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

sustainability

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

Katarzyna Kocur-Bera * and Karol Szuniewicz

Department of Geoinformation and Cartography, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland; [email protected] * Correspondence: [email protected]; Tel.: +48-895-234-583

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 Citation: Kocur-Bera, K.; Szuniewicz, municipalities under analysis have a chance for stabilization of the population size, provided local K. Socio-Spatial Aspects of Shrinking stakeholders take some targeted actions. Municipalities: A Case Study of the Post-Communist Region of Keywords: urban shrinkage; shrinking municipality; conceptual model of the standard of good North-East Poland. Sustainability living; reference model 2021, 13, 2929. https://doi.org/ 10.3390/su13052929

Academic Editor: Karina Pallagst 1. Introduction Received: 27 January 2021 Reducing poverty and inequalities and achieving economic welfare are all global Accepted: 4 March 2021 challenges in line with the United Nations Sustainable Development Goals 1, 8 and 10 [1,2]. Published: 8 March 2021 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. Publisher’s Note: MDPI stays neutral Failure to take the relevant actions results primarily in a considerable decrease in population with regard to jurisdictional claims in size at some locations despite the global forecasts indicating a steady population increase published maps and institutional affil- until the year 2100 [3–6]. Globally, the largest population increase is forecast for Asia and iations. 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 Copyright: © 2021 by the authors. half of the 20th century [10]. It applied mainly to critical demographic issues, such as Licensee MDPI, Basel, Switzerland. a low fertility rate and an increase in the elderly population [11,12]. Only recently has This article is an open access article the discourse focused on “the challenges” before urban shrinkage in terms of policy [13] distributed under the terms and and planning [14–16]. According to the definition, urban shrinkage/shrinking cities is an conditions of the Creative Commons empirical phenomenon resulting from the specific interplay of different macro-processes Attribution (CC BY) license (https:// at the local scale [7,17–19]. A population decrease there is larger than would result from creativecommons.org/licenses/by/ the trends observed in the country/region/town. Other theoretical explanations of urban 4.0/).

Sustainability 2021, 13, 2929. https://doi.org/10.3390/su13052929 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 2929 2 of 20

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 [23–26]. 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 [29–32]. 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), Warmi´nsko-MazurskieVoivodship, 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 Figure1). According to forecasts, a good demographic situation is enjoyed by municipalities situated in the following Voivodships: Małopolskie, Sustainability 2021, 13, 2929 3 of 21

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demographic situation is enjoyed by municipalities situated in the following Voivodships: Małopolskie, Pomorskie, Wielkopolskie and the central part of Mazowieckie. The regions Pomorskie,(municipalities) Wielkopolskie with the andlargest the forecast central part popu oflation Mazowieckie. increase Theare situated regions (municipalities) in the immedi- withate neighborhood the largest forecast of the populationlargest urban increase centers. are The situated increase inthe results immediate from so-called neighborhood “urban ofsprawl” the largest [34]. urbanIt is a centers.consequence The increase of the attrac resultstiveness from so-calledof large urban “urban centers sprawl” as [labor34]. It mar- is a consequencekets. of the attractiveness of large urban centers as labor markets.

FigureFigure 1.1. ForecastForecast ofof populationpopulation decreasedecrease exceedingexceeding 10%10% inin PolishPolish municipalities.municipalities. Source:Source: [33[33].].

StudiesStudies ofof locationslocations withwith aa highhigh levellevel ofof depopulationdepopulation shouldshould focusfocus onon thethe lifelife qualityquality standardsstandards [ 35[35–37],–37], as as their their unsatisfactory unsatisfactory level level often often makes makes residents residents emigrate. emigrate. Life Life quality qual- isity defined is defined as theas the extent extent to whichto which life life meets meet thes the good good life life standards standard [s38 [38].]. Evaluation Evaluation ofof thethe placeplace wherewhere oneone liveslives (home,(home, work,work, leisure,leisure, health, health, entertainment, entertainment, etc.)etc.) isis thethecentral central elementelement ofof the the objective objective (spatial) (spatial) dimension dimension of lifeof life quality. quality. Good Good life standards life standards change change from onefrom epoch one epoch to another, to another, they depend they depend on the socialon the group, social levelgroup, of wealth,level of thewealth, needs the of needs a group of undera group study under as wellstudy as as historical well as historical and cultural and factors. cultural Young factors. people, Young “singles”, people, “singles”, families withfamilies children, with children, elderly people—they elderly people—they all perceive allgood perceive life standardsgood life standards differently. differently. There are alsoThere noticeable are also noticeable differences differences between the between needs the of village needs andof village small-town and small-town residents andresi- thosedents ofand large those cities of large [2]. Young cities [2]. people Young value peop livelyle value and vibrantlively and places vibrant with places good with transport good facilities,transport short facilities, commuting short commuting times to work, times school to work, or placesschool ofor entertainmentplaces of entertainment [39]. Families [39]. withFamilies children with search children for search places for not places only situated not only close situated to their close place to their of work, place but of whichwork, arebut alsowhich safe, are with also access safe, with to schools, access kindergartens,to schools, kindergartens, playgrounds, playgrounds, shops and health shops clinicsand health [40]. Elderlyclinics [40]. people Elderly expect people to find expect places to with find good places access with to good services, access including to services, public including health clinicspublic [health41]. The clinics needs [41]. of all The these needs groups of all are these different. groups are different. TheseThese analysesanalyses focusfocus onon thethe extentextent toto whichwhich municipalitiesmunicipalities failfail toto meetmeet thethe goodgoodlife life standards.standards. ForFor peoplepeople ofof workingworking age,age, thisthis failurefailure oftenoften providesprovides aa stimulusstimulus forfor lookinglooking forfor other,other, satisfactorysatisfactory and and friendly friendly places places to to live, live, which which meet meet their their expectations. expectations. TheThe levellevel ofof goodgood life standards standards is is different different in in each each country/region. country/region. It will It will be different be different in Africa in Africa than thanin Europe in Europe and different and different in the in UK the than UK thanin Poland. in Poland. It will It also will vary also varydepending depending on the on level the levelof financial of financial resources. resources. Poland’s Poland’s accession accession to the toEU the and EU a stream and a streamof funds of aimed funds at aimed provid- at providinging equal opportunities equal opportunities to the backward to the backward areas made areas the made differences the differences present presenttwo decades two decadesago gradually ago gradually disappear. disappear. This study This examined study examined municipalities municipalities in danger in of danger the greatest of the greatestdepopulation depopulation based on based a multi-criteria on a multi-criteria conceptual conceptual model (see model Figure (see 2), Figure which2 takes), which into takesaccount into the account following the following criteria: location, criteria: location,employment, employment, security, security, living condition, living condition, health, health,social assistance, social assistance, entertainment, entertainment, level of level development of development of technical of technical infrastructure infrastructure and ef- and effectiveness of local government. fectiveness 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. Sustainability 2021, 13, 2929 4 of 21

