Turbulent Events Effects: Socioeconomic Changes in Southern Poland as Captured by the LSED Index

Abstract

Today’s generations live in uncertain times. The inflation of violent and unpredictable events over the last two decades, such as the economic crisis or COVID-19 pandemic, has affected the functioning of regions and the daily lives of their residents. Therefore, the socioeconomic level has to be monitored. This article fills the research gap regarding the identification of the impact of recent turbulent events on the development of municipalities in southern Poland. The specific goal is to identify trends in socioeconomic changes in times of change and uncertainty from 2006 to 2021 in 450 municipalities in Małopolskie, Śląskie, and Świętokrzyskie Voivodeships. The research defined model and problem areas among the municipalities regarding the level of development. The analyses employ an original synthetic Level of SocioEconomic Development index (LSED) consisting of 18 diagnostic variables. The study mixed qualitative and quantitative approaches and considered the spatial dimension in statistical analyses. We identified general trends related to the ageing population and housing shortage. Moreover, in municipalities dominated by industry, socioeconomic development was generally constant. The opposite is true for municipalities focusing on tourism or agriculture. The conclusions demonstrate that Poland’s European Union membership was the key driver of the socioeconomic development of the regions and the country at large. The rule of law crisis in Poland and ensuing cuts in EU spending could slow the development down. The crisis brought about by the COVID-19 pandemic might be consequential as well.

1. Introduction

Socioeconomic development is a process that promotes prosperity, improves the standard of living, and advances the development of society and the national economy in specific environmental and cultural circumstances. The objective of socioeconomic development is to create conditions for people to grow and improve the country’s international competitiveness. The process spans such countless domains as education, healthcare, infrastructure, the labour market, commerce, technology, innovation, or the natural environment [1]. Therefore, socioeconomic growth demands that the state government, private stakeholders, and civil society coordinate their efforts. Politics and political stability, economic freedom, infrastructural projects, and technology growth all constitute its driving factors [2].

Modern times are characterised by a high level of such unforeseeable events as the COVID-19 pandemic [3], the war in Ukraine, the economic crisis, energy crisis [4,5,6], and climate disasters [7,8] that shape the socioeconomic development of regions and countries [9,10]. The literature refers to such events as ‘turbulence’. Turbulent events are hard to foresee. They often take a violent and unpredictable course. ‘Turbulent’ hints at the unstable, often chaotic, and dynamic nature of the events [11].

Turbulent events come in many forms across a diversity of spheres of life. As regards the natural environment, for example, turbulent events include fierce storms, hurricanes, tornadoes, earthquakes, and other weather events that involve rapid changes in wind direction and strength, temperature, or intensity of precipitation [12]. In economics, turbulent events mean political declarations or unforeseeable swings in financial markets. They can lead to volatile prices, stock market crashes, public unrest, or other unexpected events that disturb the global economy [13]. Turbulent events in the public plane may involve rioting and social unrest, wars and conflicts, including racially motivated clashes, and other destabilising events that have the potential to significantly affect social and political order [14]. As a consequence, turbulent events can be detrimental to the realisation of sustainable development goals.

The literature emphasises the importance and relevance of sustainable development to humanity [15]. Sustainable development covers multiple challenges, spanning a multitude of dimensions and perspectives. Some include digitalisation and technological development, support for food security integration, sustainable development in emerging economies, and the sustainable use of resources for process optimisation [15]. Sustainable development responds to dynamic and often sudden changes in socioeconomic and cultural domains. Consequently, the challenges of shaping sustainable development strategies and policies and new business models call for new research approaches that take into account indicators for monitoring SDG progress locally as well as globally [16]. The present article offers a new approach to analysing 3 of the 17 sustainable development goals [15]: SDG 9: Industry, Innovation, and Infrastructure; SDG 10: Reduced Inequality; and SDG 11: Sustainable Cities and Communities. Our indicator analysis of socioeconomic development on the social, economic, and environmental planes is complemented by spatial aspects, such as land use and cover. Here, socioeconomic development is also assessed in terms of spatial development. This is because socioeconomic development, land development, and spatial planning are linked [17]. Therefore, the new approach to the assessment of sustainable development goals, including the sustainable development of infrastructure, reduction in social inequalities, and sustainable development of cities and communities, should take into account the spatial conditions that characterise the degree of socioeconomic development.

The objective of the paper is divided into a general and a specific one. The general research objective is to identify two types of municipalities. One are those with continual socioeconomic development maintained in turbulent times referred to as model areas. The other type, problem areas, includes municipalities where the socioeconomic development has declined. Model areas are those that exhibit special qualities, employ exemplary good practices, and inspire decision-makers in other areas that seek to improve their living standards and surroundings. One can identify features and solutions that can be implemented elsewhere thanks to analysis and insight into the unique traits. In contrast, problem areas are those with various negative issues with socioeconomic development in general and often with culture, including spatial planning and management. Problem areas often demonstrate developmental disparities, evident in living standards, emigration, and degradation of the natural environment. Note that our research approach differs from others in that it refrains from labelling areas in favour of the identification of phenomena; it focuses on phenomena and their causes rather than administrative areas as such. This way, it may be easier to identify changes in spatial management and socioeconomic policies that guide sustainable development.

The specific goal of the paper is to identify trends in socioeconomic changes in the times of change and uncertainty in three selected voivodeships in Poland considering their spatial situation reflected in the statistical data and a synthetic index of socioeconomic development.

According to our best knowledge and a literature review, no such aggregate analysis has been performed for the three adjacent voivodeships, hence the novelty of our work. Moreover, the research investigates the impact of current turbulent economic, social, and political events on the development level of the area, which is a novelty in and of itself. In addition, the study mixes qualitative and quantitative approaches and considers the spatial dimension in statistical analyses innovatively. It facilitates the demonstration of socioeconomic phenomena on thematic maps and the mapping of changes in the level of socioeconomic development. The paper investigated the following research questions:

• Which of the analysed factors of socioeconomic development actually contributed to the growth or decline in the level of socioeconomic development in the research area?
• What socioeconomic events of the turbulent times have affected changes in the socioeconomic development of the investigated Polish regions?

The remainder of the article is structured as follows: Section 2 characterises factors that affect socioeconomic development and discusses how indicator analyses can be used to assess the intensity of turbulent events and forecast them, focusing on sustainable development. Section 3 outlines the methodological framework for the research. We described the subject matter of the study, the procedure for selecting the diagnostic variables, and the research procedure. Section 4 provides the results, including the results of the analysis of the variability of the aggregate quality index and the results of the assessment of the socioeconomic development of the investigated municipalities. In addition, Section 4 offers the results of an in-depth analysis of the municipalities with the highest and lowest degrees of socioeconomic development. Section 5 discusses the results in light of the impact of turbulent events on the socioeconomic development in the municipalities (and consequently in the voivodeships). We also noted the need to identify problem areas in turbulent times, which may substantially inform decision-making. The summary focuses on the theoretical and practical implications of the research.

