Analysis of Results of Experts’ Perspectives of Sustainable Regional Competitiveness Using the Analytic Hierarchy Process Multi-Criteria Method

Abstract

Regional competitiveness is essential for sustainable development, driven by complex and interrelated factors. This study applies the Analytic Hierarchy Process (AHP) to assess experts’ perspectives of the primary factors influencing regional competitiveness, including factors like the economy, the labor market, poverty and social inclusion, health, education, environmental sustainability, transport infrastructure, technology/science and the digital society, high-tech industry growth, and innovation. From a comprehensive list of over 250 regions in the EU, selection of regions was made based on their ranked Regional Competitiveness Index (RCI), from the region with the highest index to the region with the lowest index. This involved choosing one representative region from each of the fifty RCI rankings. The selected regions included SE11 (Stockholm, Sweden, RCI: 1.08), BE22 (the Flemish Region, Belgium, RCI: 0.46), FRH0 (Île-de-France, France, RCI: 0.11), ITC3 (Lombardy, Italy, RCI: −0.30), PL43 (Masovia, Poland, RCI: −0.69), and EL53 (Western Macedonia, Greece, RCI: −1.44). By applying the AHP methodology, the prioritization sequence of the aforementioned regions was validated, confirming the robustness of the ranking derived from the Regional Competitiveness Index (RCI). The AHP analysis reinforced the importance of addressing region-specific factors and highlighted the alignment of expert judgments with the established RCI-based rankings. This study highlights the critical role of region-specific factors in driving competitiveness and sustainable development, with the AHP methodology effectively validating the prioritization of regions and providing a robust framework for aligning expert insights with established rankings.

1. Introduction

Regional competitiveness is defined as a region’s capacity to attract and retain investment, stimulate innovation, and create job opportunities while simultaneously addressing pressing social and environmental challenges [1]. We conducted an extensive review of the regional competitiveness literature, and we ended up with ten core factors that influence it: the economy, the labor market, poverty and social inclusion, health, education, environmental sustainability, transport infrastructure, technology/science and the digital society, high-tech industry growth, and innovation [2].

Following this review, we proceeded to examine the relative importance of these ten factors through a structured questionnaire and by receiving expert assessments [3]. The results reveal that experts prioritize the economy, the labor market, and health infrastructure as the most impactful factors on regional competitiveness. Addressing poverty and social exclusion through strategic improvements in these areas also emerged as essential. Secondary factors, such as education and technological development in science and the digital society, were recognized as essential to enhancing regional prosperity. Environmental sustainability and transport infrastructure highlight the increasing call for sustainable growth, while high-tech industry and innovation received lower prioritization but remained integral to long-term competitiveness [4].

As globalization continues to reshape economic landscapes, understanding the intricate dynamics of regional competitiveness becomes essential for policymakers and stakeholders aiming to create sustainable environments conducive to growth and innovation [5]. Once these ten key determinants that significantly affect regional competitiveness are identified and prioritized, it is useful to correlate the results with the order of the regions according to the RCI index [6].

This paper begins with a comprehensive review of the existing literature on the ten factors of regional competitiveness, combined with the application of the Analytic Hierarchy Process (AHP). Subsequently, the AHP method is applied, utilizing expert opinions gathered through a questionnaire, to derive the ranking of regions based on the RCI index.

The results are then presented and discussed, with a focus on the hierarchy of the regions following the application of the AHP method. Finally, the study concludes by summarizing the key findings, addressing its limitations, and suggesting directions for future research on sustainable regional development.

Literature Review

The understanding of regional competitiveness has evolved significantly, recognizing it as a multidimensional construct influenced by various interrelated factors. Recent studies emphasize the importance of integrating economic, social, and environmental dimensions when evaluating regional competitiveness [7,8]. This literature review delves into ten critical determinants of regional competitiveness, examining how the Analytic Hierarchy Process (AHP) can be employed to prioritize these factors.

