Addressing Urban Vulnerability: A Comprehensive Approach

Abstract

This study aims to investigate the impacts of urbanization and propose strategies to mitigate urban vulnerability. The study first defines and examines urban vulnerability within the context of spatial management, allowing for the introduction of indicators derived from the Sustainable Territorial Economic/Environmental Management Approach (STeMA), complemented by maps, to assess urban vulnerability. These indicators offer a holistic view of urban areas, highlighting the key areas of concern. Secondly, the study identifies highly vulnerable areas and provides targeted intervention recommendations. A European city with a population of more than 500,000 is considered as a case study. By integrating quantitative analysis and qualitative insights, this study aims to provide actionable solutions for urban planners and policymakers in order to improve sustainability in rapidly urbanizing regions.

1. Introduction

The impacts of urbanization have led to various environmental problems—such as global warming and air, water, or land pollution—while, in turn, environmental deterioration has negatively affected the quality of urban living [1,2,3]. Furthermore, rapid urbanization, together with population growth and uncontrolled expansion [4,5], has brought about urban invasion into agricultural land, creating the so-called “urban–rural fringe” [6] and resulting in inefficient city planning [5,7]. Despite these phenomena, urbanization and economic growth contribute to inequalities [8,9], resulting in difficulties in maintaining socio-environmentally resilient policies [5,10]. A significant acceleration of this negative impact is also anticipated with respect to the forecasted population growth [11,12]. Therefore, a major issue is the measurement of a system’s vulnerability toward increasing urban resilience [13,14,15].

Vulnerability (Figure 1) in urban systems refers to the degree to which a territory is exposed and sensitive to risks, combined with its capacity to adapt or recover. It is a multidimensional concept encompassing environmental, social, economic, and spatial components. In the context of urban planning, vulnerability reflects how systemic conditions—such as infrastructure, governance, population dynamics, and ecological fragility—contribute to increased susceptibility to disturbances. Assessing urban vulnerability, therefore, requires an integrated approach that uses both spatial and socio-economic indicators to guide resilient planning and targeted interventions [10,16,17,18,19,20].

Identifying priority vulnerable areas involves both quantitative and qualitative assessments [21]. According to the Intergovernmental Panel on Climate Change [22], vulnerability refers to the propensity or predisposition to be adversely affected, incorporating both sensitivity and adaptive capacity. It is distinct from exposure, which describes the presence of people, infrastructure, or assets in places that could be adversely affected.

In our study, vulnerability is understood as a multidimensional concept that combines socio-demographic and socio-economic characteristics influencing both the susceptibility and sensitivity of urban areas to uncontrolled development and their capacity for recovery. This approach incorporates multi-criteria and multi-group methods, as well as socio-spatial indicators, allowing for comprehensive socio-spatial analyses [19].

Methodologies for the assessment of urban vulnerability incorporate both its increase (susceptibility) and decrease (coping capacity), including the use of several indicators, remote sensing, and geographic information systems (GISs) to map physical vulnerability. Vulnerability mapping involves parameters such as land use or spatial analysis in terms of density (e.g., of buildings) or distribution (e.g., of public facilities) [23,24]. Indicator-based approaches, including those based on GISs, not only allow for the marking of areas requiring direct actions to mitigate the verified negative phenomenon in a given area of the city [5,7,25,26], but also the prediction of future spatial patterns of urban expansion [25,26]. The integration of GIS, or methods such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), into urban vulnerability assessments allows for the establishment of a Spatial Decision Support System (SDSS) [19]. SDSSs enhance “knowledge-based decision-making”, which is essential for solving current socio-environmental issues [27] and strengthening the development of strategies for resilience.

The implementation of such solutions is needed for spatial management, especially in cities [28] where most of the population lives (55%) and will live (68%) by 2050 [29]. A comprehensive approach to innovative urban policymaking and planning for implementing the knowledge-based agenda includes an assessment of urban development potentials as a basis for local and regional decision-making and planning mechanisms [30]. Recent approaches highlight the relevance of interdisciplinary frameworks, such as STEAM, in fostering holistic urban education and policy planning that integrates environmental, social, and economic dimensions [31]. Therefore, based on the analysis, direct actions could be provided for the most vulnerable areas. These advantages can be observed within approaches called Territorial Impact Assessment (TIA) and Strategic Environmental Assessment (SEA).

This study builds on these planning tools by demonstrating the practical application of STeMA, an integrated assessment model adapted from TIA and SEA frameworks. While the broader objective of the research aligns with promoting cohesive and sustainable urban development, the present study focuses on the operationalization of vulnerability assessment in a European urban context. Other analyses, using different methods, can be integrated to assess the physical and ecological aspects of vulnerability. The city of Wrocław (Poland) serves as a case study, in order to illustrate how spatially nuanced and data-driven methods can support the planning of decisions aligned with European territorial cohesion goals.

2. Background

For twenty years, the question of structuring evaluation processes to consider ex ante the potential impact of territorial policies, programs, plans, and projects has emerged. At the European level, these processes have been useful for gathering evidence to support cohesion policy transformation and affecting its evolution [32]. Territorial Impact Assessment (TIA) methods and related tools support this type of process [33,34].

A TIA allows for simulation and explanation ex ante of both local and European objectives (program theory), and how the planned activities will lead to the expected results (implementation theory) through spatial planning. However, TIA requires powerful data support (analytical and cartographic), as well as qualitative and quantitative models to associate judgements in response to the questions for which evaluation is required [32]. TIA relates to many dimensions, including the financial dimension, capacity building, and, particularly, better regulation [35,36]. The establishment of a coherent relationship between the ex ante and ex post evaluation of policies, programs, and plans is required by states, regions, and local systems.

