Measuring and Addressing Territorial Cohesion: A Framework for Regional Development in Portugal

Abstract

This study develops a new methodological framework to assess territorial cohesion by integrating six critical dimensions: demography, equity, competitiveness, governance, sustainability, and connectivity. Rooted in the context of Portugal, the research addresses significant spatial disparities between metropolitan and inland regions, emphasizing the necessity of place-based policies. Using publicly available data and employing normalization techniques, the methodology ensures fair comparisons across municipalities with diverse characteristics. The findings highlight profound asymmetries, including demographic decline, unequal access to services, and variations in economic and governance performance. These results underscore the need for targeted interventions that align with the unique attributes and challenges of different territories. By incorporating governance and connectivity dimensions, the study advances traditional approaches to territorial cohesion, offering an analytical model of analysis. The framework provides practical tools for policymakers to design interventions aimed at fostering balanced and sustainable development. Furthermore, its adaptability ensures relevance in varied contexts, enabling replication in other regions facing similar challenges. The study’s conclusions highlight the importance of addressing territorial disparities through integrated policies that enhance equity, sustainability, and resilience, contributing to the global discourse on regional development and public policy design.

1. Introduction

Territorial cohesion has evolved significantly since its inception, becoming a cornerstone of spatial planning and regional development policies. Initially introduced in the European policy framework during the late 1990s, territorial cohesion emerged as a response to growing concerns about disparities in development, social inequality, and regional imbalances. It emphasizes the need to foster more balanced development across regions by enhancing economic performance, promoting social inclusion, and ensuring sustainable use of resources [1]. As a multidimensional concept, territorial cohesion extends beyond economic metrics to encompass social, cultural, and environmental dimensions, aligning with broader goals of sustainability and equity [2]. The relationship between territorial cohesion and regional imbalances can be explained by understanding that both concepts address the equitable distribution of resources, opportunities, and living conditions across geographic spaces, but they focus on complementary dimensions. In practice, territorial cohesion acts as a tool to mitigate these imbalances. For example, place-based policies, tailored to the specificities of each region, can address relevant issues. Therefore, while regional imbalances represent the problem or the challenges faced, territorial cohesion is the objective or the pathway to fostering more balanced and inclusive development among regions.

One of the primary challenges of operationalizing territorial cohesion lies in its measurement. Territorial cohesion encompasses diverse and interrelated elements, including economic competitiveness, social equity, environmental sustainability, and spatial connectivity [3]. Developing metrics that adequately capture these dimensions while accounting for territorial diversity is a complex task. Traditional indicators, such as GDP per capita or employment rates, often fail to reflect the nuanced realities of territorial dynamics, particularly in peripheral and rural areas [4]. Moreover, data limitations, such as a lack of spatially disaggregated or longitudinal data, further complicate the measurement process [5]. Despite these challenges, robust metrics are essential to identify territorial disparities, monitor progress, and design effective interventions [6].

Recent research has sought to address these measurement challenges through innovative methodologies, including composite indices, spatial econometrics, and participatory approaches [7]. Composite indices, for instance, aggregate multiple indicators into a single measure, offering a solid perspective on territorial cohesion. This perspective recognizes the interconnections and interdependencies within a system and seeks to address all relevant dimensions comprehensively, which is relevant for measuring territorial cohesion. However, their construction requires careful consideration of indicator selection, weighting, and normalization to avoid misrepresentation or bias [8]. Spatial econometrics, on the other hand, allows researchers to analyze spatial relationships and dependencies, shedding light on patterns of convergence or divergence across regions [9]. Participatory approaches engage stakeholders in defining territorial priorities and evaluating cohesion, ensuring that metrics align with local realities and aspirations [10].

Territorial cohesion is integral to territorializing public policies—an approach that tailors policy interventions to the specific characteristics, resources, and needs of different regions. By recognizing and leveraging the unique attributes of each territory, territorialized policies can foster inclusive and sustainable development [11]. This approach aligns with the principles of place-based development, which emphasize local agency, endogenous resources, and community participation [12]. For example, policies promoting renewable energy production in wind-rich regions or cultural tourism in areas with rich heritage can generate economic benefits while respecting territorial identity and values [13].

The importance of territorializing public policies is particularly evident in the European Union’s cohesion policy, which aims to reduce regional disparities and promote harmonious development across member states. The policy’s place-based approach prioritizes investment in less-developed regions, urban–rural linkages, and cross-border cooperation, recognizing the diversity of territorial challenges and opportunities [14]. Similarly, the OECD’s Territorial Outlook underscores the need for integrated policies that address economic, social, and environmental dimensions, ensuring alignment with the United Nations Sustainable Development Goals (SDGs) [15].

Long before Portugal’s accession to the European Union (EU) in 1986, significant regional imbalances were already deeply entrenched, largely stemming from historical patterns of uneven economic development, rural–urban disparities, and a prolonged period of autarkic policies under the Estado Novo regime. These imbalances manifested in stark contrasts between the more developed coastal regions, particularly Lisbon and Porto, and the interior, which suffered from depopulation, weak infrastructure, and limited economic diversification. EU membership marked a turning point, as structural and cohesion funds were mobilized to address these disparities through large-scale investments in infrastructure, education, and economic modernization. Key policy instruments included the National Strategic Reference Framework (QCA I-III, followed by the Community Support Frameworks and, later, the Portugal 2020 strategy), which aimed to enhance regional competitiveness, foster innovation, and improve quality of life across Portuguese territories. However, despite these interventions, persistent asymmetries remained, with certain regions benefiting more than others due to structural weaknesses, governance inefficiencies, and an overemphasis on physical infrastructure rather than sustainable endogenous development.

The assessment of post-1986 regional policies in Portugal reveals a mixed record of successes and failures. On one hand, EU funds significantly improved basic infrastructure, expanded higher education, and enhanced connectivity, particularly through the development of road and rail networks, contributing to a more integrated national economy. On the other hand, the focus on large-scale infrastructure projects did not always translate into sustainable regional development, as many peripheral areas continued to struggle with demographic decline and economic stagnation. While some regions successfully leveraged EU support to modernize industries and enhance tourism potential, others remained overly dependent on external funding, with limited long-term economic resilience. This underscores the relevance of studies on spatial dynamics clustering, as they provide a data-driven basis for rethinking territorial cohesion policies. By identifying the specific factors that differentiate successful regions from lagging ones, such research can inform more targeted, place-based policy interventions that go beyond traditional top-down investment strategies, fostering more balanced and sustainable regional development in Portugal.

Territorial cohesion also contributes to the well-being of communities by addressing inequalities and enhancing quality of life. By reducing disparities in access to services, infrastructure, and opportunities, territorial cohesion policies promote social inclusion and resilience [16]. Moreover, fostering territorial identity and community engagement strengthens social capital, enhancing collective capacity to address challenges and seize opportunities [17]. For instance, initiatives such as participatory budgeting or local development strategies enable communities to influence decision-making processes, ensuring that policies reflect public expectations and aspirations [18].