The economy remains a primary indicator of a region’s competitiveness, encompassing metrics like GDP growth, investment levels, and employment rates [9]. Recent research highlights the growing role of digital transformation, artificial intelligence, and smart specialization strategies in shaping regional economic performance [10,11]. AHP allows experts to compare these with other factors to assess the economic impact on regional growth.

The dynamics of the labor market, including employment rates, workforce skills, and adaptability, are essential for regional competitiveness [12]. Emerging studies stress the role of automation, digital skills, and remote work trends in labor market transformation [13,14]. AHP helps prioritize labor market issues, guiding workforce development initiatives.

Poverty and social inclusion impact a region’s stability and productivity [15]. New evidence suggests that policies promoting inclusive economic growth and equitable access to digital resources significantly enhance regional competitiveness [16,17]. AHP evaluates social policies, helping experts identify effective strategies for fostering inclusivity.

Health infrastructure directly influences workforce productivity and attractiveness to businesses [18,19]. The recent literature highlights the significance of telemedicine, artificial intelligence in healthcare, and digital health solutions in improving regional health outcomes [20]. AHP helps prioritize health initiatives by comparing access, quality, and technological integration in healthcare.

High-quality education systems are fundamental to human capital development [21]. Updated studies emphasize the importance of STEM education, lifelong learning, online education platforms, and industry–academic collaboration in shaping a competitive workforce [22,23]. AHP evaluates educational factors like curriculum quality and vocational training to enhance skills driving innovation and growth.

The Analytic Hierarchy Process (AHP) is a decision making methodology that helps prioritize criteria and alternatives based on pairwise comparisons. It is particularly useful in complex decision making environments, such as evaluating regional competitiveness, where multiple factors interact [24]. Through AHP, experts assess the relative importance of factors, and a consistency ratio is used to ensure the reliability of the judgments. AHP has been widely applied in various studies to evaluate regional competitiveness, as it allows for structured analysis of both quantitative and qualitative criteria [25].

The Analytic Hierarchy Process (AHP) can effectively assess environmental policies and practices [26]. Recent research underlines the role of circular economy strategies, renewable energy adoption, and sustainable urban planning in regional resilience [27,28]. Experts can compare factors like climate adaptation strategies, carbon neutrality efforts, and sustainable development goals [29].

Efficient transport systems play a crucial role in supporting trade and mobility [30]. Recent advancements in smart mobility, electric and autonomous vehicles, and high-speed rail infrastructure are reshaping regional connectivity [31,32]. Through AHP, the importance of these transport innovations is assessed relative to other competitiveness factors [33].

Regions that promote R&D and technological innovation are better positioned in the global market [34]. New studies highlight the role of digital ecosystems, blockchain technology, artificial intelligence, and cross-border innovation networks in regional development [35,36]. AHP assesses various technological factors, such as investment in R&D, collaboration between universities and industry, and technology transfer effectiveness.

The presence of high-tech industries signifies a region’s innovative capacity [37]. Recent findings emphasize the role of startup incubators, venture capital accessibility, digital entrepreneurship, and government incentives in fostering high-tech industry growth [38]. AHP helps evaluate the importance of factors contributing to high-tech development, such as workforce training, research institutions, and infrastructure.

Innovation remains crucial for maintaining regional competitiveness in a rapidly changing world [39,40]. Recent research highlights open innovation models, digital platforms, artificial-intelligence-driven decision making, and collaborative networks as key enablers of regional innovation capacity [41,42]. With AHP, we evaluate different innovative drivers, such as government support for startups, access to funding, and intersectoral collaboration.

Thus, AHP enables experts to prioritize these factors, providing insights into their interconnectedness and how they contribute to regional prosperity and sustainability. The integration of the recent literature enhances the analytical depth of this review, ensuring a more comprehensive and up-to-date understanding of regional competitiveness.

