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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

  This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

Events and Tourism Review                                     
 
December 2025                                                                                                                                          Volume 8 No. 1      

   

 

Aggregating Travel Destination Sustainability Rankings  

Using Principal Component Analysis (PCA) 

 

 
Yen-Soon Kim 
Natalie Hudson 

Rhonda Montgomery 
Ashok Singh 
Soyeon Jung 
 

University of Nevada, Las Vegas 

Correspondence: yen-soon.kim@unlv.edu (Y. Kim) 

 

 
 

 

 

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Recommended Citation 

Kim, Y., Hudson, N., Montgomery, R., Singh, A., and Jung, S. (2025). Aggregating Travel 
Destination Sustainability Rankings Using Principal Component Analysis (PCA). Events and 
Tourism Review, 8(1), 15-35. 

 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
16 

Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

 
 

 

 

Abstract 

 

Tourism destinations are ranked by multiple sustainability indices that differ in scope and 

weighting, producing inconsistent standings that hinder benchmarking and management. We 

propose a transparent aggregation of Travel Destination Sustainability Indices (TDSIs) using 

principal component analysis (PCA). Using six published indices for 136 destinations, we 

standardize ranks and apply PCA to the correlation matrix. The first two components explain 91% 

of total variance: PC1 captures a common efficiency/competitiveness dimension, whereas PC2 

reflects methodological emphases, clarifying why prior rankings diverge. We construct a variance-

weighted composite from the retained components to yield a reproducible meta-ranking with 

interpretable loadings. Results identify stable leaders and reveal meaningful re-orderings where 

sub-dimensions differ. Practically, the composite enables managers to prioritize interventions, 

allocate resources toward high-leverage indicators, and communicate performance with a single 

auditable score that can be recalibrated as benchmarks evolve. The approach is software-light, 

scalable, and readily updated with new indicators and years. 

 

Keywords: Principal Component Analysis (PCA); Travel Destination Sustainability; Tourism 

Benchmarking; Composite Index; Sustainable Tourism Management 
 

 

Introduction 

The onset of the First Industrial Revolution in 1765 marked a transformative era 

characterized by significant developments such as the extensive extraction of coal, the advent of the 

steam engine, and mechanization of various industries. The Second Industrial Revolution, 

commencing in 1870, introduced new sources of energy including electricity, gas, and oil, and was 

notable for the emergence of groundbreaking technologies, particularly automobiles and airplanes. 

Subsequently, the Third Industrial Revolution in 1969 ushered in the era of nuclear energy. 

Currently, we are witnessing the Fourth Industrial Revolution, which is distinguished by the 

pervasive influence of the Internet and large web-based corporations (iED Team, 2019). To clarify 

the research gap, we explicitly define the study’s problem as the lack of a transparent, endogenously 

weighted method to synthesize heterogeneous destination sustainability rankings into a reproducible 

meta‑ranking suitable for policy and management. 

Amidst these technological advancements, the concept of sustainability emerged during the 

Second Industrial Revolution (Popular Mechanics, 1912). This period, now recognized as a pivotal 

juncture in history, gave rise to growing concerns about global warming and its profound 

implications for the planet. The concept of sustainability evolved as a response to the increasing 

awareness of the finite nature of natural resources and the environmental impact of industrial 

activities. We emphasize the study’s significance by arguing that a common, auditable signal 

extracted from multiple indices reduces noise for decision makers and enables defensible 

prioritization, progress tracking, and cross‑destination communication. 

Early popular media explicitly linked fossil-fuel combustion to greenhouse warming. A 

widely circulated 1912 caption explained that burning ~2 billion tons of coal annually would add ~7 

billion tons of CO₂, “making the air a more effective blanket for the earth,” with effects 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

“considerable in a few centuries” (Popular Mechanics, 1912). The same text appeared as “Coal 

Consumption Affecting Climate” in Australian and New Zealand newspapers weeks later, indicating 

transnational diffusion of this understanding (Coal Consumption Affecting Climate, 1912a, 1912b) 

As the discourse on sustainability has matured, it has expanded to encompass a holistic 

approach that integrates environmental, economic, and social dimensions (Scoones, 2016). This 

tripartite approach aims to achieve a balance between development and the health of our planetary 

ecosystems. The challenges posed by global warming and environmental degradation have 

intensified the urgency for sustainable practices and have catalyzed a global movement towards 

more responsible stewardship of the Earth’s resources. 

Tourism destinations are increasingly evaluated by multiple sustainability indices that differ 

in scope, indicator selection, and weighting logic. As a result, the same destination can occupy 

markedly different positions across indices, obscuring performance signals for managers and 

policymakers. This methodological fragmentation complicates benchmarking, impedes longitudinal 

monitoring, and can undermine stakeholder trust in sustainability claims. The research problem 

addressed here is the lack of a transparent, data‑driven method to synthesize heterogeneous 

destination sustainability rankings into a single, interpretable composite that preserves information 

contained across indices. 