Despite its relevance, achieving territorial cohesion faces several barriers, including institutional fragmentation, resource constraints, and conflicting interests. Institutional fragmentation—both vertically (across government levels) and horizontally (across sectors)—often hampers policy coherence and integration [19]. Addressing this requires robust governance frameworks that facilitate coordination, cooperation, and stakeholder engagement [20]. Resource constraints, particularly in less-developed regions, limit the capacity to implement territorialized policies effectively. Innovative financing mechanisms, such as public–private partnerships or EU structural funds, can help address these limitations [21]. Conflicting interests among stakeholders, such as between economic development and environmental preservation, necessitate inclusive and transparent decision-making processes that balance competing priorities [22].

Territorial cohesion is a policy objective that evolves with changing societal needs, environmental challenges, and technological advancements [14,23]. The COVID-19 pandemic, for instance, has highlighted the importance of resilience and adaptability in territorial cohesion policies. Remote work, digitalization, and shifts in mobility patterns have reshaped spatial dynamics, presenting both challenges and opportunities for territorial cohesion [24]. Similarly, the transition to a green economy and the pursuit of climate neutrality requires policies that address spatial disparities while promoting sustainable practices [25].

An essential yet often underexplored dimension of territorial cohesion is the role of the social economy, particularly cooperativism, in fostering inclusive and resilient development. Recognized by the European Union, the United Nations, and the OECD as a key driver of cohesion, cooperatives contribute to reducing spatial disparities by promoting local entrepreneurship, sustaining employment, and ensuring access to essential goods and services in peripheral or less-developed areas [7,14]. Their democratic governance structures and reinvestment of profits into local communities align with the principles of territorial equity and sustainability. As such, integrating cooperativism into territorial policies can strengthen social capital, enhance economic resilience, and support place-based approaches to development, complementing traditional public and private sector interventions [25].

Looking ahead, advancing territorial cohesion will require innovative approaches that integrate quantitative and qualitative insights, foster multi-level governance, and embrace territorial diversity [13,14,17]. Collaborative platforms, such as territorial observatories or regional forums, can facilitate knowledge exchange and joint action among stakeholders [26]. Moreover, leveraging emerging technologies, such as geographic information systems (GIS) or big data analytics, can enhance the capacity to monitor and evaluate territorial cohesion [27]. By embracing these innovations, policymakers can design and implement interventions that not only reduce disparities but also enhance the well-being and resilience of communities [28].

Territorial cohesion remains a critical objective for promoting balanced, inclusive, and sustainable development. While measuring territorial cohesion poses significant challenges, advances in methodologies and participatory approaches offer promising pathways for capturing its complexity. Territorializing public policies, informed by territorial values, resources, and public expectations, is essential for fostering development and well-being. By addressing institutional, financial, and societal barriers, and by leveraging opportunities such as digitalization and the green transition, policymakers can ensure that territorial cohesion contributes to a more equitable and resilient future.

The proposed research introduces a novel methodology to evaluate territorial cohesion by identifying six critical dimensions of territorial development: demography, equity, competitiveness, governance, sustainability, and connectivity. This approach integrates multiple indicators to create a comprehensive territorial assessment framework. Utilizing data from diverse sources and leveraging tools such as ArcMap for geographic analysis, the methodology ensures a nuanced understanding of territorial dynamics. The normalization method applied, prioritizing equal weighting and avoiding biases from variable magnitudes, allows for a fair comparison across municipalities. This study’s focus on Portugal as a case study underscores its utility in addressing the country’s deep-rooted spatial asymmetries.

This research goes beyond measuring the quality of life by emphasizing territorial processes that shape cohesion. While most methodologies focus on socio-economic indicators, this study integrates governance, sustainability, and connectivity, offering a broader scope of analysis and insights into how spatial structures influence development. By using normalization instead of standardization, it accommodates non-Gaussian distributions common in territorial data, ensuring robust results. These advancements provide a detailed framework for tailoring public policies to regional strengths and challenges.

The study significantly aids policymakers by offering actionable insights into disparities and potentials across municipalities. It enables more targeted interventions, such as improving connectivity in isolated areas or retaining populations in rural regions, while emphasizing participatory governance and fiscal management to foster resilience. Its multi-dimensional framework also serves as a replicable model for addressing territorial cohesion challenges in other regions, setting a new standard in regional planning and development.

2. Materials and Methods

2.1. Study Area

The study area corresponds to the entire administrative territory of Portugal (Figure 1). For statistical and cartographic analysis, the municipal scale is adopted, considering the 308 municipalities in Portugal: 278 on the mainland and 30 in the two autonomous regions (Madeira and the Azores). In the Azores and Madeira, it is important to mention their territorial fragmentation (several islands) and the double interiority of most of the municipalities located there. The concept of double interiority in the Azores and Madeira reflects the compounded isolation faced by many municipalities. First, insularity limits connectivity to the mainland, increasing dependence on maritime and air transport. Second, within the archipelagos, some municipalities experience further isolation due to their location on smaller or more remote islands or in less accessible areas of the main islands. This dual constraint heightens challenges in mobility, economic activity, and service provision, reinforcing the need for tailored policies to address these territorial disparities.

In recent decades, Portugal has undergone profound social, economic, and territorial transformations, driven by key moments such as the Carnation Revolution (1974), accession to the European Union (1986) and the euro (2001), as well as the financial crisis that led to the Troika intervention (2011). These processes shaped the country’s modernization, altering living standards, family structures, and labor dynamics while fostering greater integration into a global context. Additionally, investments in infrastructure, innovation, and urban rehabilitation contributed to territorial development, although with unequal impacts and not always aligned with national cohesion expectations.

Portuguese society has experienced significant demographic shifts, moving from a period of population growth to stagnation and aging. Between 1960 and 2021, birth rates dropped significantly, and population growth became increasingly dependent on immigration. Life expectancy has risen, but the aging index soared from 34 to 182.1 between 1970 and 2021, highlighting structural challenges such as the sustainability of social security systems and the reorganization of public services for an increasingly elderly population.

Family structures have diversified, reflecting changes in social values and women’s participation in the labor market. The patriarchal model has given way to more diverse family arrangements, including single-parent and blended families, while the average age at first childbirth rose from 24.4 to 30.9 years between 1970 and 2021. The average household size has decreased, divorce rates have increased, and the proportion of people living alone has grown, reflecting new social realities that require public policies tailored to evolving life dynamics.

The qualification of the population has improved significantly, drastically reducing illiteracy rates and promoting higher levels of education. Early school dropout rates have reached historic lows, reflecting the impact of educational reforms and the growing value placed on lifelong learning. This progress has facilitated the transition to a more service- and technology-based economy, strengthening the country’s competitiveness. However, challenges remain in adapting the labor market to the demands of digitalization and innovation.

2.2. Methods

The text proposes a new methodological framework for assessing territorial cohesion and development by structuring an approach that goes beyond traditionally used indicators. Instead of relying solely on isolated economic or social metrics, it defines critical dimensions of development and quality of life, ensuring a global and multidimensional perspective. Additionally, it establishes a clear measurement formula for each dimension, combining multiple indicators in a composite and integrated manner. This methodology captures the interactions between territorial factors and ensures a more balanced comparative analysis across different municipalities or regions. Unlike conventional approaches, which often use disaggregated or difficult-to-update indicators, this model provides an operational, replicable, and adaptable tool for different territorial contexts.