2. Materials and Methods

The research was conducted in several distinct phases, each aimed at structuring the complex problem of regional competitiveness into a manageable hierarchical framework. The initial step involved defining the overarching goal of the research, which was to evaluate the competitiveness of various regions within the European Union.

The RCI index ranks regions from those with the highest to the lowest score. The selection of regions was performed by randomly choosing one region from each group of 50 out of the initial pool of 250. From these, 6 regions were selected based on their RCI rankings, considering factors like economic structure, industrial focus, and location.

The selected regions included SE11 (Stockholm, Sweden), BE22 (the Prov. Limburg, Belgium), FRH0 (Île-de-France, France), ITC3 (Lombardy, Italy), PL43 (Masovia, Poland), and EL53 (Western Macedonia, Greece) [6].

The next phase involved identifying and defining the criteria for evaluating regional competitiveness. In our case were the ten key factors of our previous research, such as the economy, the labor market, poverty and social exclusion, health, education, the environment and energy, transport, technology/science and the digital society, high-tech industry, and innovation.

The AHP methodology was applied to assess the relative importance of the ten key factors by engaging experts in pairwise comparisons. These comparisons were used to create a judgment matrix, where each factor was evaluated against others on a scale of 1 to 9 based on their perceived importance [43]. The pairwise comparisons were then processed to generate a priority vector, which represents the relative importance of each factor [44]. The consistency of these judgments was checked using the consistency ratio (CR), with a CR value of less than 0.1 indicating acceptable consistency. This methodology, widely used in multi-criteria decision making, ensures that subjective expert opinions are systematically quantified and compared [45].

In our previous study, a questionnaire was developed to assess the preferences of ten factors of experts, focusing on regional competitiveness (see Table S1). The survey was conducted from October to December 2023 and included 45 questions involving pairwise comparisons between the factors. The survey was distributed electronically via Google Forms to experts from Greece and EU member states following GDPR guidelines [3]. The expert panel consisted of 93 participants (out of 120 invites), including regional policymakers, academics, and practitioners. Experts spanned various disciplines, such as economics, geography, statistics, data modeling, and development policy. A trial run with 10 respondents ensured the questionnaire’s clarity before full distribution.

The data were collected through pairwise comparison surveys administered by a group of experts in regional development and economics. The experts were asked to subjectively compare each of the ten criteria with the others to assess their relative importance. This was achieved using Saaty’s numerical scale (see Table S2) [46]. As a result, a pairwise comparison matrix was constructed, which was, in our case, 10 × 10, as shown in

Table 1. Opinions and positions of experts (peer reviewers) regarding pairwise criteria comparisons.

	Economy	Labor Market	Poverty and Social Exclusion	Health	Education	Environment and Energy	Transport	Technology/Science and Digital Society	High-Tech Industry	Innovation
Economy	1	2	3	3	2	2	2	3	2	3
Labor Market	0.5	1	3	2	3	2	3	2	3	2
Poverty and Social Exclusion	0.33	0.33	1	2	2	2	3	0.33	2	3
Health	0.33	0.5	0.5	1	2	3	3	3	2	3
Education	0.5	0.33	0.5	0.5	1	2	2	3	3	3
Environment and Energy	0.5	0.5	0.5	0.33	0.5	1	2	3	2	3
Transport	0.5	0.33	0.33	0.33	0.5	0.5	1	3	2	2
Technology/Science and Digital Society	0.33	0.5	3	0.33	0.33	0.33	0.33	1	3	2
High-Tech Industry	0.5	0.33	0.5	0.5	0.33	0.5	0.5	0.33	1	3
Innovation	0.33	0.5	0.33	0.33	0.33	0.33	0.5	0.5	0.33	1

.

Once the judgments are recorded, a consistency check is essential. The next step involved calculating the weights of each criterion. Because numerical values are derived from subjective preferences, some inconsistencies in the final judgment matrix may be inevitable.

