Urban Science

Urban competitiveness and vulnerability have traditionally been studied as analytically distinct dimensions, grounded in the assumption that competitive performance necessarily strengthens urban structures. However, empirical evidence from tourist cities reveals a paradox, as high levels of tourism competitiveness may coexist with cumulative processes of structural fragilisation. This article introduces urban competitive vulnerability—an urban system’s propensity to competitive erosion driven by internal fragility accumulation, even during high performance. Using panel data from six major Spanish tourist cities (Barcelona, Madrid, Valencia, Palma, Seville, and Málaga) over 2014–2024, we develop an integrated framework with four dimensions: tourism competitiveness, sectoral specialisation, territorial pressure, and governance capacity. We construct the Urban Competitive Vulnerability Index (CVI) and test four hypotheses using panel-data models with fixed effects and interaction terms. Results confirm significant positive relationships between tourism competitiveness and structural vulnerability (β = 0.540, p < 0.001). Sectoral specialisation increases vulnerability both directly (β = 0.504, p < 0.001) and indirectly through competitiveness (65.8% mediated effect). Tourist housing intensity significantly increases housing prices (β = 0.288, p < 0.001) and evictions (β = 0.125, p < 0.05). Cities with high prior vulnerability experienced more severe COVID-19 impacts (β = −3.688, p < 0.05) and slower recovery. While limited to Spanish cities, this study provides the first urban-specific framework for competitive vulnerability with direct implications for urban tourism planning and governance.

Accurate occupancy information is critical for optimizing energy efficiency in buildings. Hybrid machine learning models have demonstrated great potential in previous studies; however, their application in passive ultra-low-energy buildings remains underexplored. This study conducts an empirical evaluation of real-time occupancy rate prediction using a CNN-ResNet-RF hybrid model based on multi-source environmental and behavioral data from a passive ultra-low-energy educational building. The model integrates Convolutional Neural Networks (CNN) for local feature extraction, Residual Networks (ResNet) to enhance deep feature representation, and Random Forests (RF) for ensemble-based generalization. Indoor CO₂ concentration exhibits the strongest linear correlation with occupancy rate (r = 0.54), indicating a meaningful association with occupancy dynamics. The model demonstrates strong predictive performance on the test set, with a coefficient of determination (R²) of 0.964, a root mean square error (RMSE) of 0.054, and a residual prediction deviation (RPD) exceeding 5. Compared with baseline models such as CNN, RF, and CNN-RF, the proposed framework exhibits generally lower prediction errors and improved stability. Further lightweight compression experiments reveal that the structured compact CNN-ResNet-RF-25 variant achieves even better accuracy (R² = 0.9748, RMSE = 0.0449, RPD = 6.327) while substantially reducing model complexity, demonstrating strong deployment potential in resource-constrained environments.

Urban expansion presents significant challenges and opportunities for ecological conservation in developing countries, particularly in regions such as the Table Bay Nature Reserve in Cape Town, South Africa, where urban development interfaces with sensitive ecosystems. This article examines the complex dynamics between urban growth and ecological implications in this unique landscape, employing multi-temporal remote sensing techniques to analyze changes over time. By investigating the historical trajectory of urbanization in Table Bay, alongside its impacts on biodiversity and ecosystem services, we aim to underscore the urgent need for sustainable urban planning and conservation strategies. To analyze land use/land cover (LULC) dynamics over a 24-year period, this study leveraged a time series of satellite imagery processed within the Google Earth Engine (GEE) platform. Data can be accessed using their respective collection IDs within the GEE platform. The use of remote sensing tools aligns with Sustainable Development Goal (SDG) 15, which focuses on the protection, restoration, and sustainable use of terrestrial ecosystems. Urban encroachment analysis indicates that approximately 0.324 km² of built-up area expanded directly within the reserve boundary, highlighting a measurable degree of infringement into protected zones. The dominance of built-up and bare land classes highlights the early encroachment of urban infrastructure and anthropogenic disturbance, setting the stage for subsequent land cover transformations observed in later years (2012 and 2024). These findings demonstrate a persistent trend of urban encroachment and ecological alteration within the Table Bay Nature Reserve. With the increase in global population levels, urban expansion into protected conservation areas has become a critical environmental concern, threatening biodiversity globally. This challenge is particularly acute in developing countries as seen in regions like the Table Bay Nature Reserve in Cape Town, South Africa, where urban development is interfaced with sensitive ecosystems.