Search Results (9,526)

The Ganges–Brahmaputra–Meghna (GBM) delta is one of the most densely populated and flood-prone regions in the world. Identifying the exposure patterns of settlement expansion under different flood hazard levels in the GBM delta is of significant importance for enhancing the delta’s regional resilience. This research regionally screens settlement flood exposure by overlaying the Global Urban Expansion Simulation Dataset and the Aqueduct Floods Hazard Map at a 1 km spatial resolution. To account for inter-model variability, this study utilized the ensemble mean of five global climate models for future projections in 2030 and 2050 under SSP2-4.5 and SSP5-8.5 scenarios. Flood hazards were categorized into four specific levels based on inundation depth, namely low-hazard (0–0.15 m), medium-hazard (0.15–0.5 m), high-hazard (0.5–1.5 m), and highest-hazard (≥1.5 m). The study employed spatial overlay analysis and excluded missing pixels to avoid statistical bias from incomplete data. The findings indicate that under historical and future socioeconomic scenarios, both high- and highest-hazard zones exhibit significant settlement expansion, and the expansion rate within highest-hazard zones (270.9–357.1%) is expected to increase substantially compared to the historical baseline, reaching 1.57–1.85 times the expansion rate of flood-safe zones. Within the high- and highest-hazard categories, the contribution rate of fluvial and coastal flood coincidence zones reaches 21% to 22%. Furthermore, approximately 87% of the settlements within these fluvial–coastal coincidence zones are exposed to high-hazard levels or above. This study characterizes the variation characteristics of settlement exposure within fluvial–coastal flood coincidence zones under future socioeconomic scenarios. These results provide a first-order regional screening and macro-scale support for identifying broad exposure trends and establishing a baseline for future high-resolution assessments in the GBM delta. Full article

Coastal ecosystems are highly dynamic and vulnerable to climate change, sea-level rise, and rapid urbanization. However, many landscape ecological risk assessments are limited by fixed scales and assumptions of spatial uniformity. This study develops a geomorphology-based framework to analyze coastal ecological risk. Using multi-source data from 1980 to 2020, the optimal analytical scale was identified as 120 m (grain) and 1000 m (extent). An integrated approach combining OPGD, XGBoost–SHAP, and restricted cubic spline (RCS) models was applied to examine risk patterns and driving mechanisms across four coastal types in Guangdong, China. The results show that the importance and interactions of driving factors vary significantly among geomorphic types, with clear nonlinear responses. Key statistical thresholds were identified, indicating ranges where risk sensitivity changes, including NDVI ≈ 0.624 in the Hilly Ria Coast, slope ≈ 2.8° in the Platform Ria Coast, elevation ≈ 14.5 m in the Barrier–Lagoon Coast, and GDP ≈ 1644.65 × 10⁶ CNY/km² in the Estuarine Delta Coast. These findings provide quantitative evidence for understanding spatial heterogeneity and the nonlinear dynamics of coastal ecological risk, and offer practical references for adaptive management. Full article

This paper examines the subjective annoyance associated with shared-bike electronic alert sounds (SBeASs), an emerging urban noise source. A study was conducted by employing extensive questionnaire surveys and psychoacoustic experiments. A preliminary survey (N = 1340) indicated that 90.6% of participants reported being impacted by SBeASs, with pronounced effects on nighttime rest and daytime work efficiency. In this study, SBeAS samples were taken from three prominent Chinese bike-sharing brands: Hello Bike, Meituan Bike, and DiDi Bike. Under laboratory conditions, subjective annoyance assessments (N = 28) for SBeASs were conducted at controlled sound pressure levels (SPLs) ranging from 45 to 65 dBA, with occurrence frequencies of 1, 3, and 5 s. Simultaneously, annoyance assessments were also conducted for two reference noise types: traffic noise and street noise. The results indicated a notable increase in annoyance levels related to SBeASs with rising SPL and increased occurrence frequency. Minor variations in annoyance were identified among different bike-sharing brands, which can be attributed to their distinct acoustic features. When the SPL was above 55 dBA, the DiDi Bike SBeASs produced considerably higher annoyance than those of other brands. This can be attributed to its elevated low-frequency energy, loudness, and roughness. Moreover, individuals exhibiting increased sensitivity to noise reported notably higher annoyance ratings on the SBeAS scale (p = 0.019). Under low-SPL conditions (45–55 dBA), the annoyance attributed to frequent SBeASs can exceed that caused by traffic noise and street noise at comparable SPLs, highlighting the distinct disruptive impact of abrupt sound sources. Full article