The framework identifies six critical dimensions—demography, equity, competitiveness, governance, sustainability, and connectivity—because they collectively address the core factors influencing territorial cohesion (Figure 2). These dimensions are considered “critical” (despite not being exclusive to understanding territorial dynamics) as they represent the foundational elements of balanced territorial development and reflect both the challenges and opportunities faced by different regions. Considering that Portugal (country) is the research are, the choice of the six dimensions reflects their relevance to understanding and addressing territorial cohesion, considering the Portuguese Government expectations (presented in research project workshops) and a test with local stakeholders at the Tâmega and Sousa region. These dimensions were selected because they encapsulate key factors that influence regional disparities and development outcomes. Demography addresses population stability and human capital, essential for planning and resource allocation. Equity highlights social inclusion and access to services, focusing on fair opportunities for all. Competitiveness captures the economic vitality and innovation capacity of regions. Governance emphasizes institutional quality and the ability to implement effective policies. Sustainability underscores the environmental and long-term viability of territories, and connectivity reflects the importance of infrastructure and mobility in integrating regions. Together, these dimensions aim to cover the multi-dimensional nature of territorial cohesion.

To analyze demographic, social, economic, and territorial dynamics, various indicators were collected using data from the National Statistics Institute (INE), the Directorate-General for Territory (DGT), municipal websites, and official reports [29]. Demographic data after 2021 corresponds to Provisional Estimates of Resident Population—post-census estimates based on the 2021 Census results. The data presented do not include the revision of the Provisional Estimates of Resident Population conducted in June 2024 (which includes displaced persons from Ukraine benefiting from the Temporary Protection regime in Portugal). Part of this information was analyzed using ArcMap 10.8.2., a GIS software by ESRI. Six analytical profiles are proposed, each evaluated with several simple and composite indicators. The selected indicators were chosen based on the availability of data at the municipal level and the possibility of regular updates, ensuring consistency and comparability over time. While indicators such as the share of renewable energy or broader aspects of governance are undoubtedly relevant, their absence is justified by the difficulty in obtaining standardized and regularly updated data for all municipalities, which would compromise the robustness of the methodology. Therefore, the focus was placed on indicators that, despite some limitations, allow for consistent and replicable analysis across the entire territory.

The methodology assumes equal weighting for all indicators, based on an indicator normalization methodology. Normalization was performed for all the indexes collected in the work.

Normalization methods allow the transformation of any element within a class of equivalence of forms or data under a group of geometric transformations into a specific form, fixed for each class. This data transformation ensures that the final algorithm (municipal performance profiles) is not biased by variables with a greater order of magnitude. The approach was based on normalization rather than standardization, as the value distribution is not Gaussian, and standardizing the variables would result in a mean of 0 and a standard deviation of 1. In contrast, the normalization exercise aims to place the variables within a range of 0 to 1, or between −1 and 1 if there are negative results. The formula used for data normalization relies on the minimum and maximum values of each indicator, with the final value calculated as follows:

x = (y − min)/(max − min).

The results made it possible to identify trends and compare the performance of municipalities based on demographic, equity, competitiveness, connectivity, governance, and sustainability criteria. A general profile is analyzed for each of the six themes, ranked into seven levels that consider the country’s average performance: average values, similar to those observed in Portugal (95–105% of the national average); three groups below the national average (<75%; 75–85%; 85–95%); and three groups above the national average (>125%; 115–125%; 105–115%). These seven classification levels align with the criteria for classifying convergence regions (<75%) for the purpose of accessing EU funding and with the existing methodologies in Portugal for increasing support for projects with a territorial impact in low-density areas. In this exercise, all indicators are normalized, with the maximum value (positive) representing the nominal value of one. All the indexes refer to the municipal scale.

The six analyzed themes were calculated as follows:

• Demography

Demography is measured through a composite indicator: (i) demographic and social dynamics.

The demographic dynamics index (IDD) measures the capacity to retain and stabilize the resident population, calculated as follows:

IDD = VP + N + M + E + PA,

where

• VP = Population growth, measured through the indicator [Rate of population change (‰) (2011–2021)].
• N = Demographic dynamics, measured through the indicator [Crude birth rate (‰) average over the last five years (2019–2023)].
• M = Migration dynamics, measured through the indicator [Average migration growth rate (%) over the last five years (2019–2023)].
• E = Social dynamics, measured through the indicator [Aging index (Nº) (2023)]. Aging index is calculated considering the formula: [(Population (65+ years)/Population (0–14 years)] × 10ⁿ.
• PA = Active population retention, measured through the indicator [Renewal index of the working-age population (Nº) (2023)].

B.

Equity

Equity (EQU) of a territory is measured through three composite indicators: (i) access to goods and services; (ii) demographic and social dynamics; and (iii) social and economic inequality.

Thus,

EQU = IABS + IDDS − IDSE.

The access to goods and services index (IABS) measures a resident’s ability to access education, health, housing, and employment, calculated as follows:

IABS = ED + S − H + E,

where

• ED = Access to education, measured through the indicator [Gross pre-school enrolment rate (%) (2021/2022)].
• S = Access to health, measured through the indicator [Doctors per 1000 inhabitants (Nº) (2023)].
• H = Access to housing, measured by considering the number of years a family composed of two working persons needs to purchase a new house, assuming 60% of their income is allocated to housing costs, using the indicator [Average property value (€)/((Average monthly income × 2 × 14) × 60/100) (2020)].
• E = Access to employment, measured through the indicator [Employment rate (%) (2021)].

The social dynamics index (IDS) measures aspects of social capital, such as qualifications, employability, and financial return, calculated as follows:

IDS = ES + A + R + D,

where

• ES = Higher qualifications, measured through the indicator [Proportion of residents aged 30–34 with at least higher education (%) (2021)].
• A = Lack of qualifications, measured through the indicator [Illiteracy rate (%) (2021)].
• R = Income, measured through the indicator [Average monthly income (Nº) (2021)].
• D = Employment dynamics, measured through the indicator [Unemployment rate (%) (2021)].

The social and economic inequality index (IDSE) measures aspects of municipal social cohesion, such as dependence on social benefits, wage disparities, or insecurity, calculated as follows:

IDSE = AS + DS + I + PC,

where

• AS = Social benefits dependency, measured through the indicator [Recipients of social integration income per 1000 active-age residents (‰) (2022)].
• DS = Wage disparities, measured through the indicator [Disparity in average monthly income (by educational level %) of employed population (2022)].
• I = Insecurity, measured through the indicator [Crime rate (‰) (2022)].
• PC = Purchasing power, measured through the indicator [Per capita purchasing power (PT = 100) (2021)]. The indicator is calculated every two years, using several indicators and a coefficient of territorial variation defined by the Government.

C.

Competitiveness

Competitiveness (COM) of a territory is measured through three composite indicators: (i) productivity; (ii) business dynamics; and (iii) innovation, knowledge, and development.

Thus,

COM = IPr + IDE + IICD.