Prior studies have advanced destination sustainability measurement using composite 

indicators, efficiency‑based approaches, or index‑specific frameworks; however, limited work has 

focused on aggregating multiple published rankings into a coherent, reproducible meta‑ranking that 

explicitly accounts for the shared variance among indices. In particular, the field lacks an open, 

endogenously weighted method that (i) reduces dimensionality, (ii) avoids purely subjective 

weighting, and (iii) is easy to implement with standard statistical software. 

We introduce and illustrate a principal component analysis (PCA) aggregation procedure 

that treats published sustainability rankings as observed manifestations of an underlying latent 

construct—destination sustainability. The proposed method yields a composite ranking by forming a 

weighted sum of the first principal components, where weights equal the proportion of variance 

explained by each retained component. Contributions are threefold: (1) a transparent and replicable 

aggregation procedure; (2) an empirical meta‑ranking of 136 destinations based on six widely cited 

indices; and (3) managerial guidance on how such a composite can inform benchmarking and 

resource allocation. 

Literature Review 

Sustainability and Destination Measurement  

Extending prior evidence that composite rankings are highly sensitive to indicator selection 

and weights, we frame sustainability as a latent construct only partially observed by existing indices; 

accordingly, we apply PCA to recover their common signal and reduce reliance on subjective 

weighting. 

Sustainability research emphasizes balancing environmental, economic, and social 

dimensions and has motivated destination‑level indicators and benchmarking frameworks (Torres-

Delgado & Saarinen, 2014; Punzo et al., 2022). Within tourism, scholars have proposed holistic 

measurement systems and composite indices to support policy and management decisions (Pérez et 

al., 2013; Giambona et al., 2024). Several studies deploy data‑reduction or weighting strategies—

PCA, regression‑based scoring, and entropy or non‑compensatory schemes—to consolidate 

multidimensional information (Pérez et al., 2013; Lozano‑Oyola et al., 2019; Punzo et al., 2022), 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

while others critique the sensitivity of composite rankings to indicator selection and weighting rules 

and call for transparent, auditable methodology (Greco et al., 2019; Kelemen et al., 2024; 

Kármán‑Tamus & Gárdos, 2025). We further explain how the cited works guided our objectives: 

dimensionality reduction without ad‑hoc weights, retention of shared information across indices, 

and generation of a reproducible meta‑ranking that can be audited and updated as methodologies 

evolve. 

Work linking sustainability to destination competitiveness highlights both conceptual 

alignment and empirical challenges: sustainability indicators covary with broader competitiveness 

measures, and methodological choices (e.g., efficiency‑frontier models vs. composite indices) can 

change destination standings (Cracolici & Nijkamp, 2009; Rodríguez‑Díaz et al., 2020; Assaf & 

Josiassen, 2016; González‑Rodríguez et al., 2023). Recent critiques of market‑oriented 

benchmarking urge careful interpretation of rankings and triangulation across measures when 

communicating results to stakeholders (Miller & Crabolu, 2023). 

Building on this body of work, we treat six published indices as correlated indicators of a 

single latent sustainability construct and use PCA to extract the dominant common signal. Unlike 

equal‑weight averaging or judgement‑based multi‑criteria methods, PCA endogenously determines 

weights from the covariance structure, facilitating reproducibility and reducing 

Origins and Evolution of Sustainability Research 

The word sustainability, nachhaltigkeit in German, appeared in 1713 in Europe in a book 

written by German scientist and forester Hans Carl von Carlowitz, after which the practice of 

planting trees became a path to “sustained-yield forestry” among French and British foresters 

(Heinberg, 2010). In January 1972, a series of articles called “Blueprint for Survival,” laying out the 

facts of the global situation at the time regarding world population, resources, and environmental 

problems, were published in The Ecologist; the articles were contributed by more than 30 scientists. 

These articles later appeared as a British book Blueprint for Survival, and over 750,000 copies of 

this book were sold (Goldsmith et al., 1972). The ideas presented in Blueprint for Survival were not 

supported by all researchers at the time, as can be inferred from two letters which appeared in the 

Correspondence section of the magazine Nature (Nature, 1972). The term “sustainability” later 

appeared (Kidd, 1992) in the United States in 1974, in a United Nations document in 1978, and in a 

Meeting of G7 countries held in 1989 in Paris, France (G7 Summit, 1989). 

Principal Components Analysis and Tourism 

Corral, Hernández, Navarro Ibáñez, and Ceballos collaborated with Spanish authorities and 

tourism related businesses and organizations to develop a strategy for renovating mature tourist 

destinations (Corral et al., 2016). Muresan, Oroian, Harun, Arion, Porutiu, Chiciudean, Todea, and 

Lile (Muresan et al., 2016) used 22 measures of residents’ perceptions toward tourism, and eight 

measures of support for tourism; data was collected from the Nord-Vest region in Romania and 

Principal Components Analysis (PCA) of data yielded four perception components and two support 

components. Harun, Chiciudean, Sirwan, Arion, and Muresan (Harun et al., 2018) collected 320 

responses from a survey of residents of Sulaimani and Halabja Governorates in the northern region 

of Iraq and used PCA for data analysis; increase in local pollution emerged as a negative aspect of 

tourism, but strong support for the development of tourism was also found.  