The AHP method utilizes eigenvalue analysis to derive the priority vector from the pairwise comparison matrix [47,48]. Each entry in the matrix was normalized by dividing each value by the sum of its respective column. This produced a normalized matrix representing the relative priorities (see Table S3). The average of each row in the normalized matrix was computed to derive the priority vector, which indicates the relative importance of each criterion [49]. The Row Sum, the Column Sum, and, finally, the Table Sum are shown in

Table 2. Initial priority/weight calculation table for the criteria.

	Economy	Labor Market	Poverty and Social Exclusion	Health	Education	Environment and Energy	Transport	Technology/Science and Digital Society	High-Tech Industry	Innovation	Row Sum
Economy	1	2	3	3	2	2	2	3	2	3	23
Labor Market	0.5	1	3	2	3	2	3	2	3	2	21.5
Poverty and Social Exclusion	0.33	0.33	1	2	2	2	3	0.33	2	3	16
Health	0.33	0.5	0.5	1	2	3	3	3	2	3	18.33
Education	0.5	0.33	0.5	0.5	1	2	2	3	3	3	15.83
Environment and Energy	0.5	0.5	0.5	0.33	0.5	1	2	3	2	3	12.33
Transport	0.5	0.33	0.33	0.33	0.5	0.5	1	3	2	2	11
Technology/Science and Digital Society	0.33	0.5	3	0.33	0.33	0.33	0.33	1	3	2	11.17
High-Tech Industry	0.5	0.33	0.5	0.5	0.33	0.5	0.5	0.33	1	3	7.5
Innovation	0.33	0.5	0.33	0.33	0.33	0.33	0.5	0.5	0.33	1	4.5
Column Sum	4.83	6.33	16	18.33	15.83	12.33	11	11.17	7.5	4.5	Table Sum = 141.17

.

To address this, the AHP calculates the consistency ratio (CR), which compares the Consistency Index (CI) of the judgment matrix to the Random Index (RI) of a randomly generated matrix (see Table S4) [50].

The Consistency Index (CI) was derived using the formula:

$$CI={\displaystyle \frac{\lambda \max -n }{n-1 }}$$

where λ_max is the maximum eigenvalue and n is the number of criteria.

The consistency ratio was calculated using the formula

$$CR={\displaystyle \frac{CI}{RI}}$$

A CR value of less than 0.1 indicated acceptable consistency among the judgments.

In the AHP, CR = CI/RI. Saaty demonstrated that a CR ≤ 0.1 is acceptable for further analysis. If CR > 0.1, the judgments must be revised to identify and address inconsistencies [51]. Then, the initial judgment matrix is multiplied by the corresponding relative weights to compute the product A∗W (see Table S5). Each row of A∗W is calculated as the sum of the products of each column value in the row with the corresponding relative weight. The next step is to compute the matrix division, (A∗W)/W (see Table S6). Divide each A∗W value by the corresponding weight W, resulting in a new column [52].

After these divisions, we have to calculate the λmax with the following calculation, (values see Table S6):

λmax = (Sum of values)/n

In our case, λmax = 11.6. Then, CI = (λmax − n)/(n − 1). For n = 10, CI = 0.18. To calculate the consistency ratio (CR), divide the CI by the RI corresponding to the matrix size n = 10.

$$CR={\displaystyle \frac{CI}{RI}}={\displaystyle \frac{0.18}{1.49}}\approx 0.1$$

Because CR ≈ 0.1, which is within the acceptable range, the judgment matrix is consistent, and the decision making process using AHP can proceed.

To continue the method, alternative solutions must be selected. In our case, regions have been chosen as alternative regions, classified based on the RCI [53,54]. As mentioned previously, the six regions selected are randomly chosen from groups of fifty regions from the RCI ranking. Specifically, they are Stockholm, Sweden (SE11), from the 1st group of fifty, the Flemish Region, Belgium (BE22), from the 2nd group of fifty, Île-de-France, France (FRH0), from the 3rd group of fifty, Lombardy, Italy (ITC3), from the 4th group of fifty, Masovia, Poland (PL43), from the 5th group of fifty, and Western Macedonia, Greece (EL53), from the 6th group of fifty.