The productivity index (IPr) measures a territory’s productive capacity, calculated as follows:

IPr = PR + EX + VN,

where

• PR = Business productivity, measured through the indicator [Gross value added (€) per company (2022)].
• EX = Export importance, measured through the indicator [(Exports-Imports of goods and services (€))/Companies (Nº) (2022)].
• VN = Diversification of productive activity, measured through the indicator [Concentration of turnover in the four largest companies (%) (2022)].

The business dynamics index (IDE) measures various aspects of economic activity, such as the vitality and diversification of the business fabric, calculated as follows:

IDE = VIT + DGE + GE + DE,

where

• VIT = Business vitality, measured through the indicator [Survival rate (%) of companies created 2 years earlier (2022)].
• DGE = Dependence on large companies for employment, measured through the indicator [Concentration of staff in the four largest companies (%) (2022)].
• GE = Presence of large companies, measured through the indicator [Proportion of large companies in total companies (%) (2022)].
• DE = External employment dependency, measured through the indicator [Residents employed outside the municipality (%) (2021)].

The innovation, knowledge, and development index (IICD) measures issues associated with the qualifications of the employed population and employment in research, knowledge, and ICT, calculated as follows:

IICD = TCID + TQ,

where

• TCID = Workers in communication, research, and development, measured through the indicator [Proportion of workers employed in information, communication, or research and development activities (%) (2022)].
• TQ = Qualified workers, measured through the indicator [Proportion of employed population with higher education (%) (2021)].

D.

Governance

(Good) governance (GOV) of a territory is measured through two indicators: (i) financial management; and (ii) participation.

Thus,

GOV = IGF + P.

The financial management index (IGF) measures municipal financial performance, including sustainability and investment capacity, calculated as follows:

IGF = IF + CI + END,

where

• IF = Municipal financial independence, measured through the indicator [Proportion of own revenues in total municipal revenues (%) (2021)].
• CI = Investment capacity limitation, measured through the indicator [Personnel expenses as a proportion of total expenses (%) (2021)].
• END = Debt per inhabitant, measured through the indicator [Municipal debt per capita (€) (2019)].

P = Participation, analyzed through the participation of resident voters in the most recent municipal elections, measured using the indicator [Abstention rate in municipal elections (%) (2021)].

E.

Sustainability

The sustainability (S) of a territory is measured through three composite indicators: (i) promotion and protection of the environment; (ii) individual and collective behaviors; and (iii) pressure on the territory.

Thus,

S = IPDMA + ICIC + IPT.

The municipal environmental protection and defence index (IPDMA) measures municipal involvement in protecting and enhancing the environment, calculated as follows:

IPDMA = GR + PB,

where

• GR = Waste management, measured through the indicator [Municipal waste management expenses per 1000 inhabitants (€) (2022)].
• PB = Biodiversity protection, measured through the indicator [Municipal biodiversity and landscape protection expenses per 1000 inhabitants (€) (2022)].

The individual and collective behavior index (ICIC) measures the involvement of various agents (public, private, civil society, and third sector) in environmental protection and enhancement, calculated as follows:

ICIC = SR + CE + EA,

where

• SR = Waste separation, measured through the indicator [Proportion of selectively collected urban waste (%) (2022)].
• CE = Energy consumption, measured through the indicator [Domestic electricity consumption per inhabitant (kWh) (2021)].
• EA = Environmental companies, measured through the indicator [Companies in water, waste management, and pollution sectors (%) (2022)].

The territorial pressure index (IPT) measures the density and concentration of population distribution, calculated as follows:

IPT = DP + POV,

where

• DP = Density, measured through the indicator [Population density (inh./km²) (2022)].
• POV = Settlement dispersion, measured through the indicator [Proportion of residents in settlements with less than 2000 inhabitants (%) (2021)].

F.

Connectivity

Connectivity (CO) of a territory is measured through two indicators: (i) communications; and (ii) mobility.

Thus,

CO = CD + IMI.

CD = Digital connectivity, measured through the indicator [Fixed broadband internet access per 100 inhabitants (Nº) (2022)].

The individual mobility index (IMI) measures daily circulation patterns, calculated as follows:

IMI = TC + DP,

where

• TC = Use of public transport, measured through the indicator [Proportion of employed or student residents using public transport for commuting (%) (2021)].
• DP = Average commuting duration, measured through the indicator [Average commuting duration (minutes) of employed or student residents (2021)].

3. Results

3.1. Demography

Portugal exhibits a deeply asymmetrical demographic profile, characterized by significant differences between a small number of municipalities with positive dynamics (79) and a vast majority of councils (229) immersed in a demographic decline, often termed by government structures as a “demographic winter” (Figure 3). These spatial inequalities translate into a territorial mosaic where the strong population dynamism of the Lisbon Metropolitan Area stands out, and to a more moderate extent, some Algarve municipalities, coastal cities, or municipalities with a strong attraction capability, such as Braga (strong Brazilian immigration) or Odemira (strong concentration of intensive agriculture).

Between 2011 and 2021 (census period), 83.8% of the councils lost residents, with almost a third of the municipalities registering population losses exceeding ten percentage points (102). This depopulation process is pervasive across the country, most intensely in the northern and central inland regions of mainland Portugal. The Lisbon Metropolitan Area and the Algarve are the most dynamic regions. In 2023, only 5.2% of the 308 Portuguese municipalities had a positive natural growth rate, while only three councils (Barrancos, Resende, and Vila Franca do Campo [Azores]) recorded a negative migratory balance. Observing the dynamics over the last five years (2019–2023), it is evident that, although generally positive, migratory dynamics also vary unevenly across the territory. Once again, the Lisbon Metropolitan Area and the Algarve exhibit the highest growth, albeit accompanied by several municipalities in the Central Region. Conversely, there’s a significant loss of population due to emigration, primarily concentrated in Tâmega e Sousa and surrounding areas.

The average birth rate over the last five years supports the notion of a country operating at two speeds. Despite a general decline nationwide, significantly lower values are observed in nearly all municipalities in the northern and central inland regions of Portugal. As a result, the aging that characterizes the country (and Europe in general) is felt much more intensely in these territories, with 42.5% of the councils having 250 or more elderly per young person in 2023 [30]. Additionally, analyzing the active-age population renewal index (the ratio of people potentially entering to those exiting the workforce) highlights an associated problem, with a loss of human capital and labor, where 28.9% of the municipalities have a value below 60.

3.2. Equity

In terms of equity, which involves social cohesion, the Portuguese territory is quite asymmetrical, although the municipalities are increasingly aligning with the national average behavior (Figure 4). However, a stark difference exists between major urban spaces and the rest of the country. Only forty municipalities (12.9%) perform above the national average, notably Lisbon, Oeiras, Coimbra, Porto, Cascais, Alcochete, Braga, and Aveiro. In contrast, the most negative performance is seen in Penamacor, Freixo de Espada à Cinta, Idanha-a-Nova, Mourão, and Monforte, all located inland and close to the Spanish border.