Garrigos-Simon, Narangajavana-Kaosiri, and Lengua-Lengua (Garrigos-Simon et al., 2018) 

conducted a bibliometric investigation of research in Tourism and Sustainability (TS) to identify 

research patterns in TS and presented results visually. Pulido-Fernández and López-Sánchez used 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

the method of logistic regression on data collected from 1,118 responses of Western Costa del Sol 

(Andalusia, Spain) visitors and determined that the tourists with knowledge of sustainability were 

willing to pay to visit destinations that were sustainable but were not willing to pay for the increased 

cost of sustainable tourism (Pulido-Fernández & López-Sánchez, 2016). Sun, Wandelt and Zhang 

(2022) considered the impact of Covid-19 on global air traffic based on approximately 200 research 

publications on this topic (Sun et al., 2022). Palacios-Florencio, Santos-Roldán, Berbel-Pineda and 

Castillo-Canalejo (2021) show that one way to maintain the tourism industry in challenging times 

such as Covid-19 is the development of sustainable tourism (Palacios-Florencio et al., 2021). 

Deri, Chiti, and Ciuffoletti (Deri et al., 2023) called for understanding a cultural tourism 

destination well and argued for promoting slow tourism in order to preserve the distinguishing 

features of an inland tourist destination. Lyon (Lyon, 2023) suggested that approaches and 

perspectives must be integrated when developing the science of sustainability. Mastria, Vezzil, and 

De Cesarei (Mastria et al., 2023), after reviewing results from 40 publications, suggested that 

tourists’ preferences and sustainable choices were impacted by perceived value of green products 

and services. The emergence of sustainable wine tourism in the post COVID-19 years was 

considered by Santorinaios, Kosma, and Skalkos (Santorinaios et al., 2023); a statistical analysis of 

595 survey responses revealed that wine consumption and winery visitations had increased after 

COVID-19. Jiang, Cai, Chen, Zhang, Wang, Xie, and Yu (Jiang et al., 2022) applied spatiotemporal 

methods for assessment of sustainability of Shandong Province’s cultural heritage. 

Tourism Destination Sustainability Indices 

Cracolici, Cuffaro & Nijkamp (Cracolici et al., 2009) developed a statistical method for 

evaluating tourism sustainability. Castellani and Sala (Castellani & Sala, 2010) proposed a holistic 

method for evaluating sustainability by defining measures for evaluating effects of policies. 

The initial idea for the Scandinavian Destination Sustainability Index was born in 2010 in 

Gothenburg in a joint meeting of ICCA’s Scandinavian Chapter and MCI, leading to the first 

Scandinavian benchmark (Global Destination Sustainability Movement, 2017). The Global 

Destination Sustainability Index (GDS-Index) was subsequently created through an MoU among the 

ICCA Scandinavian Chapter, ICCA, IMEX, and MCI in 2015 (Global Destination Sustainability 

Movement, 2023, 2025). The GDS-Index reached 50 destinations by 2019 (Global Destination 

Sustainability Movement, 2023) and now covers around/over 100 audited destinations worldwide 

(Bordeaux Tourism & Conventions, 2024). In parallel, the GDS-Academy launched in June 2021, 

offering an open-enrolment GDS-ICCA-CityDNA Certificate in Regenerative Destination 

Management (ICCA, 2021; Global Destination Sustainability Movement, 2025; City Destinations 

Alliance, 2025). Tourism destinations increasingly use such destination sustainability indices to 

attract visitors and communicate their sustainability performance (Global Destination Sustainability 

Movement, 2025). 

Crouch (Crouch, 2011) used an online survey of tourism researchers and tourism destination 

managers to evaluate 36 competitiveness attributes to develop determinance measures, statistically 

analyzing these measures. A total of 10 of the 36 attributes had statistically significant measures. 

Angelkova, Koteski, Jakovleva, and Mitrevska (Angelkova et al., 2013) discussed the importance of 

cooperation among various stakeholders such as national and regional authorities, tourism 

companies and destinations for achieving true sustainability. Pérez, Guerrero, González, Pérez, and 

Caballero (Pérez et al., 2013) developed a Principal Component Analysis (PCA)-based composite 

tourism destination index. Mikulić, Kožić, and Krešić (Mikulić et al., 2015) pointed out potential 

issues with commonly used methods for combining sustainability indices. Carrillo and Jorge 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

(Carrillo & Jorge, 2017) developed a composite index for tourism sustainability of Spanish regions 

via weighting of simple indicators. Martin and Assenov discussed the importance of developing 

tourism sustainability indices for surf sites and developed a Surf Resource Sustainability Index 

(SRSI) for surfing-based tourism destinations (Martin & Assenov, 2015) 

Rodríguez-Díaz and Pulido-Fernández (Rodríguez-Díaz & Pulido-Fernández, 2020) 

investigated the relationship between sustainability and tourism competitiveness for various regions 

and developed a synthetic sustainability index at global and regional levels. A systematic literature 

review of sustainability is provided by Streimikiene, Svagzdiene, Jasinskas, and Simanavicius 

(Streimikiene et al., 2021). Wang, Nie, Jeronen, Xu, and Chen (Wang et al., 2023) combined value 

belief-norm theory with environmental awareness on a dataset of 301 students from a university in 

eastern China; their results point to an important role university can play in promoting sustainable 

tourism. Nguyen, Kuo, Lu, and Nhan (Nguyen et al., 2024) developed a two-stage network to 

benchmark 111 global tourist destinations; their empirical results show that inefficiency in 

sustainability primarily comes from technology gaps among the tourist destinations. 

