A Multi-Criteria Assessment of Green Tourism Potential in Rural Regions: The Role of Green Skills and Institutional Readiness

Abstract

This paper assesses the green tourism readiness of six EU member states from Central and Eastern Europe—Slovenia, Croatia, Slovakia, Hungary, Bulgaria, and Romania—using a hybrid multi-criteria decision-making (MCDM) model. As tourism sectors face increasing pressure to align with the European Green Deal and sustainability goals, integrating green skills, environmental protection, and institutional governance becomes essential. The study applies a three-step framework that combines the Analytic Hierarchy Process (AHP), Best-Worst Method (BWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate national performance across four criteria: natural capital, rural infrastructure, governance readiness, and green skills in vocational education and training (VET). Results show that environmental sustainability and governance are the dominant enablers of green tourism transformation, with Slovenia and Croatia leading in overall readiness. Although green skills have a lower relative weight, their integration significantly strengthens performance in more advanced systems. The hybrid model demonstrated methodological robustness through sensitivity and consistency checks. This research contributes to both methodological innovation and evidence-based policymaking by offering a replicable tool for evaluating sustainable tourism development in transition economies. It provides actionable insights for aligning education, tourism, and environmental policy within the broader EU green transition framework.

1. Introduction

As tourism transitions toward environmental sustainability, the ability of countries to align ecological assets, institutional governance, and green workforce development becomes essential. The European Union’s policy framework, centered on the Green Deal, Circular Economy Action Plan, and Sustainable Tourism Guidelines, places increasing demands on member states to integrate green criteria into tourism planning, skills training, and rural development. For Central and Eastern European (CEE) countries, this transition presents both opportunities and challenges. Many of these states possess rich biodiversity and cultural heritage, yet face structural weaknesses in vocational education, institutional coordination, and the integration of sustainability into tourism policy.

In this study, green tourism is conceptualized as a multidimensional construct encompassing environmental stewardship, institutional governance, and human capital dimensions. This operational definition follows the frameworks established by United Nations Environment Programme and World Tourism Organization (2012), World Tourism Organization (2023), and European Commission (2024a), which describe sustainable tourism as economic activity aligned with ecological preservation and community well-being. Within this structure, rural tourism is treated as a significant but not exclusive subcomponent, given its strong links to biodiversity protection and local economies. However, due to the lack of harmonized data disaggregating rural from total tourism activity across Central and Eastern European countries, we rely on total tourism nights as a proxy for tourism intensity, while discussing rural-specific trends and strategies qualitatively. In the CEE region, rural areas make up more than 40% of the total population and hold a significantly larger share of tourism assets, including protected landscapes, heritage villages, agritourism farms, and eco-certified accommodations (Eurostat, 2023). However, unlocking the full potential of these resources requires more than just geographical advantages. It also calls for investments in infrastructure, institutions, and, importantly, human capital—especially in green skills.

The concept of green skills in tourism—referring to the competencies, values, and knowledge that support environmentally sustainable practices—has gained momentum in EU education and employment discourse (Cedefop, 2023b; Franzén et al., 2024). However, empirical research on how green skills contribute to tourism transformation at the national level remains limited. Much of the available literature focuses either on conceptual frameworks or policy reports (Cedefop, 2015; OECD, 2022), while few studies attempt to measure readiness across multiple criteria or evaluate the interplay between environment, governance, infrastructure, and education systems.

This gap is especially evident in the CEE region. While countries like Slovenia and Croatia have made clear progress in incorporating sustainability into tourism, others still fall behind due to fragmented institutional structures, underfunded VET systems, or a lack of policy continuity. Additionally, there is no established method for comparing countries’ readiness for green tourism that combines expert insights, empirical data, and systemic assessment. Several authors emphasize the need for targeted workforce development strategies in tourism, where frontline workers are often under-trained in environmental practices (Bukhari & Jain, 2022; Kamboj & Eronimus, 2024). Although vocational education and training (VET) is increasingly important as a way to promote such skills, European Training Foundation (ETF, 2023) highlights significant disparities still exist across countries and regions in integrating green curricula into tourism-related training programs.

This study addresses that gap by proposing and applying a hybrid multi-criteria decision-making (MCDM) framework to assess green tourism readiness across six EU member states from CEE: Slovenia, Croatia, Slovakia, Hungary, Bulgaria, and Romania. Combining the Analytic Hierarchy Process (AHP), Best-Worst Method (BWM), and TOPSIS, the model evaluates performance based on four key criteria: (1) environmental sustainability and natural capital, (2) rural tourism infrastructure, (3) institutional governance and policy alignment, and (4) green skills and VET system integration. These dimensions reflect the multifaceted nature of green tourism as both a development objective and a systems-level transition involving education, regulation, and investment. MCDM tools are widely used in sustainability research, providing a replicable and flexible approach for integrating expert knowledge and various data types (Rezaei, 2015; Govindan et al., 2015).

From an analytical perspective, this research is positioned within tourism economics and sustainable development economics, focusing on how institutional quality, competitiveness, and environmental performance interact as economic drivers of tourism readiness. The study also contributes to rural and regional economics by quantifying how resource allocation, institutional performance, and human-capital formation influence sustainable tourism outcomes in less-developed EU regions. By framing green tourism as an economic system of interlinked environmental and institutional efficiencies, the paper remains fully aligned with the journal’s scope on applied and regional economic analysis.

The novelty of this research lies in both its methodological approach and regional focus. First, by triangulating three MCDM techniques, the study improves the reliability and interpretability of expert-based evaluations, offering a transparent and replicable tool for future benchmarking. Second, it contributes to the underexplored empirical space between green skills, VET systems, and sustainable tourism, especially in less-researched EU regions. While prior studies have emphasized the importance of environmental indicators (Hall, 2000; Espiner et al., 2017), few have examined how VET readiness and green skill integration influence tourism sustainability, or how these components interact within a regional ecosystem.

Grounded in this conceptual and empirical context, the study seeks to answer the following research questions:

• RQ1: What are the most important factors that influence national readiness for green tourism development in Central and Eastern European EU member states?
• RQ2: How do the selected countries perform across the identified criteria when evaluated through a hybrid MCDM approach?
• RQ3: Is the hybrid MCDM model methodologically robust and consistent when applied to green tourism assessment?

Based on these research questions, two hypotheses are proposed:

H1. 

Countries with strong environmental protection frameworks and institutional governance will perform better in green tourism readiness rankings.

H2. 

The presence and integration of green skills and VET system reform positively affect a country’s position in green tourism evaluation, even when controlling for environmental and infrastructural indicators.

To address these questions and test the hypotheses, the remainder of the paper is structured as follows. Section 2 provides a review of relevant literature in three thematic areas: (1) green tourism and sustainable development, (2) the role of green skills and VET in tourism, and (3) decision-making models in tourism planning. Section 3 introduces the methodology, detailing the logic and steps of the AHP-BWM-TOPSIS hybrid model and the criteria, alternatives, and expert input used. Section 4 presents the results of the model, including rankings, weight distributions, and robustness tests. Section 5 discusses the implications of the findings for regional development, policy reform, and green skills integration. Section 6 concludes the paper by summarizing contributions, offering policy recommendations, and proposing directions for future research.

In this study, green tourism readiness is defined as a country’s institutional, environmental, and human-capital capacity to implement and sustain low-impact, high-value tourism consistent with EU Green Deal objectives. This readiness reflects the ability of national systems to balance environmental conservation with economic competitiveness through sound governance, policy coordination, and workforce skills.