2. Background

2.1. Factors Contributing to Growth in Socioeconomic Development

Socioeconomic development is a comprehensive process affected by a multitude of factors. The availability of hard infrastructure is among the important indicators of sustainable socioeconomic development [18]. The growth of infrastructure stimulates economic growth, which further leads to industrialisation and is helpful in economic development [19]. Socioeconomic development is assessed in light of economic conditions and indicators as well [20]. Also of importance are access to healthcare, environmental conditions, and general quality of life [21]. Moreover, access to high-quality education for sustainable development is critical [1,22,23]. A well-educated society has a better potential to generate innovation, further technological advancement, and stimulate economic growth. However, this calls for investments in research and development, support of entrepreneurship, and the facilitation of knowledge sharing [24]. All of these processes should preferably take place in a stable institutional and legal environment, which is a critical factor for building a supporting business environment and ensuring social equality [25]. Socioeconomic development is also stimulated by an open trade system, which guarantees market availability and the exchange of technology [26]. Population demographics influence the labour market and social resources, affecting socioeconomic development [27]. The determinants mentioned above are interrelated and interdependent. Upsetting the balance can curb the socioeconomic development potential.

The in-depth analysis identified detailed relationships between individual factors of socioeconomic development. The state’s economic policy significantly impacts socioeconomic development [28]. Decisions on public projects, taxes, regulations, and subsidies affect the labour market, entrepreneurship, innovations, and trade. The political and legal stability of the state can be central to socioeconomic development. Internal conflicts, institutional instability, and convoluted regulations can hinder economic activity, reduce investor trust, and deteriorate the social environment [29]. Education and science, including the level of education and public culture, also meaningfully affect socioeconomic development. They contribute to innovativeness. Culture shapes the living conditions and competitiveness of rural and urban areas. Local culture clearly influences the potential for further improvement and positive transformations. The culture guides city-forming processes. The economy is part of culture because it is deeply rooted in it [30]. All of these processes happen in an infrastructure. Infrastructure: transport, energy generation, telecommunications, and new technologies shape socioeconomic development [31]. Better infrastructure can attract investors, facilitate access to the market, streamline production processes, and reduce unemployment [32]. Access to natural resources is important as well. Countries with plenty of natural resources can use them to help the industry, export, and public revenue grow. Moreover, globalisation also shapes socioeconomic development through openness to foreign markets, exchange of knowledge and technology, and improved competition [33]. Interaction with global markets can boost economic growth but entails a risk of increased social and economic inequalities. Therefore, institutions implement policies to identify and preserve local cultural heritage and tap its economic potential in parallel to globalisation processes.

2.2. Monitoring the Level of Socioeconomic Development in a Turbulent Environment

The intensive accumulation of turbulent events in recent years has substantially shaped producer and consumer behaviour, changes in industrial production, and the economy in general. The costs of production factors have gone up due to many derailed supply chains, leading to general price increases and a global economic slowdown [34]. Escalating tensions in various markets and political relationships also inhibit building long-term strategies of socioeconomic development. Therefore, it is necessary to monitor the level of socioeconomic development to ensure the efficiency of socioeconomic policy, prevent economic crises, and plan growth [35,36,37]. The monitoring involves the systematic collection and analysis of data to facilitate the right decisions and effective planning to boost country or regional development. Turbulent and challenging times call for tools and mechanisms to assess the effectiveness of the socioeconomic policy and verify whether or not it drives positive changes in the society and economy [38]. Moreover, the monitoring of socioeconomic development helps to identify areas in need of improvement, such as interventions of local or central authorities, and model areas that can guide others towards development in these turbulent times. Regular monitoring promotes more effective prevention of economic crises and social and economic inequality [39]. It also facilitates comparative analyses, including among countries. The right national, regional, and local decisions are only possible if data on socioeconomic development are available. They foster better planning of the development, including its objectives and tendencies, for better action planning and resource allocation [40].

2.3. Indicators and Sustainable Development

The last twenty years saw an increase in the use of indicators to assess the degree of socioeconomic development and sustainable development. It is associated with a debate on the use of indicators for evaluating sustainable development, which is yet to yield a consensus regarding the theory, methods, and use of such indicators [41]. Most often, indicators are employed to assess socioeconomic development in the economy, society, and environment [42]. This approach is gaining more ground in culture and cultural heritage research [43]. The indicators are employed to monitor trends and changes in specific processes and phenomena, including identifying developmental challenges and problem and model areas. National and international institutions use indicators to evaluate results and changes in many dimensions, such as income, education, health, and social care at the regional and local levels [44]. Aggregate indicators are used in comparative analyses (analysis of competition) and also to monitor the progress or effectiveness of the legislative effort. Aggregate indicators of socioeconomic development identify relationships between economic, social, environmental, and cultural phenomena [45]. Note that there is no one perfect indicator. This means that indicator-based research can yield relative and subjective results that often depend on the set of diagnostic (predictor, independent) variables used in the study [46]. One should, therefore, speak of indicator assessment of socioeconomic phenomena ‘under the employed research design’ for both urban and rural areas [37]. These outcomes are of potential interest to politicians, governments, and citizens to help to identify the metabolic flows and social or economic indicators to be optimised in search of sustainability [45].

The indicators can be used to synthesise complex socioeconomic phenomena. Aggregate indices represent the complexity of such relationships in a simplified manner so that the results can then be employed in decision-making to streamline the identification and resolution of sustainable development problems and improve the resilience potential through models and forecasts [41]. The function of indicators is to offer a solid foundation for planning and decision-making in all aspects so as to contribute to the sustainability of the integrated environment and development systems [47]. Indicator-based assessment helps to rank objects, such as countries, municipalities, or voivodeships, by indicator values. Therefore, indicators facilitate ranking lists and clustering (typology) of similar objects [48]. In this way, areas that conform or fail to conform to specific criteria described with the indicators can be identified. The analysis places a problem or intensity in time and space because analyses of socioeconomic development usually have a limited time frame and geographical location [49].

Indicator analyses of socioeconomic development can be particularly useful for assessing the intensity of phenomena in a turbulent environment and forecasting them. The results can be useful for controlling the impact of unforeseen events by informing the decision-making process. Barska and Jędrzejczak-Gas [49] analysed the regional differences among indicators of the economic development of regions in Poland in the context of progress in sustainable development in 2010 and 2017. They proposed a set of indicators for economic development monitoring, which takes into account other aspects of sustainable development, such as social and environmental development. Their main criterion for selecting the indicators was subject-matter relevance and data availability in the regions. The authors’ aim was to analyse economic development in the turbulent institutional and political environment, taking into account the potential and level of innovation of the economy, entrepreneurship, production, and transport, as well as the economic activity in households. They compiled a ranking list with which they identified regions resilient and susceptible to adverse socioeconomic events. Such an analysis can be useful for decision-making regarding the allocation of resources (infrastructure, etc.) or subsidies, for example. Shcherbak et al. [37] completed a multi-dimensional analysis of sustainable socioeconomic development and developmental challenges in rural Ukraine. The socioeconomic development of rural areas was assessed with statistical data, expert knowledge, and interviews with local residents. The study identified growth points and forecasted future development scenarios for volatile and unpredictable times. In contrast, Garina et al. [18] identified points of inhibition of development and stimulants of socioeconomic development.