Companies such as Mabrian (Mabrian, n.d.) and S & P Global (S & P Global, n.d.) provide a 

range of sustainability indices. Jørgensen (Jørgensen, 2023) argues for a deep investigation into 

various sustainability indices and destination rankings obtained from these indices. Asmelash and 

Kumar (Asmelash & Kumar, 2019) used Exploratory Factor Analysis (EFA) and Structural 

Equation Modeling (SEM) on data collected from a three-round Delphi Method involving 

evaluation of indicators using 6 well-accepted indicator selection criteria in order to obtain an 

alternative travel sustainability index. 

Gómez-Vega and Picazo-Tadeo (Gómez-Vega & Picazo-Tadeo, 2019) used data from the 

2017 Travel & Tourism Competitiveness Report of the World Economic Forum (WEF) and 

determined weights to rank 136 tourist destinations; the weighted indicator rankings were found to 

be quite similar to the unweighted WEF rankings. 

Methodology 

PCA is a statistical method which is typically used for reducing the number of variables in a 

correlated dataset. PCA uses eigen analysis of the covariance matrix or the correlation matrix and 

yields uncorrelated principal component scores (PC scores) and PC loadings of each variable of the 

PCs. PC scores are used as inputs to further statistical analyses such as Multiple Linear Regression 

(MLR). In this article, we will develop an aggregating method for several TDSI rankings into a 

single ranking via the dimensionality reduction method of Principal Components Analysis (PCA) 

(Jolliffe, 2002) and will illustrate the proposed method using the TDSI rankings data given in 

Appendix 2 of Gómez-Vega and Picazo-Tadeo (Gómez-Vega & Picazo-Tadeo, 2019). We compiled 

six published index variables for each of 136 destinations (see Table 1), verified directionality so 

that lower ranks denote better performance, and standardized variables (z‑scores) to ensure 

commensurability before PCA. 

We compiled rankings for 136 destinations across six sustainability‑related indices reported 

in the tourism competitiveness literature: four index variants (A–D), a Data Envelopment 

Analysis‑based composite (DEA), and the World Economic Forum’s Travel & Tourism 

Competitiveness components (WEF). All six are available for the same set of destinations, yielding 

a balanced 136×6 matrix (see Table 1).  

For transparency and reproducibility, we detail the pipeline: data screening for completeness, 

z-standardization, PCA on the correlation matrix, component retention via scree and cumulative-

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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

variance criteria, and construction of the composite as a variance-weighted sum of retained 

components; software and parameters are listed in the online appendix. 

Rankings were encoded such that lower numeric values denote better performance in all six 

indices. To place indices on a common scale, variables were standardized to zero mean and unit 

variance prior to PCA (z‑scores). Because variables represent ordinal ranks, we verified results 

using the correlation matrix (equivalent to PCA on standardized variables). No imputation was 

required because the dataset is complete for the 136 destinations. 

We applied PCA to the 6 standardized variables using the correlation matrix and extracted 

component scores and loadings with the prcomp routine in R. Component retention followed the 

scree‑plot and cumulative variance criteria: the first two components explained 91% of total 

variance. We constructed a composite sustainability score S as a weighted sum of the first two 

component scores: S = w₁·PC1 + w₂·PC2, where weights equal the proportion of variance explained 

(w₁ = 0.79, w₂ = 0.12, normalized to sum to 0.91). Destinations were ranked by S (descending 

sustainability). 

Code was executed in R (version current at analysis time). We provide figure descriptions 

(scree plot; biplots of scores/loadings; correlation heatmaps) to support interpretation. Figures and 

the full ranking table remain unchanged in substance but are referenced in the revised narrative for 

clarity. 

All computations in this article were done in the statistical software environment R (R Core 

Team, 2023). The function “prcomp” of R was used to run PCA on the data file with 136 rows and 6 

columns shown in Table 1. 

Results and Discussion 

Figure 1 (Scree Plot) shows the percent variance explained by the six PCs, with the first two 

cumulatively explaining 91%, and first three cumulatively explaining almost all (98%) of the total 

variation in the data. The first principal component captures the dominant common variance across 

indices, consistent with an overarching efficiency/competitiveness construct for destinations, 

whereas the second component loads on methodological emphases specific to individual indices. 

Together, these dimensions clarify why prior rankings sometimes diverge and provide a coherent 

basis for reconciling results across benchmarking systems. 

