Popular tourism destinations based on specific attractions along with coastal and island destinations have been considered potential candidates to suffer from overtourism. In this context, in-depth knowledge of the determinants of tourists’ choices of attractions can be used to improve policies against crowding. This paper analyzes why tourists decide to visit certain attractions instead of others in the context of an island destination with sustainability concerns. To do so, discrete choice models are used to determine if a set of 96 variables can explain why 11 attractions are visited on the island of Lanzarote. The results show that 86 variables are significant to explain visits to at least one of the attractions. The analysis also identifies both similarities and differences on the effects these variables have on the probability of visiting each of the 11 attractions. These results are useful to cluster attractions depending on the profile of those tourists most likely to visit them and to cluster variables regarding their effect on visiting attractions. Furthermore, the results provide useful information for public and private managers involved in evenly reallocating tourist flows in time and space to avoid the negative impacts of overtourism.

In this study, an identification of tourist tracking techniques, their evolution, advantages and disadvantages is made to help researchers decide which techniques to use. First, a literature review is carried out to explain how current tracking techniques work and what information can be obtained through their use. A total of 31 different tracking techniques were found. Afterwards, a bibliometric analysis was conducted to gather data regarding studies published on tourist tracking, the evolution of these studies and the most used techniques. To do so, all empirical contributions using tourist tracking techniques found in Scopus were considered for the analysis. Results point that geolocation-based techniques currently are the most frequently used ones to track tourists. Techniques such as the direct observation of tourists were the first techniques used in tourism research but are currently in disuse. Finally, based on a further literature review, the main advantages and disadvantages of the techniques found are highlighted. The study has implications for researchers interested in tracking tourists to study their behaviour and mobility patterns. It can be also used by destination managers willing to gather data of tourists to better understand tourism activity and industry.

Contemporary challenges in destination management, particularly those related to sustainability, tourism behavior, and mobility, require granular, local-scale data to inform public and private sector decisions. However, the traditional international tourism statistics standards, such as IRTS 2008 and TSA:RMF 2008, have focused on national and, to a lesser extent, regional scales, overlooking local destinations as a relevant level for the measurement and analysis of tourism. As a result, no common conceptual framework has been available for producing statistical information for local destinations, despite tourism impacts being primarily felt at this level. The endorsement by the United Nations in 2024 of the new Statistical Framework for Measuring the Sustainability of Tourism (SF-MST 2024) has addressed this gap, marking a crucial shift toward recognizing local tourism destinations within sustainability measurement. In this conceptual paper, the recent local turn within this new international statistical standard is explored. Furthermore, by comparing SF-MST 2024 with previous documents, an extended conceptual framework for tourism statistics is developed, including the spatial dimension. Finally, in this paper, the implications for the implementation of the framework in local tourism destinations are discussed.

In this paper, greenhouse gas (GHG) emissions derived from airport surface access of outbound German tourists travelling to the Canary Islands have been estimated. It is argued that carbon footprint estimations in tourism must be improved to incorporate the transport to the airports within the country of origin. To do so, statistical data from the Tourism Expenditure Survey of the Canary Islands have been used. In particular, the postcodes of a large sample of German tourists visiting the archipelago from 2012 to 2014 are used to identify their residence and the distances travelled to their chosen airport. The findings of the paper contribute to the literature on tourism carbon footprints, including an estimate of the share of airport surface access emissions in a typical outbound sun and beach holiday trip. Airport ground access accounted for 8.17% of transport-related emissions. These results have implications for urban planners, transport firms, and tourism management.

Nationality is considered a highly relevant determinant of tourism expenditure. In this paper, we use survey data to estimate the main determinants of daily tourism expenditure in 2019 for five nationalities of a major coastal destination, the Canary Islands. A total of 38 regressors, based on literature, are used with the help of quantile regression analysis to understand the behaviour patterns of different expenditure segments. To obtain segmented information on the determinants of expenditure, independent quantile regression models for each nationality are estimated and compared. Results show that there are similarities but also some remarkable differences between nationalities regarding income, age, travel group, educational level, or content of the package that can be used in destination management and marketing.