After a period during which the analysis of impacts referred to the social, economic, and environmental dimensions without considering territorial aspects [37], the need to consider a territorial dimension strongly emerged. This shift aimed to support policymakers in providing tailored responses suited to varying geographical scopes and scales [38]. Territorial impact “is essentially considered to be any impact on a given geographically defined territory, whether on spatial usage, governance, or on wider economic, social or environmental aspects, which results from the introduction or transposition of an EU directive or policy” [39].

At the same time, the tight secondary link between TIA and Strategic Environmental Assessment (SEA) becomes evident if the plan is no longer detached from policies and their development beyond the scale of relevance [40,41]. Thus, both TIA and SEA can be recognized as ex ante and ex post mechanisms for identifying impacts at national, regional, and local levels in Member States. They also facilitate the recognition of potential policy conflicts or inconsistencies. Both TIA and SEA can additionally identify a variety of possible impacts across different areas, allowing for an analysis of the geographical dimension of EU policy impacts and related plans.

The existing TIA and SEA models have some elements in common, including the territory and its specificities (geographical diversity). They are derived from methods and techniques that have been shared by the international scientific community since the 1970s [32]. From time to time, they have proven to be useful in the selection and correlation of indicators, impacts, or effects¹ [42]. In all ex ante evaluation processes, the attribution of weights to impacts and their indicators/receptors represents the most delicate phase regarding the methodological choice and the accompanying methods.

As the purpose of TIA is to measure the impact of policies, directives, and regulations, many organizations—including the CoR and DG Regio—prefer methods such as preference analysis or the Delphi method. These approaches help to detect the orientations of policymakers and stakeholders from the outset, minimizing differences in the choice of parameters and criteria for assessment [43].

While both TIA and SEA are ex ante evaluation tools used in policy and planning, they serve distinct purposes (

Table 1. Comparative overview of TIA, SEA, and STeMA frameworks in urban planning.

Feature	TIA	SEA	STeMA
Primary Focus	Spatial/policy impacts on territorial cohesion	Environmental risks of plans and programs	Urban vulnerability and sustainability integration
Application Level	Multi-scale: EU, national, regional, local	Mostly regional/local; policy/program level	NUTS 1–4; urban and regional
Main Use	Evaluate policy coherence, socio-economic outcomes	Ensure environmental compliance and mitigation	Support integrated planning and decision-making
Methods Used	Delphi method, preference analysis, simulations	Impact modeling, scenario analysis	Indicator-based matrix, GIS mapping, pairwise comparisons
Output	Spatial impact maps, policy recommendations	Environmental impact statements, mitigation plans	Vulnerability maps, sensitivity analysis, and planning recommendations
Data Required	Quantitative + qualitative indicators	Environmental, ecological, and regulatory data	Mixed: census, environmental, spatial, and historical data
Strengths	Policy alignment, spatial equity, and EU-wide applications	Early-stage environmental integration, risk avoidance	Holistic, multi-criteria analysis tailored to urban and territorial planning
Tool Integration	ESPON, RHOMOLO	SEA software2, EIA platforms	Custom GIS-supported STeMA software, integrated with SDSS3

). TIA is primarily focused on assessing how spatial policies or plans influence territorial cohesion, equity, and socio-economic development across multiple geographic scales. It is often used to understand the spatial implications of EU and national policies on local and regional territories. In contrast, SEA focuses on the environmental consequences of proposed plans and programs, aiming to ensure sustainability and prevent ecological degradation. SEA is often integrated earlier in policy cycles and is particularly useful for environmental risk identification and mitigation. Both approaches require robust data and often utilize GIS; however, they differ in emphasis: TIA is policy- and space-oriented, while SEA is environment- and risk-oriented.

Moreover, the close subsidiary relation between TIA and SEA [44] in spatial planning elaboration is becoming more widespread in Europe in order to make coherent and cohesive sustainable choices at different levels of decision-making. The vulnerability concept and its assessment are common to both.

3. Materials and Methods

3.1. Methods: STeMA Model

The Sustainable Territorial Environmental/Economic Management Approach (STeMA) model serves as a methodological foundation for analyzing urban vulnerability. It was adapted from the Territorial Impact Assessment–Strategic Environmental Assessment (TIA-SEA) methodology. Its primary objective is to describe the socio-economic, environmental, and cultural territorial framework of a location ex ante to facilitate the creation of ex post decision scenarios that reduce vulnerability risks within the planning process.

STeMA employs a systemic approach rooted in theory-based evaluation [45]. A core assumption is that the environment, economy, society, and territory function as a single integrated system from the outset of the evaluation process (time t₀), consistent with general systems theory as originally proposed by Bertalanffy in 1968 [46]. These dimensions are viewed as interrelated sets of biotic and abiotic elements [47,48,49]. From a planning perspective, this aligns with strategic spatial planning frameworks that recognize complexity, adaptability, and multi-level governance [50].

The STeMA model utilizes a set of indicators to assess territorial sustainability, integrating them at two distinct levels:

i Regional Level (NUTS 1–3): This level encompasses a comprehensive framework of 28 indicators (Figure 2) for measuring sustainable development. Metrics include CO₂ emissions, air quality, drinking water consumption, and natural risk exposure, all of which directly contribute to the calculation of urban vulnerability. This broader scope ensures a holistic foundation for planning and policy decisions within the context of sustainable development.

ii Local/Urban Level (NUTS 4–5): At this scale, each indicator is enhanced with detailed data, such as specific types of air pollutants, detailed water use information, and various risk types (e.g., hydrogeological, seismic). These indicators collectively provide information across 11 thematic dimensions or components of the STeMA SEA plan. These components are as follows: hydrology; geomorphology; natural landscape system; historical landscape system; natural and protected areas; fauna; urban; socio-economic settlement; rural agricultural system; atmosphere; air and carbon footprint; noise and vibration; and public health.