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Figure 2. Conceptual model of good life standards for a shrinking municipality (SM). Source: own study.

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 Figure 2. Conceptualworking model of goodage and life standardsthe choice for of a determinants— shrinking municipalityon the (SM). availability Source: ownof up-to-date study. data. Figure 2. Conceptual model of good life standards for a shrinking municipality (SM). Source: own study. 2.2. Materials Materials and and Methods Methods 2.1. MaterialsEach criterion of the conceptual model is represented by several determinants. The objects2.1. Materials under study were compared to the reference object, which covered the mean level The study covered two voivodships in the north-east of Poland (see Figure3). They of factorsThe prevalent study covered in Poland. two voivodshipsThe choice of in criteria the nort wash-east based of Polandon the needs(see Figure of people 3). They of are part of the so-called “eastern wall” (Warmia and Mazury and Podlaskie Voivodships). workingare part age of theand so-called the choice “eastern of determinants— wall” (Warmiaon theand availability Mazury and of Podlaskie up-to-date Voivodships). data. In total, 65 rural and urban-and-rural municipalities in danger of depopulation (over 10%) 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 2. wereMaterials analyzed. and Methods These included 21 municipalities in the Warmia and Mazury Voivodship and 44 in the Podlaskie Voivodship. 2.1.and Materials 44 in the Podlaskie Voivodship. 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.

FigureFigure 3. 3.Study Study area. area. Source: Source: [33 [33,42].,42].

The Warmi´nsko-MazurskieVoivodeship 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 Figure 3. Study area. Source: [33,42]. Sustainability 2021, 13, 2929 5 of 20

region as one of the poorest regions in Poland. The expenses of 11.2% of the Warmi´nsko- 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 Cultiva- tion [45], Headquarters of the State Fire Service [46], Warsaw Police Headquarters [47], data gathered in situ [48–50] and indices calculated from primary data according to Formula (1). They are presented in Table1.

Table 1. Data used in the study.

Description Unit Reference Mean Min Max Symbol Value Index of agricultural production space (soil P index 66.6 59.67 43.7 84.2 1 quality to agriculture development) 2 P2 Area km - - 7118 24,294 Distance to the nearest town at a higher level P km - 25.14 7 64 3 (district/voivodship town) Travel time to the nearest town at a higher level P minute 30 24.74 7 55 4 (district/voivodship town) 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 2 S9 Population density Persons/km 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 2 S15 Average flat area m 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 S Beneficiaries of social 579 1113 152 3296 19 assistance/10,000 residents index 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 Percent of buildings connected to water S % 84.6 76.02 46 97.9 25 supply network Percent of buildings connected to S % 50.6 26 0 77.7 26 sewage network 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 [44–50].

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

ai AI = (1) li

AI—index; ai—primary data; li—population of the municipality under analysis. Sustainability 2021, 13, 2929 6 of 20

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).   1 r12 r1 m R =  r21 1 r2 m  (2) rm1 rm2 1

where rmn—Pearson correlation coefﬁcient (3) for all pairs of attributes.

k ∑l (xl − x)(yl − y) rmn q (3) k 2 n 2 ∑l=1(xl − x) ∑l=1(yl − y)

where x, y—random variables x, 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– 59], the standardization affects the distribution of objects in the Euclidean space and reduces the effectiveness of cluster analysis methods.

g E = ∑ Em (4) m=1

nm pk 2 Em = ∑ ∑ (xml,k − xm,k)(xml,k − xm,k) (5) l=1 k=1 where 1 nm xm,k = ∑ xml,k (6) nm l=1

—the mean of the mth cluster for kth variable xml,k—being the score on the kth variable (k = 1, 2, ... , p) for the lth object (l = 1, 2, ... , nm) in the mth cluster (m = 1, 2, . . . , g). All the calculations were made with the licensed Statistica 13.1. Software (Stat- Soft Poland).

