Examinando por Autor "Pousa Unanue, Aitziber"

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    Calculating the carbon footprint of urban tourism destinations: a methodological approach based on tourists’ spatiotemporal behaviour
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-03) Pousa Unanue, Aitziber; Alzua Sorzabal, Aurkene; Álvarez Fernández, Roberto; Delgado Jiménez, Alexandra; Femenia Serra, Francisco
    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
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    Methodological pathways for measuring tourism carbon footprint: a framework-oriented systematic review
    (MDPI AG, 2026-01-23) Pousa Unanue, Aitziber ; Alzua Sorzabal, Aurkene ; Femenia Serra, Francisco
    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.
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    Recomendaciones turísticas de la IA generativa: variaciones lingüísticas y culturales en las representaciones de destinos turísticos
    (Subdirección General de Conocimiento y Estudios Turísticos, 2025-12-18) Herasimovich, Volha; Pousa Unanue, Aitziber; Alzua Sorzabal, Aurkene; Guereño-Omil, Basagaitz; Liang, Ruipu
    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, la exhaustividad y la estructura de las recomendaciones, dependiendo del idioma de consulta y de la forma toponímica. Se observaron mayores tasas de errores y de respuestas genéricas en lenguas extranjeras, especialmente en ruso, mientras que las lenguas locales ofrecieron recomendaciones más detalladas, aunque con cierto riesgo 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 de-ben adaptarse al contexto multilingüe del turismo mediante estrategias colaborativas que integren a desarrolladores, gestores turísticos y expertos culturales, para asegurar representaciones digitales precisas, inclusivas y culturalmente contextualizadas.