Search Results (76)

Ensuring accessibility in alpine ski areas remains a critical challenge for social sustainability and inclusive tourism because physical, seasonal and organisational constraints interact across the visitor experience. This paper reframes accessibility as a dynamic and relational landscape attribute and proposes a flow-chain framework for assessing accessibility as a sequence of interdependent phases, from pre-trip information to arrival, lift access, slope use, rest and return. Developed within the Ski-Ability project in the ArgeAlp working community, the study draws on exploratory field observations, stakeholder engagement and co-design activities conducted across seven Alpine pilot resorts. The pilot resorts are not treated as a statistically representative sample, but as field cases used to understand current operational conditions in a context where academic literature, technical standards and regulatory guidance specific to accessible ski areas remain limited. The framework is operationalised through a qualitative toolkit based on Basic, Comfort and Plus levels, priority categories and non-compensatory decision rules. The results provide methodological validation and practical guidance rather than quantitative benchmarking. They show that accessibility in alpine ski areas depends on the continuity of routes, services, information and assistance, and on coordination among multiple actors. The paper contributes to social sustainability research by linking Universal Design, accessible tourism and mountain governance within a transferable, process-oriented assessment model. Full article

As a critical component of ice-snow tourism, land cover classification for ski resorts is crucial to ice-snow resource management. However, there is currently a scarcity of datasets and methods capable of high-precision mapping for such fine-grained scenarios. Although Transformers with long-sequence interactions and convolutional neural networks (CNNs) have emerged as mainstream solutions, their performance remains limited on high-resolution remote sensing data characterized by small datasets and high heterogeneity. Targeting land cover classification in ski resort areas, this study proposes a triple-branch segmentation framework integrating CNNs and Transformers to extract global, detail and boundary features (GDBNet), and constructs the first high-resolution ski resort land cover dataset with a resolution of 0.75 m using JiLin-1 satellite constellation (LULC_SKI). The framework employs a backbone combining SegFormer with dual CNN branches. SegFormer captures global semantic context, while dual ResNet-18 branches extract local semantics and edge details respectively. The neck integrates two specialized feature interaction modules, the proposed Pixel-Guided Feature Attention (PG-AFM) and Boundary-Guided Feature Attention (BG-AFM), which synergistically fuse these heterogeneous feature representations for enhanced multi-scale modeling. For the segmentation head, a multi-task learning approach supervises both semantic and edge outputs. LULC_SKI covers seven representative ski resorts in Jilin Province, China, comprising 10,000 multi-seasonal images annotated with six land cover classes, including roads, vegetation, built-up areas, ski runs, water bodies, and cropland. Experiments demonstrate GDBNet achieves 85.44% mIoU and 91.84% mF1 on LULC_SKI, outperforming other advanced models with particularly significant improvements for linear objects like roads and ski runs. Extensive experimental comparisons show that GDBNet delivers consistently excellent performance on both the iSAID and LoveDA datasets, underscoring the superiority of our proposed method. Ablation studies validate the effectiveness of the triple-branch architecture, attention modules, and multi-task supervision. This work proposes a modular framework for land cover classification in complex ski resort scenarios. Full article

The rapid expansion of ski tourism in Northeast China has triggered extensive land use and land cover change (LULCC), yet the micro-scale spatial mechanisms linking historical land conversion to the accessibility of tourist services remain largely unquantified. This study addresses this gap by integrating annual 30 m CLCD land cover data with GIS network analysis of Points of Interest (POIs) around 30 major ski resorts (2018–2023). Specifically, it makes a novel distinction between the accessibility outcomes of construction-oriented and agriculture-oriented land transitions. Results indicate that while forest-to-construction conversion significantly predicts reduced travel distances to services (e.g., hotels: r = −0.532, p < 0.01), a distinct and previously unreported agri-tourism synergy emerges: forest-to-cropland conversion is positively associated with higher per capita tourist spending (r = 0.366, p < 0.05). This finding challenges the conventional zero-sum view of land use competition and suggests that cultivated landscapes can function as complementary tourism assets. These empirical patterns provide an evidence-based framework for integrated land-transport planning in emerging winter sports destinations. Full article

