S8	Unemployment rate	%	11.79	10.66	2.5	33.3
S9	Population density	Persons/km2	123	23.97	10	53
S10	Business entities/10,000 residents	index	1080	254.95	73	950
S11	Local hazards/municipality		110	37.86	11	106
S12	Criminal offenses rate/1000 residents	index	20.75	13.88	9.23	22.16
S13	Employment rate/1000 people	index	251	73.68	0	201
S14	Number of flats/1000 people	index	375.7	391	261	695
S15	Average flat area	m2	74.4	141.81	71	288.5
S16	Number of health clinics		8.5	2.43	0	9
S17	Number of people per one pharmacy		2651	3049	1017	6275
S18	Number of medical consultations per person	index	8.4	3.66	0	15.8
S19	Beneficiaries of social assistance/10,000 residents	index	579	1113	152	3296
S20	Number of libraries		3.2	1.43	1	4
S21	Number of supermarkets/markets		-	1.35	0	4
S22	Members of sport clubs		422	59.17	0	276
S23	Number of people receiving housing benefits		1306	371	0	3612
S24	Consumers of natural gas		-	47	0	1585
S25	Percent of buildings connected to water supply network	%	84.6	76.02	46	97.9
S26	Percent of buildings connected to sewage network	%	50.6	26	0	77.7
S27	EU funds for projects in 2014–2020 per capita	PLN	17,201	1986.85	76.13	16,471
S28	Municipality income per capita	PLN	4900	5251	4204	7035

Source: own study on [44,45,46,47,48,49,50].

.

2.2. Methods

The primary data were transposed as per the Formula (1):

$$A_I=\frac{a_i}{l_i}$$

(1)

$A_I$—index;

$a_i$—primary data;

$l_i$—population of the municipality under analysis.

The number of variables was optimized by reduction of multidimensionality with the use of expert criteria and the correlation matrix [51], according to the Formula (2).

$$R=\left[\begin{array}{ccc}1& r_{12}& r_{1 m}\\ r_{21}& 1& r_{2 m}\\ r_{m1}& r_{m2}& 1\end{array}\right]$$

(2)

where $r_{mn}$—Pearson correlation coefficient (3) for all pairs of attributes.

$$r_{mn}\frac{{\sum }_l^k\left(x_l-\overline{x}\right)\left(y_l-\overline{y}\right)}{\sqrt{{\sum }_{l=1}^k{\left(x_l-\overline{x}\right)}^2}{\sum }_{l=1}^n{\left(y_l-\overline{y}\right)}^2}$$

(3)

where

x, y—random variables

$\overline{x},\overline{y}$—mean values

K—number of variables.

Grouping of municipalities with respect of taxonomic measure of good life standards (TMGL) was based on agglomeration by the Ward method—formulas (4) and (5) [52], with the use of the Euclidean distance [53]. Distances between clusters in these methods are determined by the analysis of variance. It consists of minimization of the sum of squares of deviations within clusters. A pair of clusters is chosen at each stage from among all those that can be connected, which gives a cluster of the minimal variability [54,55]. This yields groups of objects of the maximum homogeneity within a group and the maximum heterogeneity between groups. Raw data were used because it allowed for showing the natural properties of the determinants under analysis [56]. According to many authors [57,58,59], the standardization affects the distribution of objects in the Euclidean space and reduces the effectiveness of cluster analysis methods.

$$E={{\displaystyle \sum }}_{m=1}^g{E}_m$$

(4)

$$E_m={{\displaystyle \sum }}_{l=1}^{nm}{{\displaystyle \sum }}_{k=1}^{pk}\left(x_{ml,k}-\overline{x_{m,k}}\right){\left(x_{ml,k}-\overline{x_{m,k}}\right)}^2$$

(5)

where

$$\overline{x_{m,k}}=\left(\frac{1}{n_m}\right){{\displaystyle \sum }}_{l=1}^{nm}{x}_{ml,k}$$

(6)

—the mean of the m_th cluster for k_th variable

$x_{ml,k}$—being the score on the k_th variable (k = 1, 2, …, p) for the l_th object (l = 1, 2, …, nm) in the m_th cluster (m = 1, 2, …, g).

All the calculations were made with the licensed Statistica 13.1. Software (StatSoft Poland).

