Achieving balanced, sustainable, and inclusive tourism requires a comprehensive approach addressing environmental, economic, and social dimensions. Museums, as repositories of a nation’s cultural capital, play a significant role in this framework. This study aims to quantify museums’ sustainability, classify them based on their sustainability practices, and examine the influence of structural variables on sustainability levels and transitions. Using data from the Survey on Museums and Other Cultural Institutions, the research employs Latent Transition Analysis (LTA) to group museums into classes based on sustainability indicators across the years 2018, 2019, 2021, and 2022. Results indicate that Italian museums fall into three sustainability classes, with a trend towards lower sustainability over time. Almost all the covariates included in the model significantly impact initial classification, with a lower significant effect on the transition probabilities. Finally, a Support Vector Machine (SVM) algorithm for the museums’ post-classification not included in the LTA analysis and a mobility index for transition matrices were employed. This research highlights the importance of tailored policy interventions to enhance sustainability practices. By integrating these findings, policymakers can better preserve cultural heritage, promote local development, stimulate economic regeneration, and enhance social cohesion, thus fostering a more sustainable and inclusive tourism sector
Quantifying the sustainability of cultural heritage in Italy / Carla Galluccio, Francesca Giambona, Laura Grassini. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1618-2510. - STAMPA. - (2025), pp. 1-37. [10.1007/s10260-025-00812-1]
Quantifying the sustainability of cultural heritage in Italy
Carla Galluccio
;Francesca Giambona;Laura Grassini
2025
Abstract
Achieving balanced, sustainable, and inclusive tourism requires a comprehensive approach addressing environmental, economic, and social dimensions. Museums, as repositories of a nation’s cultural capital, play a significant role in this framework. This study aims to quantify museums’ sustainability, classify them based on their sustainability practices, and examine the influence of structural variables on sustainability levels and transitions. Using data from the Survey on Museums and Other Cultural Institutions, the research employs Latent Transition Analysis (LTA) to group museums into classes based on sustainability indicators across the years 2018, 2019, 2021, and 2022. Results indicate that Italian museums fall into three sustainability classes, with a trend towards lower sustainability over time. Almost all the covariates included in the model significantly impact initial classification, with a lower significant effect on the transition probabilities. Finally, a Support Vector Machine (SVM) algorithm for the museums’ post-classification not included in the LTA analysis and a mobility index for transition matrices were employed. This research highlights the importance of tailored policy interventions to enhance sustainability practices. By integrating these findings, policymakers can better preserve cultural heritage, promote local development, stimulate economic regeneration, and enhance social cohesion, thus fostering a more sustainable and inclusive tourism sector| File | Dimensione | Formato | |
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s10260-025-00812-1.pdf
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