This manuscript explores a key challenge of sustainability, one of climate change vulnerability across diverse destinations. We propose a cross-pollination approach, examining destinations via a novel, dual framework: the relational perspective of actor-network theory (ANT) and the strategic focus of resource dependence theory (RDT). Applying actor-network theory to compare risks of climate change in a tourism setting, this study broadens academic inquiry, reinforces the legitimacy of trans-destination studies, and offers potential managerial insights to improve management and increased sustainability in the context of uncertainty. We advocate for a cross-pollination approach to research, arguing that insights from one context can enrich understanding and innovation in the other. Examining two types of destinations, ski and islands, this research suggests that sharing can contribute to a deeper understanding of destination resilience, mitigation and adaptation, encouraging more holistic and interdisciplinary strategies to address global vulnerabilities and challenges from a destination management and development lens. Full article

The positive economic, social, and environmental influences of the active sport tourism market are well documented today. This study aimed to map brand association patterns across the different stages of the Psychological Continuum Model (PCM) within the context of winter skiing. The PCM was used as the theoretical framework to categorize participants into stages according to their skiing involvement levels. The data was collected from recreational skiers at two major ski resorts in Greece. Participants were classified into the PCM stages. The findings revealed that associations with the activity significantly discriminated against PCM stages. Product delivery associations were salient only at the Attraction stage, indicating the importance of functional evaluations for novice participants. In contrast, tradition and peer acceptance associations consistently predicted membership across all stages, highlighting their enduring symbolic and social relevance. Escape-related associations were diminished in higher commitment levels, whereas importance and affective associations emerged as key predictors in the Attachment and Allegiance stages. The study extends the PCM by integrating brand association theory in the context of sport tourism and offers practical implications for stage-specific branding strategies in participatory sports services. Full article

Snow avalanches pose a serious hazard in snow-covered, mountainous areas. In order to protect inhabited areas and infrastructure such as roads and railway lines, avalanche protection measures need to be taken. In addition to permanent, technical protection measures, temporary, organizational measures, which are based on risk assessments by local avalanche warning commissions, are utilized. These avalanche risk assessments rely on regional avalanche bulletins, weather forecasts, local expertise, and information on current snowpack conditions. Our research seeks to enhance knowledge of current snowpack and avalanche conditions by providing in situ monitoring of potential avalanche slopes. Therefore, we developed a novel sensor box array, peakr, consisting of multiple sensor units deployed by hand or by drone at key avalanche slope locations throughout the winter season. The sensors continuously measure temperature, humidity, position, and snowpack movement. Data are transmitted via LoRaWAN and GSM, stored locally, and accessed through a web platform. Automated analysis using a decision tree and event-detection algorithm triggers immediate alerts to responsible personnel via SMS and email. This paper presents an overview of the peakr sensor array and web platform, focusing on data analysis and avalanche events from the Arlberg ski resort in winter 2023/2024, supported by webcam time-lapse validation. Full article

When snow is present, the snow surface is the interface between the atmosphere and the Earth’s surface. The snowpack energy balance is dictated in part by snow surface roughness, which can be quite dynamic. At the Sierra Nevada ski resort in Spain, we measured several snow surface forms: natural, with the presence of dust, with the presence of sun cups, and groomed snow (tracked and between tracks). The snow surface was assessed in 2-dimensions from snow roughness boards and in 3-dimensions from iPad surface scanning to measure across resolutions. Both data collection methods yielded similar roughness estimates via random roughness (RR) and variogram analysis (scale break, SB, and fractal dimension, D) for each distinct surface, yet the roughness differences between the surfaces were substantial. The geometry-based aerodynamic roughness length ($z_0$) was computed for the iPad-scanned surfaces, yielding an order-of-magnitude variability in $z_0$. This produced an order-of-magnitude difference in modelled sublimation. This work can inform snow management at ski areas and reflects some of the snow-surface conditions encountered in a natural snowpack. Full article