3. Results

Altogether, 65 municipalities situated in the Warmińsko-Mazurskie and Podlaskie Voivodships were analyzed, with 28 indices determined to describe the socio-spatial factors and included in the conceptual good life standard model (see Figure 3). The spatial factors described both the position relative to the economic center/higher level location (district/voivodship town) as well as internal factors (forest cover, soil quality, etc.).

The municipalities under study differ with respect to both the area (P₂) and population (S₇). The smallest municipality—Przytuły (Podlaskie Voivodship) occupies an area of 71.18 km² and the largest—Pieniężno (Warmińsko-Mazurskie Voivodship) has an area of 242.94 km². Dubicze Cerkiewne is the municipality with the smallest population—1484 people (2018) and Biała Piska is the largest—11,827 people (2018). The municipality of Dubicze Cerkiewne has the lowest population density (S₉) (10 people/km²). Wiżajny is the municipality with the poorest soils (P₁) (the index of 43.7/100) and Barciany has the best soils (84.2/100). The municipality of Ruciane Nida has the largest forest cover (P₆) (71.7%). In terms of the transport availability, there is no public transport in eight of the municipalities under study (P₅), which is why residents have to use their own cars. The largest number of bus links to the nearest higher level economic center (county/voivodship town) can be found in Bielsk Podlaski (24 lines). The municipality of Michałowo is the farthest from the district town (P₃)—64 km and the municipality of Szepietowo is the closest (7 km). Figure 4 shows the distance between particular municipalities and the economic centers of the higher level and the time it takes to travel there. The municipality numbers and positions are listed in Appendix A).

Socio-economic factors make up another group included in the conceptual model of the good life standards. It is larger and it covers the life quality criteria that are most vital from the human perspective. These include employment (unemployment rate—S₈, number of the employed—S₁₃, number of jobs—S₁₀), security (local natural hazards—S₁₁, criminal offenses—S₁₂), living conditions (number of flats—S₁₄, average flat area—S₁₅), healthcare (number of health clinics—S₁₆, number of pharmacies—S₁₇, number of medical consultations—S₁₈), social assistance (number of social assistance beneficiaries—S₁₉, number of people receiving housing benefits—S₂₃), technical infrastructure (consumers of natural gas—S₂₄, % of buildings connected to the water supply network—S₂₅ and to the sewage network—S₂₆), entertainment (number of libraries—S₂₀, number of sport club members—S₂₂, number of supermarkets/markets—S₂₁) and effectiveness of local government (amount of funds from the EU received in 2014–2020—S₂₇, municipality income per capita—S₂₈).

The mean unemployment rate (S₈) varies. The mean unemployment rate during the period of 2004–2019 in Poland was 11.8% [60]. When it comes to the area under study, the highest unemployment rate was recorded in the municipality of Lelkowo (Warmińsko-Mazurskie Voivodship)—33.3% and the lowest—in the municipality of Przytuły (Podlaskie Voivodship)—3.1%. This index was higher than the reference value in nearly 32% of the municipalities under study (see Figure 5).

Business activity development level is very low in the municipalities under study. There are 1080 business entities per 10,000 residents (S₁₀) in Poland. None of the SMs under study achieved the reference value, with the municipality of Bisztynek being the closest (950). The number of business entities is under 50% of the reference value in the other SMs. Security is one of the most important factors taken into account when the place of living is chosen, especially by families. Two security-related attributes were considered in this study—the number of criminal offences/1000 residents (traffic-related, economic) (S₁₂) and local hazards/municipality (natural) (S₁₁). It is a destimulating criterion (a high index has a negative impact on the good life standards). S₁₁ is below the reference value. The municipalities of Pieniężno, Orzysz and Ruciane-Nida are the least secure. Considering the second index related to crime (S₁₂), the reference value (20.75) was exceeded in two municipalities—Janowo (22.16) and Janowiec Kościelny (22.16). Both objects are situated in the Warmińsko-Mazurskie Voivodship (see Figure 6) close to the border with the Mazowieckie Voivodship.

In general, the housing conditions (S₁₄/S₁₅) in the objects under study are highly satisfactory (see Figure 7). However, a more thorough analysis shows that it is a consequence of the area depopulation, which leaves many flats empty [11,12] and creates problems for their owners, usually individuals. The S₁₅ index exceeds the reference value for 21 municipalities. The greatest problem concerning empty flats is encountered in the municipality of Kobylim-Borzymy (the index is more than three times higher than the reference value).