3. Results Altogether, 65 municipalities situated in the Warmi´nsko-Mazurskieand Podlaskie Voivodships were analyzed, with 28 indices determined to describe the socio-spatial factors and included in the conceptual good life standard model (see Figure3). 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—Pieni˛ezno˙ (Warmi´nsko-MazurskieVoivodship) 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 Sustainability 2021, 13, 2929 7 of 21

1 , = , (6)

—the mean of the mth cluster for kth variable ,—being the score on the kth variable (k = 1, 2, …, p) for the lth object (l = 1, 2, …, nm) in the mth 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 Sustainability 2021, 13, 2929 71.18 km2 and the largest—Pieniężno (Warmińsko-Mazurskie Voivodship) has an area7 ofof 20 242.94 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). Wi2żajny is the Dubicze Cerkiewne has the lowest population density (S9) (10 people/km ). Wizajny˙ is municipality with the poorest soils (P1) (the index of 43.7/100) and Barciany has the best the municipality with the poorest soils (P1) (the index of 43.7/100) and Barciany has the soils (84.2/100). The municipality of Ruciane Nida has the largest forest cover (P6) (71.7%). best soils (84.2/100). The municipality of Ruciane Nida has the largest forest cover (P6) In(71.7%). terms of In the terms transport of the availability, transport availability, there is no there public is notransport public in transport eight of in the eight munici- of the 5 palitiesmunicipalities under study under (P study), which (P 5is), whichwhy residents is why residentshave to use have their to useown their cars. own The cars. largest The numberlargest of number bus links of bus to the links nearest to the higher nearest level higher economic level economic center (county/voivodship center (county/voivodship town) cantown) be found can be in foundBielsk inPodlaski Bielsk Podlaski(24 lines). (24 The lines). municipality The municipality of Michał ofowo Michałowo is the farthest is the fromfarthest the district from the town district (P3)—64 town km (P 3and)—64 the km municipality and the municipality of Szepietowo of Szepietowo is the closest is (7 the km).closest Figure (7 km).4 shows Figure the distance4 shows between the distance particular between municipalities particular municipalitiesand the economic and cen- the terseconomic of the higher centers level of the and higher the time level it take ands the to travel time it there. takes The to travel municipality there. The numbers municipality and positionsnumbers are and listed positions in Appendix are listed A). in AppendixA).

70 64 60 55 53 50 45 42 41 40 40 40 40 38 35 35 35 35 36 32 34 32 34 32 30 28 28 29 28 29 25 27 27 26 23 24 24 23 23 23 22 20 20 21 19 19 16 16 18 16 18 10 13 12 7' 0 distance from nearest district town/travel time town/traveldistrict nearest fromdistance 0 10203040506070 number of object

distance from the nearest district town [km] travel time to the nearest dostrict town [minute]

Figure 4. The distance between the seat of the municipality (P ) and the nearest economic center of a higher level [km] (e.g., Figure 4. The distance between the seat of the municipality (P33) and the nearest economic center of a higher level [km] (e.g.,district/voivodship district/voivodship town) town) and and the the travel travel time time (P4) (P [minute]4) [minute] for individualfor individual object. object. Number Number and and name name of object of object can becan found be foundin Appendix in AppendixA. Source: A. Source: own studyown study on [44 on,45 [44,45].].

Socio-economicSocio-economic factors factors make make up upanother another group group included included in the in theconceptual conceptual model model of theof good the good life standards. life standards. It is larger It is larger and it and covers it covers the life the quality life quality criteria criteria that are that most are vital 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 Figure5). 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 Sustainability 2021, 13, 2929 8 of 21

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 Sustainability 2021, 13, 2929 8 of 20 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 sew- livingage network—S is chosen,26 especially), entertainment by families. (number Two of libraries—S security-related20, number attributes of sport were club considered mem- inbers—S this study—the22, number of number supermarkets/markets—S of criminal offences/100021) and effectiveness residents (trafﬁc-related, of local government economic) (amount of funds from the EU received in 2014–2020—S27, municipality income per cap- (S12) and local hazards/municipality (natural) (S11). It is a destimulating criterion (a high ita—S28). index has a negative impact on the good life standards). S11 is below the reference value. The municipalitiesThe mean unemployment of Pieni˛ezno,˙ rate Orzysz (S8) varies. and The Ruciane-Nida mean unemployment are the least rate secure. during Consid- the period of 2004–2019 in Poland was 11.8% [60]. When it comes to the area under study, the ering the second index related to crime (S ), the reference value (20.75) was exceeded highest unemployment rate was recorded in12 the municipality of Lelkowo (Warmińsko- in two municipalities—Janowo (22.16) and Janowiec Ko´scielny(22.16). Both objects are Mazurskie Voivodship)—33.3% and the lowest—in the municipality of Przytuły (Pod- situated in the Warmi´nsko-MazurskieVoivodship (see Figure6) close to the border with laskie Voivodship)—3.1 %. This index was higher than the reference value in nearly 32% the Mazowieckie Voivodship. of the municipalities under study (see Figure 5).

300

251 250

200 195 201 187 187 working people on working 159 %]/ 150 145 147 136 127 124 116

1000 inhabitants 100 100 94 88 92 92 86 88 79 81 84 83 73 71 64 65 67 68 68 59 55 56 58 55 6157 58 50 49 51 53 47 51 49 53 41 41 43 4144 40 41 33.3 36 37 33 33 33 38 38 21.9 20.9 26 13.8 18.9 14.3 18 12.2 11.5 17.1 unemployment unemployment [ rate 13 11.8 10.7 9.9 6.4 11.3 11.2 8.2 12.3 6.9 0 6 4.1 4.5 3.1 3.6 3.2 0 10203040506070 number of the objects

unemployment rate [%] average in Poland working people on 1000 inhibitants average in Poland