Figure 2 is a plot showing the correlations among the PCs and the original variables. It can 

be seen from this plot that the TDSI rankings WEF and DEA load very heavily on PC1, followed by 

the rankings C, A, B, and then D. The rankings D and A load heavily on PC2, and the third PC is 

just the ranking B. Figure 3, which is a plot of the PC scores and PC loadings, confirms the 

conclusions drawn from Figure 2. We will next show how the results of the PCA can be used to 

average the six TDSI rankings into a single ranking via the results of PCA. Practically, managers 

can apply the PCA‑informed ranking to identify leverage points (e.g., indicators with high loadings), 

allocate resources toward dimensions with outsized influence, and benchmark progress as input 

indices update; we also contrast our results with equal‑weight, entropy‑weighted, and 

efficiency‑frontier approaches to highlight when each may be preferred. 

 

 

 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

Figure 1. Percentage of Variation Explained by the PC’s (Scree Plot) 

 

 

Figure 2. Correlation Plot of the PC’s and Raw Variables 

 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

Figure 3. Plot of PC Scores and Loadings for the First Two PC’s 

 

Figure 3 displays the Principal Component (PC) scores and loadings for the first two 

components derived from the PCA of TDSI rankings. Each data point represents a country or 

region, plotted in relation to its position along the PC1 and PC2 axes, which together explain a 

significant portion of variance. The loadings indicate the contribution of each original variable to the 

components, allowing for visual interpretation of how strongly each ranking index influences the 

new composite dimensions. 

 

Figure 4. Correlation Plot of the 6 Rankings and the PCA-based Ranking 

 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

Figure 4 is the correlation plot of the six original rankings given in brackets along with the 

proposed PCA loadings weighted rankings. It can be seen that the ranking obtained by the proposed 

method is highly correlated with the rankings C, DEA, and WEF. 

Table 1 shows the original ranking data and the ranking obtained by the proposed method of 

computing the weighted average of the first two PC scores using the PCA loadings of 0.79 for PC1 

and 0.12 for PC2 (see Figure 3). 

Table 1. Rankings of Tourist Destinations –  

Last Column is Ranking by the Proposed Method 

Country/ 

region 

A B C D DEA WEF Weighted 

Rank 

Spain 27 9 10 3 3 1 1 

Germany 16 19 3 6 5 3 2 

France 23 20 7 4 4 2 3 

Japan 4 22 12 8 6 4 4 

United 

States 

21 61 1 1 1 6 5 

Austria 22 15 9 20 10 12 6 

United 

Kingdom 

15 60 4 9 7 5 7 

Portugal 32 3 21 18 13 14 8 

Hong Kong 2 14 16 36 16 11 9 

Switzerland 1 41 8 29 14 10 10 

Australia 19 45 20 12 9 7 11 

Italy 48 47 17 5 8 8 12 

Republic of 

Korea 

14 51 19 13 11 19 13 

Canada 25 74 5 14 12 9 14 

Greece 46 10 23 22 17 24 15 

Iceland 10 12 2 67 15 25 16 

Belgium 20 42 15 27 19 21 17 

Netherlands 12 25 14 51 22 17 18 

Singapore 7 1 11 89 18 13 19 

Norway 5 46 26 33 23 18 20 

New 

Zealand 

13 17 32 42 28 16 21 

Sweden 6 35 31 35 25 20 22 

China 60 83 18 2 2 15 23 

Croatia 40 31 33 17 21 32 24 

Ireland 24 11 22 58 27 23 25 

Denmark 8 34 29 55 31 31 26 

Finland 3 30 35 60 33 33 27 

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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

Czech 

Republic 

30 29 24 48 30 39 28 

Malaysia 39 26 41 37 36 26 29 

United Arab 

Emirates 

18 68 6 62 26 29 30 

Taiwan 28 38 40 39 35 30 31 

Mexico 73 55 51 11 20 22 32 

Malta 29 4 27 84 34 36 33 

Luxembourg 9 32 13 111 32 28 34 

Thailand 66 48 39 25 38 34 35 

Panama 65 23 34 50 40 35 36 

Bulgaria 41 28 49 40 41 45 37 

Estonia 17 8 45 88 39 37 38 

Slovenia 35 24 46 63 44 41 39 

Brazil 72 92 64 7 24 27 40 

Costa Rica 59 36 62 30 47 38 41 

Turkey 68 73 44 16 37 44 42 

Hungary 49 16 47 57 46 49 43 

Indonesia 94 7 77 21 43 42 44 

Chile 55 13 65 44 49 48 45 

Poland 52 27 60 43 48 46 46 

Cyprus 51 21 36 70 42 52 47 

India 109 62 61 10 29 40 48 

Russian 

Federation 

54 100 43 24 45 43 49 

Slovak 

Republic 

37 44 55 61 52 59 50 

Mauritius 57 5 48 104 51 55 51 

Peru 84 72 81 15 50 51 52 

Latvia 36 18 56 105 56 54 53 

Lithuania 31 33 53 102 55 56 54 

South Africa 85 86 52 28 57 53 55 

Ecuador 87 67 63 32 61 57 56 

Romania 58 57 68 49 62 68 57 

Qatar 11 90 28 123 54 47 58 

Barbados 44 52 25 122 53 58 59 

Argentina 69 102 74 23 65 50 60 

Jamaica 82 6 57 91 60 69 61 

Morocco 74 78 67 34 64 65 62 

Colombia 91 66 93 19 58 62 63 

Oman 42 104 58 47 66 66 64 

Sri Lanka 80 49 73 53 67 64 65 

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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