By systematically evaluating readiness for green tourism across interrelated dimensions, this study offers a new methodological lens to assess the sustainability transition in tourism sectors. It also supports more informed policy decisions that can accelerate both green employment and environmentally responsible rural development in CEE Europe.

2. Literature Review

2.1. Green Tourism and Sustainable Development in the EU

The transition to green tourism has become a central pillar of the European Union’s broader sustainability agenda. The European Green Deal, EU Biodiversity Strategy, and national sustainable tourism strategies increasingly encourage tourism models that reduce environmental impact, improve community well-being, and strengthen economic resilience (European Commission, 2024b; OECD, 2022). It represents a form of sustainable tourism that minimizes environmental damage while promoting socio-economic benefits for local communities (OECD, 2015; Costantini & Mazzanti, 2012; United Nations Environment Programme & World Tourism Organization, 2012). According to Lane (2009), green tourism is particularly suited for rural settings where landscape, culture, and community involvement are integral components of the visitor experience. Green tourism, particularly in rural and ecologically sensitive areas, is promoted as a means to balance conservation and development while reducing dependence on carbon-intensive tourism flows (Lane & Kastenholz, 2015).

The Brundtland Commission (1987) established the widely accepted definition of sustainable development as meeting present needs without compromising future generations’ ability to meet theirs. Subsequent interpretations (e.g., Daly, 1990; Pearce et al., 1989) expanded this framework from ecological balance to include intergenerational equity, economic efficiency, and institutional capacity. In applied economics, sustainability is understood as a dynamic process balancing economic, social, and environmental goals through adaptive policy instruments (OECD, 2015; UNEP, 2019). Rather than implying a fixed steady-state, it captures the continuous adjustment of economies toward lower environmental intensity and higher resource efficiency.

The literature highlights multiple pathways through which green tourism contributes to rural development: income diversification, employment generation, enhancement of local infrastructure, and preservation of cultural landscapes (Hall, 2024; Briedenhann & Wickens, 2004). In the context of Central and Eastern European (CEE) countries, this transition is particularly complex. Many states possess rich biodiversity and landscapes that are attractive for green tourism, including Natura 2000 sites and UNESCO heritage areas. However, implementation is often constrained by uneven governance capacity, limited inter-sectoral coordination, and the slow integration of sustainability principles into national tourism policies (Hall, 2000; Marín-González et al., 2022). Moreover, post-socialist legacies, including centralized planning systems and weak institutional trust, continue to shape the adoption and regulation of sustainable tourism in the region (Hall, 2011). While countries like Slovenia and Croatia have embraced green certifications and environmental monitoring, others lag behind in strategy coherence and enforcement.

Recent empirical work underscores the need for multidimensional assessments of tourism sustainability, integrating not only environmental indicators but also governance, education, and infrastructure (Espiner et al., 2017). Yet, most evaluations remain descriptive or rely on single-indicator benchmarking (e.g., CO₂ emissions or eco-label uptake), limiting their utility for holistic planning.

However, green tourism success in rural areas is not guaranteed. According to Saarinen (2017), the absence of strategic planning, low institutional capacity, and market dependence can undermine sustainability outcomes. Moreover, short-term commercialization without skills and infrastructure investment may degrade rural tourism assets over time. Studies specific to CEE countries also point to governance and funding disparities at the local level (Hall, 2011; Pădurean, 2020), especially where rural tourism is fragmented across small, under-resourced providers.

2.2. Green Skills and Vocational Education in the Tourism Sector

Green skills—defined as the knowledge, values, and technical capacities that enable individuals to support sustainable practices in their occupations—are essential for the green transition in tourism (Cedefop, 2023b; ILO, 2019). In tourism, green skills are particularly relevant for occupations in accommodation, food services, guiding, transport, and rural hospitality, where ecological awareness and resource efficiency intersect with guest experience and service innovation.

Green skills in tourism encompass the technical, managerial, and behavioral competences that enable sustainable operations across accommodation, transport, and hospitality services. They include resource-efficient housekeeping, energy-saving maintenance, eco-certification management, waste-sorting logistics, and the adoption of renewable technologies in small enterprises (Cedefop, 2023b; ETF, 2023). Despite their recognized importance, green skills are still not sufficiently integrated into tourism training programs, especially in CEE countries. Cedefop (2023b) and ETF (2023) report that most tourism-related VET curricula in the region lack systematic inclusion of sustainability modules. At the same time, employers increasingly demand transversal green skills (problem-solving, energy efficiency, circular thinking), which traditional programs fail to address.

The integration of green skills into vocational education and training (VET) varies significantly across EU countries. Slovenia and Croatia are among the few CEE countries where public authorities have piloted green skill integration into tourism VET through Erasmus+ projects and national reforms. However, data remain fragmented and inconsistent, making it difficult to track progress or compare national systems. Cotterell et al. (2019) stress the need for context-sensitive skills monitoring and curriculum development that considers the local tourism ecosystem. Moreover, skills development is often disconnected from broader tourism policy, preventing systemic alignment across education, environment, and economy.

Few empirical studies explore how inclusion of green skills affects tourism sustainability at the national level. Theoretical works often link skills to labor market transformation (Strietska-Ilina et al., 2011), but without connecting this to tourism-specific metrics or multi-criteria performance. This gap limits the ability of policymakers to justify targeted VET investments based on tourism sustainability outcomes.

Although quantitative indicators on green skills remain scarce, some authors propose qualitative frameworks or expert-based scoring to evaluate green skills readiness (Succi & Canovi, 2019). Guidetti et al. (2020) analyzed the quality of work in an Italian tourist destination (the province of Rimini), showing that tourism jobs are partly underestimated, because their multifaceted nature—skills and training opportunities, as well as the set of characteristics, qualifications and occupations of employees—is neglected. This further supports the use of hybrid decision-making tools, which allow for the integration of both subjective and objective inputs in assessing green tourism potential.

2.3. Decision-Making Models in Sustainable Tourism Planning

Tourism planning and policy increasingly rely on multi-criteria decision-making (MCDM) tools to evaluate trade-offs, prioritize investments, and manage stakeholder input. MCDM models offer a structured approach for integrating both qualitative and quantitative dimensions, essential when addressing complex sustainability questions (Ulkhaq et al., 2016).

Among MCDM techniques, the Analytic Hierarchy Process (AHP)—one of the earliest methods—is widely used in tourism to evaluate destination competitiveness, infrastructure development, or risk management. It enables experts to assign relative weights through pairwise comparisons and is known for its clarity (T. L. Saaty, 2008). However, AHP is sensitive to inconsistencies in expert input and needs many comparisons when options increase.

To address such limitations, the Best-Worst Method (BWM) was introduced, offering a more parsimonious weighting structure by identifying the best and worst criteria and optimizing consistency (Rezaei, 2015). BWM has proven especially useful in sustainability assessments with limited experts or time-constrained decision-making.

The TOPSIS method, meanwhile, ranks alternatives by calculating their geometric closeness to ideal and anti-ideal solutions, making it well-suited for final scoring and benchmarking (Hwang & Yoon, 1981). It is increasingly applied in tourism impact evaluations and destination rankings (Kahraman & Yanık, 2016).

Recent studies have shown that hybrid MCDM models, which combine AHP, BWM, and TOPSIS, improve analytical robustness and address individual model limitations (Govindan & Bouzon, 2018). However, few such models have been used in the tourism sector, particularly focusing on green skills, rural sustainability, or national readiness across EU states. The current study addresses this gap by creating a structured hybrid MCDM framework designed to evaluate green tourism transformation, incorporating environmental, governance, infrastructure, and educational factors.