Then, for each region, specific values for each criterion were selected from the Well Being and Eurostat databases, corresponding to each of the ten factors [55,56]. A table was created for each criterion, listing the values for each region. The result is the “local” weight or priority for each alternative (region). This analysis is presented in

Table 3. Values and weights for selected regions.

Region	Value	Weight (W)
Stockholm, Sweden (SE11)	63.93	0.25
Flemish Region, Belgium (BE22)	46.23	0.18
Île-de-France, France (FRH0)	45.73	0.18
Lombardy, Italy (ITC3)	34.43	0.14
Masovia, Poland (PL43)	40.16	0.16
Western Macedonia, Greece (EL53)	18.67	0.07
Sum	249.15

. The same process was followed for all criteria, and then we proceeded to the results, which are subsequently presented.

In Table 3, the weight represents the relative importance or priority of each region based on the criteria used in the analysis. It reflects how significant each region is compared to the others when considering the various factors. A higher weight indicates greater importance in the overall evaluation, while a lower weight signifies less importance.

3. Results

The findings of this study, based on the literature, expert assessments, and the use of the Analytical Hierarchy Process (AHP), provide two detailed classifications, firstly concerning the main factors influencing regional competitiveness and secondly the ranking of the regions selected based on the RCI indicators.

The economy emerged as the most critical factor, receiving a weighted importance score of 0.691. Closely following the economy, the labor market was ranked second, with a score of 0.647. Health infrastructure was identified as another crucial factor, with a score of 0.529. Poverty and social inclusion were also recognized as important factors, with a score of 0.508.

While the RCI rankings provide a general view of regional competitiveness, our AHP-based analysis allows for a more nuanced understanding of the relative importance of different factors. For instance, our results highlight that while the economy remains paramount, factors like labor market dynamics, social inclusion, and technological development also play key roles, which might not be as apparent in traditional indices.

Other factors, such as education and technological development in science and the digital society, also play significant roles, with scores of 0.419 and 0.392, respectively. Environmental sustainability (0.367) and transport infrastructure (0.357) were identified as secondary but still vital components of regional competitiveness. High-tech industry development (0.325) and innovation (0.282) were ranked lower but are still important, reflecting the need for technological advancement and innovation to maintain competitiveness in the long term.

Table 4. Ranking of regional competitiveness factors and their respective scores.

Factor	Score
Economy	0.691
Labor Market	0.647
Health Infrastructure	0.529
Poverty and Social Inclusion	0.508
Education	0.419
Technological Development	0.392
Environmental Sustainability	0.367
Transport Infrastructure	0.357
High-Tech Industry	0.325
Innovation	0.282

below summarizes the ranking of these factors and their respective scores.

These results highlight the multifaceted nature of regional competitiveness, with economic, labor market, and health factors being of primary importance. However, social inclusion, education, technology, sustainability, and infrastructure are also integral to fostering long-term competitiveness. These findings align with the insights presented in our first review and in our analysis in our article, which also emphasize the interconnectedness of these factors. Finally, they corroborate the views of the experts, who identified these same dimensions as critical for regional development.

Furthermore, the application of the AHP method confirmed the ranking of the regions in the following order, as in the RCI index: SE11 (Stockholm, Sweden) was ranked in the highest position, followed by BE22 (Prov. Limburg, Belgium), FRH0 (Île-de-France, France), ITC3 (Lombardy, Italy), PL43 (Masovia, Poland), and EL53 (Western Macedonia, Greece) in the lowest position. This ranking, based on the weights and values associated with each criterion, was consistent with the experts’ assessments and further validated the results obtained through the AHP methodology.