These methods have been updated and applied as part of the TIA methodology in several major projects. Notably, they were used in the evaluation of cohesion policy programs (2008–2011 and 2018–2020) across Italian regions and provinces [40]. They were also applied to the assessment of large infrastructure projects funded under the Recovery and Resilience Facility Plan [51]. In the context of SEA, the method was used for the Metropolitan Plan of Rome (2003–2020), its surrounding intermunicipal areas (2020–2024), and several municipal plans in Italy (e.g., Latina, Arborea, Venezia-Padua), as well as in Mediterranean countries such as Lebanon, Jordan, Tunisia, and Spain.

STeMA Workflow

A comprehensive analytical framework (Figure 3) is used for assessing urban vulnerability using the STeMA supported by Geographical Information System (GIS) tools. The methodology enables both abstract and practical evaluations of territorial transformations, in line with sustainability principles [32,40,48].

The initial stage of the STeMA methodology involves the critical task of defining the analytical framework, specifically the selection of the appropriate territorial scale—regional or urban—based on the spatial objectives and context of the analysis. This selection is essential, as it conditions the relevance and applicability of indicators, the spatial resolution of data, and the interpretative lens through which territorial phenomena are assessed. Establishing the scale of analysis provides the structural basis for organizing spatial information and enables the coherent planning of subsequent analytical and decision-making stages within the STeMA process [49].

Data acquisition encompasses both non-spatial and spatial dimensions. Non-spatial data include statistical indicators such as population figures, demographic structure (e.g., total population, age cohorts, employment by sector), and indices of environmental stress. Spatial data, on the other hand, comprise orthophotos, cartographic representations of the built environment, infrastructural networks, and the historical evolution of land use. All spatial datasets are standardized in shapefile (.shp) format and aligned to a unified EPSG coordinate reference system, ensuring compatibility and interoperability across analytical platforms. These datasets, primarily sourced from national statistical authorities, offer a high degree of reliability and methodological rigor for urban assessment.

A central component of the methodology is the construction of spatial typologies of a settlement (STSs), which provide a functional classification of the urban fabric. The typologies are derived by aggregating BRUs into morphologically and functionally homogeneous zones, in accordance with the European CEMAT (Conference of Ministers Responsible for Spatial/Regional Planning; [52]) guidelines for delineating functional urban areas (FUAs). The STS framework identifies five distinct classes: Class A (central urban cores), Class B (grid-like reticular patterns), Class C (linear or ribbon-type settlements), Class D (rural and dispersed habitations), and Class E (natural, uninhabited areas). Each STS unit is further enriched with demographic and environmental variables, including population density, age distribution, economic activity, and pollution metrics [40].

The comprehensive database thus developed is processed within the STeMA-GIS analytical environment, which is compatible with the ArcGIS and QGIS platforms. This system facilitates the computation of key urban indicators, such as population density, settlement load, degree of urbanization, and anthropization levels (classified on a seven-point ordinal scale ranging from A2 to G2). Such GIS-based approaches to spatial typology and risk mapping align with established methods for geocomputation and spatial vulnerability assessment found in broader urban planning practice [53,54].

The carrying capacity is calculated as the ratio between developed land and population-induced pressure. In addition, the model incorporates measures of territorial vulnerability and sensitivity, derived through the weighted aggregation of normalized indicators. These are expressed on a standardized 0–1 scale for Territorial Impact Assessment (TIA) and a 0–3 scale for Strategic Environmental Assessment (SEA). Indicator prioritization is established through pairwise comparison methods [48], enabling the assignment of relative weights to individual metrics.

The analytical outputs are presented in the form of spatial vulnerability maps, which depict the multidimensional aspects of urban stress. These visualizations illuminate spatial patterns with respect to demographic concentration, infrastructural burden, and environmental degradation.

Ultimately, the STeMA model establishes a procedural linkage between spatial assessment and planning praxis through distinguishing between the ex ante territorial baseline—referred to as the Initial Territorialized Value (VTI)—and ex post simulated policy scenarios—referred to as the Final Territorialized Value (FTI) [51]. This enables planners to assess the prospective impacts of spatial interventions and to strategically prioritize actions in line with sustainable development objectives. The model supports scenario-based planning and informed decision making, promoting adaptive territorial strategies that integrate ecological thresholds with socio-economic imperatives.

3.2. Materials

This study used data representing spatial and non-spatial information. The STeMA model, depending on the research focus, uses a range of metadata for assessment (see more in Appendix A). For the evaluation and drafting of maps via the STeMA-SEA methodology using the STeMA GIS software (related to ArcGIS, QGIS, or similar), some protocols are needed, which are required to respect the following:

• Orthophotos of the area to be analyzed, with a reference system attached;
• Use of a reference system (EPSG) common to all maps submitted;
• Each map must be provided in shapefile format (.shp file);
• In this study, data from official statistics provided by the national statistical office were utilized, represented as census tracts in polygon format. These tracts formed the basis for analyses and served as the reference unit for creating the spatial typologies of a settlement (STSs) map, a critical element in the evaluation process.