SustainabilityFigure 5. 2021 The, 13 unemployment, 2929 rate (S8) average for 2004–2019 and working people on 1000 inhabitants (S13) relative to the 9 of 21 Figure 5. The unemployment rate (S8) average for 2004–2019 and working people on 1000 inhabitants (S13) relative to the reference value for individual object. Number and name of object can be found in Appendix A. Source: own study on [56]. reference value for individual object. Number and name of object can be found in AppendixA. Source: own study on [ 56]. Business activity development level is very low in the municipalities under study. 120 There are 1080 business entities per 10,000 residents (S10) in Poland. None of the SMs under 110 106 study achieved the reference value, with the municipality of Bisztynek being the closest 100 (950). The number of business entities is under 50% of the reference value in the other 91 89SMs. Security90 is one of the most important factors taken into account when the place of 85 living is chosen, especially by families. Two security-related attributes were considered in 80 this study—the number75 of criminal offences/1000 residents (traffic-related, economic) (S12) /local natural hazards [case] 73 72 and local hazards/municipality (natural) (S11). It is a destimulating criterion (a high index 64 64 60 has a negative impact on the good life standards60 ). S11 is below the reference value. The mu- 56 55 54 53 53 52 51nicipalities of Pieni50 ężno, Orzysz and Ruciane-Nida are the least secure. Considering the 48 45 46 43 second index related to crime (S12), the reference value (20.75) was exceeded in two mu- 40 39 nicipalities—Janowo (22.16) and Janowiec36 Kościelny (22.16). Both objects are situated in 32 31 31 30 30 29 29 29 the rate of criminal offences of criminal the rate 28 the Warmi27 ńsko-Mazurskie27 Voivodship (see26 Figure 6) close to the border with the Ma- 24 24 22.16 23 22 20 20.75 21 20 21 21 20 19 zowieckie18.45 18 Voivodship. 18 1918 16.1 15.9 17 17 16 16 17 14.4 15.41 14.4 14.8 13.5 14 14.1 15 14 14 13 11.4 12 12 10 9.2 9.9 11 10.4 10.4 number of the object 0 0 10203040506070

local natural hazards average in Poland [case]

the rate of criminal offenses/per 1 000 inhibitants average in Poland/per 1000 inhibitants

Figure 6. The rate of criminal offences/per 1000 inhabitants (S12) in individual municipalities and the number of natural Figure 6. The rate of criminal offences/per 1000 inhabitants (S12) in individual municipalities and the number of natural hazards (S11) relative to the reference value (average for 10 years) for individual object. Number and name of object can be hazards (S ) relative to the reference value (average for 10 years) for individual object. Number and name of object can be found in11 Appendix A. Source: own study on [41,56]. found in AppendixA. Source: own study on [41,56].

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. Sustainability 2021, 13, 2929 9 of 20

In general, the housing conditions (S14/S15) in the objects under study are highly satisfactory (see Figure7). 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

Sustainability 2021, 13, 2929 municipality of Kobylim-Borzymy (the index is more than three times higher10 than of 21 the reference value).

800

700 695 673 640 619 600 609 605 581

509 522 500 491 436 419

average flat area 375.7 411 400 396 392 387 360 365 341 350 325 338 324 336 325 300 310 298 305 302 282 274 265 261 248 288.5 215.5 number of flats/ number of 200 191 182 184.7 179 172.6 163.5 158 150 134 130 116 108 120 100 92 74.4 71 number of object 0 0 10203040506070

flats/1000 inhabitants average in Poland average flat area [m2] average in Poland

Figure 7. The number of flats per 1000 residents (S14) and the average flat area (S15) relative to the reference value for Figure 7. The number of ﬂats per 1000 residents (S ) and the average ﬂat area (S ) relative to the reference value for individual object. Number and name of object can be14 found in Appendix A. Source: own15 study on [38,56]. individual object. Number and name of object can be found in AppendixA. Source: own study on [38,56]. The largest number of medical consultations was recorded in the municipality of Accessibility of places where residents can seek medical advice is an important aspect Michałowo (Podlaskie Voivodship)—15.8—and it is nearly twice as large as the reference of life in places outside large economic centers (number of health clinics, number of value (8.4). residents per pharmacy, number of medical consultations). In each municipality there The level of technical infrastructure in the municipalities under study is very low (see is a pharmacy (S17) where one can buy medicines. In one municipality, the number of Figure 8). There is a water supply network (S25) in each of the municipalities under study, residentsbut only per46% pharmacy of the buildings is three are times connect highered thanto the the network reference in valuethe municipality (Barciany—6275, of withSzudzia theł referenceowo and nearly value—2651). 100% of the There buildings is not in a the health municipality clinic in of every Korsze. municipality The situation (S 16), which makes people use such facilities situated in the neighboring municipalities. is much worse when it comes to the sewage system (S26). The set under analysis includes municipalitiesThe largest (Przytu numberły, Milejczyce, of medical Perlejewo, consultations Kulesze was Ko recordedścielne) without in the a municipality central sew- of Michałowoage system. (Podlaskie The residents Voivodship)—15.8—and have to use the sewage it installations is nearly twice made as on large their as own—usu- the reference valueally cesspits (8.4). or individual wastewater treatment plants. The reference value for the sewage Thesystem level (number of technical of buildings infrastructure connected in to the the municipalitiessewage system) under is 50.6% study and this is very level low (seeis achieved Figure8 ).only There in four is a watermunicipalities—Narewka, supply network (S Ruciane25) in each Nida, of theJanowo municipalities and Srokowo. under study,Due to but the onlyclimate 46% in ofPoland, the buildings people have are to connected use heating to systems. the network They inare the usually municipality pow- ofered Szudziałowo by gas (S24), and although nearly there 100% are of increasingly the buildings more in systems the municipality which use renewable of Korsze. en- The situationergy (photovoltaics, is much worse heat when pumps, it comes etc.) because to the sewage they are system subsidized (S26). Theby the set government. under analysis includesThe percentage municipalities of buildings (Przytuły, connected Milejczyce, to such systems Perlejewo, in the Kulesze municipalities Ko´scielne)without under study a centralis very sewagesmall—a system. gas supply The network residents was have available to use only the in sewage nine of installations them. Coal-fired made boilers, on their own—usuallywith emissions cesspits contributing or individual to smog formation, wastewater are treatment still the most plants. popular The reference heating devices value for thein Poland sewage [61,62]. 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- Sustainability 2021, 13, 2929 10 of 20

