This study investigates the influence of urban tourists’ behaviour on the environmental performance of a destination, particularly in terms of carbon emissions. Tourist-related emissions are shaped by their choices and behaviours, impacting the overall carbon footprint of the locations they visit. To assess this impact, we introduce a methodology for quantifying greenhouse gas emissions linked to tourists’ energy consumption. This approach considers key tourism components—activities, accommodation, and transportation—analysing their roles in emissions across a trip’s temporal and spatial dimensions. By integrating tourists’ spatiotemporal behaviour with emissions data, our framework offers insights that can support local climate-responsive urban and tourism policies. We empirically apply the proposed model to the destination of Donostia/San Sebastián (Spain), where the primary travel sequences of visitors are analysed. We utilise cartographic techniques to map the environmental footprints of different tourist profiles, such as cultural and nature tourists. The findings indicate that visitors primarily motivated by nature and outdoor recreation constitute the segment with the highest greenhouse gas emissions (with a minimum footprint of 30.69 kg CO₂-equivalent per trip), followed by cultural tourists, and finally, other categories of visitors. The results highlight the practical applications of the proposed model for sustainable tourism management, providing valuable guidance for urban planners and policymakers in mitigating the environmental impacts of tourism.

Tourism is increasingly acknowledged as a major driver of global greenhouse gas emissions. However, efforts to accurately assess its carbon footprint remain hindered by methodological inconsistencies and a reliance on fragmented case studies. This study undertakes a systematic review of 166 peer-reviewed research papers to critically evaluate prevailing approaches for quantifying tourism-related carbon emissions. Leveraging a structured framework encompassing four analytical dimensions and fourteen parameters, the analysis reveals that energy consumption and emission factors constitute the core elements of prevailing models. Nevertheless, only half of the papers account for indirect emissions, and the majority of studies are confined to national or subnational scales, offering limited insight into destination-specific impacts. This methodological heterogeneity undermines the comparability of results and constrains their utility in formulating coherent, evidence-based climate policies. By synthesising these diverse approaches, this review identifies critical methodological gaps, advocates for the harmonisation of best practices, and delineates a roadmap for more robust and context-sensitive carbon accounting within the tourism industry. The insights gained are practical for researchers and policymakers seeking to align tourism development with climate mitigation objectives, thereby fostering greater transparency and efficacy in carbon governance within the sector. Ultimately, such initiatives aim to fortify the sector’s contribution to global decarbonisation efforts.

La relevancia del turismo y la proliferación de las viviendas de uso turístico (VUT) de los últimos años en España, hace necesario el estudio del impacto que estas generan en relación con la actual crisis de acceso a la vivienda. Se requieren estudios nacionales que consideren las particularidades regionales y municipales para obtener una visión integral del problema, con el objeto de legislar y planificar adecuadamente la incidencia de la VUT en ciertos territorios. Por eso, mediante un análisis geoespacial basado en el índice de Moran I y en relación con la evolución de los precios de vivienda en alquiler, esta investigación examina la dependencia espacial de la VUT en diversas provincias y ciudades españolas en el periodo pospandémico. Los resultados de este indicador clave destacan la existencia de regiones donde su prevalencia agrava el acceso a la vivienda, lo que afecta a los residentes y supone, unido a otros factores, un posible riesgo de una nueva crisis inmobiliaria. Se muestra la importancia de integrar el análisis espacial de las VUT en la planificación urbana para abordar la situación actual, prevenir futuras inestabilidades del mercado de vivienda y regular en pro de la convivencia. Este estudio enfatiza la necesidad de estrategias de planificación urbanística que consideren la concentración espacial de la VUT, además del resto de usos turísticos, con el objetivo de promover un desarrollo urbano sostenible y equitativo en España.

The growing process of touristification and the effects of climate change are some of the most pressing issues for cities today. This research examines the complex relationship between the implementation of low-emission zones (LEZs) as a strategy to mitigate climate change and adapt urban spaces to its widespread ramifications, and the touristification of cities. The study analyses 48 LEZs in Spanish cities, employing a combination of statistical (i.e., fsQCA) and spatial analyses. Despite presenting heterogeneous realities, the findings reveal a dual effect. On one hand, the implementation of LEZs and related pedestrianisation promote air quality improvement and reduces emissions. On the other hand, this traffic calming initiative leads to further touristification by facilitating commercial activity aimed at visitors, occupation of public space, and increased walkability of streets. However, results also indicate that the geographical display of LEZs responds to manifold factors that do not follow this logic in all cases. Three distinct categories of cities are identified according to different implementation strategies and objectives. An additional in-depth analysis of the case of Madrid, a major urban hub and tourist destination, is conducted to further illustrate this complex interplay of factors. This research, which looks at the ongoing implementation of a national law, demonstrates the close relationship between touristification and implementation of LEZs, while it paves the way for future research inquiries into the progression of air quality indices and tourism intensification processes within cities.

Este estudio analiza las variaciones lingüísticas y culturales en las recomendaciones turísticas generadas por modelos de lenguaje de gran tamaño (LLM), con foco en el comportamiento de ChatGPT ante consultas formuladas en español, euskera, inglés y ruso. Mediante un diseño experimental de métodos mixtos, se evaluaron 210 respuestas generadas a partir de prompts estructurados sobre tres destinos del País Vasco: Zumaia, Zarautz y Donostia-San Sebastián. Los resultados evidencian diferencias significativas en la precisión, exhaustividad y la estructura de las recomendaciones, dependiendo del idioma de consulta y la forma toponímica. Se observaron mayores tasas de errores y respuestas genéricas en idiomas extranjeros, especialmente en ruso, mientras que los idiomas locales ofrecieron recomendaciones más detalladas, aunque con ciertos riesgos de inexactitud factual. El reconocimiento de destinos se mostró sensible a la escritura, siendo menor en sistemas no latinos. Los resultados reflejan una disparidad lingüística y cultural en la generación automatizada de contenido turístico, probablemente vinculada a sesgos en los datos de entrenamiento. Se concluye que los sistemas de IA generativa deben adaptarse al contexto multilingüe del turismo, mediante estrategias colaborativas que integren desarrolladores, gestores turísticos y expertos culturales, para asegurar representaciones digitales precisas, inclusivas y culturalmente contextualizadas.

Artificial Intelligence is reshaping the traveller journey and transforming tourism business models; especially evident in museums, which have long embedded digital technologies to enhance visitor experiences. Yet, research on AI functionalities in museum applications remains case-specific and loosely connected to current market developments. This chapter addresses this gap by offering a critical synthesis of the field. It outlines an integrative classification grouping key AI capabilities for museum contexts into (1) visit planning tools, (2) spatial guidance, (3) entertainment features, (4) accessibility and (5) real-time connectivity. Through a functions-based benchmarking analysis, these insights are compared with evidence from leading apps—Ask Mona, Smartify, The Met Replica, and Google Arts & Culture. The findings show notable technological progress, yet current solutions remain specialised and fragmented, failing to fully exploit emerging AI capabilities. The chapter concludes with recommendations for ethical, user-centred design to ensure AI enhances visitor experiences.