The census data included a statistical identifier for each tract and the population count. Additionally, spatial data on buildings, urban development, infrastructure, and the historical evolution of the city were required. These datasets provided detailed insights into building types, the age of structures, and urban layouts, contributing to a comprehensive understanding of the city’s development patterns.

The basic data used in this study, including census data and other spatial information about Wrocław, represent the most recent official statistics available at the time this study was carried out. They accurately reflect recent urban dynamics and provide an up-to-date picture of the situation, enabling an assessment of the city’s vulnerability. The reliability of the data is ensured by its origin from the national statistical office. These institutions adhere to rigorous data collection methodologies and quality control standards, minimizing errors and ensuring a high degree of reliability.

The analysis was conducted at the level of official census areas, which are smaller, geographically defined areas for which statistical data are collected. This provides detailed spatial resolutions, enabling the identification of local patterns of vulnerability in the urban structure of Wrocław. Although this aggregated level provides a solid overview of the situation, it should be noted that this scale may not reflect highly dynamic, real-time urban processes at the microlevel or certain nuances of socio-economic behavior that occur below the census tract level. The inherent constraints of utilizing periodic official statistical data pose challenges for conducting urban assessments. However, in the case of multidimensional vulnerability assessment using the STeMA model, the resolution of these data provides a balanced and representative basis. The integration of STeMA with a GIS system is also crucial to mitigate these limitations, enabling the overlay and analysis of data from multiple sources and enriching the interpretation of less detailed statistical information with precise spatial attributes.

These data were applied to the STeMA-GIS tool, which was also used to support the management of the assessment process, and then mapped using QGIS. This study was conducted at two levels:

• Application of the STeMA-TIA (Territorial Impact Assessment) process and tool to evaluate the impact of the territorial planning innovative policy at this stage. This section is not within the scope of the present paper, but an illustrative example can be found in Prezioso 2024 [51];
• Application of STeMA-SEA and its tools to assess the ex ante vulnerability of the urban system.

Data Collection and Preparation

The vulnerability assessment required data preparation, which included verifying and updating census tract boundaries to account for newly developed residential areas and infrastructure, splitting or aggregating polygons where necessary, and estimating population data for various economic sectors (agriculture, industry, and services) based on proportions derived from available statistical data for the city of Wrocław. Additionally, the preparation involved ensuring that each tract’s area and perimeter were accurately represented. The STS map was created based on additional spatial data, which enabled a comprehensive analysis of urban development and its evolution in the city. These additional data included information on building types, the age of buildings, urban layouts, and the historical context of urban development.

The vulnerability of an urban system takes into account all occurrences related to human presence and socio-economic behaviors within the territory. These elements are described using themes and indicators deduced from the economic–political geography and regional–territorial economies, such as the distribution of inhabitants, population variations, and the presence of services and infrastructure (not considered according to their capacity to satisfy the existing demand, but exclusively evaluated depending on their impact on the territory). Therefore, the assessment requires each feature/polygon of the submitted layer to be associated with the data in the table, according to the specifications of the map of the system under analysis. This is also required for the delimitation of the spatial typology of a settlement (STS).

The STS delimitation requires the following data:

-

pop: Total population in the spatial typology of a settlement;

-

act1: Active population in the primary sector (agriculture);

-

act2: Active population in the secondary sector (industry);

-

act3: Active population in the third sector (services);

-

popcor: Population of the core;

-

popcen: Population of the centers;

-

insclass: Spatial typology of the settlement class (A, B, C, D, and E).

Optionally, the following fields can also be added, according to the planned needs and the indicators to be evaluated:

-

super: The land surface area, expressed in hectares;

-

pcdi: Domestic pollutant pressure index (decimal number);

-

pcii: Industrial pressure pollutant index (decimal number);

-

pcai: Index of agricultural pollution pressure (decimal number);

-

pcti: Transport pollution pressure index (decimal number);

-

pop15: Population under 15 years;

-

pop65: Population over 65 years.

Alternatively, more granular data should be obtained depending on the scale of the examined phenomenon (e.g., flats of a building).

3.3. Case Study

This study focused on Wrocław, the largest city in Lower Silesia and the fourth most populous city in Poland, which serves as a key economic, social, and cultural hub for the region. Its spatial development reflects a complex history and dynamic urban transformations. The city’s historical growth began with its medieval core, characterized by compact high-intensity block developments, which, guided by regulatory urban planning, continued through the 19th century with the expansion of areas beyond the city walls. This expansion encompassed both the development of representative garden city-inspired residential neighborhoods and the dynamic industrialization processes that led to the construction of prefabricated housing estates in the second half of the 20th century.

In the 20th century, the city attracted population inflows from rural and industrial areas, accelerating urbanization and necessitating the development of modern residential neighborhoods. These processes reflected changing social needs, ranging from intimate interwar residential districts to the prefabricated housing estates of the 1960s and 1970s, which are characterized by high population densities and rapid construction to meet growing housing demands [55].

In recent decades, Wrocław’s spatial development has focused on modern residential developments and the adaptation of former rural and industrial areas within the city. These processes have been facilitated by the integration of diverse building types, from large high-density housing complexes to more cohesive single-family housing structures [56].

The development of Wrocław reflects urban processes typical of dynamically evolving European cities, where history, space, and contemporary challenges create a complex urban landscape. The vulnerability analysis of this city thus incorporated these multilayered aspects, providing a comprehensive picture of the risks and opportunities within the context of its dynamic spatial growth and its impact on residents’ quality of life.