Sustainability 2021, 13, 2929 ﬁred boilers, with emissions contributing to smog formation, are still the most11 popular of 21 heating devices in Poland [61,62].

120.0

100.0 99.7 98.1 97.1 99.1 97.9 95.8 94.6 92.7 91.6 92.4 93.5 90.0 90.5 91.1 89.2 88.3 86.4 86.1 80.0 77.7 78.8 77.6 78.0 78.5 72.0 68.9 68.8 67.2 68.5 67.2 64.0 66.1 60.0 61.1 61.5 56.1 50.6 52.4 50.2 48.9 50.2 50.0 50.6 46.0 40.0 37.0 33.8 34.5 30.8 30.6 29.8 29.5 28.7 27.7 29.2 27.1 21.5 22.1 20.0 18.1 16.0 17.7 16.1 13.1 12.7 13.5 6.6 7.3 5.3 0.0 2.1 building building connected towater supply/sewage system [%] 0 10203040506070

-20.0 name of the object

buildings connected to the water supply [%] average in Poland [%]

buildings connected to the sewage system [%] average in Poland [%]

Figure 8. Access to a water supply network (S25) and a sewage network (S26) in individual municipalities [%] relative to Figure 8. Access to a water supply network (S25) and a sewage network (S26) in individual municipalities [%] relative to the the reference value for individual object. Number and name of object can be found in Appendix A. Source: own study on reference value for individual object. Number and name of object can be found in AppendixA. Source: own study on [ 38]. [38]. The active involvement of local authorities in funds acquisition for actions aimed The active involvement of local authorities in funds acquisition for actions aimed at at stemming the urban shrinkage is of key importance [63,64]. Local authorities initiate stemming the urban shrinkage is of key importance [63,64]. Local authorities initiate ac- actions in their municipalities, seek funds to finance the development of the area, of tions in their municipalities, seek funds to finance the development of the area, of business business activity and training aimed at reducing unemployment. Two measures related to activity and training aimed at reducing unemployment. Two measures related to this cri- this criterion were taken into consideration in the area under study—EU funds acquired in terion were taken into consideration in the area under study—EU funds acquired in 2014– 2014–2020 (S27) and the municipality income per capita (S28). The analyses show that local 2020 (S27) and the municipality income per capita (S28). The analyses show that local gov- governments’ernments’ effectiveness effectiveness in acquiring in acquiring EU funds EU funds for municipality for municipality development development is low. The is low. Themunicipalities municipalities closest closest to the to thereference reference valu valuee (PLN (PLN 17,201 17,201 per per capita) capita) include include Sidra Sidra (PLN (PLN 16,47116,471 per per capita), capita), as as well well as Perlejewo,as Perlejewo, Szczuczyn, Szczuczyn, Grodzisk, Grodzisk, Narew, Narew, Pieni˛e Pienizno˙ ęż andno and Reszel (seeReszel Figure (see9 ).Figure This 9). index This is index lower is thanlower 20% than of 20% the of reference the reference value value in the in other the other SMs, SMs, which meanswhich thatmeans the that local the government local government effectiveness effectiveness in this in regard this regard is very is very poor. poor. The The situation situ- is aation little is better a little when better it comeswhen it to comes the S 28to index.the S28 index. The municipality The municipality income income per capita per capita (S28 ) is below(S28) is the below reference the reference value in value 30% in of 30% the municipalitiesof the municipalities under under study. study. It is similar It is similar or slightly or higherslightly in higher the other in the municipalities. other municipalities. The municipalityThe municipality of Mielnik of Mielnik stands stands out out because because the incomethe income per capitaper capita in it in reaches it reaches 170% 170% of theof the reference reference value. value. SolvingSolving the the secondsecond researchresearch problem,problem, i.e., identifying clusters clusters of of municipalities municipalities simi- similar inlar terms in terms of the of criteria the criteria under under analysis, analysis, required required reducing reducing the dimensionalitythe dimensionality by theby expertthe methodexpert method and a correlation and a correlation matrix matrix (Appendix (AppendixB). The B). reduction The reduction aimed aimed to choose to choose only only those variablesthose variables which which can be can used be used to discriminate to discrimina againstte against individual individual observations observations because because they arethey included are included in a specificin a specific cluster cluster [56 ][56] Descriptive Descriptive determinants determinants and and those those withwith a neu- neutral impacttral impact (municipality (municipality area—P2, area—P2, forest forest cover—P6, cover—P6, population—S7, population—S7, population population density—S9) den- assity—S9) well as as correlated well as correlated variables variables with a similar with a information similar information range—P3 range—P3 (distance (distance fromthe nearestfrom the higher nearest level higher town), level S21 town), (number S21 of(number markets), of markets), S22 (number S22 (number of sport of club sport members), club S23members), (number S23 of (number people receivingof people receiving housing benefits),housing benefits), S24 (natural S24 (natural gas consumers) gas consum- were excludeders) were fromexcluded further from analyses. further analyses. In total, In 65total, SM 65 objects SM objects were were clusterized clusterized and and each each was describedwas described with sevenwith seven criteria criteria (19 determinants). (19 determinants). This This allowed allowed for identifyingfor identifying groups groups of SM (shrinkingof SM (shrinking municipality) municipality) of various of various speeds. speeds. Five clusters Five clusters were identifiedwere identified by this by method this (formulamethod (formula 4 and 5). 4 The and first5). The cluster first cluster included included 20 SM, 20 the SM, second—16 the second—16 SM, SM, the third—21the third— SM, the21 fourth—6SM, the fourth—6 SM and SM the and fifth—2 the fifth—2 SM (see SM Figure (see Figure 10). 10).