Accessibility of places where residents can seek medical advice is an important aspect of life in places outside large economic centers (number of health clinics, number of residents per pharmacy, number of medical consultations). In each municipality there is a pharmacy (S₁₇) where one can buy medicines. In one municipality, the number of residents per pharmacy is three times higher than the reference value (Barciany—6275, with the reference value—2651). There is not a health clinic in every municipality (S₁₆), which makes people use such facilities situated in the neighboring municipalities.

The largest number of medical consultations was recorded in the municipality of Michałowo (Podlaskie Voivodship)—15.8—and it is nearly twice as large as the reference value (8.4).

The level of technical infrastructure in the municipalities under study is very low (see Figure 8). There is a water supply network (S₂₅) in each of the municipalities under study, but only 46% of the buildings are connected to the network in the municipality of Szudziałowo and nearly 100% of the buildings in the municipality of Korsze. The situation is much worse when it comes to the sewage system (S₂₆). The set under analysis includes municipalities (Przytuły, Milejczyce, Perlejewo, Kulesze Kościelne) without a central sewage system. The residents have to use the sewage installations made on their own—usually cesspits or individual wastewater treatment plants. The reference value for the sewage system (number of buildings connected to the sewage system) is 50.6% and this level is achieved only in four municipalities—Narewka, Ruciane Nida, Janowo and Srokowo. Due to the climate in Poland, people have to use heating systems. They are usually powered by gas (S₂₄), although there are increasingly more systems which use renewable energy (photovoltaics, heat pumps, etc.) because they are subsidized by the government. The percentage of buildings connected to such systems in the municipalities under study is very small—a gas supply network was available only in nine of them. Coal-fired boilers, with emissions contributing to smog formation, are still the most popular heating devices in Poland [61,62].

The active involvement of local authorities in funds acquisition for actions aimed at stemming the urban shrinkage is of key importance [63,64]. Local authorities initiate actions in their municipalities, seek funds to finance the development of the area, of business activity and training aimed at reducing unemployment. Two measures related to this criterion were taken into consideration in the area under study—EU funds acquired in 2014–2020 (S₂₇) and the municipality income per capita (S₂₈). The analyses show that local governments’ effectiveness in acquiring EU funds for municipality development is low. The municipalities closest to the reference value (PLN 17,201 per capita) include Sidra (PLN 16,471 per capita), as well as Perlejewo, Szczuczyn, Grodzisk, Narew, Pieniężno and Reszel (see Figure 9). This index is lower than 20% of the reference value in the other SMs, which means that the local government effectiveness in this regard is very poor. The situation is a little better when it comes to the S₂₈ index. The municipality income per capita (S₂₈) is below the reference value in 30% of the municipalities under study. It is similar or slightly higher in the other municipalities. The municipality of Mielnik stands out because the income per capita in it reaches 170% of the reference value.

Solving the second research problem, i.e., identifying clusters of municipalities similar in terms of the criteria under analysis, required reducing the dimensionality by the expert method and a correlation matrix (Appendix B). The reduction aimed to choose only those variables which can be used to discriminate against individual observations because they are included in a specific cluster [56] Descriptive determinants and those with a neutral impact (municipality area—P2, forest cover—P6, population—S7, population density—S9) as well as correlated variables with a similar information range—P3 (distance from the nearest higher level town), S21 (number of markets), S22 (number of sport club members), S23 (number of people receiving housing benefits), S24 (natural gas consumers) were excluded from further analyses. In total, 65 SM objects were clusterized and each was described with seven criteria (19 determinants). This allowed for identifying groups of SM (shrinking municipality) of various speeds. Five clusters were identified by this method (formula 4 and 5). The first cluster included 20 SM, the second—16 SM, the third—21 SM, the fourth—6 SM and the fifth—2 SM (see Figure 10).

Table 2. Comparison results for the criteria of the conceptual model of good life standards for the SM.