4. Results

4.1. STS Units

The STEMA-GIS tool allowed us to assign identifiers and STS classes to each census unit. Therefore, each STS (Figure 4) within an attribute table is described by its identifier (COD_PL), total population (TOTAL_POP), active population in the primary sector (agriculture) (act1), active population in the secondary sector (industry) (act2), active population in the third sector (services) (act3), TSI identifier (id_tsi), and membership in a particular STS class (inclass).

The assessment of vulnerability begins here. The STeMA-SEA software and GIS tool automatically carry out calculations within each STS:

• Population in absolute values;
• STS surface and the density of the population;
• Settlement load match between (1) and (2);
• Grade of urbanization, applying the following formula: act 2 + act3/act1;
• Level of urbanization as matched between (3) and (4);
• Vulnerability value matching (5) and STS values.

In the case of Wrocław (Figure 5), the center represents the central places or poles, characterized by dense and continuous urbanization (Class A, depicted in red). These areas are highly urbanized, with a high density of buildings and infrastructure. This central zone serves as the core of the city, where regional and interregional transportation routes converge, making it the hub of activity and urban development.

Surrounding the central core, Class B (shown in blue) forms a reticular structure, with urban settlements arranged in a grid-like pattern. These areas are well-connected to the central Class A region through local and regional transportation routes. The settlements in this zone are integrated into the city’s broader network, forming a structured expansion outward from the urban core. The blue zones are prominent in the immediate periphery of the red center, extending outward in multiple directions and highlighting the spatial continuity of urban development.

Notably, the map does not show a distinct linear pattern for Class C, suggesting that these areas may be less prominent in Wrocław. It can be seen that these areas are mainly concentrated around the inner city (Class A and the surrounding Class B), as well as in the west of the city.

Beyond the reticular structure, Class D (marked in pink) dominates the outer portions of the map, representing rural areas and isolated nuclei. These zones consist of scattered and less dense settlements where no clear orientation pattern is observed. The morphology of the land plays a significant role in shaping these areas, which are primarily used for agricultural or natural purposes. These rural zones encircle the urban and suburban areas, creating a clear distinction between the densely built city and the sparsely settled outskirts.

4.2. STeMA Results

The research addressing the geography of Wroclaw was carried out in stages, starting with an analysis of data on the city population and the degree of investment in the area of the city of Wroclaw, along with identifying vulnerability value matches and STS values. For this purpose, the basic statistical units of reference (official census sections) were aggregated, within which observations were carried out and considered as reliable sources of statistical information. Firstly, the spatial typology of settlements was obtained, considering the distribution structure of residents and the population in absolute values (Figure 6;

Table 2. Population and settlement load by STS Class.

STS Class	Description	Avg. Population	Population Density (pop/ha)	Settlement Load (pop/ha)
A	Central urban cores	>15,000	High	High
B	Reticular/grid patterns	4000–15,000	Medium–High	Medium
C	Linear settlements	1400–4000	Medium	Medium–Low
D	Rural and scattered settlements	<1400	Low	Low
E	Natural/uninhabited areas	0	N/A	N/A

).

Following the adopted rule, the aggregated units of reference with a population of more than 15,000 inhabitants are denoted as A. The units in the range of 4000–15,000 inhabitants are labelled with B, whereas those presenting a lower number of inhabitants are denoted with C and D. The analysis revealed a typically centric population distribution pattern. The highest population rate was observed in the strict center of the city. This was followed by the downtown city areas (Class B), while Class C (1400–4000 inhabitants) covered the intermediate and peripheral areas of the city.

The adopted local indicators—used to determine the settlement’s carrying capacity (Figure 7;

Table 3. Settlement carrying capacity classification.

Carrying Capacity Class	Description	Dominant Area(s)
C1	High	Central and downtown zones
D1	Average	Intermediate and peripheral zones
E1	Weak	Peripheral/rural zones
F1	Poor	Outskirts, underdeveloped zones

)—place selected areas of the city of Wroclaw at the level of C1 (high), D1 (average), E1 (weak), and F1 (poor). The indicator is generated based on the area of land suitable for development (ASS) (in this case, developed) divided by the population number (p) and by the area factor (requirements).

Based on the SCC results, it is possible to indicate the leading role of the city center in terms of the settlement’s carrying capacity and the areas in the downtown zone. The spatial distribution of the indicator’s intensity takes on higher values in the central city and Cameral zones based on morphological types. They include the oldest part of the city and the settlements built during the interwar period. The developments classified in these areas (morphological types) are characterized by an old residential fabric presenting a high density rate. The city’s peripheral zone only includes the F1, E1, and D1 types according to the spatial construction typology, with type F1 (poor) being the most numerous.

The city’s urbanization degree (Figure 8;

Table 4. Urbanization degree by zone.

Urbanization Class	Evaluation Criteria (act2 + act3/act1)	Dominant Zone Type
A—High	>threshold ratio	Most of the city, including the periphery
B—Medium	Moderate ratio	Downtown pockets
C—Low	Low ratio	Western intermediate zone

) constitutes an important element of the assessment. The spatial distribution of the urbanization degree of the city of Wroclaw is based on the adopted evaluation scale in the STeMA typology: high (A), medium (B), and low (C).

The majority of the city was classified as rank A, presenting a high degree of urbanization. This indicator was determined based on the sum of secondary and tertiary sector activities divided by primary sector activity. The results are rather surprising because the high rank of the indicator was also assigned to the study units, which are characterized by low development intensity and low population density, and are located in the city’s peripheral zones.

In turn, in the intermediate zone of the city, the areas with a low degree of urbanization are situated to the west of the center, which could constitute a reserve for the development of buildings, as these areas are simultaneously located in the impact zone of the highway bypass of Wroclaw. The areas that have a B rank—characterized by a medium degree of urbanization—occupy small areas in the city’s downtown zone.