The study also covered urban areas. Gonzalez-Garcia et al. [45] proposed a multi-criteria approach combining three methodologies: material flow analysis (MFA), life cycle assessment (LCA), and data envelopment analysis (DEA). They applied it to a sample of 26 representative Spanish cities with different characteristics. The combined approach identified non-sustainable cities considering an offset of indicators from three pillars of sustainability. Moreover, this multi-criteria method allows the setting of target values for the assessed indicators, which become objectives for the non-sustainable cities to evolve toward a more sustainable performance [45]. Strezov et al. [48] developed the normalised average sustainability index (NASI). They found out that Switzerland, followed by Norway and Sweden, are the highest-ranking countries, while Burundi, followed by Sierra Leone and Niger, are the countries with the lowest NASI rankings. The studies referred to above usually involved the selection of a set of predictor variables, data acquisition, and aggregation of results to present complex phenomena in a simplified manner with a single aggregate quality index. This led to ranking lists for decision-making. The latter should concentrate on steering the socioeconomic development of urban and rural areas towards more sustainable paths, which can substantially improve their turbulent-event resilience.

3. Materials and Methods

3.1. Research Area

The research area covers three voivodeships in southern Poland, Małopolskie, Świętokrzyskie, and Śląskie (Figure 1).

The research area was selected intentionally because of the differences affecting socioeconomic conditions, such as different spatial arrangements, structures of the settlement networks, land-use structures, landscape, and topography. In particular:

• Małopolskie Voivodeship exhibits a substantial share of tourism in its economy, landscape and topography variability, and a substantial scattering of built-up areas combined with patchworked land;
• Świętokrzyskie Voivodeship’s economy focuses on agriculture. It is dominated by rural areas and a rather uniform (flat) topography. Any developments are dense;
• Śląskie Voivodeship has an industry-based economy and mostly urbanised areas with a hilly or flat topography.

The basic assessment fields for the analysis are municipalities in the voivodeships. The research area covered about 39,000 km² and 450 municipalities (182 in Małopolskie, 167 in Śląskie, and 101 in Świętokrzyskie).

3.2. Selection of Diagnostic Variables

The research is founded on diagnostic variables. Their selection is one of the crucial and most challenging stages of the research process. The quality of the variables determines the reliability of the results, and consequently, the decisions taken using the results. Therefore, the classification procedure should include only variables capable of discriminating elements of the set. Still, it is unreasonable to aim for the largest possible number of variables [50].

The most common method for variable selection is substantive or substantive and formal analysis with the former being the most common starting point [51]. The substantive selection of variables is mostly subjective. The shortlist of potential variables should meet the two criteria: (1) cost-effectiveness (costs of obtaining variable information) and (2) availability and reliability of statistical data. Such a list of variables emerges from expert knowledge and statistical traditions [52].

The present research design uses absolute values of variables. The diagnostic variables and their number were selected arbitrarily through substantive analysis, mostly based on sets of variables employed in similar studies [53,54,55,56,57,58,59]. They conformed to the requirement of cost-effectiveness, availability, and reliability of statistical data. The statistical methods used in the research aggregated the diagnostic variables to identify trends in socioeconomic changes in the three voivodeships, taking into account spatial, environmental, social, and economic conditions and delimiting the points of interest, model and problem areas in turbulent times.

The selection of variables was also guided by data availability (the data had to be available for all municipalities in the voivodeships). The results of the statistical analysis are presented in spatial terms. Spatial analyses can reveal regularities hidden in numbers and relate them to objects with specific physical locations.

3.3. Indicator Analysis

We used 18 diagnostic variables to build the synthetic level of socioeconomic development index (LSED) to quantify socioeconomic development in turbulent times (

Table 1. Attributes for assessing socioeconomic development.

Variable Code	Diagnostic Variable Designation	Unit	Type of Variable
X1	Agriculture and hunting (municipal expenditures)	[PLN]	LDB
X2	Community services and environment protection (municipal expenditures)	[PLN]	LDB
X3	Length of active sewerage system	[km]	LDB
X4	Total per capita income	[PLN]	LDB
X5	Budgetary income of municipalities and towns with district status, independent income	[PLN]	LDB
X6	Length of active water distribution system managed or administered by the municipality	[km]	LDB
X7	Water consumption by the national economy and population during the year per capita	[dam3]	SDB
X8	Elderly dependency rate	[persons]	SDB
X9	New flats per 1000 persons	[–]	LDB
X10	Total unemployed registered in municipalities	[persons]	SDB
X11	Working population according to a different category than the classification of business activities: working population in municipalities by sex	[persons]	LDB
X12	Total mixed waste collected in a year per capita	[t]	LDB
X13	Registered national economy entities per 1000 persons	[–]	LDB
X14	Elderly population over 65 to total population	[persons]	SDB
X15	Living area of new flats per 1000 persons	[m2]	LDB
X16	Distance to voivodeship capital (core city)	[–]	SDB
X17	Land cover change	[–]	LDB
X18	Growth in anthropogenic areas	[–]	LDB

Note: LDB—the larger the better, SDB—the smaller the better. All units for the variables are set by Statistics Poland. [PLN]—Polish złoty. Code PLN was introduced during the 1995 denomination. [t]—metric tonne. [–]—no unit, according to the Local Data Bank of Statistics Poland.

). The source data for 15 statistical-type variables (X1–X15) come from the Local Data Bank [60] (for 2006, 2012, 2018, and 2021). The other three diagnostic variables (X16–X18) come from a geospatial analysis. We used resources from the state boundary and border register and land-cover data from Corine Land Cover (for 2000, 2006, 2012, and 2018).

The distance to the voivodeship capital (X16) comes from spatial analyses of administrative data (state boundary and border register). It was calculated by classifying municipalities as:

• Central municipalities (voivodeship capitals): Kraków in Małopolskie, Katowice in Śląskie, and Kielce in Świętokrzyskie. Assigned value: 4;
• Periurban municipalities in a 10 km ring zone around the central municipality. Assigned value: 3;
• Transitional municipalities in a 10 km ring zone around periurban municipalities. Assigned value: 2;
• Peripheral municipalities that is all the remaining municipalities, the most distant to the voivodeship capitals. Assigned value: 1.

The classification was automatized with GIS algorithms. Each municipality in a specific zone was assigned a score as defined above.