Criterion	Symbol	Impact	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
location	P1	stimulant	62.03	58.51	55.34	60.93	67.75
	P4	destimulant	27.2	20.88	27.0	21.17	18.0
	P5	stimulant	7.4	4.6	5.5	8.0	9.5
employment	S8	destimulant	13.6	9.2	8.4	10.0	17.8
	S10	stimulant	306.6	229.5	189.3	253.8	273.5
	S13	stimulant	76.9	92.7	64.2	54.2	48
security	S12	destimulant	13.9	14.4	13.5	13.5	14.8
	S11	destimulant	41.8	35.9	32.3	42.5	59
living condition	S14	stimulant	361.2	387.9	422.5	408.7	351
	S15	stimulant	139.5	143.1	144.5	132.9	153.3
healthcare	S16	stimulant	2.8	2.6	2.2	1.8	1
	S17	destimulant	3279	4139	2124	2519	3324
	S18	destimulant	3.9	3.5	3.7	3.4	2.4
social assistance	S19	destimulant	1227.6	1149.6	1005.9	1024.2	1059
entertainment	S20	stimulant	1.6	1.3	1.4	1	2
technical infrastructure	S25	stimulant	82.7	82.7	82.4	75.1	92.3
	S26	stimulant	31.4	25.7	23.4	16.5	39.8
effectiveness of local government	S27	stimulant	1235	872	1404	9713	1373
	S28	stimulant	4965.3	5761.4	5095.6	5465.8	5002.1

Source: own study.

shows the result of a comparison of individual clusters in the nine criteria under analysis. The determinants were divided into stimulants and destimulants (the higher the index, the worse condition of the SM) of the good living standards (see Table 2).

The comparison results show that the municipalities grouped in two clusters, 3 and 5, are significantly close to the reference values and they even achieved better results for some determinants than the reference model (see Table 2).

Cluster 3 and cluster 5 are the most promising. Cluster 5 includes only two municipalities (Pozezdrze and Sępopol), which have the best indices in five criteria (see Table 2). In consequence, it has the best chance of mitigating the scenario in the population-related forecast. This cluster has the best indices for location, living conditions, healthcare, entertainment and technical infrastructure. Soils in these municipalities are good (P₁) and cultivated [65] because the agriculture land is leased to active farmers, due to their location features (distance to higher-level towns and the number of bus lines) one can find a job outside one’s place of residence. The housing conditions are good, the population health is relatively good (a low number of medical consultations) and the technical infrastructure level (water supply and sewerage networks) is also good. Agriculture is developing (soils are good) and the natural and landscape values (lakes, forests, varied land relief, low pollution level, nature parks) favor tourism development. In the municipality of Pozezdrze there are bunkers and fortifications (Himmler’s field headquarters), tourist routes, castles and 19th century mansions, sacral objects and monuments [66] The municipality of Sępopol is a craft and service center, with services for agriculture dominating in the past. The downfall of the local food processing industry took place after 1990 [67]. The situation in cluster 3 is very similar. The level of the determinants under study is the best in five criteria: location, employment, security, health and social assistance (see Table 2). Unlike in cluster 5, it has a good location and healthcare, as well as positive indices regarding employment, safety and social assistance. The unemployment rate is lower than the reference value (11.79) and it is below the reference value booth in terms of natural hazards (S₁₁) and crime rate (S₁₂). The level of social assistance (S₁₉) is the lowest in this cluster. Both clusters can be regarded as including first speed municipalities (approx. 35% of the municipalities under study). The probability that the depopulation forecasts for these municipalities will not be fulfilled is the greatest. It is much lower in clusters 1, 2 and 4. Each of the clusters is distinguished by two criteria. Cluster 1—employment and healthcare (see Table 2). The number of business entities index (S₁₀) is higher than in the other clusters, but it accounts only for 30% of the reference value. The number of health clinics in this group of municipalities is also the largest. Among the determinants that distinguish cluster 2 include employment and effectiveness of local government. The municipality income per capita (S₂₈) is higher than the reference value and the employment rate (S₁₃) is lower, but it is higher than in the other clusters. Cluster 4 is distinguished by the safety and effectiveness of local government. The latter criterion is particularly important because the municipal authorities noticed the trend and took actions to acquire funds for development.

4. Discussion

The phenomenon of urban shrinkage was noted several decades ago. It involves a considerable population decrease compared to the trends in other regions. It was noted that not only in cities/urban areas, but also in smaller units in terms of area and population, such as municipalities and villages [68,69,70]. The objects affected by this phenomenon are influenced by demographic, economic and political factors in ways which lead to their disappearance. According to current estimates, approx. 40% of European towns are losing their populations [71]. Therefore, it is a problem in many European regions [72]. Rink et al. [73] report that the major causes of urban shrinkage in most East European towns include economic decline, which has been observed since 1989. One can agree with this opinion. The acquisition of funds for the area development, new investments in infrastructure and creating new jobs have a positive impact on the population’s welfare [70]. In contrast, their absence results in people looking for other, more attractive places to live.