The anthropization degree of Wroclaw was classified according to the adopted seven-point rating scale from A2 to G2 in the STeMA model (Figure 9;

Table 5. Anthropization levels (A2–G2 scale).

Rank	Description	Observed Areas
B2	Very High	Inner-city clusters, near dense cores
C2	High	Downtown and intermediate zones
D2	Medium	Housing estate areas, central districts
E2	Low	Most are built-up but less dense areas
F2	Very Low	Peripheral or transitional urban zones
G2	Insignificant	Outskirts with minimal human influence
A2	Absolute (None)	Not observed

).

The analysis of anthropization levels within the urban sub-system reveals a predominantly moderate transformation of the environment due to human activity. Most of the investigated areas—particularly in the central city, Cameral, and housing estate zones—are classified as D2 (medium) and E2 (low), reflecting a balanced integration of built structures with open spaces. Pockets of C2 (high) and B2 (very high) anthropization appear mainly in the intermediate and downtown zones, indicating localized concentrations of dense development and urban infrastructure.

However, no areas reach the A2 (absolute) level, suggesting that even the most developed parts of the city retain some ecological or spatial permeability. In contrast, the peripheral regions are dominated by F2 (very low) and G2 (insignificant), representing low-density or semi-natural areas. Overall, the STeMA model indicates that urban growth has occurred with a relatively restrained environmental impact, resulting in a heterogeneous but generally moderate level of anthropization across the city.

The results of the analysis—in terms of vulnerability using the STeMA method (Figure 10;

Table 6. Vulnerability rankings (A–F scale).

Vulnerability Rank	Description	Zone Characteristics
A	Absolute	Not observed
B	Very High	Historic downtown, subsidized housing zones
C	High	Cameral zones, inner urban fabric
D	Average	South and west development corridors
E	Weak	Eastern districts, lower urban intensity areas
F	Poor	Peripheral natural/rural zones

)—show that the entire area of Wroclaw has a minimum rank of F—indicating poor vulnerability—with respect to its typology; moreover, none of the analyzed research units received a rank of A—absolute vulnerability.

The highest degree of vulnerability, according to the adopted criteria and following the applied STeMA method, was recorded in the city center, including the adjacent downtown areas. The study units for this area fell under ranks B (very high vulnerability) and C (high vulnerability). These areas are built up with old housing fabric dating back to the turn of the 19th and 20th centuries; at the same time, they feature the highest density of dwellings owned by the Municipality of Wroclaw (commonly referred to as subsidized housing), intended for people on low incomes. The average age of the buildings owned 100% by the Municipality of Wroclaw reaches approximately 100 years, and out of the 1281 buildings with residential units, 581 buildings were constructed before 1900 (45.35%), while the buildings erected between 1900 and 1940 account for a total of as many as 587 buildings (45.82%). Among the buildings owned entirely by the Municipality of Wroclaw, only 104 were built after 2000. These buildings are characterized by significant technical and functional wear and tear, and they continue to deteriorate each consecutive year, with an increasing number of buildings with residential units that are suitable for either general renovation or demolition. Dwellings in tenement houses are usually inhabited by elderly people in the post-working age; at the same time, they earn lower incomes, which adds to the housing problems (rent arrears, keeping rent low, lack of mobility, etc.).

The identified high-vulnerability areas in the center of Wrocław and the surrounding inner-city areas—resulting mainly from outdated housing infrastructure and problems related to social housing—require concentrated revitalization activities. These should include comprehensive technical modernization programs with respect to old buildings and socio-economic support initiatives for low-income residents. It is worth considering innovative public–private partnerships to attract investments, implement rent stabilization mechanisms, and increase access to social services. It will be important to implement historical heritage protections in order to preserve the character of these areas and, at the same time, improve the quality of life of residents. The aim is not only to modernize the infrastructure, but also to revitalize society and prevent the exclusion of current residents.

The areas with assigned vulnerability ranks of C (high) and D (average) in the STeMA typology were identified in the south and west of the city’s core (center). These areas contain both old housing estates (Cameral) and housing blocks and estates. The significant size of the zone classified as type D (average) is noticeable, which develops in the southern direction and conforms to the southbound catchment area of the city of Wroclaw (development of urban functions). The west is the second directional area presenting higher vulnerability, which is simultaneously another potential city development area. The eastern directional area, except for a few enclaves, is rated under E (weak) on the urban vulnerability scale.

These areas, associated with the development of urban functions, indicate the need for strategic spatial planning. Policies should ensure that development is sustainable and that new residential and commercial development is linked to appropriate investments with respect to public and social infrastructure. Such an approach will prevent uncontrolled sprawl and ensure that development contributes to—rather than weakening—the resilience of the city and the quality of life of its residents. In turn, lower sensitivity towards the east, combined with areas of low development intensity identified in the analysis of urbanization degree, indicates the potential for strategic investments. Decision-makers should consider concentrated infrastructure development (e.g., improving transport accessibility, technical networks) and stimulating diversified housing development and economic activity in these areas.

5. Discussion

This study originated from a broader aim: to explore best practices in territorial planning for sustainable, polycentric, and cohesive urban development. Through the application of the STeMA methodology, this study translates this goal into an operational assessment of vulnerability, allowing for targeted spatial planning responses. This shift from conceptual policy objectives to detailed empirical analyses reflects the need for practical tools that enable decision-makers to address urban sustainability challenges at the local scale, while remaining consistent with European spatial planning principles.