Unequal performance in terms of equity is influenced by various factors (Figure 5), with a key one being unequal access to goods and services, reflecting the division between major urban centers and the rest of the country. Municipalities such as Lisbon, Coimbra, Loulé, Cascais, and Porto have the best indicators, while those in the interior, such as Tabuaço, Freixo de Espada à Cinta, and Idanha-a-Nova, show less favorable conditions. Education, healthcare, housing, and employment are areas with significant disparities, with pre-school education being one of the least asymmetric, although there are some challenges in the metropolitan areas of Lisbon and Porto. Healthcare access also varies, with greater availability in major urban centers. Employment is more concentrated in industrial areas, such as Vizela and Paços de Ferreira, but housing access is a much more pronounced issue in large cities like Loulé, Cascais, and Lisbon, where high prices make access more difficult.

The second dimension of analysis focuses on social dynamics and social capital, reflecting greater inequalities in the coastal strip from Viana do Castelo to Setúbal. Metropolitan areas and some cities like Aveiro, Braga, and Coimbra stand out in terms of qualifications and employability, while the interiors of the country, such as Pampilhosa da Serra and Idanha-a-Nova, shows the worst results. The concentration of young adults with higher education is predominantly urban, reflecting the coastal polarization. Illiteracy rates still show territorial asymmetries, with the worst indicators in the interior of Northern and Central Portugal and in the Alentejo. Monthly income is higher in metropolitan areas, with Lisbon and Castro Verde, influenced by the Somincor mine, having the highest earnings. Unemployment rates also vary significantly between urban areas and the interior regions, such as in the North and the Algarve.

The third dimension analyses social and economic inequalities, with the best performance seen in the Central Region and some areas of Greater Lisbon, such as Oeiras, Mafra, and Leiria, and in certain municipalities in the Azores. Inequality is higher in municipalities like Ribeira Grande (Azores), Mourão, and Monforte. Dependence on social benefits, such as the Social Insertion Income (RSI), is an area with the greatest concentration, particularly in the Alentejo and inland areas of the North and Azores. The number of RSI beneficiaries per thousand active-age inhabitants shows large variations, with municipalities like Ribeira Grande (Azores) and Mourão registering over 125 beneficiaries per thousand inhabitants, while areas like Barcelos and Oliveira de Frades have very low numbers. Wage disparities are also notable, with large differences between municipalities, especially in metropolitan areas. Furthermore, crime rates are concentrated in Alentejo, Algarve, and the Azores, with Lisbon and Porto also showing high values. The safest areas include Sernancelhe and Calheta (Madeira), with rates below 15‰.

Finally, the study of municipal purchasing power reveals significant economic inequality, with only 10.1% of municipalities performing above the national average, including Lisbon, Oeiras, and Porto. In contrast, 20.8% of municipalities have purchasing power below 70, with Ponta do Sol and Porto Moniz, among others, showing the poorest results.

3.3. Competitiveness

The dimension of territorial competitiveness is characterized by a territorial mosaic with municipal performance significantly below the national average, encompassing 97.4% of municipalities (Figure 6). Indeed, only eight municipalities exhibit higher economic competitiveness, in this order of magnitude: Lisbon, Porto, Oeiras, Coimbra, Funchal, Vila Velha de Ródão, Aveiro, and Braga. Negative performance is widespread across almost the entire country, though it is most pronounced and concentrated in the Alto Alentejo and the Central Region. Municipalities recording the lowest values include Alvito, Azambuja, Vila Nova da Barquinha, Lajes das Flores, Sardoal, Sobral de Monte Agraço, Porto Moniz, Mourão, and Mangualde.

This analysis of the economic competitiveness of Portuguese territories focuses on three composite indices: economic productivity, business dynamics, innovation, knowledge, and development (Figure 7).

The first indicator, economic productivity, reveals a regional divide, with areas such as Ave, Cávado, Tâmega e Sousa, and the Leiria Region showing high productivity levels, particularly in industrial sectors. At the municipal level, the highest performers are Felgueiras, Lisbon, Guimarães, and Águeda, while municipalities like Crato and Azambuja are at the bottom. Productivity is assessed through business output, exports, and activity diversification. While major urban centers and the Central Region show high gross value added (GVA) per company, the interior regions exhibit lower productivity levels. Exports reveal a positive trade balance in 51% of municipalities, with places like Castro Verde performing well, while Lisbon and Azambuja show negative trends. Business activity is also concentrated, with some areas relying heavily on the turnover of their largest companies, indicating uneven economic landscapes.

The second index, business dynamics, reflects the vitality and growth capacity of businesses. Municipalities in the Northern and Central regions, as well as parts of the Azores and Alentejo, show high business dynamics. Indicators include business survival rates, dependence on large companies, company size, and external employment. Northern regions exhibit higher business survival rates, while some inland areas show low rates, with survival below 50% in places like Castelo de Vide. Several municipalities are highly dependent on large companies for employment, such as Campo Maior and Castro Verde, while others, like Sesimbra and Albufeira, have a more balanced employment distribution. Most municipalities are characterized by small and medium-sized enterprises, with larger companies being more significant in municipalities like Vila Velha de Ródão and Oeiras. Urban areas, particularly Lisbon and Porto, show a high dependency on external employment, with over 55% of the workforce commuting from municipalities such as Seixal, Barreiro, Odivelas, and Amadora.

The third indicator, innovation, knowledge, and development, highlights significant territorial disparities. Major urban centers like Lisbon, Porto, and Oeiras, along with cities with higher education institutions like Coimbra and Aveiro, exhibit the best performance. This index includes two indicators: employment in knowledge-intensive sectors (communication, research, and development) and the share of the population with higher education. While these sectors are concentrated in metropolitan areas, they remain underdeveloped in the interior, with 17.5% of municipalities lacking jobs in these fields. Lisbon and Porto lead in the proportion of the employed population with higher education, surpassing 45%, while rural and industrial areas show much lower rates, with municipalities like Cinfães and Corvo (Azores) having fewer than 9% of the employed population with higher education.

In summary, Portugal’s economic competitiveness is uneven, with significant disparities between coastal and interior regions, urban and rural municipalities, and areas with industrial versus knowledge-based economies.

3.4. Governance

The dimension related to the governance of territories once again shows a country with considerable asymmetries, where most municipalities (65.9%) perform below the average for Portugal (Figure 8). However, the intensity of these differences is less than what we observe in other development dimensions, as performances that approach the national average or are moderately above or below these values predominate. Notably positive are various municipalities in the Northern Region around the Porto Metropolitan Area, extending to Ave, Cávado, Alto Minho, and Tâmega e Sousa, as well as several councils in the Central Region (especially concentrated near Leiria and Coimbra) and the Algarve. The municipalities with the best records include Lagoa (Algarve), Sernancelhe, Penafiel, Arouca, Leiria, Castelo de Paiva, Viseu, Póvoa de Lanhoso, Cascais, and Barcelos. Conversely, the poorest performances are particularly concentrated in the Alentejo and appear frequently, albeit more isolated, in the interior of the Northern Region and the Eastern group of the Azores. Fornos de Algodres, Melgaço, Cartaxo, Alfândega da Fé, Freixo de Espada à Cinta, Tarouca, and Serpa show the worst records.