The RCI is calculated by evaluating the factors that influence regional competitiveness, which are scored based on the AHP method. The combined evaluation of these parameters, using the respective weights for each factor, results in the final RCI score. Negative values in the RCI reflect regions with lower performance relative to the average of the regions, indicating areas that face challenges or have lower competitiveness compared to others.

Table 5. Ranking of regions based on the AHP method and the RCI scoreboard.

Rank	Region	RCI Score
1	SE11—Stockholm, Sweden	1.08
2	BE22—Flemish Region, Belgium	0.46
3	FRH0—Île-de-France, France	0.11
4	ITC3—Lombardy, Italy	−0.30
5	PL43—Masovia, Poland	−0.69
6	EL53—Western Macedonia, Greece	−1.44

presents the ranking of the regions based on the AHP method and the RCI scoreboard, while the detailed values are shown in Table S7.

4. Discussion

The findings of this study, based on expert assessments using the Analytic Hierarchy Process (AHP), align with the existing literature on priority order and interrelationships of the factors influencing the regional competitiveness of EU regions based on RCI ranking. The analysis highlights that the prioritization and weighting of factors, such as the economy, the labor market, and infrastructure, are closely tied to the specific characteristics and development profiles of individual regions.

The region of Stockholm (SE11) ranks as the most competitive region due to its exceptional strengths in the economy, the labor market, and innovation [57]. Its economic stability and robust job market are complemented by advanced technological development and sustainability practices, solidifying its leading position with an RCI score of 1.08 [58]. This aligns with the literature, which frequently emphasizes the synergy between economic stability, innovation, and sustainability in driving regional competitiveness.

The Flemish Region (BE22), positioned second, benefits from a strong education system, a healthy labor market, and efficient transport infrastructure [59]. These elements enhance connectivity and workforce adaptability, with an RCI score of 0.46 reflecting its balanced development [60]. Such findings corroborate existing research that underscores the role of education and infrastructure in fostering adaptability and regional cohesion.

As the economic hub of France, Île-de-France (FRH0) excels in transportation, high-tech industries, and healthcare infrastructure, securing its third-place ranking [61]. Its advanced networks and investments in innovation drive regional productivity, with an RCI score of 0.11. This observation is consistent with the literature linking innovation networks and infrastructure to enhanced economic productivity.

Lombardy (ITC3) ranks fourth, demonstrating strengths in education and environmental sustainability while facing challenges in poverty reduction and social inclusion [62]. Its mixed performance, with an RCI score of −0.30, highlights the need for targeted policy interventions to address socioeconomic disparities. The literature also emphasizes that regions excelling in education and sustainability can face disparities in social inclusion, reflecting Lombardy’s experience.

Masovia (PL43), positioned fifth, shows potential in technological development but struggles with lower performance in healthcare and poverty levels [63]. Its fragmented transport and environmental infrastructure further hinder growth, resulting in an RCI score of −0.69 [64]. These findings align with research indicating that fragmented infrastructure often limits the full realization of technological potential in regional development. Addressing these structural deficiencies could improve Masovia’s competitive standing.

Western Macedonia (EL53), ranked last, faces significant challenges across multiple dimensions, including the economy, the labor market, and education [65]. Limited investment in innovation and transport infrastructure restricts its capacity to attract investment, reflected in its RCI score of −1.44 [66]. This supports existing research that suggests that underinvestment in critical sectors exacerbates regional underperformance. The persistent weaknesses in Western Macedonia emphasize the need for substantial policy interventions to stimulate economic diversification and social development.

Based on the above discussion, the analysis underscores that regional competitiveness is shaped by the interplay of multiple factors, which exert varying influences depending on a region’s developmental stage. High-performing regions, such as Stockholm and the Prov. Limburg, leverage economic stability, innovation, and infrastructure to maintain their competitive edge. Conversely, less competitive regions like Western Macedonia require targeted strategies that prioritize investment in education, healthcare, and sustainable development to foster resilience and long-term economic growth. Strengthening policy frameworks that support innovation, social inclusion, and infrastructure development will be crucial in bridging regional disparities and enhancing the overall competitiveness of EU regions.