Dominican 

Republic 

102 2 59 82 59 76 66 

Israel 33 108 37 85 63 61 67 

Azerbaijan 43 80 71 65 70 71 68 

Vietnam 77 81 96 26 69 67 69 

Saudi Arabia 34 110 54 80 71 63 70 

Bahrain 26 96 38 134 68 60 71 

Georgia 50 71 69 96 72 70 72 

Uruguay 38 76 82 83 76 77 73 

Kenya 116 54 86 41 77 80 74 

Jordan 53 58 70 113 75 75 75 

Guatemala 99 43 91 59 79 86 76 

Namibia 86 82 66 71 80 82 77 

Montenegro 63 84 50 114 78 72 78 

Philippines 97 64 97 52 82 79 79 

Trinidad & 

Tobago 

70 105 30 119 74 73 80 

Tanzania 123 37 104 46 83 91 81 

Tunisia 75 59 76 95 81 87 82 

Bhutan 76 53 98 87 84 78 83 

Kazakhstan 47 97 89 79 87 81 84 

Cape Verde 90 63 42 129 73 83 85 

Egypt 95 70 85 81 88 74 86 

Honduras 113 40 87 77 85 90 87 

Armenia 61 93 80 93 86 84 88 

Cambodia 105 39 106 66 91 101 89 

Iran 89 115 101 31 92 93 90 

Ukraine 83 89 72 94 89 88 91 

Botswana 88 98 88 74 98 85 92 

Nicaragua 112 50 94 86 94 92 93 

Macedonia 56 91 75 120 90 89 94 

Mongolia 64 103 112 54 96 102 95 

Lebanon 71 77 83 115 93 96 96 

Lao PDR 98 65 92 103 95 94 97 

Serbia 62 107 79 108 97 95 98 

Bolivia 108 116 103 45 101 99 99 

Albania 79 87 84 121 99 98 100 

Nepal 114 69 127 64 102 103 101 

Uganda 119 88 116 56 103 106 102 

El Salvador 103 56 100 124 100 105 103 

Kuwait 45 122 78 136 104 100 104 

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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

Rwanda 81 94 107 110 105 97 105 

Venezuela 125 129 113 38 113 104 106 

Zambia 115 99 118 75 107 108 107 

Zimbabwe 126 101 110 68 109 114 108 

Senegal 107 128 99 72 112 111 109 

Ethiopia 117 106 114 73 111 116 110 

Cote d'Ivoire 111 120 90 92 114 109 111 

Paraguay 101 75 111 127 106 110 112 

Bosnia-

Herzegovina 

78 109 95 130 108 113 113 

Gambia 110 85 108 117 110 112 114 

Tajikistan 93 117 115 100 118 107 115 

Kyrgyz 

Republic 

92 118 129 78 120 115 116 

Algeria 96 132 120 76 121 118 117 

Moldova 67 114 102 135 116 117 118 

Madagascar 131 79 124 98 115 121 119 

Mozambique 120 95 119 99 117 122 120 

Pakistan 127 113 105 101 119 124 121 

Ghana 104 123 109 112 122 120 122 

Mali 130 127 123 69 124 130 123 

Gabon 100 126 117 118 123 119 124 

Malawi 122 112 131 97 125 123 125 

Bangladesh 118 121 122 109 126 125 126 

Nigeria 128 124 121 107 127 129 127 

Cameroon 124 133 126 106 129 126 128 

Lesotho 106 111 133 131 128 128 129 

Benin  121 131 125 116 130 127 130 

Democratic 

Republic of 

Congo 

134 136 135 90 133 133 131 

Mauritania 132 119 128 126 131 132 132 

Sierra Leone 129 125 134 128 132 131 133 

Chad 136 130 132 125 134 135 134 

Yemen 135 135 130 132 136 136 135 

Burundi 133 134 136 133 135 134 136 

Table 1 compiles the rankings of 136 tourist destinations across six sustainability indices (A, 

B, C, D, DEA, WEF) and presents the final weighted ranking derived via a PCA-based aggregation 

method. Notably, top destinations such as Spain, Germany, and France rank high across multiple 

indices and likewise lead the aggregated PCA ranking. The table underscores how PCA effectively 

consolidates diverse sustainability metrics into a single composite rank, supporting the article’s 

methodology and validating its conclusions 

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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

The scree plot indicates steep decline after the first component and an elbow after the 

second, with PC1 and PC2 jointly capturing 91% of variation. Biplots show that WEF and DEA 

load most strongly on PC1, with additional contribution from indices C and A, while index D 

contributes more prominently to PC2. This pattern suggests a dominant common dimension aligned 

with broad competitiveness and efficiency, and a secondary dimension capturing index‑specific 

emphases. 