Sustainability 2021, 13, 2929 12 of 21

Sustainability 2021, 13, 2929 11 of 20 Sustainability 2021, 13, 2929 12 of 21

20000 18000 17201 1600020000 16471 1400018000 17201 16471 1200016000 14000 10000 9988 12000 9093 8000 8359 10000 7647 9988 6725 9093 6000 5137 8359 8000 7647 6725 40006000 5137 3864 2709 3235 20004000 3864 1518 1956 1001 1007 2709 1124 1094 3235 822 2000 590 668 1956 316 665 655 76 0 1001 1007 1124 1518 1094 590 668 316 665 655 822 -2000 0incom per 0capita/projects finaced from UE [PLN] 1020304050607076

0incom per capita/projects finaced from UE [PLN] 10203040506070 -2000 name of the object name of the object

amountamount of of founds founds perper capita capita for for EU EU finaced finaced projects projects average inaverage Poland in Poland

incomeincome per per capita capita average municipalityaverage municipality incom per capita incom in Poland per capita in Poland

Figure 9. The municipality income per capita (S28) and the amount of founds per capita for EU financed projects (S27) FigureFigure 9. 9. The municipalitymunicipality income income per capitaper capita (S28) and (S28 the) and amount the ofamount founds perof founds capita for per EU capita ﬁnanced for projects EU financed (S27) relative projects (S27) relative to the reference values (for 2014–2020) for individual object. Number and name of object can be found in Appendix relativeto the reference to the reference values (for values 2014–2020) (for 2014–2020 for individual) for individual object. Number object. and Number name ofand object name can of be object found can in be Appendix found inA .Appendix A. Source: own study on [38,43]. A.Source: Source: own own study study on [on38, 43[38,43].].

Figure 10. Cont. Sustainability 2021, 13, 2929 12 of 20 Sustainability 2021, 13, 2929 13 of 21

FigureFigure 10. 10.The The clusterization clusterization result result for for TMGL. TMGL. Source: Source: prepared prepared by by the the author author on onStatistica Statistica 13.1. 13.1.

TableTable2 shows 2 shows the the result result of aof comparison a comparison of individualof individual clusters clusters in thein the nine nine criteria criteria underunder analysis. analysis. The The determinants determinants were were divided divided into into stimulants stimulants and and destimulants destimulants (the (the higherhigher the the index, index, the the worse worse condition condition of of the the SM) SM) of of the thegood good living living standards standards(see (see Table Table2). 2). The comparison results show that the municipalities grouped in two clusters, 3 and 5,Table are significantly 2. Comparison close results to thefor referencethe criteria valuesof the conc andeptual they model even achieved of good life better standards results for for the someSM. determinants than the reference model (see Table2). Cluster 3 andSym- cluster 5 are the most promising. Cluster 5 includes only two munici- Criterion Impact Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 palities (Pozezdrze andbol S˛epopol),which have the best indices in five criteria (see Table2). In consequence, it has the best chance of mitigating the scenario in the population-related P1 stimulant 62.03 58.51 55.34 60.93 67.75 forecast. This cluster has the best indices for location, living conditions, healthcare, en- location P4 destimulant 27.2 20.88 27.0 21.17 18.0 tertainment and technical infrastructure. Soils in these municipalities are good (P1) and cultivated [65] becauseP5 the agriculturestimulant land7.4 is leased4.6 to active farmers,5.5 due8.0 to their location9.5 features (distance toS8 higher-level destimulant towns and13.6 the number9.2 of bus8.4 lines)10.0 one can find17.8 a job outsideemployment one’s place ofS10 residence. stimulant The housing306.6 conditions 229.5 are good,189.3 the population253.8 health273.5 is relatively good (a lowS13 number stimulant of medical 76.9 consultations) 92.7 and64.2 the technical54.2 infrastructure48 level (water supplyS12 and seweragedestimulant networks) 13.9 is also14.4 good. Agriculture13.5 is13.5 developing 14.8 (soils security are good) and the naturalS11 anddestimulant landscape values41.8 (lakes,35.9 forests, 32.3 varied land42.5 relief, low59 pollution level, nature parks)S14 favorstimulant tourism development.361.2 387.9 In the municipality422.5 408.7 of Pozezdrze 351 there living condition are bunkers and fortificationsS15 stimulant (Himmler’s 139.5 field headquarters),143.1 144.5 tourist 132.9 routes, castles153.3 and 19th century mansions, sacral objects and monuments [66] The municipality of S˛epopolis a S16 stimulant 2.8 2.6 2.2 1.8 1 craft and service center, with services for agriculture dominating in the past. The downfall healthcare S17 destimulant 3279 4139 2124 2519 3324 of the local food processing industry took place after 1990 [67]. The situation in cluster 3 is S18 destimulant 3.9 3.5 3.7 3.4 2.4 Sustainability 2021, 13, 2929 13 of 20

very similar. The level of the determinants under study is the best in five criteria: location, employment, security, health and social assistance (see Table2). 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 Table2). 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.