The detection and monitoring of asbestos–cement roofing remain a critical public health and environmental challenge, especially in urban and suburban areas where asbestos-containing materials are still widespread due to their extensive use in the 20th century. Although hyperspectral and high-resolution multispectral remote sensing have proven effective for mapping asbestos–cement roofs, many existing approaches rely on proprietary software, limiting transparency, reproducibility, and large-scale adoption. This study presents a fully reproducible, cost-free Python-based workflow for the detection and temporal monitoring of asbestos–cement roofing using high-resolution multispectral WorldView-3 imagery. The workflow integrates atmospheric correction (using the Py6S radiative transfer model), spatial preprocessing, supervised pixel-based classification, postprocessing, and building-level aggregation within an open framework. A Maximum Likelihood Classifier is applied to VNIR and SWIR data using empirically defined roof typologies to enhance class separability. Pixel-level results are aggregated to the building scale through adaptive thresholding enabling the translation of spectral classifications into meaningful building-level information. Tested over the city of Mantua (Italy), the approach achieved reliable classification performance and enabled multi-temporal comparison to identify changes potentially due to roof remediation. Evaluation metrics (precision, recall, and F1-score) highlight the importance of carefully choosing the building-level threshold. By relying exclusively on open-source tools, the workflow enhances transparency, reproducibility, and scalability for long-term monitoring. Full article

Urban agglomerations have become the dominant spatial platform of urbanization, regional coordination, and economic transformation in China. Yet whether the expansion of agglomeration scale at the urban-agglomeration level alleviates or intensifies energy use remains insufficiently understood. Extending the scale of analysis from individual cities to integrated urban agglomerations, this study investigates 64 cities in four major Chinese urban agglomerations, including Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu–Chongqing, over the period 2006–2023. Using panel data models, this study examines the impact of the scale agglomeration within urban agglomeration on urban energy intensity. The results show that the overall agglomeration scale generated by urban agglomeration formation significantly suppresses energy intensity while indicating a robust energy-saving effect: every 10% increase in agglomeration scale is associated with a decline of approximately 0.0893 million tons of standard coal per CNY 100 million of GDP. This finding remains stable after addressing endogeneity concerns and performing a series of robustness checks. Mechanism analyses further suggest that this effect operates primarily through talent agglomeration, technological progress, and public transportation expansion. In addition, the energy-saving effect is more pronounced in smaller cities, cities with lower administrative rank, cities with weaker factor mobility, and cities characterized by poorer air quality but stronger public environmental attention. These findings contribute to the literature on urban agglomeration and green development by showing that the agglomeration scale within urban agglomerations can generate inclusive energy-efficiency gains, especially for relatively disadvantaged cities, thereby offering important implications for spatial governance and low-carbon transition in rapidly urbanizing economies. Full article

In the context of climate change and the challenge of strengthening urban biodiversity, this paper examines the potential of speculative drawing as a methodological tool for developing biodiversity scenarios of the cityscape within the framework of the New European Bauhaus initiative. The research is based on the initiative’s core values of beautiful, sustainable, and together, and is conducted using a drawing-based methodology grounded in inductive reasoning across three spatial scales in Block 30, which is part of the spatial cultural-historical unit of the Central Zone of New Belgrade. The potentials for biodiversity development are explored at the scale of the apartment, the facade, and the open space of the block. By examining the interactions between the indoor and open spaces of mass housing, ecological potentials emerge. The experimental process demonstrates that drawing can function as a methodological tool that reveals opportunities for community engagement through drawing practices. The proposed layering of drawings offers interpretations of cityscape transformation at each of the three scales. Through speculative scenarios, the drawings provide a methodological tool to co-create biodiversity interventions in mass housing as a sensitive architectural layer within the design process, fostering a new understanding of the relationship between nature and the cityscape. Full article