As global climate change intensifies, research on low-carbon practices has become a critical component of sustainable tourism development. The carbon emission profile of ski tourism differs significantly from other tourism sectors. Ski resorts have a mountainous terrain and typically maintain relatively high levels of vegetation, endowing them with inherent advantages for pioneering low-carbon and sustainable tourism practices. However, the substantial energy demands associated with artificial snowmaking systems and advanced infrastructure pose significant challenges to reducing carbon emissions in ski resort operations. This study gathers first-hand data on sustainable tourism development in the Chongli ski resort—the region that hosted the 2022 Winter Olympics—through field investigations and interviews with key industry stakeholders. It develops a comprehensive framework accounting for carbon emissions in ski resorts by integrating input–output analysis with enterprise-level data, focusing on four core operational sectors: catering, skiing, wholesale and retail, and leasing and business services. Furthermore, this study examines the coupling relationship between carbon emissions and operating revenue. Using correlation and regression analyses, this study identifies the key drivers of carbon emissions across these operational departments within the ski tourism sector. The results indicate that carbon emissions from these four sectors in the Chongli ski resort exhibit periodic fluctuations with an overall upward trend year by year. Nevertheless, progress in low-carbon development is evident, suggesting that the resort is on a trajectory toward achieving peak carbon emissions and eventual carbon neutrality. The inclusion of natural endowments, market-scale effects, festival and special events, and capital investment in ski tourism collectively serve as crucial drivers for low-carbon sustainability in Chongli. Based on these findings, this study proposes targeted recommendations to support low-carbon sustainable development, offering scientific insights for similar Winter Olympics host cities. This study integrates top-down input–output analysis with bottom-up enterprise data, taking Chongli, the host city of the Winter Olympics, as a timely case study. It constructs a four-dimensional low-carbon development model based on the identification of key natural, social, and economic driving factors, and strengthens the reliability of the conclusion by relying on first-hand field research and operator interview data. Our study provides an analysis of methodological innovation, framework integrity, and solid empirical evidence that accounts for micro-scale carbon emissions in ski resorts. Full article

GPS tracking has made ski injury data abundant, yet few studies have mapped where incidents actually occur or how those patterns differ between skiers and snowboarders. To address this gap, we analyzed 8719 GPS-located incidents (4196 skier; 4523 snowboarder) spanning four seasons (2017–2022, excluding 2019–2020 due to COVID-19) at a large West Coast resort in California. Incidents were aggregated into 45 m hexagons and analyzed using Getis–Ord Gi* hot spot analysis, Local Outlier Analysis (LOA), and a space–time cube with time-series clustering. Hot spot analysis identified both activity-specific and overlapping high-injury concentrations at the 99% confidence level (p < 0.01). The LOA revealed no spatial overlap between skier and snowboarder High-High classifications (areas with high incident counts surrounded by other high-count areas) at the 95% confidence level. Temporal analysis exposed distinct patterns by activity: Time Series Clustering revealed skier incidents concentrated at holiday-sensitive locations versus stable zones, while snowboarder incidents separated into sustained high-activity versus baseline areas. These findings indicate universal safety strategies may be insufficient; targeted, activity-specific interventions may warrant investigation. The methodology provides a reproducible framework for spatial injury surveillance applicable across the ski industry. Full article

Artificial (technical) snow production is an increasingly common practice in alpine regions, yet little is known about its role in shaping microbial communities at the molecular level. In this study, we combined culture-based methods with high-throughput 16S rRNA gene sequencing and functional trait prediction (FAPROTAX) to investigate bacterial communities across the full technical snowmaking cycle in one of Polish ski resorts. The molecular profiling revealed that technical snow harbors dominant taxa with known cold-adaptation mechanisms, biofilm-forming abilities, and stress tolerance traits (e.g., Brevundimonas, Lapillicoccus, Massilia, with a relative abundance of 2.95, 2.14, 3.38 and 5.61%, respectively). Functional inference revealed a consistent dominance of chemoheterotrophy (up to 38% in relative abundance) and aerobic chemoheterotrophy (up to 36%), with localized enrichment of fermentation (6.9% in cannon filter and 6.5% in sediment) and aromatic compound degradation (3.7% in source waters, 3.8% in cannon filter and 4.6% in sediment). Opportunistic and potentially pathogenic genera (e.g., Acinetobacter, Flavobacterium, Nocardia) persisted in sediments (7.4%, 21.4% and 3.5%) and meltwater (34.9% and 2.31% for the latter two), raising concerns about their environmental reintroduction. Our findings indicate that technical snowmaking systems act as selective environments not only for microbial survival but also for the persistence of molecular traits relevant to environmental resilience and potential pathogenicity. Our study provides a molecular ecological framework for assessing the impacts of snowmaking on alpine ecosystems and underscores the importance of monitoring microbial functions in addition to taxonomic composition. Full article