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 P1 stimulant 62.03 58.51 55.34 60.93 67.75 location P4 destimulant 27.2 20.88 27.0 21.17 18.0 P5 stimulant 7.4 4.6 5.5 8.0 9.5 S8 destimulant 13.6 9.2 8.4 10.0 17.8 employment S10 stimulant 306.6 229.5 189.3 253.8 273.5 S13 stimulant 76.9 92.7 64.2 54.2 48 S12 destimulant 13.9 14.4 13.5 13.5 14.8 security S11 destimulant 41.8 35.9 32.3 42.5 59 S14 stimulant 361.2 387.9 422.5 408.7 351 living condition S15 stimulant 139.5 143.1 144.5 132.9 153.3 S16 stimulant 2.8 2.6 2.2 1.8 1 healthcare 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 S25 stimulant 82.7 82.7 82.4 75.1 92.3 infrastructure S26 stimulant 31.4 25.7 23.4 16.5 39.8 effectiveness of local S27 stimulant 1235 872 1404 9713 1373 government S28 stimulant 4965.3 5761.4 5095.6 5465.8 5002.1 Source: own study.

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 [68–70]. 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 Sustainability 2021, 13, 2929 14 of 20

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 [74–76]. 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, Pieni˛ezno˙ 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 main- taining their economic vitality. The formulated rules mainly concern [77–81]: (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 adminis- trative 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 Sustainability 2021, 13, 2929 15 of 20

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.

Author Contributions: Conceptualization, K.K.-B.; methodology, K.K.-B.; software, K.K.-B.; validation, K.K.-B.; formal analysis, K.K.-B.; investigation, K.K.-B.; resources, K.K.-B., K.S.; data curation, K.K.-B., K.S.; writing—original draft preparation, K.K.-B.; writing—review and editing, K.K.-B.; visualization, K.K.-B.; supervision, K.K.-B.; project administration, K.K.-B.; funding acquisition, K.K.-B. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: https://bdl.stat.gov.pl/BDL/dane/teryt/jednostka (accessed on 20 September 2020). https://mapadotacji.gov.pl/ (accessed on 20 September 2020) https://www.gov.pl/web/kgpsp/ (accessed on 20 September 2020) http://policja.pl (accessed on 20 September 2020). http://gugik. gov.pl (accessed on 20 September 2020). Data resulting from the analysis is contained within the article (AppendicesA andB). Conﬂicts of Interest: The authors declare no conﬂict of interest. Sustainability 2021, 13, 2929 17 of 21 Sustainability 2021, 13, 2929 16 of 20

Appendix A. Map with the Location, Names, and Numbers of the Analyzed Appendix A. Map with the Location,Municipalities Names, and Numbers of the Analyzed Municipalities

Numer of object Name of the object

1 Banie Mazurskie 2 Barciany 3 Biała Piska 4 Bielsk Podlaski 5 Bisztynek 6 Boćki 7 Brańsk 8 Budry 9 Czeremcha 10 Czyże 11 Dąbrowa Białostocka 12 Drohiczyn 13 Dubeninki 14 Dubicze Cerkiewne 15 Dziadkowice 16 Godkowo 17 Górowo Iławeckie 18 Grodzisk 19 Janowiec Kościelny 20 Janowo 21 Janów 22 Jaświły 23 Jedwabne 24 Kleszczele 25 Klukowo 26 Kobylin-Borzymy 27 Kolno 28 Korsze 29 Krynki 30 Kulesze Kościelne 31 Lelkowo 32 Lipsk 33 Michałowo 34 Mielnik 35 Milejczyce 36 Narew 37 Narewka 38 Nowy Dwór 39 Nurzec-Stacja 40 Orla 41 Orzysz 42 Perlejewo 43 Pieniężno 44 Poświętne 45 Pozezdrze 46 Przytuły 47 Puńsk 48 Rajgród 49 Reszel 50 Ruciane Nida 51 Rutka-Tartak 52 Rychliki 53 Sępopol 54 Sidra 55 Srokowo 56 Stawiski 57 Suchowola 58 Szczuczyn 59 Szepietowo 60 Sztabin 61 Szudziałowo 62 Trzcianne 63 Wąsosz 64 Wiżajny 65 Wyszki