Mediterranean water systems face intensifying drought pressure under climate change, yet the long-term macroeconomic consequences of recurrent water restrictions remain largely unquantified at the metropolitan scale. This study estimates the cumulative economic costs of drought-induced water restrictions in the Barcelona Metropolitan Area (AMB) over 2016–2099 using a supply-driven Input–Output (Ghosh) model driven by six hydro-climatic projections. Drought conditions persist in more than half of all simulated months across all climate projections, generating substantial cumulative undiscounted losses of €52–61 billion through repeated restriction episodes rather than isolated extreme events. The present value of total GDP losses ranges between €8.4 and €41.4 billion depending on the discount rate applied (1%, 3% and 5%). Losses concentrate in service sectors due to strong intersectoral propagation effects, despite agriculture exhibiting the highest direct water dependence. The framework provides a transferable approach for assessing long-term climate-driven drought costs in metropolitan urban or regional economies. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Protected areas are increasingly expected to reconcile biodiversity conservation with socio-economic sustainability, yet operational tools for assessing local sustainability are limited. This study develops a replicable viability index as an operationalization of socio-economic sustainability at the settlement scale, focusing on the capacity of rural communities to maintain demographic balance and housing dynamics over time. The framework was applied to the West Estonian Archipelago Biosphere Reserve (WEABR), an inhabited UNESCO “Man and the Biosphere” site. Using harmonized census data from 1979 to 2021, the index combines three village-level binary indicators: population dynamics, residential construction activity, and demographic balance. Binary scoring reduces statistical volatility in small settlements and enables comparison across time. Approximately 60% of rural settlements remained viable over four decades, while highly viable settlements declined from 14% to 7%. Population stabilization increased, but ageing intensified and new construction decreased. Viability concentrates near urban centres, ports, transportation corridors, and coastal areas, while inland peripheral villages stagnate. Compared with mainland rural Estonia, WEABR shows a relatively resilient middle tier of viable settlements. The framework provides a transferable tool for monitoring settlement level socio-economic sustainability in inhabited protected areas. Full article

China’s rapid economic growth is partly due to the productivity gains from agglomeration, whereby firms and workers in larger and denser cities benefit from proximity through knowledge spillovers, thicker labor markets, and shared infrastructure. This study examines the changing nature and location of agglomeration economies in China using resident-based measures of urban scale from the 2000, 2010, and 2020 population censuses. Chinese “cities” are administrative jurisdictions that contain both dense urban districts and lower-density counties, so the agglomeration elasticities are estimated separately for districts and counties for a balanced panel of 298 prefectural jurisdictions. Agglomeration economies occur only in urban districts, while coefficients on urban scale for counties and county-level cities are close to zero or significantly negative. Moreover, district-level elasticities decline over time, from 0.24 in 2000 to 0.15 in 2020, assuming no feedback from productivity to urban scale. Allowing for such feedback, the temporal decline is even greater, from 0.24 in 2000 to 0.08 in 2020. However, urban growth is shifting increasingly toward counties rather than districts, foregoing the potential agglomeration effects. Changes in location of construction workers also shows this dispersed urban growth. Hence, recent urban growth is increasingly in locations without agglomeration benefits. Full article

Land subsidence (LS) is a widespread geoenvironmental problem driven by both natural processes and human activities. Identifying the main factors controlling LS susceptibility and their spatial contribution patterns is essential for LS management and mitigation. In this study, an interpretable earth observation framework was developed for the North China Plain (NCP) to quantify both spatial and non-spatial contributions of dominant LS drivers. Land displacement was derived from Sentinel-1A SAR images using Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) processing. The displacement map was then combined with nine geoenvironmental variables to construct an LS susceptibility model using the eXtreme Gradient-Boosting (XGBoost) algorithm. The model performed well, with an R² of 0.96, an EVS of 0.96, and an MAE of 2.25 mm/yr. SHapley Additive exPlanations (SHAP) analysis was employed to quantify feature contributions and their effects on LS susceptibility. The results show that a deep groundwater level (DGL) was the dominant factor, followed by elevation and a shallow groundwater level (SGL). The effect of DGL was strongest when it ranged from −75 to 20 m. Elevation showed a clear effect on LS occurrence when values fall between 30 and 50 m. Relatively high subsidence sensitivity was mainly observed in areas where SGL was below −7 m. Interaction effects, particularly those between DGL and elevation and between DGL and SGL, further increased LS susceptibility in specific areas. The highest predicted susceptibility occurred in areas with DGL below −20 m and elevations below 30 m. Higher susceptibility was also identified where DGL was high and SGL ranged between −20 and −10 m, and where DGL was low and SGL ranged from 15 to 20 m. In contrast, factors such as slope and aspect had limited influence at the regional scale. The contributions of the predominant factors show obvious marginal effects and significant spatial heterogeneity to LS susceptibility. The results clarify where and how key factors shape subsidence and can inform targeted mitigation measures and urban planning by local authorities. Full article

