Events and Tourism Review 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) For Authors Interested in submitting to this journal? We recommend that you review the About the Journal page for the journal's section policies, as well as the Author Guidelines. Authors need to register with the journal prior to submitting or, if already registered, can simply log in and begin the five-step process. For Reviewers If you are interested in serving as a peer reviewer, please register with the journal. Make sure to select that you would like to be contacted to review submissions for this journal. Also, be sure to include your reviewing interests, separated by a comma. About Events and Tourism Review (ETR) ETR aims to advance the delivery of events, tourism and hospitality products and services by stimulating the submission of papers from both industry and academic practitioners and researchers. For more information about ETR visit the Events and Tourism Review. 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. https://creativecommons.org/licenses/by/4.0/ mailto:yen-soon.kim@unlv.edu http://journals.iupui.edu/index.php/jet/about http://journals.iupui.edu/index.php/jet/about/submissions#authorGuidelines http://journals.iupui.edu/index.php/jet/user/register http://journals.iupui.edu/index.php/jet/user/register http://journals.iupui.edu/index.php/index/login https://journals.iupui.edu/index.php/ETR/user/register http://journals.iupui.edu/index.php/ETR 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 https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 17 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), https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 18 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 https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 19 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 https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 20 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- https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 21 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. https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 22 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 https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 23 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 https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 24 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 https://creativecommons.org/licenses/by/4.0/ Kim et al. 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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 https://creativecommons.org/licenses/by/4.0/ Kim et al. 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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 https://creativecommons.org/licenses/by/4.0/ Kim et al. 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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 https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 28 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 https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 29 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 https://creativecommons.org/licenses/by/4.0/ Kim et al. (2025) / Events and Tourism Review, 8(1), 15-35. 30 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. https://creativecommons.org/licenses/by/4.0/ Kim et al. 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