1,720,963 research outputs found
Evaluating Sustainable Tourism: A Composite Index for European Destinations
No full textThis paper focuses on the analysis of sustainable tourism indicators for the 27 European Union (EU) countries to address the challenges faced by the tourism sector. The impact of sustainable tourism indicators on destination competitiveness was evaluated using the EU Tourism Dashboard, a scheme funded by the European Commission. The Mazziotta-Pareto Index was used to construct the indicator of sustainability by incorporating the three pillars of indicators: Environmental impact, Digitalisation, and Socio-economic vulnerability. The proposed approach provides greater tractability and flexibility for decision-makers to adjust the number of indicators to meet specific case conditions. The sustainable tourism indicators aim to provide critical information for resource allocation and policymaking in the conservation of tourism sites, as well as improving the welfare and inclusiveness of local communities. The findings of the analysis highlight significant disparities among destinations in terms of the examined indicators, emphasizing variations in sustainability profiles and performance within the tourism sector. These disparities underscore the need for personalized and targeted approaches to address the specific challenges and opportunities faced by each destination in achieving sustainable tourism development. Furthermore, the proposed ranking system, updated at regular intervals, can enhance the image and reputation of European areas as high-quality and sustainable destinations, attracting increased tourism demand. Additionally, the ranking system can foster knowledge sharing and the adoption of benchmarking practices, incentivizing countries to maintain and improve their position in the ranking. This work contributes to addressing the evidence gap in tourism sustainability policymaking and provides a comprehensive framework for the development of sustainable tourism analysis in the EU context
Enhancing Tourism demand forecasting in Italy: Integrating OTA data and CPI trends with statistical and Deep Learning models
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Harnessing machine learning to investigate the socio-demographic determinants of sports habits in Italy
No full textExplaining Tourism Expenditure Patterns in Italy: A Comparative Study of Domestic and Incoming Tourists Using XGBoost and SHAP
No full textThis study introduces a novel approach in analyzing the determinants of tourism expenditure in Italy, with a particular focus on the differences between domestic and incoming tourism. The analysis is based on microdata from two surveys: ISTAT’s “Travels and Holidays” and the Bank of Italy’s “International Tourism”. Expenditure forecasts for both categories were calculated using the eXtreme Gradient Boosting (XGBoost) machine learning algorithm, ensuring high predictive accuracy. To identify the main variables influencing tourism expenditure, SHapley Additive exPlanations (SHAP) values were calculated, providing a deeper interpretation of the contribution of each one of them. The results highlight both differences and common points in the determinants driving domestic and incoming tourism expenditure, underscoring the need for targeted policy interventions to address the distinct characteristics of these two segments. The methodological integration of machine learning techniques and SHAP values offers a tool for understanding and forecasting tourism expenditure trends in Italy, with relevant implications for policymakers and industry stakeholders
Measuring Culture for Well-Being and Sustainability: The New Frontier of Tourism Development
No full textThe definition of culture and cultural activities is not yet statistically well-defined. Nevertheless, the PNRR (National Recovery and Resilience Plan) has decided to allocate significant sources of funding to culture, both in terms of improving its accessibility and in terms of enhancing the value of the territory. It remains challenging to determine the economic value of the demand for culture, as well as its connection with tourist flows. The link between the cultural enhancement of the territory and tourist flows remains uncertain. Cultural travels represent an important motivation, although family expenditure still has a relatively small share compared to the total expenditure. Similarly, municipalities show significant variability in terms of public spending on culture. It is advisable to include regional and sub-regional level indicators to complement the data, and how possible to provide a cultural satellite account
Forecasting tourist nights spent in Italy: a comparative analisys of statistical and neural network models
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