We developed two variants of aggregate land-cover change indices (X17 and X18) using Corine Land Cover (CLC) vector models of land-cover data. The analyses employ the following aggregate land-cover classes (CLC second level of detail) shown in

Table 2. Land-cover classes used in the analysis.

Level 1	Level 2
Artificial surfaces	1.1.	Urban fabric
	1.2.	Industrial, commercial, and transport units
	1.3.	Mine, dump, and construction sites
	1.4.	Artificial, non-agricultural vegetated areas
Agricultural areas	2.1.	Arable land
	2.2.	Permanent crops
	2.3.	Pastures
	2.4.	Heterogeneous agricultural areas
Forest and seminatural areas	3.1.	Forests
	3.2.	Shrub and/or herbaceous vegetation associations
	3.3.	Open spaces with little or no vegetation
Wetlands	4.1.	Inland wetlands
	4.2.	Coastal wetlands
Water bodies	5.1.	Inland waters
	5.2.	Marine waters

.

The values of X17 and X18 reflect changes in land cover and are calculated for intervals of 2000–2006, 2006–2012, 2012–2018, and 2018–2021. The CLC database does not contain land-cover data for 2021. We extrapolated land-cover change for 2018–2021 to ensure data consistency in terms of sources and time. To this end, we determined the rolling average for three known data points. To extrapolate one step forward, we had to check whether the rolling average was decreasing or increasing from point one to point two. This stage is described with the following decision tree (1):

$$\begin{array}{c}\mathrm{IF} \{\left(x_i+\frac{x_{i+1}-x_i}{2}\right)<\left(x_{i+1}+\frac{x_{i+2}-x_{i+1}}{2}\right)\}\\ \mathrm{THEN} \left(x_i+\frac{x_{i+1}-x_i}{2}\right)+\left(x_{i+1}+\frac{x_{i+2}-x_{i+1}}{2}\right)\\ \mathrm{ELSE} \left(x_i+\frac{x_{i+1}-x_i}{2}\right)-\left(x_{i+1}+\frac{x_{i+2}-x_{i+1}}{2}\right)\\ \mathrm{END}\end{array}$$

(1)

where:

x_i is the value for the first data point;

x_i+1 is the value for the second data point;

x_i+2 is the value for the third data point;

IF, THEN, ELSE, END are the decision tree control functions.

If the rolling average between Points 2 and 3 is higher than the rolling average between Points 1 and 2, the rolling average between Points 1 and 2 has to be enlarged by the value of the rolling average between Points 2 and 3. Otherwise, if the rolling average between Points 2 and 3 is lower than the rolling average between Points 1 and 2, the rolling average between Points 1 and 2 has to be reduced by the value of the rolling average between Points 2 and 3.

Index X17 covers any type of change among the land cover classes. In contrast, Index X18 reflects land-use change towards artificial surfaces (the area of land in each municipality transformed into an artificial surface category 1.1–1.4) to demonstrate the intensity of anthropogenic pressure.

The variables are grouped into those that promote better results (LTB) and inhibit good results (STB). Values of the variables were normalised (Formulas (2) and (3)) so they could be aggregated and the phenomenon described with the synthetic measure of development. The data were transformed with zero unitarization. It is a recommended method for analysing complex phenomena.

$${\mathrm{z}}_{ij}=\frac{{\mathrm{x}}_{ij}-mi{n}_i\left\{{\mathrm{x}}_{ij}\right\}}{{\mathrm{r}}_j}$$

(2)

$${\mathrm{z}}_{ij}=\frac{ma{x}_i\left\{{\mathrm{x}}_{ij}\right\}-{\mathrm{x}}_{ij} }{{\mathrm{r}}_j}$$

(3)

where z_ij ∈[0,1]. And:

z_ij is a normalised variable;

z_ij = 0 ⇔ x_ij = mini{x_ij};

z_ij = 1 ⇔ x_ij = maxi{x_ij};

x_ij is the value of a non-normalised variable;

min_i{x_ij} is the minimum value of the variable before normalisation;

max_i{x_ij} is the maximum value of the variable before normalisation;

r_j is the range for the jth variable.

After normalisation, the values are limited to the closed interval [0,1]. The socioeconomic development of each municipality is then described with the synthetic aggregate LSED index (Formula (4)), which is a sum of the normalised values that does not exceed 18 units (under the current research design).

$${\mathrm{LSED}}_i={\displaystyle \sum }_{k=1}^{18}{\mathrm{X}}_{ki}$$

(4)

where:

LSED is the level of socioeconomic development index;

X_k are the diagnostic variables;

i is the time interval.

4. Results

4.1. Analysis of the Variability of the Aggregate LSED Index

The distribution of the LSED index in Figure 2 does not exhibit significant spatial trends regarding the level of socioeconomic development in the voivodeships. Municipalities with various levels of socioeconomic development are not located according to a specific pattern relevant to the core city.

The analysis of the socioeconomic development in the voivodeship was a ‘reverse analysis’. It was aimed to identify decreases rather than increases in index values as often happens in other analyses. The macro-scale ‘reverse’ analysis demonstrates that all of the voivodeships exhibited socioeconomic development evident from a regular reduction in the number of declining diagnostic variables from 2012 to 2021. The only exception is the period of 2012–2018 in Małopolskie and Świętokrzyskie Voivodeships, where socioeconomic development slowed down. The situation improved in the next analysis period from 2018 to 2021.

In 2006, the largest number of municipalities (157) was classified as ‘low’. They dominated the northern and eastern parts of Małopolskie and Świętokrzyskie (Figure 2). Śląskie was dominated by ‘average’ municipalities. The area’s socioeconomic development improved in the next period (until 2018). The dominant municipality type in that period was ‘average’ (164 in 2012 and 139 in 2018). The number of ‘very low’ municipalities also shrunk significantly (from 90 in 2006 to 51 in 2012 and 33 in 2018), as did the set of ‘low’ municipalities (to 131 in 2012 and 123 in 2018). Furthermore, the number of ‘moderate’ and ‘good’ municipalities also grew (94 in 2012 and 138 in 2018 and 10 in 2012 and 17 in 2018, respectively), mainly in Śląskie Voivodeship. Nearly half of the analysed municipalities (49%, 221) continuously grew in terms of socioeconomic development from 2006 to 2018. Until 2012, 319 municipalities grew continuously. The number improved to 328 between 2012 and 2018.

The year 2021 saw significant changes in the level of socioeconomic development in the study area. The value of the LSED index declined in 56% of the municipalities from 2018 to 2021. Virtually all municipalities in Śląskie Voivodeship suffered a deterioration in this regard. The number of ‘good’ and ‘moderate’ municipalities plunged to 5 and 85, respectively. The number of ‘average’ municipalities grew significantly to 217 and dominated Śląskie and Małopolskie Voivodeships in 2021. At the same time, Świętokrzyskie Voivodeship had mostly ‘low’ municipalities.