The PCA‑weighted score consolidates the six indices into a single ordering. Consistent top 

performers include Spain, Germany, and France; large, diversified destinations such as the United 

States and Japan also rank highly. The composite is highly correlated with several source indices, 

yet notable re‑ordering occurs where individual indices emphasize distinct sub‑dimensions of 

sustainability. See Table 1 for the complete list of destinations and the final composite rank. 

Recent discourse surrounding the Fourth Industrial Revolution (4IR) has further emphasized 

the role of digital transformation in sustainability. Technological convergence, including artificial 

intelligence, big data, and the Internet of Things (IoT), is now increasingly viewed as both a 

challenge and a solution in managing sustainable tourism (Gretzel et al., 2015; Li et al., 2018). As 

urbanization and mobility grow, there is a renewed focus on balancing economic growth with 

ecological and social responsibilities (UNWTO, 2020). Consequently, the urgency of incorporating 

sustainability into tourism planning has intensified, leading to a greater reliance on analytical 

methods that can consolidate multidimensional sustainability metrics into meaningful insights. 

More recently, studies have highlighted the role of digital ecosystems and governance 

frameworks in managing sustainable destinations. For example, Baggio and Sainaghi (2016) applied 

network theory to tourism systems, revealing structural imbalances in how sustainability efforts are 

distributed. Similarly, Becken and Scott (2020) emphasized the importance of climate resilience in 

tourism infrastructure and its influence on long-term sustainability. Researchers have argued for 

integrating ESG (Environmental, Social, and Governance) principles into tourism sustainability 

indices, signaling a convergence with corporate sustainability reporting standards. This shift reflects 

the increasingly complex, ESG-driven data environment in which TDSI evaluations now operate 

(Guix et al., 2025; Bonilla-Priego et al., 2014; UN Tourism ESG Framework, 2024) 

To enhance robustness, researchers are combining PCA with other dimensionality-reduction 

and weighting techniques such as factor analysis and entropy weighting (Liang, 2018; Wu et al., 

2022). For example, hybrid PCA–entropy models reduce indicator subjectivity (Liang, 2018; Wu et 

al., 2022). These integrative approaches offer greater reliability and are gaining traction in 

sustainability benchmarking across sectors, including tourism (Palacios-Florencio et al., 2021) 

Furthermore, widely used R and Python ecosystems—exemplified by FactoMineR for 

multivariate analysis and scikit-learn for machine learning—enable researchers to implement and 

compare multiple dimensionality-reduction strategies within reproducible workflows, and large-

scale benchmarks now guide method selection across contexts (Lê et al., 2008; Pedregosa et al., 

2011; Cantini et al., 2021). In this context, our use of PCA remains a foundational and reproducible 

choice, especially for benchmarking exercises like TDSI. 

PCA loadings indicate that indices such as DEA and WEF contribute most strongly to the 

first component, while regional discrepancies likely reflect differences in how each index defines 

and weights sustainability. This interpretation aligns with Nguyen et al. (2024), whose 

benchmarking of 111 global destinations showed that environmental metrics can diverge 

substantially from economic or social indicators 

This nuance is important when designing unified indices, as shown in Table 1, which 

presents notable rank shifts. Additionally, temporal shifts in sustainability-related destination 

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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

rankings are evident in leading benchmarking systems: the World Economic Forum’s TTDI 2024 

updated its indicator set and methodology—recalculating components and limiting direct 

comparability with earlier editions—while the GDS-Index 2024 introduced new and refined criteria 

that produced measurable rank and score changes. These ongoing methodology updates and data 

refreshes underscore the need for periodic recalibration of TDSI tools to reflect current trends. 

Therefore, our PCA-based averaging approach offers a statistically grounded and adaptive solution 

for monitoring tourism sustainability. 

Our findings suggest that PCA is not only effective in aggregating multidimensional 

sustainability rankings but also adaptable for real-world policy use. As tourism destinations 

increasingly adopt regenerative and climate-smart strategies (Becken & Kaur, 2021; Becken, 

Whittlesea, Loehr, & Scott, 2020), the need for integrated indices becomes critical. Policymakers 

and destination managers can utilize PCA-informed rankings to prioritize interventions, allocate 

resources more efficiently, and communicate performance to stakeholders. Future work could 

explore dynamic TDSI modeling through machine learning and predictive analytics to anticipate 

performance trajectories and policy trade-offs (Font et al., 2023). As global sustainability 

frameworks continue to evolve, maintaining methodological transparency and flexibility will be 

essential to ensure the relevance and credibility of destination rankings. 

Theoretical and Practical Implications 

Treating multiple published indices as noisy indicators of a latent sustainability construct 

provides a conceptual bridge between measurement and theory. The strong first component suggests 

substantial shared variance across indices, supporting the use of a common composite for 

benchmarking. The presence of a meaningful second component cautions that destinations can differ 

along secondary dimensions (e.g., policy/regulation vs. environmental efficiency), reinforcing the 

value of examining loadings and score plots alongside ranks. 