Sustainability 2021, 13, 2929 17 of 20

Appendix B. Correlation Matrix

P1 P3 P4 P5 S8 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20S S21 S22 S23 S24 S25 S26 S27 S28 P1 1.000 0.062 0.060 0.140 0.503 0.355 0.198 0.163 0.071 −0.276 −0.031 0.105 0.208 −0.090 0.198 0.200 0.135 0.007 0.259 0.174 0.136 0.059 0.058 −0.161 P3 0.062 1.000 0.893 0.210 0.211 0.068 0.046 0.110 0.053 −0.066 −0.112 0.113 −0.082 0.364 0.095 0.244 0.065 0.049 0.062 0.033 −0.162 0.121 −0.047 −0.186 P4 0.060 0.893 1.000 0.200 0.176 0.033 0.086 0.137 −0.036 −0.158 −0.058 0.173 −0.068 0.315 0.146 0.181 0.039 0.010 0.071 0.038 −0.119 0.102 −0.055 −0.157 P5 0.140 0.210 0.200 1.000 0.212 0.335 0.313 −0.011 −0.006 −0.032 0.086 0.023 0.092 −0.069 0.007 0.146 0.171 0.157 0.185 0.042 0.112 0.065 0.042 −0.207 S8 0.503 0.211 0.176 0.212 1.000 0.242 0.386 0.410 0.071 −0.137 −0.461 0.125 0.050 0.066 0.362 0.317 0.227 0.224 0.403 0.131 0.201 0.403 0.032 −0.170 S10 0.355 0.068 0.033 0.335 0.242 1.000 0.535 0.237 0.439 −0.060 −0.137 0.471 0.176 0.228 −0.004 0.433 0.490 0.551 0.563 0.190 0.020 0.328 −0.012 −0.077 S11 0.198 0.046 0.086 0.313 0.386 0.535 1.000 0.294 0.258 −0.145 −0.204 0.505 0.039 0.238 0.188 0.380 0.560 0.480 0.560 0.216 0.156 0.451 0.083 0.015 S12 0.163 0.110 0.137 −0.011 0.410 0.237 0.294 1.000 0.061 0.123 −0.288 0.173 0.088 −0.048 0.282 0.283 0.237 0.089 0.330 0.075 0.052 0.365 0.020 −0.108 S13 0.071 0.053 −0.036 −0.006 0.071 0.439 0.258 0.061 1.000 0.185 −0.080 0.316 0.167 0.297 0.039 0.311 0.447 0.370 0.374 0.418 −0.104 0.373 −0.020 0.356 S14 −0.276 −0.066 −0.158 −0.032 −0.137 −0.060 −0.145 0.123 0.185 1.000 −0.236 −0.166 −0.325 −0.095 0.014 −0.073 −0.059 −0.177 −0.131 0.015 −0.065 −0.071 0.028 0.104 S15 −0.031 −0.112 −0.058 0.086 −0.461 −0.137 −0.204 −0.288 −0.080 −0.236 1.000 −0.079 0.183 −0.070 −0.105 −0.128 −0.059 −0.194 −0.099 −0.014 0.053 −0.251 −0.034 −0.127 S16 0.105 0.113 0.173 0.023 0.125 0.471 0.505 0.173 0.316 −0.166 −0.079 1.000 0.139 0.489 0.211 0.380 0.583 0.423 0.610 0.320 −0.144 0.375 0.027 0.080 S17 0.208 −0.082 −0.068 0.092 0.050 0.176 0.039 0.088 0.167 −0.325 0.183 0.139 1.000 −0.093 0.112 0.022 0.141 0.152 0.263 0.000 0.048 0.153 −0.134 0.112 S18 −0.090 0.364 0.315 −0.069 0.066 0.228 0.238 −0.048 0.297 −0.095 −0.070 0.489 −0.093 1.000 −0.076 0.181 0.335 0.418 0.234 0.116 −0.116 0.231 0.059 0.084 S19 0.198 0.095 0.146 0.007 0.362 −0.004 0.188 0.282 0.039 0.014 −0.105 0.211 0.112 −0.076 1.000 0.270 0.226 −0.128 0.433 0.304 −0.022 0.371 −0.067 0.155 S20 0.200 0.244 0.181 0.146 0.317 0.433 0.380 0.283 0.311 −0.073 −0.128 0.380 0.022 0.181 0.270 1.000 0.456 0.159 0.523 0.273 0.122 0.336 −0.037 0.009 S21 0.135 0.065 0.039 0.171 0.227 0.490 0.560 0.237 0.447 −0.059 −0.059 0.583 0.141 0.335 0.226 0.456 1.000 0.469 0.709 0.560 0.025 0.509 0.071 0.159 S22 0.007 0.049 0.010 0.157 0.224 0.551 0.480 0.089 0.370 −0.177 −0.194 0.423 0.152 0.418 −0.128 0.159 0.469 1.000 0.446 0.133 −0.006 0.344 0.162 0.070 S23 0.259 0.062 0.071 0.185 0.403 0.563 0.560 0.330 0.374 −0.131 −0.099 0.610 0.263 0.234 0.433 0.523 0.709 0.446 1.000 0.438 0.143 0.500 −0.006 −0.001 S24 0.174 0.033 0.038 0.042 0.131 0.190 0.216 0.075 0.418 0.015 −0.014 0.320 0.000 0.116 0.304 0.273 0.560 0.133 0.438 1.000 0.010 0.314 0.121 0.228 S25 0.136 −0.162 −0.119 0.112 0.201 0.020 0.156 0.052 −0.104 −0.065 0.053 −0.144 0.048 −0.116 −0.022 0.122 0.025 −0.006 0.143 0.010 1.000 0.141 −0.039 −0.074 S26 0.059 0.121 0.102 0.065 0.403 0.328 0.451 0.365 0.373 −0.071 −0.251 0.375 0.153 0.231 0.371 0.336 0.509 0.344 0.500 0.314 0.141 1.000 −0.064 0.082 S27 0.058 −0.047 −0.055 0.042 0.032 −0.012 0.083 0.020 −0.020 0.028 −0.034 0.027 −0.134 0.059 −0.067 −0.037 0.071 0.162 −0.006 0.121 −0.039 −0.064 1.000 0.158 S28 −0.161 −0.186 −0.157 −0.207 −0.170 −0.077 0.015 −0.108 0.356 0.104 −0.127 0.080 0.112 0.084 0.155 0.009 0.159 0.070 −0.001 0.228 −0.074 0.082 0.158 1.000 Sustainability 2021, 13, 2929 18 of 20

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