Loss of population and the resulting transformations present a serious challenge to local stakeholders. It is because the elderly population grows and the group of people of working age shrinks. Therefore, society in an SM requires different services. These trends have serious consequences regarding the adaptation of buildings, transport, services and the physical environment, particularly healthcare, social welfare, transport (location) and services [74,75,76]. This can be noted in the objects under study. However, additional financial support is required to meet the needs of the transforming population. Many decision-makers find it difficult to accept the financial outlay increase for this purpose [7,72]. Despite many EU and national programs for small and medium-sized towns and villages, the municipalities under study benefited from them to a very small extent. One municipality (Sidra) achieved nearly the level of the reference object and five (Perlejewo, Szczuczyn, Grodzisk, Narew, Pieniężno and Reszel) stood out against the others. However, they were included in cluster 4, with municipalities not classified as first speed for the non-fulfilment of the forecasts due to very low indices in all the other criteria.

In many countries subject to the phenomenon of depopulation, research is conducted on the specificity of managing shrinking regions, especially from the perspective of maintaining their economic vitality. The formulated rules mainly concern [77,78,79,80,81]:

(1)

getting used to the prospect of shrinking population and economic potential;

(2)

the need for long-term thinking—decision-makers and planners should take this process into account in works related to the development and maintenance of infrastructure;

(3)

better cooperation between local government units in order to reduce costs and improve the quality of provided public services (creation of joint centers of administrative and social services);

(4)

preferences for “compact growth”, i.e., development focused on relatively densely populated areas without encouraging an excessive dispersion of the population;

(5)

holistic thinking taking into account the interdependencies between the activities carried out, being aware of the existing synergies.

The practical application of these principles should be adapted to local conditions and needs.

According to Schlappa, Neil [72], new local governments need to be elected in many locations to stimulate the dialogue with local stakeholders (entrepreneurs, public sector and civil society) and to identify the real development opportunities suited to the local reality. These needs can be diverse since the good life standards are perceived in various ways. Focusing on local needs is of key importance. For example, supporting local entrepreneurs and policies aimed at redesigning services and infrastructure to suit the age and needs of the local population. Therefore, the ideas and strategies being implemented should concern both elderly people and children. People are willing to settle in locations with the healthy surroundings, the friendly landscape, the local identity and free from natural hazards [82,83], where the physical and social environment is being created and improved continuously, where community resources are expanded to enable people to support each other and to develop their potential.

In general, actions to strengthen the demographic potential should be undertaken as part of a comprehensive and long-term social policy, consistently implemented at the national, regional and local level. Without comprehensive action, it will not be possible to stop or mitigate the negative demographic trends [84]

5. Conclusions

The phenomenon of urban shrinkage is noticeable all over Europe—not only in towns, but also in units of smaller area and population. Depopulation of some locations is attributed to many factors, but it is usually caused by economic and political factors. The local authorities, realizing that the shrinkage is taking place, can provide a stimulus for modernization, both with respect to the infrastructure and to the municipality management.

An analysis of the extent to which good life standards are met was performed in municipalities situated in two regions of the so-called “eastern wall” of Poland. These regions have the largest number of municipalities with forecast depopulation indexes above 10% by 2030. The conceptual model of the good life standards for the rural and urban-and-rural municipalities was proposed, including such criteria as location, employment, security, health, social assistance, entertainment, living conditions, technical infrastructure and effectiveness of local government and a reference object was created for comparison. The research questions formulated for the study enabled the authors to diagnose the condition of the objects with respect to the socio-spatial criteria and the similarity between them. The majority of the municipalities under study failed to achieve the reference values in nearly all the criteria. Homologous municipalities and municipalities of two speeds were identified by the grouping methods. The first speed municipalities (about 35%) dominate in five criteria and they allow for the optimistic assumption that if actions are taken to develop a feasible strategy, it is possible to stop or mitigate the fulfilment of the depopulation forecast. However, this cannot be achieved without the involvement of local entrepreneurs, public authorities, the community and targeted settlement policies.