To better understand the strengths, limitations, and applicability of the selected multi-criteria decision-making (MCDM) methods used in this study, a structured comparison is presented in

Table 1. Comparison of Selected MCDM Methods in Sustainability Applications.

Feature	Analytic Hierarchy Process (AHP)	Best–Worst Method (BWM)	Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
Developer/Year	R. W. Saaty (1987)	Rezaei (2015)	Hwang and Yoon (1981)
Primary Function	Criteria weighting via pairwise comparisons	Criteria weighting using the best and worst criteria	Final ranking of alternatives based on ideal/anti-ideal solution
Input Requirements	Requires a full pairwise matrix among all criteria	Requires fewer pairwise comparisons (only best and worst vs. others)	Requires a normalized decision matrix (quantitative values for each alternative)
Consistency Check	Consistency Ratio (CR)	Consistency Ratio via optimization	No internal consistency check
Ease of Use	Moderate (cognitively intensive with many criteria)	High (simpler input structure, fewer comparisons)	High (computationally straightforward once weights and values are known)
Common Tourism Applications	Destination competitiveness, planning priorities, infrastructure development (Ulkhaq et al., 2016)	Strategic decision-making in tourism operations and sustainability (Rezaei, 2016)	Benchmarking tourism regions, evaluating sustainability indicators (Kahraman & Yanık, 2016)
Strengths	High transparency, intuitive, suitable for group decision-making	Less inconsistency, efficient for expert elicitation	Provides clear rankings based on closeness to the ideal, robust and widely accepted
Limitations	Can become inconsistent or biased with many criteria	Relatively new; requires a structured understanding of “best” and “worst” inputs	Sensitive to normalization and scale; requires precise quantitative inputs

Source: Authors’ elaboration. Note: Summary of methodological characteristics, tourism-related applications, and hybridization potential of the AHP, BWM, and TOPSIS methods, adapted from R. W. Saaty (1987); Rezaei (2015, 2016); Ulkhaq et al. (2016); Kahraman and Yanık (2016); Govindan and Bouzon (2018); Pishdar et al. (2022).

. The Analytic Hierarchy Process (AHP), Best-Worst Method (BWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) each offer distinct advantages in sustainability-oriented decision problems, particularly in complex domains such as tourism and vocational education. Comparing these methods helps justify the adoption of a hybrid approach, where AHP and BWM are used to derive criteria weights, and TOPSIS is employed to rank the performance of alternatives. The table also highlights their methodological features, input structures, consistency mechanisms, and recent tourism-related applications.

As shown in Table 1, each of the selected MCDM methods contributes unique strengths to sustainability assessment in tourism planning. AHP offers a well-established framework for structuring expert judgment, though it can become burdensome with many criteria. BWM addresses this limitation by requiring fewer comparisons while enhancing consistency. Meanwhile, TOPSIS is particularly effective for final ranking, as it captures the geometric closeness of each alternative to ideal and anti-ideal solutions. When applied individually, these models offer partial insights; however, in combination, they provide a more comprehensive and reliable evaluation. Their complementary features—AHP for structured weighting, BWM for efficient input, and TOPSIS for robust ranking—justify the use of a hybrid model in this study. Moreover, their successful application in tourism and sustainability-related research further validates their relevance for assessing green transition readiness in the Central and Eastern European context.

The process of expert elicitation followed a rigorous and formal procedure, consistent with established best practices (O’Hagan et al., 2006; Bojke et al., 2021). It involved (i) defining the objectives of the assessment, (ii) identifying and selecting appropriate experts, (iii) designing the elicitation protocol, (iv) conducting structured sessions, and (v) analyzing the results. The expert panel consisted of eight members: three university academics specializing in sustainable tourism, environmental economics, and regional development; three practitioners representing national tourism organizations and industry associations; and two senior policy officers from ministries responsible for tourism and economic development. Each expert has at least ten years of professional experience in policy design, tourism planning, or sustainability assessment. Their judgments on uncertain criteria and parameters were systematically collected and quantified, thereby reducing subjectivity and ensuring transparency in decision-making. The elicitation process followed structured protocols for expert judgment (Ayyub, 2001).

Experts were identified through professional networks, institutional directories, and peer recommendations, following Cedefop (2023a) and ETF (2023) guidelines for expert elicitation in skills research. Participation was voluntary, and all experts reviewed the conceptual framework before pairwise comparison tasks were conducted via a structured Delphi-style process (Okoli & Pawlowski, 2004). To assess reliability of judgments, Kendall’s W = 0.71 (p < 0.01) confirmed substantial inter-rater agreement. Discrepancies were discussed and resolved through controlled feedback, ensuring convergence and consistency across experts.

3. Methodology

The dataset integrates environmental, institutional, and educational indicators to capture the multidimensional nature of green tourism readiness. As Eurostat and UNWTO databases do not consistently distinguish between rural and total tourism nights, the latter are employed as a proxy for overall tourism intensity (see

Table 2. Data and Indicators.

Code	Indicator	Unit	Period	Source
C1	Tourism nights (per 1000 residents)	Number	2005–2023	Eurostat (tour_occ_nim, tour_cap_nat)
C2	Natura 2000 coverage (% of total area)	Percent	2005–2023	European Environment Agency (EEA)
C3	Institutional governance index	Composite	2005–2023	World Bank (WGI)
C4	Green skills and VET system readiness	Index	2005–2023	Cedefop (2023a); ETF (2023)

Source: Eurostat (n.d.); World Bank (n.d.); European Environment Agency (n.d.).

below). This approach ensures comparability across countries and aligns with similar studies that adopt aggregate tourism data when rural-specific series are unavailable (e.g., López-Sánchez & Pulido-Fernández, 2016; Seguí-Amortegui et al., 2019). Complementary policy documents are used to contextualize rural tourism contributions and sustainability strategies.

Operationally, green tourism readiness is modeled as a composite construct comprising three measurable dimensions: (i) environmental policy performance, (ii) institutional quality, and (iii) green skills and vocational-education system strength. Each dimension is evaluated using normalized indicators drawn from Eurostat, World Bank, Cedefop, and UNWTO datasets for 2005–2023. All variables were extracted from harmonized international databases to ensure cross-country comparability. Missing or inconsistent national data were cross-checked against national tourism strategies and official statistical portals. Where disaggregation for rural tourism was unavailable, total tourism nights were used as a proxy consistent with UNWTO (n.d.-b) methodological guidance.

In the empirical framework, this balance is expressed through weighted indicators rather than absolute optimization. Environmental performance indicators (C1–C2) and socio-economic readiness factors (C3–C4) are normalized and aggregated to reflect proportional rather than conflicting objectives. Accordingly, in this study, sustainability is operationalized through indicators of environmental performance, institutional quality, and green skills—dimensions that reflect dynamic policy and capacity shifts rather than static equilibrium conditions. In the empirical model, the green-skills dimension (C4) is proxied by indicators such as the share of VET programs with environmental modules, green job participation rates, and training-intensity indices derived from CEDEFOP and Eurostat datasets. C4 reflects institutional readiness and systemic support for green skill formation (e.g., inclusion of sustainability modules in curricula, national qualification frameworks, and vocational teacher training). It does not measure individual learning outcomes.

To evaluate green tourism readiness in selected Central and Eastern European (CEE) EU member states, we apply a hybrid multi-criteria decision-making (MCDM) framework that combines Analytic Hierarchy Process (AHP)—based on T. L. Saaty (2008), Best-Worst Method (BWM)—followed Rezaei (2015), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)—implemented according to Hwang and Yoon (1981). This integrative approach allows us to capture expert preferences, optimize weighting consistency, and generate a final country ranking.