5. Conclusions

Our research delves into the intricate dynamics of regional competitiveness, emphasizing the indispensable role of expert insights in understanding these complexities. The findings confirm that the expert opinions from previous research align with the results obtained through the application of the AHP methodology, reinforcing the validity of the insights provided.

Through the application of the AHP method and comprehensive reviews of the existing literature, we discern the multifaceted interaction of various factors that shape regional success. Notably, the selected regions represent a spectrum of performance levels across the RCI rankings, from the highly competitive Stockholm to the less competitive Western Macedonia.

The economy is the most critical factor for regional competitiveness, driving growth, stability, and productivity. Regions like Stockholm and the Flemish Region thrive due to strong economic stability and sustainable development, while regions like Western Macedonia face economic resilience challenges.

The labor market also plays a vital role in regional growth. Strong labor markets, like in Île-de-France, attract investment and create jobs. In contrast, regions like Masovia must address structural labor market issues to enhance competitiveness.

Addressing poverty and promoting social inclusion are key to creating equitable environments for sustainable development. Regions with targeted policies, like Lombardy, contribute to long-term stability and social cohesion, while others must focus on reducing inequality to improve outcomes.

Health infrastructure is essential for a productive workforce. Stockholm leads in healthcare innovation, while regions like Masovia and Western Macedonia face challenges that impact competitiveness.

Education is crucial for developing human capital and innovation. The Flemish Region and Île-de-France benefit from advanced education systems, while Western Macedonia struggles with limited resources.

Technological development boosts productivity and global competitiveness. Stockholm excels in technological investments, while regions like Masovia and Lombardy need to focus more on digital infrastructure.

Environmental sustainability, though secondary, is important for long-term growth. The Flemish Region invests in green technologies, while others lag in sustainability initiatives.

Well-developed transportation networks facilitate trade and market access. Île-de-France’s efficient infrastructure supports its competitiveness, while Western Macedonia and Masovia face connectivity challenges.

High-tech industries drive future competitiveness. Stockholm leads in sectors like biotechnology and IT, while lower-ranked regions need to prioritize R&D and innovation.

The practical contributions of this research lie in its ability to provide policymakers and regional stakeholders with a structured, data-driven approach to assessing and enhancing regional competitiveness. By identifying key determinants of competitiveness and evaluating them through the AHP methodology, decision makers can prioritize policy interventions and allocate resources effectively.

From a theoretical perspective, this study contributes to the existing literature by demonstrating the applicability and robustness of the AHP method in analyzing regional competitiveness. The integration of expert opinions with a multi-criteria decision making framework enhances the methodological rigor and offers a replicable approach for future research.

It is important to acknowledge the limitations of our research, including potential biases in expert opinions and the subjectivity of data collection, which may affect the findings. Additionally, regional dynamics are constantly evolving, requiring ongoing monitoring to maintain the relevance of the insights.

Another limitation is the diverse socioeconomic, cultural, and policy contexts of the selected regions. Each region’s unique characteristics may affect the generalizability of the findings, as factors driving competitiveness in leading regions like Stockholm may not have the same impact in lower-ranked regions like Western Macedonia.

Future research could build on these findings by exploring their relevance in areas like climate change, policy proposals, and civil protection. Specifically, further investigation into the application of the AHP method in different regional contexts could refine our understanding of its effectiveness and adaptability. Integrating environmental sustainability with regional competitiveness could provide valuable insights into how leading regions can maintain their edge, while others, like Western Macedonia, can overcome challenges and build resilience. This would promote a more sustainable future for all regions such that they are better prepared to address both environmental and societal challenges.