Dietary transition is reshaping cropland demand and intensifying the challenge of matching food demand with land supply in rapidly urbanizing regions. This study examines how different dietary structure scenarios generate differentiated cropland demand, how these demands match with land supply under alternative development pathways, and how the land system responds when diet-driven demand is incorporated into land-use simulation. Using Jiangsu Province, China, as a case study, we developed a coupled diet–land simulation framework. On the demand side, five dietary structure scenarios—current, balanced, U.S., Japanese, and Greek—were constructed based on seven food categories, and their cropland demand in 2035 and 2050 was estimated using the cropland footprint approach and LSTM forecasting. On the supply side, the GeoSOS-FLUS model was used to simulate future land-use patterns under four development scenarios: natural development, cultivated land protection, ecological protection, and economic development. The cropland demand associated with each dietary scenario was then introduced into the land-use simulation process as an external demand constraint to identify land-system feedbacks and scenario differences. The results show that cropland demand differs markedly across dietary scenarios, forming a clear gradient from moderate-demand to high-demand diets. These differences are driven primarily by changes in the composition of key food categories, especially grains, livestock and poultry meat, plant oils, and fruits, rather than by proportional increases across all foods. In terms of supply–demand matching, the cultivated land protection scenario provides the strongest support for high-demand diets, whereas the natural development, ecological protection, and economic development scenarios are more compatible with moderate-demand dietary pathways. Once diet-driven demand is incorporated into land-use simulation, the land system shows clear sensitivity and strong scenario dependence. High-demand dietary scenarios intensify cropland compensation pressure and trigger structural reallocation among cultivated land and flexible land types. Under natural development, the response is mainly reflected in cropland expansion and grassland compression; under cultivated land protection and ecological protection, it is expressed more through substitutions among grassland, water bodies, and unused land; under economic development, the most prominent feedback is the competitive reallocation among cultivated land, construction land, and water bodies, with high dietary demand even constraining construction land expansion. Overall, the robustness of cropland supply–demand matching depends not only on the scale of dietary demand but also on how different dietary pathways interact with development-oriented land-use structures. Full article

Dynamic wireless power transfer can reduce electric vehicles’ charging downtime and range anxiety, but its benefits depend on route characteristics and system design. This work develops an integrated numerical framework combining (i) route-specific drive-cycle analysis, (ii) identification of candidate charging segments based on speed, stops and slope constraints, (iii) a physics-informed inductive wireless power transfer model and (iv) a Matlab/Simulink vehicle energy model to quantify energy demand, transferred energy and state-of-charge evolution. Two vehicle types (a passenger light-duty vehicle and a light commercial van) and multiple Lisbon Metropolitan Area routes are analyzed, including commuting, ride-hailing and urban logistics operations. Results show that low-speed, stop-rich urban corridors achieve the highest transfer rates (typically 0.4 kWh/km and over 2 kWh for more than 15 stops in the analyzed cases), whereas expressway deployments are much less effective (down to 0.1 kWh/km and 0.5 kWh below 5 stops) unless congestion lowers average speeds. The proposed workflow provides a replicable basis to identify candidate segments and to size wireless power transfer and corridor length for city-scale deployment scenarios. Full article

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process (FAHP) with a rigorous technical benchmarking of multiple navigation APIs to improve routing decisions under volatile Bangkok traffic. By employing a normalized cost function (scale 0–1), we evaluated the performance of localized (Longdo Map) versus global (Google Maps and OpenStreetMap) platforms across day and night scenarios. Results: Experimental results, yielding normalized costs between 0.464 and 0.748, identified Bon Kai as the optimal response node, whereas Chan Road showed the lowest efficiency. Interestingly, OpenStreetMap provided the highest temporal consistency for emergency logistics. Conclusions: These findings offer a practical decision-support tool for authorities, proving that integrated API assessment is essential for building resilient and responsive urban mobility infrastructures. Full article

Railways constitute a core component of China’s national comprehensive transportation network, and their spatial organization and temporal accessibility jointly shape transport integration and system efficiency. Identifying their evolution from the dual perspectives of spatial expansion and time compression is therefore of both theoretical and practical significance. Drawing on fractal theory, this study examines the structural characteristics, evolutionary trends, and driving factors of railway networks in China’s five major urban systems from 2014 to 2024 from a “space–time” dual perspective. The results show that railway networks exhibit a staged pattern of “spatial filling preceding temporal correlation”, with a lag of approximately 1–8 years—about 1 year in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), 5 years in the Middle Yangtze River (MYR) region and Beijing–Tianjin–Hebei (BTH), and up to 8 years in the Chengdu–Chongqing (CC) region. In addition, clear regional differences are observed: the Yangtze River Delta (YRD) is polycentric, with the greatest potential, projected to continue rapid spatial growth until 2027 and to remain in a fast-growth phase of temporal correlation; GBA is highly coordinated; BTH is developed but characterized by dual-core agglomeration; CC grows rapidly with lagging functionality; and MYR is corridor-dependent with limited potential. These findings indicate that network functionality does not emerge synchronously with infrastructure expansion, but depends on subsequent improvements in operational organization and service capacity. Compared with single-scale-based indicators, the “spatial distribution–temporal correlation” framework more effectively captures network performance and provides quantitative support for transport optimization and coordinated regional development. Full article