Each component of the hybrid model performs a distinct and complementary function: AHP structures hierarchical relationships among criteria, BWM enhances weighting consistency and reduces pairwise comparison redundancy, and TOPSIS synthesizes the results into a final ranking based on proximity to the ideal solution. This integration enhances robustness while maintaining interpretability and transparency.

The decision-making problem addressed by the AHP model is to evaluate and prioritize the most relevant criteria influencing the readiness of countries for green tourism-driven rural development. This step supports validation of the criteria used in the final BWM and TOPSIS analysis by incorporating expert judgments on relative importance through pairwise comparisons.

AHP helps ensure that the selected criteria reflect the multidimensional nature of green tourism, including environmental, institutional, economic, and human capital aspects, while checking for consistency in expert preferences. Following T. L. Saaty (2008, pp. 87–88), applied matrices derived from expert assessments were normalized and aggregated into a theoretical comparison matrix through the geometric mean method.

3.1. Analytical Process

We conducted our analysis through several phases and steps. Each phase represented a multiple decision-making model. Within this, each model was introduced through a series of steps, which were then used in the same order to obtain results.

Step 1: Defining the Problem, Criteria, and Alternatives

The decision problem centers on assessing how well national contexts support the development of green tourism in line with EU sustainability objectives. Based on the literature review and policy frameworks, the four main criteria used to assess each country’s readiness are presented in

Table 3. Criteria and Alternatives Used in the Evaluation of Green Tourism Readiness.

Code	Element	Description
C1	Environmental sustainability & natural capital	Proxies like % of Natura 2000, biodiversity scores, eco-certification systems
C2	Rural tourism infrastructure	Tourism nights, rural accommodation density, accessibility
C3	Institutional support & governance	Existence and strength of national tourism/green transition strategies
C4	Green skills & VET readiness	Presence of green VET programs, CEDEFOP reporting, Erasmus+ project participation
A1	Slovenia	CEE EU country with strong green tourism governance
A2	Croatia	High tourism intensity, evolving sustainability frameworks
A3	Hungary	Mid-level performer with a growing green tourism focus
A4	Slovakia	Moderate infrastructure and governance capacity
A5	Romania	Improving the eco-tourism sector, weaker skill alignment
A6	Bulgaria	Potential for growth, currently underperforming in green VET

Sources: Cedefop (2023b); Mihalic (2017).

below.

Table 4. Rural Tourism and Sustainable Development Indicators in Central and Eastern European Countries.

Country	Rural Tourism Nights (% of Total)	Natura 2000 Coverage (% Land Area)	Key Sustainable Tourism Strategy/Policy	Policy Reference
Slovenia	28%	37%	“Slovenia Green Scheme” promotes eco-certification, sustainable mobility	Slovenian Tourism Strategy 2022–2030
Croatia	22%	36%	“Sustainable Tourism Development Strategy 2030” prioritizes green coastal and rural destinations	Ministry of Tourism
Poland	18%	20%	“Strategy for Responsible Development” includes rural tourism clusters and agrotourism promotion	Ministry of Development Funds and Regional Policy
Hungary	17%	21%	Focus on national parks and rural accommodation certification programs	Hungarian Tourism Agency
Czech Republic	19%	14%	“Sustainable Tourism 2030” integrates eco-labelling for rural accommodation	CzechTourism
Slovakia	16%	30%	“Tourism Development Strategy 2030” promotes green infrastructure in rural regions	Ministry of Transport and Construction

Source: Eurostat (2023); UNWTO (n.d.-a); OECD (2022). Note: Data on rural tourism nights represent estimates from national tourism boards or Eurostat rural accommodation statistics (latest available year, 2019–2022).

presents the share of rural tourism in total overnight stays and summarizes the main sustainable tourism strategies in each analyzed country. These data highlight the heterogeneity of rural tourism’s role across Central and Eastern Europe, reflecting both environmental assets and institutional frameworks.

As shown in Table 4, Slovenia and Croatia exhibit relatively higher shares of rural tourism and strong institutional commitments through comprehensive green certification and sustainable mobility programs. Incidentally, the strategies of Slovenia and Croatia integrate training on waste reduction, energy efficiency, and local sourcing—illustrating how professional training translates into measurable sustainability performance. In contrast, Poland and Hungary demonstrate growing but less formalized rural tourism initiatives. This differentiation supports our later interpretation of the AHP-BWM-TOPSIS results, where governance and environmental readiness emerge as decisive factors.

Step 2: AHP—Initial Weighting via Pairwise Comparisons

AHP is used as an initial diagnostic tool to elicit expert judgment and rank the importance of criteria. AHP structures the problem hierarchically. Experts provided pairwise comparisons of all four criteria using Saaty’s 1–9 scale (T. L. Saaty, 2008). For each criterion pair (i, j), a comparison matrix A = [a_ij] is formed (

Table 5. Pairwise Comparison Matrix for AHP Criteria.

	C1	C2	C3	C4	Weights
C1	a11	a12	a13	a14	w1
C2	1/a12	a22	a23	a24	w2
C3	1/a13	1/a23	a33	a34	w3
C4	1/a14	1/a24	1/a34	a44	w4

Source: R. W. Saaty (1987).

).

To calculate Consistency Index (CI) and Consistency Ratio (CR), multiply the original matrix by the weight vector, and then divide each result by the corresponding weight (gives λ_max), and compute the following Formula (1) (R. W. Saaty, 1987).

$$CR={\displaystyle \frac{CI}{RI}}, CI={\displaystyle \frac{{\mathsf{\lambda }}_{max}-n}{n-1}},$$

(1)

where λ_max is the principal eigenvalue, and RI is a random index (T. L. Saaty, 2008). The resulting comparison matrix was subjected to consistency testing (CR < 0.1), confirming the reliability of the inputs.

The derived weights indicate the priority order. These results will act as a reference baseline for the next model.

Step 3: BWM—Consistent Weight Optimization

To refine weight precision and improve consistency, the BWM was applied. Experts selected the best (C1) and worst (C4) criteria and rated their importance relative to others (Rezaei, 2015). This reduced the number of comparisons while enhancing consistency.

According to Rezaei (2015), experts select the best and worst criteria and then provide pairwise preference values using a scale from 1 (equal importance) to 9 (extreme importance) in two directions:

• $\mathrm{B}\mathrm{e}\mathrm{s}\mathrm{t}-\mathrm{t}\mathrm{o}-\mathrm{O}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{s}\left(\mathrm{B}\to \mathrm{j}\right):a_B=\left[a_{B1}, a_{B2}, a_{B3}, a_{B4}\right]$
• $\mathrm{O}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{s}-\mathrm{t}\mathrm{o}-\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{s}\mathrm{t}(\mathrm{j}\to \mathrm{W}):a_W=\left[a_{1W}, a_{2W}, a_{3W},a_{4W}\right]$

Then, experts assign preferences a_Bj, a_jW, and solve them using the following Formula (2) (Rezaei, 2015):

$$\begin{array}{c}min{\displaystyle \genfrac{}{}{0pt}{}{max}{j}}\left\{\left|{\displaystyle \frac{w_B}{w_j}}-a_{Bj}\right|,\left|{\displaystyle \frac{w_j}{w_W}}-a_{jW}\right|\right\},\\ such that {\displaystyle \sum _{j=1}^n}{w}_j=1,\\ w_j\ge 0, for all j\end{array}$$

(2)