The rapid expansion of ski tourism and climate change-induced snow shortages have led to intensified ski run maintenance, including extensive earthworks, artificial snowmaking, and regular snow grooming. While these activities are known to cause significant land degradation, quantitative geomorphological studies, specifically on the effects of snow grooming, are limited. This study addresses this knowledge gap by quantitatively assessing the impact of snow grooming on erosion processes and hillslope morphology by comparing them with natural landforms. We achieved this by determining the spatial distribution, morphometry, and long-term persistence of studied landforms. The study area consisted of a unique ski resort at Kasprowy Wierch, which does not use artificial snowmaking or extensive earthworks. We combined detailed field mapping with the analysis of multi-temporal Digital Elevation Models (DEMs) and orthophotos from 2012, 2019, 2020, and 2023. Our methodology also included the calculation of volumetric changes using the DEM of Difference (DoD) analysis. We distinguished two groups of eroded areas, natural landforms (e.g., shallow landslides, debris flow tracks, nivation niches) and snow groomer-induced forms, which were concentrated on ski runs. Natural landforms were elongated and deeper, with higher edges, clustered along debris flow tracks, and occurred on steeper slopes (mean 26.8°). They were more persistent and extensive, with a total area ranging from 3891 m² in 2012 to 3452 m² in 2023. In contrast, groomer-eroded landforms, located on gentler slopes (mean 23.4°), were smaller, more angular, less persistent, and concentrated on narrower, intensively used ski run sections. Their total area decreased from 2122.71 m² to 1762.25 m² over the same period, despite an increase in their count. The volumetric analysis revealed distinct dynamics: over the long term (2012–2023), natural forms showed a total deposition of +8.196 m³, while groomer-eroded forms experienced total erosion of −2.070 m³. During an extreme rainfall event in 2020, natural landforms experienced vast erosion of −163.651 m³, nearly five times greater than the −33.765 m³ observed on snow groomer-eroded landforms, demonstrating their greater susceptibility to high-magnitude events. Importantly, a comparison with other studies reveals that the scale of erosion from snow grooming is relatively small compared to the severe impacts of artificial snowmaking. Our findings are relevant for managing protected areas, such as Tatra National Park, where the focus should be on mitigating anthropogenic impacts to preserve natural processes, which in turn implies that the development of new ski infrastructure should be prohibited. Full article

Climate change and declining economic revenues are driving the closure of many ski resorts in mountainous regions worldwide, particularly at lower elevations, where winter snow cover is becoming increasingly sporadic. This abandonment is impacting wide areas of the Alps, previously managed to reduce erosion and to control trees/shrubs encroachment. As result, natural rewilding processes may lead either to the environmental degradation or to the restoration of pre-disturbance conditions, each with different implications for sustainability. Our aim was to assess the rewilding state and the drivers of sustainability at an abandoned ski resort in the Italian Alps (Valcanale, Bergamo), where the ecosystem has been evolving under minimal human pressure since the ski facilities were decommissioned in 1993. The assessment focused on pedological/vegetational perspectives, with particular attention to soil-based ecosystem services (SBESs). The results show that the interventions made during ski run construction significantly influenced the recovery of SBESs (and thus their long-term sustainability). Areas with minimal disturbance (e.g., forest vegetation removal without soil movement) now support SBESs at levels comparable to nearby undisturbed areas. Conversely, ski runs that underwent slope reshaping/grading support poorly developed soils and significant sheet/gully erosion, rendering them hazardous for pedestrians. Nevertheless, plant biodiversity has benefited in some cases, as many rare/endemic protected species colonize stony/eroded ski runs soils, extending their distribution beyond their original habitat. Full article