One of the sub-dimensions considered for analyzing good governance of territories is related to the financial management capacity of municipalities (Figure 9). This analysis once again highlights a strong difference between the coastal strip from Viana do Castelo to Setúbal, the Algarve, and the two archipelagos with their own regional administrative structures, compared to the rest of the territory. Lagoa (Algarve), Cascais, Loulé, Albufeira, Oeiras, Lisbon, Leiria, Caldas da Rainha, Lagos, and Mafra stand out as the municipalities with the highest performance, all located in the center or south of mainland Portugal. Municipalities that show greater vulnerabilities in this index include Fornos de Algodres, Freixo de Espada à Cinta, Alfândega da Fé, Mourão, Alandroal, Mesão Frio, and Celorico da Beira.

This analysis involves three indicators. The first assesses the financial independence of municipalities. In 2019, only 22.7% of Portuguese councils were considered financially independent, defined as having at least 50% of their total revenues from their own sources. Coastal areas and the Algarve mostly comprise these municipalities, with notable ones like Lisbon and Loulé having over 80% of their revenues from their own sources. In contrast, places like Corvo (Azores) and Pampilhosa da Serra have less than ten percent. The second indicator looks at investment capacity limitations due to personnel expenses. This shows distinct regional differences, with Northern and Central regions and the Autonomous Regions performing better than others. Municipalities like Porto Santo and Cartaxo have personnel expenses exceeding 45% of total expenditures. Finally, the debt map shows spatial heterogeneity, with coastal areas differing from the interior. Arronches and Penedono, for example, have debt of less than thirty euros per capita, whereas, in places like Fornos de Algodres and Vila Real de Santo António, it exceeds three thousand euros per inhabitant.

The second sub-dimension of governance analysis involves civic engagement and participation, measured by the abstention rate in the 2021 local elections. Higher abstention rates in metropolitan areas and the Algarve suggest that in populous municipalities, voter disengagement is more pronounced. For instance, in Sintra and Loulé, abstention exceeded 58%, whereas it was below 25% in smaller or less populous areas like Corvo (Azores) and Freixo de Espada à Cinta.

3.5. Sustainability

Sustainability is a universally recognized paradigm, especially in its association with territorial-based development. When analyzing this dimension, we find that Portugal still exhibits significant spatial asymmetries, with the most concerning performances concentrated in the North and Central regions, and lower incidence in the South and metropolitan areas, except for Lisbon and Porto (Figure 10). At the municipal level, the differences are quite significant; only 19.2% of municipalities perform above the national average—notably Funchal, Barrancos, Monchique, Redondo, Alcochete, Chamusca, Seixal, Lagoa (Azores), Castelo de Vide, and Campo Maior—with particularly weak performances in Alandroal, Povoação (Azores), Alvaiázere, and Ribeira Brava (Madeira).

The sustainability analysis of Portuguese municipalities is structured around three key dimensions: environmental protection and defense, individual and collective behaviors impacting the environment, and territorial pressure (Figure 11).

The first dimension evaluates environmental protection efforts. Municipalities in the interior of the Central Region, Madeira, Alentejo, and Algarve show strong performance in this area. This includes investment in waste management, biodiversity, and landscape protection. Regions like the Algarve and parts of the Central Region stand out, with municipalities like Barrancos, Monchique, Mação, and Sardoal showing the highest per capita investments in environmental initiatives. However, other areas, such as Vila Nova de Poiares, Tomar, and Covilhã, perform less well in terms of investment in environmental protection.

The second dimension focuses on behaviors that directly affect the environment, particularly waste management and energy consumption. There are regional differences in these behaviors, with the highest values concentrated in Lisbon and the Azores. Waste separation for recycling remains low across most municipalities, with only a few municipalities, such as Lajes das Flores and Lagoa (Azores), exceeding 40% of urban waste being selectively collected. Energy consumption is notably higher in the Algarve, linked to tourism activity, with municipalities like Loulé and Albufeira exhibiting the highest consumption. Additionally, the number of environmental companies remains low in most municipalities, with some regions, like Corvo (Azores) and Chamusca, having a significant presence in this sector.

The third dimension examines territorial pressure, which highlights the impact of population density and settlement dispersion. Major urban areas and metropolitan regions, along with parts of Central Alentejo, experience the highest pressure. For example, municipalities such as Alcochete, Sines, and Vila Franca de Xira show the most significant pressures on the territory. Population density is notably higher in urban areas like Lisbon, Oeiras, and Amadora, while rural areas exhibit much lower density, with municipalities like Alcoutim and Mértola having fewer than five inhabitants per square kilometer. Settlement dispersion is especially pronounced in the Autonomous Regions and in parts of the North and Central regions, where more than 90% of the population lives in small settlements, which creates challenges for infrastructure development and resource management.

This analysis highlights the disparities in sustainability efforts and challenges across Portugal, with coastal and urban areas often performing better in environmental protection and behavior, while rural and inland regions face greater pressures related to population dispersion and limited resources.

3.6. Connectivity

The dimension of connectivity analyses issues related to digitalization and individual mobility patterns, based on available data. The overall map reveals a context marked by significant asymmetries, particularly evident in the contrast between the cities of Lisbon and Porto, the Algarve, and the island municipalities, compared to a considerable portion of municipalities in the North and Central regions of Portugal (Figure 12). At the municipal level, the councils with the best performance in connectivity are Lisbon, Vila do Bispo, Lagos, Albufeira, Loulé, Porto, Porto Santo (Madeira), Lagoa (Azores), Porto Moniz (Madeira), and Nordeste (Azores). Conversely, the most significant challenges are observed in Montemor-o-Velho, Condeixa-a-Nova, Barrancos, Vimioso, Oleiros, and Arruda dos Vinhos.

The digital connectivity map reflects these asymmetries, further highlighting the poorer performance of the inland areas of the Northern and Central regions of Portugal. However, it is worth noting that only 22.4% of municipalities have digital connectivity indicators—measured by the ratio of internet access to residents—above the national average. The municipalities of Albufeira, Loulé, Lagos, Vila do Bispo, Lagoa (Algarve), Lisbon, and Portimão stand out with the highest values, almost all located in the Algarve. Conversely, Sernancelhe, Vimioso, Celorico de Basto, Penedono, Aguiar da Beira, Cinfães, and Resende record the lowest values.

Regarding individual mobility behaviors, the overall indicator presents a territorial mosaic with smaller differences and no clear territorial patterns, except for better performance in the Autonomous Regions of Madeira and the Azores (Figure 13). The municipalities of Nordeste (Azores), Porto Moniz (Madeira), Calheta (Madeira), Câmara de Lobos (Madeira), Santa Cruz da Graciosa (Azores), and São João da Pesqueira have the highest values, contrasting with Arruda dos Vinhos, Condeixa-a-Nova, Montemor-o-Velho, and Salvaterra de Magos, which record the lowest values.

This dynamic is reflected in the differentiated use of public transport and varying durations of commuting movements. In the first case, only 26.6% of municipalities report that public transport is used by 15% or more of the employed or student resident population, making this primarily a metropolitan phenomenon, although with notable values in areas like Tâmega e Sousa. Indeed, the municipalities where public transport use is most common (above 27%) are Barreiro, Amadora, Seixal, Almada, Moita, Odivelas, Lisbon, and Baião. In Corvo (Azores), Bragança, São Brás de Alportel, Marinha Grande, and São João da Madeira, this figure is below 5%.