Currently, the increased importance of ex ante assessments regarding the influence of territorial policies and the use of Territorial Impact Assessment methods, as well as related tools, to support spatial decision-making is observed in Europe. Among the tools supporting the effective management of policies, programs, and plans—in addition to those allowing the territorial dimension to be taken into account when assessing the potential impacts of actions at different geographic scales—the following methods can be listed: Territorial Impact Assessment (TIA) and Strategic Environmental Assessment (SEA). The TIA method is based on simulations of performance against local and European targets, whereas the SEA method is focused on identifying environmental risks related to spatial plans. SEA is applied on a higher decision-making level in order to assess the environmental impacts of policies, plans, and programs (PPPs). Furthermore, SEA is designed to help countries make their policies, plans, and programs more sustainable [57].

Applying TIA to evaluate the outcomes of policies is also increasingly common in academic publications [58,59,60]. Furthermore, the European Commission argues that this analysis should be complemented by a Territorial Impact Assessment (TIA), which could result in a better understanding of the impact of a given policy on different territorial units. In this context, the preparation and implementation of any policy should be preceded by thinking about its implications in not only sectoral but also territorial terms [61]. TIA aims at assessing territorial impacts by ex ante analyzing the compatibility of policy objectives with the results of actions at different spatial scales, while SEA enables the identification of environmental and spatial risks. These methods support the integration of territorial dimensions into the evaluation of policies at different governance levels, allowing not only for the assessment of effects resulting from the adopted policies but also their adaptation to specific spatial conditions while taking specific geographic needs into consideration [37,38]. Both methods require advanced technological support, especially GIS tools for data visualization and the integration of spatial models.

The STeMA-GIS tools used in this study allowed for a systematic assessment of spatial vulnerability and risks based on the indicators of anthropization and environmental sensitivity. According to Fischer et al. [39], in the assessment of the coherence between ex ante and ex post observations, there is a need for better synchronization of pre- and post-implementation policy assessments, which can prevent conflicts and result in consistent actions at the local and regional levels.

The theoretical contribution of this study lies in operationalizing urban vulnerability through a systemic, multi-scalar assessment tool (STeMA) that integrates spatial typologies, socio-economic conditions, and environmental stressors. Compared to other recent studies from Central and Eastern Europe—such as participatory climate impact assessment in Brno, Pilsen, and Prague [62]; urban vulnerability assessment in Kraków or Lviv [63]; or ecosystem service degradation analyses in Vilnius [64]—this approach uniquely embeds urban vulnerability within a territorial sustainability framework. Unlike single-risk or purely socio-economic methods, STeMA combines environmental, demographic, and governance-related insights into a spatially nuanced planning methodology.

In the broader European context, the STeMA model not only complements but also extends existing frameworks, such as ESPON’s Urban Typologies and the RHOMOLO territorial simulation model. While ESPON provides comparative urban classifications and RHOMOLO supports economic forecasting across EU regions [34], STeMA introduces a complete territorial diagnosis workflow by integrating ex ante vulnerability analysis with a Spatial Decision Support System (SDSS). It also diverges from traditional ecological assessments—such as those based on the Urban Atlas or Corine Land Cover—by addressing socio-political transformation, policy coherence, and resilience capacities in a single system.

These indicators are integrated into the vulnerability assessment via the anthropization and sensitivity analysis modules within STeMA. Their inclusion allows the model to capture not only the socio-economic dynamics of urban growth, but also the ecological constraints that are critical for long-term territorial resilience.

The results of the assessment carried out for the city of Wroclaw show a clear diversification of both demographic and related spatial structures. A centric population distribution pattern is observed, with central areas (class A) characterized by the highest population density and population number. The assessment of the population structure was complemented by the typology of the settlement carrying capacity, which provides insights into the development potential of different areas. In turn, having analyzed the spatial distribution of the city’s urbanization degree (based on the sum of secondary and tertiary sector activity divided by primary sector activity), it was found that the city is dominated by rank A areas, indicating a high degree of urbanization. The highest-ranking assessment also included areas characterized by low development intensity and low population density.

The continuation of analyses based on the STeMA model shows that Wroclaw is characterized by a low degree of anthropization in most urban areas (F2 rank, poor); in contrast, only small areas—mainly in the downtown zones—have reached a higher degree of anthropization (C2, high). The central zone of Wroclaw, along with the adjacent downtown areas, is characterized by the highest vulnerability and high vulnerability (ranks B, very high; and C, high vulnerability). This is associated with outdated housing infrastructure and a high proportion of subsidized housing units (from the housing stock of the municipality), which are primarily inhabited by low-income tenants and people of post-working age. These results are consistent with the technical condition diagnosis presented by urban housing and confirm the high decapitalization degree of the downtown housing fabric. The technical condition of buildings is an undeniable problem in Polish cities, and it includes buildings from municipal resources, along with council flats, which were rated the worst in terms of the technical quality assessment. The majority of buildings require renovations with respect to staircases, facades, windows, and door woodwork, in addition to requiring sanitary installations [65].

The results of the conducted analyses show that the vulnerability indicators in the southern and western directions (D rank, average) indicate the development of urban functions in these areas, thus reflecting the spatial catchment of the city. In contrast, the eastern directional area was rated as weak in terms of development and characterized by low vulnerability (E, weak).