The role of inhibiting variables (STB) in this research design is different. The lower their values, the better for socioeconomic development. The number of declines in diagnostic variables was lower in Małopolskie Voivodeship in 2021 than in 2006, which means the base level grew from 82.17% to 84.68% (Appendix A 

Table A1. The number of municipalities where diagnostic variables declined from 2006 to 2021.

Voivodeship	Time Interval	Diagnostic Variable																		Total	Decline Potential (DP)	DP Percentage (%)	Increase (%)
		X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	X11	X12	X13	X14	X15	X16	X17	X18
Małopolskie	Trend	↕	↓	↕	↕	↕	↓	↑	↕	↓	↑	↑	↑	↕	↕	↓	↕	↓	↑	↓	↕	↓	↑
	2018–2021	56	14	0	0	0	0	54	0	29	118	42	68	0	0	39	0	24	58	502	3276	15.32	84.68
	2012–2018	56	22	0	0	5	15	29	0	71	175	26	34	0	0	75	0	48	51	607	3276	18.53	81.47
	2006–2012	39	31	4	1	0	56	64	74	34	45	29	55	5	86	31	0	8	22	584	3276	17.83	82.17
Śląskie	Trend	↕	↑	↓	↕	↓	↓	↑	↕	↑	↓	↑	↓	↕	↑	↑	↕	↑	↓	↓	↕	↓	↑
	2018–2021	58	26	5	0	3	2	77	1	29	84	85	71	0	3	3	0	22	31	500	3006	16.63	83.37
	2012–2018	70	22	6	0	5	13	62	0	65	167	46	78	2	0	0	0	20	58	614	3006	20.43	79.57
	2006–2012	54	39	11	4	4	18	72	28	59	89	45	73	12	51	51	0	9	21	640	3006	21.29	78.71
Świętokrzyskie	Trend	↓	↑	↑	↑	↓	↓	↑	↑	↓	↑	↑	↓	↕	↑	↓	↕	↓	↓	↕	↕	↕	↑
	2018–2021	34	26	13	9	10	6	50	13	29	87	44	26	9	11	31	0	43	61	502	2106	23.84	76.16
	2012–2018	49	23	8	6	11	33	33	5	37	102	38	35	8	5	38	0	53	86	570	2106	27.07	72.93
	2006–2012	43	22	6	1	2	12	36	1	10	87	35	45	19	70	14	0	17	43	463	2106	21.98	78.02
↑	a growth in declines in values of diagnostic variables—hindered development
↑	a growth in declines in STB diagnostic variables is desirable
↓	a reduction in declines in values of diagnostic variables—further development
↕	constant trend or no changes
	STB variable
↑	a socio-economic growth
↑	a slower socio-economic growth

Note: DP percentage (%) = value of (column) Total/decline potential (DP) × 100%; Increase (%) = 100% − DP percentage (%).

). The development level grew in Śląskie Voivodeship as well, from 78.71% to 83.37%. Świętokrzyskie Voivodeship exhibited the lowest growth dynamics. In Małopolskie, there were a number of declines in diagnostic variables of the working population according to a different category than the classification of business activities: working population in municipalities by sex (X11), and total mixed waste collected in a year per capita (X12) grew from 2018 to 2021. In Śląskie, positive trends in the following variables slowed down: community services and environment protection (municipal expenditures) (X2), new flats per 1000 persons (X9), and the working population according to a different category than the classification of business activities: working population in municipalities by sex (X11). The change was not as spectacular in the case of the elderly population over 65 to the total population (X14) and the living area of new flats per 1000 persons (X15) (in Milówka (2), Marklowice (2), and Wielowieś (2)). Note here that although the declines were not numerous, they may herald disturbing demographic tendencies (increasing the share of the elderly population over 65 to the total population) and a decline in the living area of new flats per 1000 persons. It is advisable to continue monitoring the indicators in this group to prevent the expansion of undesirable socioeconomic issues. The number of declines in X11 and X14 also grew in Świętokrzyskie followed by the variables X2, X3, and X4 linked to municipal resources management, which calls for an in-depth investigation.

4.2. Assessment of the Socioeconomic Potential of the Municipalities

We identified model and problem areas (with the highest and the lowest LSED score) in each voivodeship separately, taking into account the division into (1) urban municipalities, (2) rural municipalities, and (3) mixed urban–rural municipalities. This decision is founded on different conditions for socioeconomic development in each voivodeship. Every voivodeship exhibits a different socioeconomic situation, which affects the LSED score. Moreover, each of the voivodeships has a different number of municipalities, which affects the model’s potential as the maximum score that the municipalities can achieve in the voivodeship. The potential of the research model regarding socioeconomic development can be described as the maximum available score (for normalised values). The full potential is theoretically feasible under laboratory conditions of perfect similarity of municipalities (in terms of environmental, socioeconomic, and spatial conditions).

Małopolskie and Świętokrzyskie Voivodeships have two strong urban areas, Kraków and Kielce, respectively. These cities scored relatively higher than Gliwice, which leads the ranking for Silesian Voivodeship (Appendix A Table A1). In our in-depth analysis, Kraków (Małopolskie) reached the highest value of LSED. At the same time, Małopolskie has municipalities with the lowest indicator values (under the present research design). This fact may demonstrate a certain polarisation and division into municipalities with a strong politically and economically well-founded position and municipalities with a lower socioeconomic potential. Our macro-scale analysis of all municipalities revealed that they achieved a total of 2518.36 LSED points by the average LSED from 2006 to 2021, which is about 31% of the maximum score. The highest score was recorded for Śląskie and the lowest for Małopolskie, which may be consistent with the developmental disparities across the latter and the generally accepted discourse on the high socioeconomic development level in Śląskie as a rich region.

Another potential sign of the significant socioeconomic development differentiation among Małopolskie municipalities is the relatively large range of LSED values between the model municipality (Kraków) and the municipality with the lowest LSED (rural municipality of Słaboszów). The difference is 4.52 points, which is more than the minimum LSED in the study. Świętokrzyskie has just as significant differences (Appendix B 

Table A2. Selected statistics of realised potential for socioeconomic development in the municipalities by LSED.

Voivodeship	Małopolskie		Śląskie		Świętokrzyskie		Total (Number)	Percentage (%)
	Number	Percentage (%)	Number	Percentage (%)	Number	Percentage (%)
No. of municipalities	182	100	167	100	101	100	450	100
Model’s potential *	3276	100	3006	100	1818	100	8100	100
LSED score	969.25	29.59	1015.51	33.78	533.61	29.35	2518.36	31.09
Maximum LSED	8.58	47.67	7.66	42.56	8.46	47.00	24.7	45.7
Minimum LSED	4.06	22.56	5.06	28.11	4.38	24.33	13.5	25.0
Range	4.52	25.11	2.6	14.44	4.08	22.67	N/A	N/A

Note: * the maximum municipality LSED score in the voivodeship. N/A—Not applicable.