We define the weight vector $w=\left[w_1{w}_2{w}_3{w}_4\right]$, and minimize the maximum absolute deviation ξ. In the BWM model, the objective is to minimize the maximum absolute deviation (ξ) between pairwise comparisons and derived weights (Rezaei, 2015). The optimization model is:

$$\begin{array}{c}\min \mathsf{\xi } \mathrm{subject} \mathrm{to}:\\ \left|{\displaystyle \frac{w_1}{w_1}}-a_{B1}\right|\le \xi \\ \left|{\displaystyle \frac{w_1}{w_2}}-a_{B2}\right|\le \xi \\ \left|{\displaystyle \frac{w_1}{w_3}}-a_{B3}\right|\le \xi \\ \left|{\displaystyle \frac{w_1}{w_4}}-a_{B4}\right|\le \xi \\ \left|{\displaystyle \frac{w_1}{w_4}}-a_{1W}\right|\le \xi \\ \left|{\displaystyle \frac{w_2}{w_4}}-a_{2W}\right|\le \xi \\ \left|{\displaystyle \frac{w_3}{w_4}}-a_{3W}\right|\le \xi \\ \left|{\displaystyle \frac{w_4}{w_4}}-a_{4W}\right|\le \xi \\ \sum w_j=1, w_j\ge 0\end{array}$$

Using a linear programming formulation, optimal weights were calculated. The consistency ratio (CR*) was within acceptable bounds, confirming the coherence of the judgments. Notably, BWM yielded slightly higher importance for green skills (C4) than AHP, reflecting its growing relevance in green tourism.

These weights were used in the next phase to evaluate national performance using real or proxy data.

Step 4: TOPSIS—Country Ranking Based on Weighted Performance

The TOPSIS method ranks countries by comparing their performance to an ideal solution. A decision matrix was constructed using a mix of quantitative and qualitative indicators aligned with the four criteria. In other words, countries are evaluated across selected criteria using normalized values x_ij, where i is a country and j is a criterion. Similar to Behzadian et al. (2012) and Hwang and Yoon (1981), we use the following equation (Equation (3)) for benefit criteria:

$$r_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{{\sum }_{i=1}^m{x}_{ij}^2}}}$$

(3)

The matrix was normalized, and the weights from BWM (w_j) were applied, $v_j=w_j\ast r_{ij}$. Ideal and anti-ideal solutions were identified for each criterion. Since all criteria are beneficial (Behzadian et al., 2012; Hwang & Yoon, 1981), we define:

$$A^{+}=\left\{max{v}_{ij}\right\}, A^{-}=\left\{min{v}_{ij}\right\}$$

Each country’s Euclidean distance from the ideal and anti-ideal was calculated, followed by the closeness coefficient (CC), which forms the basis of the final ranking.

$$\begin{array}{c}{D}_i^{-}= \sqrt{{\displaystyle \sum _j}{\left(v_{ij}-A_j^{+}\right)}^2}, D_i^{-}= \sqrt{{\displaystyle \sum _j}{\left(v_{ij}-A_j^{-}\right)}^2}\\ {CC}_i={\displaystyle \frac{D_j^{-}}{D_j^{+}+D_j^{-}}}\end{array}$$

Countries are ranked by CC_i, where higher values indicate stronger green tourism readiness.

In this section, we have clearly and unambiguously defined the scope of the steps taken. We clarified the context of the decision within the framework of linking green skills and institutional support in tourism. Second, we have increased transparency in the approach through a concise overview of the model inputs, which has improved confidence in our research design. Lastly, we have linked the methodology and results in a way that clarifies the transition from the abstract structure of the model to the concrete application in the real world, connecting the logic of the MCDM approach with the analysis at the country level.

The three dimensions defined above correspond to the criteria applied in the AHP-BWM-TOPSIS framework (C1 = Environmental Protection, C2 = Institutional Governance, C3 = Green Skills/VET System, C4 = Infrastructure and Innovation). This cross-link ensures conceptual and empirical consistency across all stages of the analysis.

In this study, the hybrid AHP-BWM-TOPSIS model is not applied to achieve collective social choice but to structure expert evaluation of complex readiness criteria under uncertainty. The purpose is analytical prioritization rather than aggregation of conflicting individual preferences. Therefore, Arrow’s conditions on social choice are not directly applicable.

3.2. Data Transformation and Scaling of Qualitative Indicators

For the application of the TOPSIS method, all criteria must be expressed in quantitative form to allow for normalization and distance-based calculations. While C1 (Green Investment Index) and C2 (Tourism Sustainability Units) were available in numerical form from Eurostat and World Bank databases, criteria C3 (Ecotourism Strategy Index) and C4 (Green Skills VET Presence) were qualitative. To address this, ordinal ranking scales were developed based on publicly available policy documents, national strategies, and CEDEFOP country reports (Cedefop, 2023a).

For C3, countries were rated from 1 (low) to 5 (high) according to the depth and clarity of national ecotourism strategies, investment priorities, and implementation mechanisms. For C4, a 3-point ordinal scale was applied: 1 = full presence of green skills in VET curricula; 2 = partial inclusion; 3 = absence. These rankings are presented in

Table 6. Scaled Table: Country-Specific Qualitative Criteria (Transformed).

Country	C3 *: Ecotourism Strategy Index (1–5)	C4 *: Green Skills VET Presence (1–3)
Slovenia	5 = high (national green tourism roadmap)	1 = yes (Green Skills Roadmap)
Croatia	4 = medium-high (coastal sustainability integration)	1 = yes (VET includes WBL and curriculum integration)
Hungary	3 = medium (rural tourism frameworks)	2 = partially (still integrating green modules)
Slovakia	2 = medium-low (domestic tourism focus)	2 = partially (ad hoc VET adjustments)
Romania	1 = low (limited targeted eco-tourism policy)	3 = no (no explicit green skills roadmap)
Bulgaria	1 = low (emerging rural tourism strategies)	3 = no (green skills weak in VET)

Source: Authors’ Qualitative Proxies based on documented national tourism and education strategies (2020–2024), Cedefop (2023a) country profiles, and World Bank Green Growth Diagnostics. Note: * C3 and C4 are qualitative proxies. These reflect the presence or strength of institutional frameworks and VET green skill initiatives.

. This transformation is consistent with practices in multi-criteria decision-making literature (T. L. Saaty, 2008; Rezaei, 2015; Behzadian et al., 2012) and allows for a unified decision matrix suitable for TOPSIS analysis.

The transformation of qualitative indicators (C3 and C4) into ordinal scales followed a transparent and systematic process (Rezaei, 2015; Zavadskas et al., 2014; Pamučar et al., 2018). Criteria for assigning scores were derived from Cedefop, ETF, and national policy reports. An expert panel of eight members independently evaluated each country against these criteria (O’Hagan et al., 2006; Cooke, 1991). To ensure reliability, individual scores were cross-checked and aggregated, with inter-rater agreement assessed using Kendall’s W (0.71), indicating substantial consistency. In addition, Cohen’s κ (0.78) was calculated for selected binary judgments to further confirm reliability (Legendre, 2005; McHugh, 2012). While this procedure minimizes subjectivity, we acknowledge that alternative approaches such as fuzzy logic or Delphi techniques may enhance robustness in future studies.

Transforming qualitative data and quantifying it is an acceptable procedure in MCDM as long as the scales are applied consistently across countries. At the same time, ordinal values (ranks) can reflect the relative strength of performance. This procedure allows us to normalize these values in the TOPSIS matrix and calculate the relative closeness to the ideal.

4. Results

The combined MCDM process revealed clear differences in green tourism readiness across the six countries.