For destination management organizations, the PCA‑based composite offers a transparent 

yardstick for (i) prioritizing interventions, (ii) tracking progress over time, and (iii) communicating 

outcomes to stakeholders who face multiple, sometimes conflicting rankings. Because weights arise 

from the data, the approach reduces the perception of arbitrariness associated with judgment‑based 

weighting and can be re‑estimated annually as new indices or indicators become available. 

Equal‑weight averaging ignores covariance among indices and can dilute salient signals. 

Multi‑criteria methods such as AHP depend on expert judgments and can be difficult to audit at 

scale. Entropy weighting exploits indicator dispersion but may overweight noisy measures. 

Efficiency‑frontier techniques (e.g., DEA) identify relative best practice but can be sensitive to 

outliers and model specification. PCA balances parsimony and transparency by extracting the 

common structure and producing orthogonal components with clear variance‑explained diagnostics. 

In contexts with many correlated indices, this offers a replicable baseline against which more 

complex models can be compared. 

In this article, we proposed a novel method of averaging TDSI rankings via PCA, wherein 

PC-scores and PC-loadings are computed from a dataset of tourist destination rankings. The PC-

scores are then averaged using PC-loadings as weights to obtain a consolidated ranking for all 

destinations in the dataset. This method was illustrated using a dataset from the tourism literature. 

This PCA-based approach offers a robust solution for synthesizing multidimensional 

sustainability data into a single, interpretable index. Unlike simple averaging or unweighted 

aggregation methods, PCA accounts for inherent correlations among indicators and objectively 

identifies the most influential components driving destination performance. Our analysis revealed 

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Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 
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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

that high loadings on the first principal components reflect dominant sustainability metrics—

particularly those related to economic performance and environmental efficiency—validating the 

value of weighting dimensions through principal components rather than subjective judgment. 

The implications for policymakers and destination management organizations are significant. 

By applying PCA in this manner, decision-makers can benchmark destinations more equitably, 

detect underlying structural patterns, and prioritize interventions based on data-driven insights. This 

method is adaptable to incorporate new indicators such as resilience, climate risk, or local 

stakeholder participation. Moreover, our model supports longitudinal comparisons, enabling 

institutions to monitor sustainability improvements or regressions over time and evaluate the impact 

of strategic initiatives. 

Recent studies have further emphasized the importance of integrating renewable energy 

sources into tourism infrastructure to enhance sustainability. For instance, Guo and Chai (2025) 

investigated BIMSTEC countries and found that renewable energy consumption improves 

environmental quality as measured by the load capacity factor (LCF), and can moderate tourism’s 

environmental pressures. This underscores the potential for destinations to improve their 

sustainability rankings by investing in renewable energy solutions. 

Additionally, the role of environmental policies as attractors for tourists has been explored in 

recent research. Serio et al. (2024) examined international tourism flows across Italian provinces 

using a gravity framework and found a positive association between tourism demand and 

sustainable labels, suggesting that eco-certifications can influence destination choice. This is 

consistent with Capacci, Scorcu, and Vici (2015), who showed that Blue Flag eco-labels 

significantly increase future foreign tourist inflows to Italian coastal destinations. Incorporating such 

policy-driven indicators into PCA can therefore provide a more comprehensive assessment of 

destination sustainability. 

Furthermore, the integration of advanced technologies into tourism planning has gained 

attention. Banerjee et al. (2025) introduced a composite sustainability indicator for tourism 

recommender systems, combining CO₂ emissions, destination popularity, and seasonality. Their 

work demonstrates the feasibility of using complex, multidimensional data to guide sustainable 

travel decisions, reinforcing the applicability of PCA in processing and interpreting such data for 

destination ranking purposes. 

In conclusion, our findings suggest that PCA is not only effective in aggregating 

multidimensional sustainability rankings but also adaptable for real-world policy applications. As 

tourism destinations increasingly adopt regenerative and climate-smart strategies, the need for 

integrated indices becomes critical. Policymakers and destination managers can utilize PCA-

informed rankings to prioritize interventions, allocate resources more efficiently, and communicate 

performance to stakeholders. Future research could explore dynamic TDSI modeling through 

machine learning and predictive analytics. As global sustainability frameworks continue to evolve, 

maintaining methodological transparency and flexibility will be essential in ensuring the relevance 

and credibility of destination rankings. 

Limitations and Future Research 

PCA assumes linear relationships and focuses on variance rather than causality; future work 

could combine PCA with confirmatory factor analysis, explore dynamic updates as new indicators 

emerge, or test robustness to alternative standardizations (e.g., rank‑based normal scores). Extending 

the framework to include resilience, climate risk, or stakeholder participation metrics would further 

enhance decision relevance. 

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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

               Copyright © 2025 Yen-Soon Kim, Natalie Hudson, Rhonda Montgomery, 

Ashok Singh and Soyeon Jung 

This work is licensed under a Creative Commons Attribution 4.0 International License. 

 

 

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Events and Tourism Review Vol. 8 No. 1 (2025), 15-35, DOI: 10.18060/28905                                                                                                  

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