). For the sake of comparison, the LSED range in Śląskie is 2.6 points. This may indicate that municipalities in Śląskie Voivodeship are more evenly developed in terms of the socioeconomic domain, while Małopolskie and Świętokrzyskie exhibit greater developmental discrepancies.

The model areas in Małopolskie Voivodeship are those municipalities that exceeded an LSED of 6.4, which is more than 35.5% of the maximum possible score. Problem areas, that is municipalities with the lowest LSED scores, include those that failed to reach 24% of the potential (

Table 3. Ranking list of municipalities according to average LSED in 2006–2021.

Małopolskie Voivodeship
Rank	Municipality	2006	2012	2018	2021	Average for 2006–2021	Percentage (%) *
Municipalities with the highest LSED (model areas)
1	Kraków (1)	8.87016	8.613165	8.567362	8.258815	8.58	47.65
2	Mucharz (2)	7.155955	6.362138	7.280348	6.337516	6.78	37.69
3	Zakopane (1)	6.873251	6.425827	6.856306	6.391696	6.64	36.87
4	Michałowice (2)	6.305934	6.682243	6.536411	6.550244	6.52	36.22
5	Zielonki (2)	6.255813	6.321124	6.698127	6.63506	6.48	35.99
6	Wielka Wieś (2)	5.422531	6.196229	6.958621	7.066122	6.41	35.62
Municipalities with the lowest LSED (problem areas)
178	Wietrzychowice (2)	3.877112	4.40204	4.425403	4.67112	4.34	24.13
179	Kozłów (2)	4.020516	4.234999	4.381591	4.60335	4.31	23.95
180	Radziemice (2)	3.947113	4.222017	4.270877	4.737846	4.29	23.86
181	Gręboszów (2)	3.920253	4.213141	4.276446	4.310924	4.18	23.22
182	Słaboszów (2)	3.858796	3.990011	4.034311	4.338272	4.06	22.53
Śląskie Voivodeship
Municipalities with the highest LSED (model areas)
1	Gliwice (1)	8.017773	8.012679	7.686603	6.940416	7.66	42.58
2	Katowice (1)	7.580392	7.300473	8.178297	7.21119	7.57	42.04
3	Bielsko-Biała (1)	7.991302	8.090002	7.321302	6.64324	7.51	41.73
4	Rybnik (1)	7.848313	7.468648	7.159499	6.649321	7.28	40.45
Municipalities with the lowest LSED (problem areas)
162	Będzin (1)	6.006029	5.449189	5.245441	4.878435	5.3947733	29.97
163	Przyrów (2)	5.178392	5.548366	5.644653	5.061427	5.36	29.77
164	Łaziska Górne (1)	5.226781	5.804004	5.287344	5.110349	5.36	29.76
165	Irządze (2)	4.990853	4.822829	5.830856	5.615629	5.32	29.53
166	Dąbrowa Zielona (2)	4.870187	5.107365	5.611285	5.29363	5.22	29.00
167	Koniecpol (3)	5.076839	5.088317	5.216197	4.869811	5.06	28.13
Świętokrzyskie Voivodeship
Municipalities with the highest LSED (model areas)
1	Kielce (1)	8.751332	8.521683	8.568724	8.016937	8.46	47.03
2	Nowiny (2)	6.504277	6.990864	6.086851	6.374502	6.49	36.05
3	Sandomierz (1)	6.718439	6.882495	5.578658	5.85485	6.26	34.77
4	Miedziana Góra (2)	5.873577	6.699218	6.158201	6.138744	6.22	34.54
5	Busko-Zdrój (3)	6.423144	6.08959	6.254384	6.091996	6.21	34.53
Municipalities with the lowest LSED (problem areas)
97	Stąporków (3)	4.491491	4.853752	4.439446	4.574043	4.59	25.50
98	Tarłów (2)	4.423021	4.695179	4.622971	4.503819	4.56	25.34
99	Czarnocin (2)	4.271622	4.417235	4.324348	5.04688	4.52	25.08
100	Bejsce (2)	4.055953	4.29708	4.725434	4.731595	4.45	24.74
101	Działoszyce (3)	4.075643	4.298015	4.516458	4.62653	4.38	24.33

Note: (1) urban municipality; (2) rural municipality; (3) mixed urban–rural municipality. The typology of municipalities follows the recommended approach of the Polish Central Statistical Office and the Local Data Bank. * Percentage (%) = Average for 2006–2021/18 (100% of the development potential) × 100%.

). The model areas in Śląskie Voivodeship are those municipalities that exceeded LSED of 7.28, which is more than 40.0% of the maximum possible score. Problem areas, that is municipalities with the lowest LSED scores, include those that failed to reach 30% of the potential (Table 3). In Świętokrzyskie Voivodeship, model areas are those that exceeded 34.5% LSED, and problem areas, those below 25.5% of the maximum potential LSED.

4.3. In-Depth Analysis of Municipalities with the Highest and the Lowest LSED

Those municipalities that have the highest LSED values—Kraków, Zakopane, Gliwice, and Kielce—score high (often over 0.6 normalised units for the maximum of 1) for the following indicators: length of active sewerage system (X3), total per capita income (X4), budgetary income of municipalities and towns with district status, independent income (X5), water consumption by the national economy and population during the year per capita (an STB variable) (X7), total unemployed registered in municipalities (X10), total mixed waste collected in a year per capita (X12), and registered national economy entities per 1000 persons (X13). The in-depth analysis revealed that the values of community services and environment protection (municipal expenditures) (X2) and budgetary income of municipalities and towns with district status, independent income (X5) affect differences in LSED between the top-ranking Kraków and the other towns in Małopolskie (Mucharz and Zakopane) the most. Moreover, these municipalities have low values of agriculture and hunting (municipal expenditures) (X1), old-age dependency ratio, i.e., elderly dependency rate (X8) and elderly population over 65 to total population (X14), new flats per 1000 persons (X9), and living area of new flats per 1000 persons (X15). Their values are relatively low, around 0.2 units. Therefore, the statistical data reveal certain generalised problems with socioeconomic development relevant to the ageing population (the variables X8 and X14) and housing shortages (the variables X9 and X15).

The problem areas are very diversified. Their classification depends on the complexity and characteristics of individual municipalities. What is more, the in-depth analysis of model areas identified special and municipality-specific micro-scale problem areas. This means that even municipalities ranking high can—and usually do—have ‘trouble spaces’ in need of improvement (optimisation). This is consistent with the relatively significant untapped potential. Every municipality could score the maximum of 18 LSED points, but even the best city of Kraków reached merely 8.58 units, which is 47.65% of the potential maximum. Moreover, the total score of the municipalities was 31% of all possible LSED points.

Those indicators that varied after normalisation, such as community services and environment protection (municipal expenditures) (X2) and length of active sewerage system (X3), constitute a separate group. Some top-ranking municipalities reached high values for these indicators, while others fared worse. This may be directly related to the size of the municipality both in terms of population and area.