In the second case, longer commuting durations are observed in the two metropolitan areas and adjacent regions—albeit to a lesser extent in Coimbra—reflecting greater congestion. In 17.2% of municipalities, these daily journeys typically last more than 20 min, exceeding 25 min in eleven municipalities: Barreiro, Moita, Seixal, Almada, Sesimbra, Baião, Sintra, Vila Franca de Xira, Cinfães, Loures, and Odivelas. In Corvo (Azores) and Porto Santo (Madeira), commuting times are below 10 min.

4. Discussion

This study aims to contribute to the understanding of territorial dynamics and characteristics through three fundamental elements: (i) the definition of a methodology for assessing territorial development, anchored in six core dimensions and the development of new, concrete indicators; (ii) the analysis of the main territorial dynamics in Portugal, advancing beyond previous studies (which focused on evaluating the financial and physical execution of projects) by introducing an assessment of their effects on development and quality of life; (iii) the proposal for the creation of a territorial knowledge base capable of supporting public decision-making, the formulation of public policies, and the allocation of EU funds to projects and intervention areas that align with the specific characteristics of the territory—an issue that the existing literature [3,8,10,14] identifies as a chronic problem in Portugal. In fact, the analysis of the results reveals a set of territorial dynamics that confirm the persistence of demographic and socio-economic asymmetries in Portugal. These inequalities, which are reflected in the various dimensions addressed, highlight a clear duality between coastal regions and the interior, as well as between large urban centers and more peripheral territories. Below, eight central ideas that emerge from the research results are outlined, which contribute to understanding current trends and provide support for future territorial development strategies.

• Persistent demographic asymmetries and the escalating dynamics of aging

Demographic data reveal that Portugal continues to display a highly asymmetric profile, with a marked polarisation between dynamic urban centers, such as the Lisbon Metropolitan Area and the Algarve, and the country’s interior, which faces a clear process of population decline. Demographic aging is far more pronounced in the interior, where nearly half of the municipalities have an elderly-to-young ratio exceeding 250, a situation that exacerbates challenges to social and economic sustainability. This phenomenon is supported by existing literature on population aging in peripheral regions of Europe [30], which highlights the weakening of the active population base and the increase in social dependency.

2.

The contrast between urban dynamics and the poverty of the interior

The multiple polarization (between the coastal regions, particularly metropolitan areas and large urban hubs, and the interior regions), which is the focus of national debates and policies, was confirmed by this study, and is becoming clear that it is common to several dimensions and not only a demographic issue. The territorial inequalities observed in terms of population growth, access to services, and economic performance confirm the theory of “development islands” [25], where certain areas, such as Lisbon, Oeiras, and Porto, stand out, while most municipalities in the interior experience low growth rates, difficulties in accessing goods and services, and high levels of dependency on social benefits, such as the social insertion income (RSI). The concept of “development islands” describes the uneven spatial distribution of economic growth and opportunities, where certain regions or cities become highly developed while surrounding or more remote areas remain underdeveloped. These islands emerge due to factors such as concentrated investment, better infrastructure, skilled labor attraction, and institutional advantages, creating self-reinforcing cycles of prosperity. Meanwhile, less favored areas, often lacking these structural advantages, struggle to compete, leading to persistent disparities. This phenomenon is closely linked to core-periphery dynamics in regional development, with wealth and innovation activities clustering in specific locations, leaving other areas increasingly dependent or marginalized. Addressing this requires policies that enhance connectivity, decentralize opportunities, and leverage the unique strengths of lagging regions to foster more balanced territorial development.

3.

Autonomous regional governance in Azores and Madeira

The autonomous regional governance of Madeira and Azores, which grants these regions greater decision-making capacity in policy design and implementation, is also a very relevant issue. This governance model allows for tailored strategies to address the specific challenges of insularity, territorial fragmentation, and socio-economic disparities. However, its impact on the indicators analyzed remains a relevant question. While autonomy may enable more effective responses to local needs, it also introduces potential disparities in resource allocation, infrastructure development, and economic performance across municipalities within each archipelago. Therefore, it is crucial to assess whether regional autonomy has contributed to reducing intra-regional inequalities or if significant asymmetries persist, mirroring the broader territorial dynamics observed on the mainland.

4.

Local and regional asymmetries within the same territory

Beyond the well-documented coastal–interior divide, significant intra-regional asymmetries exist within Portugal, where municipalities in the same region experience starkly different development trajectories. A clear example is the Lisbon Metropolitan Area, where recent administrative changes to the NUTS classification allowed some municipalities, such as Mafra and Sesimbra, to access additional EU funding due to their lower socio-economic performance compared to central urban areas. This reflects the growing disparities between highly developed urban cores and their surrounding municipalities, which often struggle with lower investment, weaker infrastructure, and social vulnerabilities despite geographic proximity to economic hubs. Another striking case is the Tâmega e Sousa region, which exhibits a dual reality: while some municipalities, such as Felgueiras and Paços de Ferreira, thrive due to strong industrial specialization, others remain predominantly rural, with lower economic dynamism and limited access to services. These asymmetries highlight the need for policies that go beyond broad regional classifications, recognizing and addressing sub-regional inequalities to ensure more balanced territorial development.

5.

The discrepancy between economic and social indicators

While some economic indicators, such as productivity and business dynamics, point to greater vitality in urban and industrial areas, social disparities, including access to education, healthcare, housing, and employment, continue to worsen territorial inequalities. In fact, the interior regions face an acute lack of workforce qualification, with higher illiteracy rates and lower monthly income compared to urban areas. This scenario underscores the discrepancy between economic growth and social inclusion, a phenomenon also observed in other peripheral regions of Europe.

6.

Economic competitiveness and innovation dependent on geography

The study of economic competitiveness reveals a strong dependence on geographical location, with coastal and urban regions exhibiting much higher productivity, innovation, and business dynamism. Municipalities such as Lisbon, Porto, and Oeiras stand out in the creation of economic value, while interior regions continue to rely largely on large companies or traditional sectors. This pattern is supported by studies on the role of innovation in regional development [31,32], which point to the need for more effective decentralization policies and support for economic diversification in interior regions.

7.

Inequality in governance capacity and local financial management

The analysis of municipal governance capacities and financial management confirms the existence of a significant territorial fracture. Most municipalities (65.9%) perform below the national average in terms of governance, with some areas standing out for their high dependence on external transfers and limitations in investment capacity due to the high weight of personnel costs. These difficulties are more pronounced in the interior, where financial resources are limited, and reflect the fragility of local management in many peripheral areas, corroborating literature that calls for greater autonomy and financial empowerment of municipalities.

8.

Unequal territorial sustainability and environmental challenges in the interior

The environmental dimension reveals the persistence of significant disparities, with interior and rural areas facing greater challenges in terms of sustainability. Urban and coastal areas perform better in terms of environmental protection and pro-environmental behaviors, such as waste management and the use of renewable energies. On the other hand, the interior continues to suffer from territorial pressure due to population dispersion, which hampers the implementation of effective public policies in areas such as waste management and biodiversity conservation. This pattern reflects a common challenge in various peripheral regions of Europe, where sustainability faces additional obstacles due to low population density and lack of resources [33,34].