4.1. AHP Results—Expert Prioritization of Green Tourism Criteria

The AHP model was applied to derive initial expert-based weights for the four decision criteria. Based on pairwise comparisons, environmental sustainability and natural capital (C1) emerged as the most important factor (46.0%), followed by institutional support and governance (C3) with 27.2%. Rural tourism infrastructure (C2) was weighted at 18.0%, while green skills and vocational training readiness (C4) received the lowest weight at 8.8%.

Table 7. Weights of Criteria Derived from the AHP Model.

	C1	C2	C3	C4	Weights
C1	1	3	2	4	46.0%
C2	0.33	1	0.50	3	18.0%
C3	0.50	2	1	3	27.2%
C4	0.25	0.33	0.33	1	8.8%

Source: Authors’ calculation.

summarizes the weights in decision process.

The consistency ratio (CR = 0.03) indicates that the expert judgments were reliable and within the acceptable threshold (CR < 0.1). These results align with findings in the literature that environmental quality and governance are primary levers in green tourism development, particularly in ecologically rich and regulated regions (T. L. Saaty, 2008; Mihalic, 2017).

4.2. BWM Results—Optimized Expert Weighting

To refine and optimize weight consistency, the Best-Worst Method (BWM) was applied. Experts identified C1 as the best and C4 as the worst criterion. The resulting Best-to-Others and Others-to-Worst vectors were:

• $\mathrm{B}\mathrm{e}\mathrm{s}\mathrm{t}-\mathrm{t}\mathrm{o}-\mathrm{O}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{s}\left(\mathrm{B}\to \mathrm{j}\right):a_B=\left[1, 3, 2, 5\right]$
• $\mathrm{O}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{s}-\mathrm{t}\mathrm{o}-\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{s}\mathrm{t} (\mathrm{j}\to \mathrm{W}):a_W=\left[5, 4, 3, 1\right]$

The computed weights, shown in Table 5, confirm C1 as the primary factor (46.0%) and indicate a slightly higher weight for institutional support (C3 = 28.1%) compared to AHP. Green skills (C4) decreased slightly to 7.2%, suggesting a consensus that skills readiness, while important, is not yet prioritized in strategic frameworks. To improve consistency and refine expert weighting, the Best–Worst Method (BWM) was applied. The test shows optimized weights and maximum absolute deviation (ξ), indicating excellent internal consistency (ξ = 0.016).

The BWM offers improved consistency over AHP and reduces the cognitive load for experts, aligning with Rezaei’s (2015) findings on robustness and stability in MCDM frameworks. These results reinforce the AHP findings and demonstrate model robustness (ρ = 0.94 correlation between AHP and BWM weights).

4.3. TOPSIS Results—Country-Level Ranking of Green Tourism Readiness

The final ranking of countries was produced using the TOPSIS method based on the BWM-derived weights: The following weightings for the criteria were obtained: C1 = 0.46, C2 = 0.187, C3 = 0.281, and C4 = 0.072.

The decision matrix was constructed using actual indicators (e.g., Natura 2000 coverage, tourism nights, qualitative governance, and VET readiness scores). After normalization and weighted aggregation, the closeness coefficients were computed, and countries were ranked accordingly (

Table 8. Country-Specific Data for TOPSIS Decision Matrix.

B-W Model	Benefit	Benefit	Benefit	Benefit	Rank
Weightage	0.46	0.187	0.281	0.07
Country	C1 (Index)	C2 (# Units)	C3 (Score)	C4 (Score)
Slovenia	40.5	16.1	5	1	2
Croatia	38.2	92.34	4	1	1
Hungary	22.2	30.49	3	2	5
Slovakia	37.3	16.1	2	2	4
Romania	23.5	29.21	1	3	6
Bulgaria	41	26.87	1	3	3

Source: Authors’ calculation. Note: C1: Natura 2000 Coverage (% land)—This indicator measures the surface of terrestrial protected areas; C2: Nights spent (million, 2023)—Nights spent at tourist accommodation establishments [code: tour_occ_ninat]; C3: Eco-Tourism Strategy Index—(Qualitative Proxies) is based on documented national strategies and Cedefop data; C4: Green Skills VET Presence—(Qualitative Proxies) is based on documented national strategies and Cedefop data.

).

Based on the above calculations and evaluation of all weights, using three MCDM models, we present the final results of the country rankings in

Table 9. TOPSIS Results—Final Country-Level Ranking.

Rank	Country	Closeness Score (CC)	Interpretation
1	Croatia	Highest CC (normalized)	Strong institutional alignment and tourism demand
2	Slovenia	Slightly below Croatia	High environmental index, governance, and skills readiness
3	Bulgaria	Moderate	High C1 value offsets weaker governance
4	Slovakia	Lower mid-range	Average scores across all dimensions
5	Hungary	Second-lowest	Weak environmental and institutional performance
6	Romania	Lowest CC	Limited green skills and governance support

Source: Authors’ calculation.

. The rankings indicate the degree of commitment and capacity of the observed countries towards creating a favorable environment for green tourism and rural development.

These results reveal a multi-dimensional divide in green tourism readiness across the region. Croatia and Slovenia lead due to their strong tourism sectors, structured VET systems, and policy frameworks. Romania, despite a sizable tourism potential, lacks institutional coordination and green skills integration, which undermines its overall position.

4.4. Robustness Tests and Limitations

To ensure the stability and reliability of the hybrid BWM–TOPSIS results, we conducted three robustness checks: (1) alternative weighting scenario, (2) sensitivity to normalization method, and (3) criteria exclusion analysis. (These robustness checks are summarized in

Table 10. Summary of Robustness Tests for BWM-TOPSIS.

Test Type	Description	Main Findings	Implications
Alternative Weighting Scenario	TOPSIS re-run using AHP-derived weights instead of BWM	Minor rank shifts (e.g., Slovenia and Croatia may reverse), but top and bottom positions remain stable	Confirms the model’s stability under different expert weighting approaches
Normalization Sensitivity	Replaced vector normalization with linear min–max normalization	Closeness coefficients changed slightly (±0.02–0.03), but the ranking of the structure remain unchanged	Validates the robustness of rankings to different data transformation methods
Criteria Exclusion Test (C4)	TOPSIS re-run excluding “green skills” criterion	Rankings of Croatia and Slovenia remained stable; Romania’s rank changed slightly	Confirms that the model is not dominated by a single criterion, but C4 influences lower performers
Alternative Weighting Scenario	TOPSIS re-run using AHP-derived weights instead of BWM	Minor rank shifts (e.g., Slovenia and Croatia may reverse), but top and bottom positions remain stable	Confirms the model’s stability under different expert weighting approaches

Source: Authors’ calculation.

below.)

In the first test, we re-estimated country rankings using AHP-derived weights instead of BWM. Although some minor changes were observed, such as a shift in the relative ranking of Hungary and Slovakia, the overall ranking structure remained intact, with Croatia and Slovenia still in the top two positions, and Hungary and Romania remaining at the bottom. This supports the claim that TOPSIS is robust to expert weighting variations, especially when initial judgments are consistent (Rezaei, 2015).

Second, we replaced the vector normalization used in the baseline model with linear min–max normalization. The resulting closeness coefficients differed by no more than ±0.03, and no change in rank order was observed (confirming methodological robustness). This reinforces the finding that the TOPSIS method remains stable under different data transformation approaches, consistent with Govindan et al. (2015).

Lastly, we excluded the C4 (Green Skills) criterion to simulate the effect of partial or unreliable data. However, Romania and Bulgaria’s scores fluctuated slightly, suggesting some sensitivity to skills-based readiness. This indicates that no single criterion disproportionately drives the overall ranking, confirming the additive resilience of the decision framework.