Municipalities with a lower LSED tend to generally have low or zero values for all normalised variables. At the same time, the variables exhibit some diversification, which seems only natural and consistent with the diversified characteristics of the municipalities. The LSED of municipalities with low values of the index is affected mostly by total per capita income (X4), but also to some degree by water consumption by the national economy and population during the year per capita (X7)—considered an STB variable—and such economic variables as housing stock, i.e., new flats per 1000 persons (X9) and living area of new flats per 1000 persons (X15). In general, the indicator values in these municipalities may be controlled by certain ‘natural limitations’ due to their geographical location (both point in space and spatial extent) and socioeconomic conditions (smaller populations, labour markets, service stock, and agriculture) that contribute to a smaller natural development potential.

5. Discussion

5.1. Turbulent Events Affecting Socioeconomic Development of Municipalities in Poland

Turbulence has been a characteristic feature of global politics since the Middle Ages. It was true when ecological imperialism and European colonialism laid waste to the world after 1500. It was also true during the First World War (1914–1918), the Second World War (1939–1945), and the Cold War (1947–1991) [61,62]. The Polish economy also went through numerous turbulences. One such event not so long ago was the spectacular socioeconomic transformation from real socialism to capitalism in the 1980s and 1990s [63]. Since then, the economy has been guided by free market principles. After an initial period of declining production and increased unemployment, the transformation brought economic stability around 1994 and continued socioeconomic development. According to The Conference Board [64], the GDP of Poland grew by over 135% from 1990 to 2014, which was the largest increase among all Central and Eastern European countries. Poland achieved the most rapid economic growth among all of the new EU member states (EU13), twice as high as the average for EU15. This gradual rise in the socioeconomic development level is reflected in the values of the LSED index.

Another breakthrough in the socioeconomic development of Poland was its accession to the European Union in 2004. It forced the state to implement the institutional and structural reforms required by the EU. The economic development of the country and its regions were directly affected by such important mechanisms as direct international investments, migrant remittances, EU funds, more open markets, and access to new technologies [65]. Their impact is reflected in the values of the LSED index and its constituent indicators, in particular the budgetary income of municipalities and towns with district status, independent income (X5), and the length of the active water distribution system managed or administered by the municipality (X6). The gradual increase in the number of municipalities where these indicators grew reflects the impact of EU funds on the improvement of the socioeconomic level in the municipalities. Socioeconomic development is not equal in all of the investigated regions. One can hardly imagine the same number of foreign investment projects that go on in Kraków to be implemented in a typical rural municipality. Our analysis of LSED demonstrated a significant polarization in Małopolskie Voivodeship, the highest level of socioeconomic development in Śląskie Voivodeship, and the lowest development dynamics in Świętokrzyskie Voivodeship. Research by Wójcik [66] also shows that development dynamics are not even in all regions of Poland after the country joined the EU, which remains a challenge for EU programmes.

The 2007 global economic crisis happened in the period of the regular socioeconomic development in Poland. According to Adamowicz and Adamowicz [67], the financial crisis reached Poland a little later and was not as impactful as in other developed countries. Poland’s was the only EU economy to dodge the recession in 2007–2009 [68]. Also, the investigated area exhibited no significant reductions in LSED during the global economic crisis.

However, the present results do suggest a slowdown in socioeconomic development after 2018. The value of LSED dropped in more than half of the municipalities between 2018 and 2021. The reduction meant that each voivodeship had more municipalities with declining values of such diagnostic variables as X11 related to the labour market and X14 related to demographics. It was also a time of gradual cutbacks on EU funds in response to the rule of law crisis caused by justice system reforms [69]. The values of the diagnostic variable X2 related to public budgets declined significantly especially in municipalities in voivodeships focused on agriculture (Świętokrzyskie) and tourism (Małopolskie). Agricultural and tourism functions receive significant EU subsidies. This is why upset EU funding affects the developmental results for these voivodeships, whereas in Śląskie, which is focused on the industrial function, EU funds were not as poignant. However, this may change due to expensive changes brought about by the new EU climate policy. In these circumstances, the region will soon face the daunting challenge of transforming its economy to conform to the new Green Deal. Without EU funds, it will be in serious trouble.

The recent turbulent events, such as the COVID-19 pandemic from 2020, which significantly affected the global socioeconomic development have been consequential as well [2,70]. The level of economic activity dropped within a few months after the outbreak in many countries, leading to more unemployment. This was mostly due to various restrictions on economic freedom and travel [71,72]. The restrictions were intended to control the spread of the virus, but they also hurt the economy [73] and society [74]. They drove changes in socioeconomic policy priorities [75]. The controls also led to changes in the legal system, service provision, and work patterns. Many countries focused on improving their healthcare and stimulating the economy through investments and subsidy programmes for businesses and social groups that were hit the worst by the pandemic. In the long run, the pandemic may contribute to permanent changes in society and the economy, such as wider acceptance for telework and more dynamic digitalisation but also a lower quality of life for some social groups in a material and mental sense. All this leads to a greater need for monitoring socioeconomic development [75], which can identify changes and their directions in the post-pandemic era compared to the pre-pandemic time. The analysis can also help to forecast future trends [76,77].

5.2. The Need to Identify Problem Areas in Turbulent Times

Today’s turbulences grow ever more consequential for the entire planet [62]. One of the consequences is the rise of problem areas and new challenges. These are strategic intervention areas with identified or potential functional links or special social, economic, or spatial characteristics that determine development barriers or permanent, feasible development capacities, where public interventions are carried out consisting of investments—particularly economic, infrastructural, or in human resources—diversified funding, or regulations. In the context of spatial planning and socioeconomic development, problem areas exhibit particular management issues and require complex efforts to shape and manage land.

Urban areas typically have spatial management problems, such as a shortage of green spaces, very dense developments, polluted air, noise, or transport issues [78]. However, all of these features do not make a municipality a problem area. The context of this label has been proven to be much broader and related to regional- or country-wide socioeconomic circumstances. Rural areas face such problems as depopulation, no investment projects, poor availability of infrastructure, or difficulties with winning developmental funds [79], which is demonstrated for the problem areas identified in the present study as well.

In the context of regional policies, a problem area is dysfunctional due to its geographic limitations or level of socioeconomic development—concentration of socioeconomic-development issues. Problem areas also include peripheral rural areas at risk of marginalisation, towns and cities and other areas where past socioeconomic functions are expiring, borderland areas, mountain areas, and areas with the lowest availability of goods and services that determine development capabilities in spatial planning and regional policy. They should be identified and delineated at a regional level to improve the effectiveness of public project planning when the population dwindles and functions can vanish at any moment. The present study identified such areas. In Małopolskie and Świętokrzyskie Voivodeships, all problem municipalities are peripheral municipalities on the fringes of the voivodeships. In Śląskie Voivodeship, five out of six problem municipalities are peripheral.