Data centers are a part of the digital economy, but they are great contributors to energy use and carbon emissions. The site selection for data centers can, therefore, be an important tool in the optimization of energy efficiency and sustainability, especially in those areas where climatic and infrastructural factors can keep environmental impacts as low as possible. This study will evaluate seven possible sites for a data center in Turkey, focusing on high-altitude ski resorts with natural cooling advantages. In the paper, the research uses q-Rung Orthopair Fuzzy Numbers (q-ROFs) in combination with methods like Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), taking environmental, technical, and socio-economic criteria into consideration, such as energy efficiency, disaster risk, infrastructure quality, and accessibility. To strengthen result reliability, a sensitivity analysis by varying the q parameter and a comprehensive study by performing different multi-criteria decision-making (MCDM) methods were employed. Analysis revealed that Erciyes and Uludağ consistently ranked among the top alternatives across all methods and q values, indicating their robustness. The results confirm the effectiveness of the proposed methodology in supporting sustainable and high-altitude-aware site selection decisions. This research presents a holistic and Sustainable Site Selection framework for data centers; therefore, it provides actionable insights into stakeholders who balance operational efficiency with environmental responsibility. Future studies should subsequently delve into dynamic modeling and global applications to enhance the versatility of the framework. Full article

The construction of sports spaces such as ski resorts leads to deforestation, soil degradation and carbon (C) loss. However, the impact of ski pistes on soil C and nutrients remains unclear. The impact of an 18-year-old ski piste operation on the stock and stoichiometry of soil C, nitrogen (N), phosphorus (P), bulk density, and water content across a 0–100 cm profile in a forest area in Northeast China was quantitatively assessed using the equivalent soil mass method and fixed depth method. The fixed depth method overestimated soil C, N and P stocks of the SP by 5% to 8% of 0–100 cm stocks of soil C and nutrients relative to the equivalent soil mass method used as a reference. The equivalent soil mass method demonstrated that the ski piste soil C, N, and P stocks were significantly reduced by 27.4%, 21.3%, and 27.5%, respectively, in comparison to the undisturbed forest. Surface layers (0–10 cm) exhibited the highest C and N losses, while deep soil (>50 cm) showed significant C, N and P depletion. The ski piste significantly reduced surface C:N (15.8%) and C:P (38.0%) ratios, indicating decoupled nutrient constraints on C loss. Soil compaction increased bulk density in surface layers (0–10 cm) but reduced it in deeper strata, correlating with altered C physical interdependencies. The findings highlight the vertical stratification of disturbance effects, emphasizing the critical role of stoichiometric controls and methodological considerations in assessing anthropogenic impacts on soil ecosystems. These insights are vital for the sustainable management of ski resorts to mitigate soil degradation. Full article

Although climate warming-induced snow cover reduction, as well as the development of ski tourism in hot and dry countries, is shifting industries toward the use of technical snowmaking, its use raises hydrological, health-related, and environmental concerns. This study was aimed at enhancing our current understanding of the impact of technical snowmaking on the environment and human health. Culturable bacteriological indicators of water quality (Escherichia coli, fecal enterococci, Salmonella, and Staphylococcus), the presence and concentration of antimicrobials, genes determining bacterial antibiotic resistance (ARGs), and next-generation sequencing-based bacterial community composition and diversity were examined from river water, technological reservoirs, and technical snow from five ski resorts. The number of culturable bacteria and prevalence of most ARGs decreased during snowmaking. The concentration of antimicrobial agents changed irregularly, e.g., ofloxacin and erythromycin dropped in the snowmaking process, while cefoxitin was quantified only in technical snow. The bacterial community composition and diversity were altered through the technical snowmaking process, resulting in the survivability of freezing temperatures or the presence of antimicrobial agents. Water storage in reservoirs prior to snowmaking allows us to reduce bacterial and ARG contaminants. Frequent and thorough cleaning of snowmaking devices may aid in reducing the negative impact snowmaking can have on the environment by reducing contaminant input and limiting the disturbance of the ecological balance. Full article