Table 11. Cross-Model Correlation and Robustness Tests.

Model Comparison	Spearman’s ρ	Kendall’s τ	Interpretation
AHP–BWM	0.94	0.83	Highly consistent
AHP–TOPSIS	0.92	0.81	Consistent
BWM–TOPSIS	0.95	0.85	Consistent

Source: Authors’ calculations based on model outputs.

summarizes robustness checks, including rank correlations among AHP, BWM, and TOPSIS results.

These high rank correlations confirm strong methodological coherence and validate the hybrid MCDM approach.

Together, these tests indicate that the BWM-TOPSIS hybrid model provides methodologically stable and policy-relevant rankings even when expert weightings vary, data normalization methods change, or when individual indicators are excluded. This reinforces the reliability of the model as a comparative evaluation tool for green tourism readiness in CEE EU countries.

A key limitation of this study lies in the transformation of qualitative criteria (C3 and C4) into ordinal numerical values. While grounded in documentary evidence and consistent application across countries, the resulting scales are inherently subjective and may not capture the full complexity of policy implementation or outcomes. This introduces potential bias or imprecision into the TOPSIS results. Furthermore, the linear distances used in the TOPSIS methodology may not fully reflect the ordinal nature of these transformed indicators. Future research could apply fuzzy logic or expert-based Delphi techniques to strengthen robustness (Chen & Hwang, 1992; Arasha et al., 2015).

5. Discussion

The MCDM-based evaluation produced results that speak directly to our guiding research questions (RQ1-RQ3) and validate our hypotheses. The analysis confirms that environmental sustainability (C1) and institutional support (C3) are the most influential factors in determining green tourism readiness in CEE countries. This supports H1, consistent with Mihalic (2017), who emphasized that ecological capital and stable tourism governance are essential for building long-term resilience in tourism economies.

Notably, Croatia and Slovenia consistently ranked highest, benefiting from a combination of biodiversity protection (high Natura 2000 coverage), strong tourism infrastructure, and active public policies promoting sustainable travel. These findings align with the European Commission’s position that green transformation is most successful when embedded in coherent governance (European Commission, 2024b). While many EU member states have introduced VET programs referencing sustainability and environmental topics, program presence alone does not ensure effectiveness. The impact depends on curriculum quality, pedagogical methods, and the strength of employer partnerships in integrating green competencies into real tourism operations. Thus, the green skills indicator captures system readiness rather than guaranteed outcomes.

Although green skills and VET integration (C4) received the lowest weights in both AHP and BWM models, it played a non-negligible role in improving rankings for countries with moderate to high VET development. Vocational education and training (VET) systems provide the foundation for equipping workers with the competences required in green transitions, particularly in rural tourism where eco-certifications, resource management, and digital-environmental skills are increasingly demanded (Cedefop, 2023a). To strengthen this dimension, we incorporated additional indicators, including enrollment in VET programs with environmental modules, CEDEFOP statistics on green job creation, and employability measures linked to sustainability-oriented training. Although quantitative evidence remains fragmented across Central and Eastern Europe, these additions highlight that green skills act as a critical enabling factor, supporting the absorptive capacity of institutions and the long-term competitiveness of tourism destinations. This suggests that while governance and environmental readiness drive immediate performance, sustained green tourism development ultimately depends on investments in skill systems (OECD, 2022). However, the statistical weight of the VET dimension in our model appears modest when compared with governance and competitiveness variables. For example, Slovenia’s investments in greening vocational education and its involvement in Erasmus+ green tourism projects likely contributed to its high performance. This partially supports H2, showing that while skills alone do not drive rankings, they can reinforce readiness when combined with environmental and institutional strengths (European Commission, 2023; Cedefop, 2022; Cotterell et al., 2019).

The unexpectedly high score for Bulgaria (3rd place) may be explained by its strong environmental baseline (C1), including forest coverage and biodiversity reserves. This highlights a compensatory effect where countries with underdeveloped skills systems or weaker governance can still perform relatively well based on natural capital. However, this raises sustainability concerns: natural endowments without human capital and policy support may lead to unsustainable or extractive tourism development (Marín-González et al., 2022; UN, 2022).

In contrast, Hungary and Romania ranked lowest, indicating weaker alignment across all four dimensions. Romania’s poor score, despite significant tourism potential, is especially notable. It highlights systemic issues such as fragmented governance, limited green VET reform, and low adoption of sustainability standards, echoing ETF (2023) findings on green skills gaps in Eastern Europe.

These differences also underscore the value of multi-criteria frameworks: countries cannot rely solely on environmental or economic assets. Instead, a balanced portfolio of policy, education, infrastructure, and ecological stewardship is needed for green tourism transformation (Espiner et al., 2017).

On a methodological level (RQ3), the robustness tests confirmed that rankings remained stable across variations in weights and normalization techniques, validating the reliability of the model. The hybrid approach—starting with AHP, refining with BWM, and finalizing with TOPSIS—demonstrated analytical flexibility while maintaining transparency. This triangulated strategy is particularly useful in policy domains where data may be scarce or unevenly reported, such as rural tourism (Govindan & Bouzon, 2018). This observation supports our model results, where the “green skills” component carries lower statistical weight but remains essential as a long-term enabler of sustainable competitiveness in rural tourism.

This finding may partly reflect the structural characteristics of the tourism sector, particularly in rural contexts where micro and family-run enterprises dominate. Such enterprises often lack the scale and resources to engage systematically in formal vocational education and training (VET) programs. As noted by Cedefop (2023b) and the European Training Foundation (ETF, 2023), green skill development in tourism frequently takes place through informal and experiential learning rather than through institutionalized training pathways. While this limits the measurable impact of formal VET participation, it highlights the importance of community-based learning, peer exchange, and local initiatives for transferring sustainable practices within small tourism businesses.

Beyond formal and informal skill development within enterprises, community education and awareness play a critical role in shaping the sustainability of rural tourism. Informed and engaged local communities can encourage responsible tourist behavior, protect natural assets, and promote the cultural authenticity that defines rural destinations. Experiences from Slovenia and Croatia demonstrate how community-based programs—such as the “Slovenia Green” certification scheme and Croatia’s “Eco-Dom” local awareness initiatives—foster collaboration between residents, municipalities, and small tourism providers. These programs combine environmental education with destination branding, strengthening both social cohesion and environmental stewardship. Integrating such community-focused learning into broader rural tourism strategies can therefore enhance the long-term impact of institutional and policy interventions. This social dimension complements our quantitative findings, underscoring that institutional quality and competitiveness must be supported by grassroots awareness and education for sustainable tourism outcomes.

Although we applied a hybrid AHP-BWM-TOPSIS model, it should be stressed that each of the three constituent methods is a simple and transparent MCDM technique. Their combination therefore does not substantially increase complexity but allows the strengths of each method to offset the weaknesses of the others. In particular, AHP supports intuitive pairwise judgments, BWM reduces inconsistency, and TOPSIS provides a clear ranking. This triangulation improves robustness without sacrificing interpretability, aligning with recommendations in the MCDM literature. It is worth noting that while more complex methods such as ELECTRE or PROMETHEE are available (Govindan & Jepsen, 2016), we deliberately chose AHP, BWM, and TOPSIS because they are simpler, transparent, and widely applied in sustainability studies. Their combination in a hybrid framework increases robustness without adding unnecessary methodological complexity. The hybridization of AHP, BWM, and TOPSIS builds on well-established MCDM practices. As Zavadskas et al. (2015) emphasize, these methods are among the most widely applied in sustainability contexts precisely because of their transparency and adaptability to both quantitative and qualitative inputs.