9.

Challenges of connectivity and territorial mobility

The analysis of issues related to digital connectivity and individual mobility confirms the increasing digitalization of urban areas, while the interior continues to experience significant gaps, particularly in terms of internet access and public transport infrastructure. While the Lisbon and Porto metropolitan areas lead in digital connectivity, more peripheral regions, such as the interior of Northern and Central Portugal, show very low indicators. In fact, the Regional Digital Index reveals that the Lisbon Metropolitan Area remains significantly ahead in digitalization, while regions such as the North and Center lag, exacerbating regional disparities. These digital gaps influence access to essential services like healthcare, education, and housing, ultimately impacting quality of life across different municipalities. Furthermore, research by the European Centre for Political Studies for the European Commission emphasizes that Portugal faces inequalities in the growth of digitalized sectors and the vulnerability of occupations to automation, further deepening socioeconomic risks. The combined effects of these disparities reinforce patterns of economic stagnation and depopulation in less digitalized regions, widening the development gap within the country [35,36,37].

Individual mobility, though more homogeneous, also reflects a clear polarization, with urban areas benefiting from better transport infrastructure and greater mobility, while interior territories face significant difficulties.

To address Portugal’s territorial asymmetries, policymakers should adopt targeted, place-based strategies that reinforce social, economic, and environmental cohesion. In interior regions facing demographic decline and aging, incentives for population retention—such as fiscal benefits for young families, remote work hubs, and improved healthcare access—are essential. Economic diversification should be prioritized through investment in innovation hubs, sector-specific business support, and the decentralization of public administration functions. Governance reforms should enhance municipal autonomy by increasing financial independence and optimizing resource allocation, particularly in peripheral areas. Additionally, bridging the digital divide through expanded broadband infrastructure and strengthening public transport networks in rural areas will improve connectivity and integration. Sustainability measures must be reinforced by supporting decentralized renewable energy projects and enhancing waste management in less densely populated areas. A monitoring system, leveraging the study’s multidimensional indicators, should guide the continuous adaptation of policies to regional needs.

The methodological contribution is also a result of this research, as it stands out by focusing on the territorial impact of policies, in contrast to other studies in Portugal that often concentrate on evaluating the number of projects completed or the percentage of funds invested [38,39,40]. Most of the analyses conducted so far are limited to quantifying financial and operational indicators, such as the number of resources allocated, without systematically and comprehensively considering the real effects of these interventions on territorial development and the well-being of local populations. By addressing public policies from a perspective centered on their territorial effectiveness, this study contributes to a deeper understanding of local dynamics, allowing for an assessment of how different regions of Portugal are being transformed and the specific challenges they face.

Additionally, the proposed methodology combines rigorous quantitative assessment with a qualitative analysis of the territorial effects of policies. By integrating dimensions such as demography, equity, competitiveness, governance, sustainability, and connectivity, the research provides a view of regional disparities, identifying not only the most developed areas but also those that most need intervention. Furthermore, it goes further than the previous studies in Portugal [38,39,40]—for example, the ones translated into law—that focus mainly on the distribution of inhabitants and population density. Therefore, this research goes beyond that, providing an integrated approach, that considers several dimensions, as well as social, economic, and environmental indicators, along with demography. This approach enables the creation of municipal performance profiles that are essential for designing more effective public policies tailored to the specifics of each territory. The use of normalized indicators, adjusted to the local reality, ensures the consistency and comparability of results over time, providing a solid foundation for the formulation of more inclusive and sustainable territorial development strategies.

However, the study is not without limitations. It relies heavily on publicly available data, which may not fully capture the most current or nuanced aspects of territorial dynamics. The equal weighting of indicators, while methodologically consistent, may oversimplify the relative importance of certain dimensions in specific contexts. Moreover, the lack of qualitative data, such as stakeholder perspectives, limits the depth of analysis in understanding local priorities and challenges. The static nature of the data used also does not account for temporal changes, such as the long-term impacts of policy interventions or sudden socio-economic shifts.

Future research should explore the dynamic interactions between the dimensions analyzed in this study. For example, how do improvements in governance influence other dimensions like sustainability or competitiveness? In concrete, future research can prioritize the establishment of a territorial observatory that systematically collects, updates, and analyzes data on the six dimensions examined in this study. This observatory would serve as a dynamic platform for monitoring territorial performance, identifying emerging trends, and assessing the long-term impact of policy interventions. Regular updates to indicators, incorporating both traditional statistical data and real-time information from local institutions, would enhance the accuracy and relevance of analyses. Additionally, new research should expand the methodological approach by integrating longitudinal studies to track territorial evolution over time and incorporating spatial econometrics to better understand the interactions between governance, sustainability, competitiveness, and social equity. Engaging in qualitative research, such as interviews and participatory workshops, would ensure that local knowledge and stakeholder perspectives are included in decision-making. By combining data-driven insights with community-based knowledge, future studies can provide more nuanced recommendations and foster truly place-based, adaptive policy strategies.

5. Conclusions

The results of this study offer a contribution to understanding and addressing territorial cohesion, providing a multi-dimensional framework for analyzing territorial development. By examining six core dimensions—demography, equity, competitiveness, governance, sustainability, and connectivity—this research presents an evaluation of territorial disparities in Portugal. These findings extend existing studies by adopting an integrated approach to territorial asymmetries, thus informing more targeted policymaking.

This study introduces a multi-dimensional approach to territorial cohesion that integrates governance and connectivity alongside traditional indicators such as demographic stability, access to services, and sustainability. By normalizing data for comparability, the framework provides a comprehensive assessment of territorial disparities, offering practical insights for policy development. The study not only deepens understanding of regional imbalances in Portugal but also has broader implications. Its reliance on publicly available data and adaptable methodology makes it suitable for application in other contexts, allowing regions facing similar challenges—such as demographic decline, governance limitations, and unequal service provision—to implement tailored strategies. This flexibility enables its replication across different geographic settings, supporting evidence-based policymaking to foster balanced and sustainable territorial development.

Beyond its methodological contribution, this research provides insights relevant to global debates on regional equity and resilience. The inclusion of governance and connectivity as key dimensions aligns with international trends emphasizing decentralized decision-making and digital transformation in territorial planning. Many regions worldwide, from aging rural areas in Southern Europe to economically struggling post-industrial zones in North America, face similar asymmetries. The framework proposed in this study offers a replicable tool to assess disparities, prioritize interventions, and guide the transition toward more resilient, inclusive, and sustainable territories. Furthermore, as climate change and digitalization reshape regional dynamics, this methodology can be expanded to incorporate indicators addressing environmental vulnerability, smart infrastructure, and emerging mobility patterns, making it a valuable resource for both developed and developing regions adapting to new socio-economic realities.

In short, the research introduces an alternative methodological approach by shifting focus from merely quantifying policy investments to assessing their territorial impact. By emphasizing the importance of analyzing official statistics, conducting thorough research, and implementing evidence-based policies, this study contributes to a more strategic and informed approach to territorial development, ensuring that policy interventions are tailored to the specific needs of each region and grounded in robust, data-driven decision-making.