Additionally, while the main focus of this study was on the structural and spatial determinants of urban vulnerability (e.g., settlement typology, urbanization degree, anthropization), we recognize the critical role of socio-economic variables in shaping urban resilience. In particular, previous research in Wrocław [8,10] has demonstrated how poverty and ageing populations contribute to increased vulnerability. These issues are evident in our case study as well, where the most vulnerable zones correspond to areas with older municipal housing stock predominantly occupied by low-income and elderly residents. While the current STeMA-based analysis did not isolate these socio-economic dimensions explicitly, they are inherently reflected in the vulnerability classifications derived from the combined demographic and infrastructural characteristics. We consider this a promising area for model enhancement in future iterations of the framework.

Undoubtedly, mitigating the identified urban vulnerability is a challenge for the local authorities; however, they will be supported by the central government and EU-derived programs. Above all, the successive and planned renovation of tenement houses can keep young people in the city center and downtown areas, while also improving the quality of life experienced by residents. In addition, the eastern and northern parts of the city should receive reinvestment in terms of transportation and infrastructure; in this manner, disproportionality can be mitigated and the shifting of the center towards the south of Wroclaw can be prevented.

Study Limitations and Future Research

While this study demonstrates the comprehensive application of the STeMA methodology, it is important to address its limitations. Firstly, as discussed in Section 3.1, while official census and statistical data provide a reliable basis, their temporal and spatial resolution may present a challenge due to inherent questions of reliability. Urban areas are often in flux and, therefore, even data that are updated every couple of years may not fully capture the dynamics of socio-economic conditions or physical infrastructure. Furthermore, one of the benefits of the STeMA model is that it is further complemented by a GIS approach, enabling the spatial disaggregation and re-aggregation of data such that we can carry out a multi-scalar analysis of existing structures and processes, which is much more suitable and can enhance the utilization of available statistics.

Secondly, related to the STeMA methodology, STeMA’s validation is linked with its theoretical consistency concerning theory-based evaluations and its application across a number of European settings to evaluate territorial policy [40,51]. The value of STeMA is in its systemic perspective, which includes qualitative and quantitative elements. As with every multi-indicator assessment framework, the selection and relative weighting of indicators may influence outcomes. While STeMA uses an appropriate pairwise comparison method to define importance and normalize values—which further encourages systematic selection and normalization—variations could theoretically arise from different expert judgements or the application of alternative conceptual models, etc. This aspect, which is relevant here, is captured in the wider discussion about issues of sensitivity and validation regarding the use of composite indicators in a spatial analysis context [66]. Future research may utilize sensitivity analyses to measure the implications of different indicator selections or weightings.

Finally, this study provides a significant and thorough case study of Wrocław. The identified patterns of vulnerability in the city, such as the ageing buildings in the historic city centers and socio-economic challenges in subsidized housing areas, resonate strongly with common urban challenges observed in many other older European cities [67]; this is also confirmed by studies examining deprivation and urban poverty in the European Union [68]. This suggests that the STeMA framework, with its standardized multidimensional approach, can provide a solid methodological basis for future systematic comparative research. Comparable efforts, such as those led by European urban poverty observatories, also emphasize the importance of structured data collection and localized vulnerability insights [69]. This allows for a deeper understanding of the common patterns of vulnerability and the effectiveness of resilience-building strategies in different European contexts. While the findings offer value and built-in replicability, the direct quantitative comparison of vulnerability levels with other cities across Europe is not possible in the context of a single case study. Such comparisons would require the application of the same STeMA framework or a fully harmonized indicator set across urban contexts.

Future exploration aimed at advancing this type of research might prioritize undertaking comparative investigations in various European cities. This would allow for the structured comparative analysis of urban vulnerability patterns and an assessment of the effectiveness of different mitigation strategies. It would be valuable to monitor changes in urban vulnerability over time through temporal analyses of STeMA implementations in successive periods, which would provide insight into the dynamic nature of urban systems and the long-term effects of policy interventions. Moreover, a detailed sensitivity analysis of the STeMA model, examining the impacts of different variables (e.g., changes in indicator weights, inclusion/exclusion of indicators) on the final results, could increase the transparency of the model. Future studies might investigate the possibility of incorporating other data as well, such as those from geolocation sources on social media or information obtained through participatory processes. The aim would be to supplement official statistics and capture more detailed, up-to-date urban dynamics affecting vulnerability.

6. Remarks

In the early stages of this study, the scope of this research was to determine best practices in territorial planning in order to promote sustainable, cohesive, and polycentric development. The application of the STeMA-SEA model was an appropriate tool in the advancement of the Europeanization of urban planning.

Furthermore, the promotion of real and sustainable socio-economic development in the partner territories through actions stressing sustainable cultural planning in accordance with vulnerability is the main added value of this study. Starting from territorial evidence and economic and social needs and demands, an ex ante evaluation was carried out to address strategic fields and opportunities for cities in order to encourage investment and promote global trade with joint local initiatives, with the aim of further promoting sustainable economic, social, and environmental development. Measures for social inclusion, gender equality, non-discrimination, youth employment, and social protection are included in the new concept of vulnerability, in accordance with European strategies.

STeMA—a powerful method that is consistent with a territorial integrated approach to planning by assessment—confirmed the initial thesis: by knowing the territorial impacts potentially produced by a policy or a plan, it is possible to adopt adaptive, compensatory, and mitigatory actions. At the same time, the costs deriving from the absence of political coordination with respect to the territorial action (both at the horizontal and vertical levels) can be minimized.

7. Declaration of AI

This study tested the trial version of the Scopus AI solution, which was used to find the relevant literature according to a planned article concept. This study used ChatGPT 4o to enhance readability and for text editing. The AI model was also used to generate flow diagrams and tables summarizing the study’s results. However, the authors verified and adjusted the obtained results.