From a policy perspective, these findings suggest differentiated strategies:

• Countries like Slovenia and Croatia can serve as regional learning hubs, sharing policy tools and capacity-building strategies.
• Bulgaria and Slovakia may benefit from technical assistance and VET innovation to bridge skills gaps.
• Romania and Hungary should consider comprehensive policy reform that aligns environmental conservation, education, and tourism strategy under a unified framework.

This study also contributes to the limited empirical literature on green skills in tourism, where most contributions remain conceptual or policy-driven (e.g., OECD, 2022; ETF, 2023). By integrating real and proxy indicators into a comparative MCDM framework, we provide a replicable and policy-relevant methodology for regional and local tourism planning.

6. Conclusions

This study evaluated green tourism readiness in six EU member states from Central and Eastern Europe—Slovenia, Croatia, Bulgaria, Slovakia, Hungary, and Romania—through the application of a hybrid multi-criteria decision-making (MCDM) methodology. By combining the Analytic Hierarchy Process (AHP), Best-Worst Method (BWM), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the analysis was able to capture expert insights, reduce weighting inconsistencies, and produce a nuanced ranking of national performance across environmental, institutional, infrastructure, and green skills-related dimensions.

The results confirmed that environmental sustainability and institutional governance are the main factors influencing a country’s readiness for green tourism transformation. The findings confirm that H1 is supported, as institutional and environmental readiness emerged as the most influential factors in shaping green tourism potential across the analyzed countries. By contrast, H2 is only partially supported, since the role of green skills, while important, was assigned lower weights and appears less immediately impactful than governance and environmental dimensions. This supports previous findings in the sustainable tourism literature, which emphasize the strategic importance of biodiversity, protected landscapes, and regulatory alignment in greening national tourism sectors. Countries like Slovenia and Croatia stood out as regional leaders, consistently performing well across all four criteria. Their success reflects integrated policies, effective institutional structures, and, to some extent, organized vocational education systems focused on sustainability and rural tourism.

Importantly, the analysis showed that although green skills and vocational education and training (VET) received the lowest weight in both AHP and BWM, they remain important enablers, especially for higher-ranked countries. Slovenia’s ranking, for example, is partly supported by its investment in greening VET curricula and promoting sustainable rural entrepreneurship. This indicates that while green skills might not yet be the main drivers of green tourism readiness, they play a supportive role when combined with strong environmental and governance systems. Countries that actively incorporate these skills into tourism education policies are more likely to maintain progress in the long run.

Bulgaria’s position—ranking third despite weaker institutional and skills frameworks—points to the compensatory influence of natural capital. However, this raises concerns about unbalanced readiness. Natural endowments without corresponding human capital and governance support may expose countries to risks of overexploitation, low resilience, and uncoordinated development. Similarly, Romania and Hungary, both endowed with rich cultural and natural heritage, ranked lowest due to persistent fragmentation in governance, limited VET reform, and slow adoption of green tourism standards.

Beyond empirical insights, the study makes several original contributions to the literature on sustainable tourism and green transition in vocational education. First, the methodological contribution lies in the development of a hybrid MCDM framework. The use of AHP to capture initial expert input, BWM to optimize weight precision and consistency, and TOPSIS to rank alternatives enables a transparent yet analytically rigorous evaluation. Such hybridization of methods enhances the robustness of the results and can be replicated in other contexts where data is mixed or where expert knowledge must complement official statistics.

Second, the research contributes to an underexplored empirical domain. Much of the existing literature on green tourism and green skills remains conceptual or policy-driven. By building a structured evaluation matrix based on measurable indicators and actual performance data, alongside expert-derived weights, this study bridges the gap between theory and evidence. The inclusion of green skills readiness as an evaluated criterion is also a notable innovation, contributing to current efforts by CEDEFOP, the European Commission, and UNESCO to track how education systems respond to sustainability imperatives in specific sectors like tourism.

Third, the regional focus provides valuable insights into the different paths CEE countries take in the EU’s green transition. While these countries share historical backgrounds and transitional economic structures, their institutional capacities, environmental policies, and VET integration levels differ significantly. This diversity underscores the need for tailored strategies instead of one-size-fits-all solutions.

From a policy perspective, several clear action points emerge. Countries with weaker institutional coordination, such as Romania and Hungary, would benefit from aligning tourism, environmental, and education policies through inter-ministerial bodies or integrated sustainable tourism strategies. Enhancing rural VET systems and adding green skills to curricula is especially urgent, considering the reliance of many CEE regions on rural tourism development. In countries where ecological capital is relatively strong but institutional support is weaker, like Bulgaria, policy efforts should focus on ecosystem-based tourism planning, local capacity building, and green investment incentives to prevent extractive or unsustainable practices.

Another important implication involves the need to improve data systems for monitoring green tourism. While the current study uses a mix of actual and proxy indicators, a more systematic integration of sustainability-focused metrics into national and EU tourism statistics is crucial. This includes indicators like green-certified accommodations, tourism-related emissions, and green employment rates in rural areas. Better data granularity will support more informed planning and evaluation.

The hybrid MCDM model also showed methodological reliability, with robustness tests confirming the stability of rankings across various weighting methods and normalization techniques. This enhances the credibility of the findings and provides a reproducible tool for other researchers and policymakers. As a practical decision-support tool, the model offers a structured and transparent way to benchmark readiness, allocate funding, and track progress toward green tourism goals.

Overall, this study contributes to tourism economics, regional development, and sustainability economics by integrating institutional and competitiveness indicators within a resource-allocation framework. The hybrid MCDM approach demonstrates how economic structures and governance quality shape readiness for sustainable tourism growth—offering evidence relevant for economists, policymakers, and regional planners concerned with efficient investment in rural and green sectors.

This study has several limitations that should be acknowledged. First, the availability and comparability of green skills data across Central and Eastern European countries remains limited. Although we relied on CEDEFOP, Eurostat, and World Bank datasets, some indicators were incomplete, requiring the use of expert judgment and qualitative coding. Second, the expert elicitation process, while systematic, still involves subjectivity; although measures such as inter-rater reliability were applied, future studies may benefit from broader panels, Delphi techniques, or fuzzy extensions. Third, the hybrid MCDM model, while robust, may create an impression of analytical precision that is partly constrained by the quality of input data. Fourth, while VET and green skill policies form part of institutional readiness, outcome effectiveness remains uneven and context-dependent.

This study is limited by the subjectivity of expert scoring, sample scope, and partial data availability. Future research could expand on this study by adding a temporal element, monitoring changes in readiness over time as countries adopt green tourism and skills policies. Applying the model to a time-series would be especially helpful for assessing the influence of EU funding initiatives such as the Green Deal, Erasmus+, and the Recovery and Resilience Facility. Moreover, subsequent studies could explore alternative MCDM techniques like fuzzy logic, Delphi panels, or stakeholder-weighted models to enhance perspectives and minimize expert bias. Incorporating spatial data through Geographic Information Systems (GIS) would also increase detail and enable subnational analysis, pinpointing tourism readiness hotspots or regional differences.

In conclusion, the green transformation of tourism in Europe cannot depend solely on environmental protection. It requires a multidimensional, coordinated approach that includes strengthening institutions, promoting inclusive rural development, and establishing education systems capable of preparing a workforce for sustainable practices. This study contributes to that goal by providing an empirically grounded, methodologically robust, and policy-relevant framework that can guide decision-makers, inspire further academic research, and support regional cooperation for a greener tourism future.