Identification of problem areas at a local level can support decision-making regarding target land development in line with its assets and limitations due to special predispositions to specific functions. In spatial planning and land management, the identification of problem areas may contribute to an improved quality of local life [80]. Moreover, the identification of specific issues in problem areas helps to better understand local problems and implement solutions to improve the quality of life of residents. This ensures a more effective use of resources and funds focused on key problems [81]. All this boosts the capabilities to improve living standards and land management more effectively and faster. Thanks to the identification of problem areas, experts can draft development plans and strategies and better coordinate collaboration among spatial planning and land-management institutions and stakeholders to take into account the characteristics of each area and its issues. In this way, spatial and social conflicts can be avoided and efforts can be more effective.

6. Conclusions

The study answered the research questions. Regarding Question 1, the decrease in the socioeconomic development of the municipalities after 2018 is related mostly to the decline in the variable X11 reflecting the labour market situation. Other variables that dwindled in many municipalities after 2018 include X2 reflecting public spending, X9 reflecting industry and construction, X12 reflecting environment protection and community services, and X14 reflecting the demographic structure and age dependency ratio. The significant increase in LSED from 2006 to 2012 coincides with a larger number of municipalities in all of the voivodeships, where the value of the variables X5 and X6 grew, reflecting more public expenditures and better community services. The statistical data revealed some general trends related to the ageing population (the variables X8 and X14) and housing shortage (the variables X9 and X15).

The second research question involves socioeconomic phenomena that affected changes in the socioeconomic development of the investigated regions in Poland. The results demonstrate that the accession to the European Union and the inflow of EU funds and foreign investments affected the socioeconomic development in the regions and Poland in general the most over the study period. The Polish rule of law crisis that started in 2015 led to a conflict with the EU and cutbacks on EU funds, which could be a reason for the economic slowdown in the investigated municipalities evident in lower values of LSED after 2018. The crisis brought about by the COVID-19 pandemic may be consequential as well.

Our analyses yielded the following conclusions relevant to the three sustainable development goals: SDG 9: Industry, Innovation, and Infrastructure; SDG 10: Reduced Inequality; and SDG 11: Sustainable Cities and Communities:

• Municipalities in a region with a dominant industrial function (Śląskie) maintain a constant level of socioeconomic development, while regions with mainly tourist (Małopolskie) or agricultural (Świętokrzyskie) functions exhibit significant disparities among municipalities;
• The highest level of socioeconomic development was found in municipalities in Śląskie Voivodeship and the lowest, in Świętokrzyskie;
• Nearly all municipalities with the lowest LSED (problem areas) in the investigated voivodeships are located on the fringes (except for one);
• The areas with the highest values of LSED (model areas) are mostly core cities and key cities in the regions and—in Małopolskie and Świętokrzyskie—suburban municipalities.

Turbulence has been a main feature of global politics for a long time. The escalating environmental climate crisis is one of the reasons for turbulence in global politics leading to upset international relations and public life. Inequality is growing intranationally and internationally. More and more people from conflict zones in impoverished counties are trying to find safe and sustainable places to live abroad. COVID-19 restrictions since 2020 and Russian aggression against Ukraine in 2022 overwhelmed the global economy, supply chains, and mass tourism. All this while the next global financial crisis may be round the corner. On the other hand, a turbulent environment opens opportunities to build resilience to change and uncertainty and imagine new ways to organise life on Earth. Therefore, it is vital and crucial to monitor the level of socioeconomic development to ensure effective socioeconomic policy, prevent economic crises, and ensure the strategic planning of development, mostly in the face of new turbulent events consequential for development.

6.1. Theoretical and Practical Implications

The article proposes a concept for the LSED index, which considers tabulated (statistical) predictor variables and spatial attributes. It is a new contribution to the methodology of indicator-based assessment of complex phenomena and a new outlook on analysing socioeconomic development because most analyses involve statistical variables only.

The selection of the independent variables and the aggregation method are critical points of the study. When ranking objects—such as municipalities—for decision-making, shifting an item as little as one place may be consequential. It may mean, for example, that a municipality is excluded from a funding scheme or a plan of infrastructural projects if an indicator analysis is used in the process. Therefore, it is important to select the diagnostic variables with subject-matter criteria so that the ones most representative (relevant) of the phenomenon are chosen. Outcomes of indicator analysis emerge from the diagnostic variables and methods employed, which may be of utmost practical impact.

The research yielded a ranking list of the municipalities. The results are shown on a map with colours representing the phenomenon’s intensity. It revealed clusters, problem areas and model areas (under the employed research design). Map visualisation is more effective than tabulated data and easier to interpret for the reader. In practice, it means that decision-makers can more easily explain the grounds for their decisions to interested parties (residents, clients, or stakeholders).

The study identified problem areas needing legislative corrective solutions along with areas with a greater degree of sustainable development. The independent variables used in the study that would be of the greatest interest to decision-makers are the stimulation of the labour market, old-age dependency ratio, problems with community services, and environmental consequences. The overall indicator analysis (macro-scale) could offer detailed recommendations (micro-scale) but only in conjunction with insights into particular problems of the municipality. Waste management, water management, healthcare, and public transport significantly affect sustainable socioeconomic development, but specific recommendations can be given and implemented by mayors only if they have the data necessary to take action.

6.2. Limitations and Further Research

Socioeconomic development is described using many diverse variables. It is, therefore, necessary to build complex models that offer real-time results of analyses and can be controlled with an interactive dashboard. The direction analytical tools are going in is set by the growing amount of data, the diversity of socioeconomic indicators, and the need for taking environmental and cultural variables into account. The inclusion of the largest possible number of variables in a model can increase its diversity and the number of analysed attributes. This, in turn, can improve the objectivity of the analysis due to the sheer number of variables. On the other hand, the aggregation of a large number of variables can blur some dependencies evident in micro-scale analysis. A larger number of variables opens more possibilities for macro-scale comparative analysis but can render individual problems unrecognisable. Therefore, a concept of analytical software should accommodate large amounts of data (big data) from various sources of primary data and a complexity of attributes for each variable. Another challenge is to follow the principles of Analytics 3.0 in practice to make decisions based on big data, also in real time. It is feasible in everyday business management of resources or work organisation, but in the case of assessment of socioeconomic development, it means generating ad hoc thematic reports from a database using an interactive dashboard. This concept involves real-time access to a diversified set of big-data diagnostic variables for public servants to reduce thematic report generation time to the time necessary to collect the variables (data) manually, justify the choice of variables, and perform the statistical analysis. Although the analysis remains retrospective, the results are more readily available, which can streamline the decision-making process. Future research can focus on developing a model that could be used to build an application capable of aggregating big data and controlled with a dashboard. The challenge lies in the range of variables to be included in the database and attributes with which they are described, i.e., metainformation. Another path for the research is to build scenarios of future socioeconomic directions and use them to build an optimisation strategy for local government as a kind of recommendation.