Search Results (296)

Balancing protection and development is essential for mitigating anthropogenic threats and achieving sustainable development in coastal regions. However, integrated spatial planning that links marine protected areas (MPAs) with developed spaces and incorporates land–sea coordination remains insufficiently explored—despite global frameworks such as the “Post-2020 Global Biodiversity Framework” advocating for such integration. In this study, we used Xiamen, a typical bay city in China, as an example, assessed its habitat suitability through the MaxEnt model, and determined its key development areas through hotspot analysis, aiming to coordinate protection and development, as well as land and marine utilization in coastal areas. The results indicate the following: (1) existing protected areas require adjustments; (2) multiple development hotspots overlap, while several cold spots with limited potential for functional development were identified; (3) prioritizing MPAs in decision-making led to an approximate 42.8% increase in MPA coverage in Xiamen. Overall, this study produced a comprehensive plan that integrates both ecological and social objectives. Full article

Urban surface subsidence is primarily induced by intensive above-ground and underground construction activities and excessive groundwater extraction. Integrating InSAR techniques for safety monitoring of urban subway infrastructure is therefore of great significance for urban safety and sustainable development. However, single-track high-spatial-resolution SAR imagery is insufficient to achieve full coverage over large urban areas, and direct mosaicking of inter-track InSAR results may introduce systematic biases, thereby compromising the continuity and consistency of deformation fields at the regional scale. To address this issue, this study proposes an inter-track InSAR correction and mosaicking approach based on the mean vertical deformation difference within overlapping areas, aiming to mitigate the overall offset between deformation results derived from different tracks and to construct a spatially continuous urban surface deformation field. Based on the fused deformation results, subsidence characteristics along subway lines and in key urban infrastructures were further analyzed. The main urban area and the eastern and western new districts of Zhengzhou, a national central city in China, were selected as the study area. A total of 16 Radarsat-2 SAR scenes acquired from two tracks during 2022–2024, with a spatial resolution of 3 m, were processed using the SBAS-InSAR technique to retrieve surface deformation. The results indicate that the mean deformation rate difference in the overlapping areas between the two SAR tracks is approximately −5.54 mm/a. After applying the difference-constrained correction, the coefficient of determination (R²) between the mosaicked InSAR results and leveling observations increased to 0.739, while the MAE and RMSE decreased to 4.706 and 5.538 mm, respectively, demonstrating good stability in achieving inter-track consistency and continuous regional deformation representation. Analysis of the corrected InSAR results reveals that, during 2022–2024, areas exhibiting uplift and subsidence trends accounted for 37.6% and 62.4% of the study area, respectively, while the proportions of cumulative subsidence and uplift areas were 66.45% and 33.55%. In the main urban area, surface deformation rates are generally stable and predominantly within ±5 mm/a, whereas subsidence rates in the eastern new district are significantly higher than those in the main urban area and the western new district. Along subway lines, deformation rates are mainly within ±5 mm/a, with relatively larger deformation observed only in localized sections of the eastern segment of Line 1. Further analysis of typical zones along the subway corridors shows that densely built areas in the western part of the main urban area remain relatively stable, while building-concentrated areas in the eastern region exhibit a persistent relative subsidence trend. Overall, the results demonstrate that the proposed inter-track InSAR mosaicking method based on the mean deformation difference in overlapping areas can effectively support subsidence monitoring and spatial pattern identification along urban subway lines and key regions under relative calibration conditions, providing reliable remote sensing information for refined urban management and infrastructure risk assessment. Full article

Objectives: This study aimed to investigate the protective effect of the Changchun Baike varicella vaccine in Yanji City from 2018 to 2024. Methods: Varicella surveillance data from 2018 to 2024 and vaccination records from 2018 to 2020 were collected from the China Disease Prevention and Control Information System and analyzed. Results: In total, 2452 varicella cases were reported in Yanji from 2018 to 2024, with an average annual incidence rate of 62.71 per 100,000 population. Notably, the annual incidence rate decreased from 142.37 per 100,000 in 2018 to 55.25 per 100,000 population in 2024. Additionally, the highest and lowest incidence rates were observed in the 10–14 and ≥40 years age groups, respectively. Moreover, the vaccine demonstrated high protective effectiveness of 98.0–99.0% for one dose and 99.0% for two doses across the study period. These estimates were derived from propensity score-matched cohorts ranging from 686 to 6990 individuals (343 to 3495 matched pairs) across three overlapping observation periods (2018–2022, 2019–2023, and 2020–2024). Conclusions: The two-dose varicella vaccination schedule demonstrated superior protective efficacy compared with the single-dose schedule. Full article

Street trees are vital components of urban ecosystems, contributing to air purification, microclimate regulation, and visual landscape enhancement. Thus, accurate segmentation of individual trees from point clouds is an essential task for effective urban green space management. However, existing methods often struggle with noise, crown overlap, and the complexity of street environments. To address these challenges, this paper introduces a multi-constraint and shortest path optimization method for individual urban street tree segmentation from point clouds. In this paper, object primitives are first generated using multi-constraints based on graph segmentation. Subsequently, trunk points are identified and associated with their corresponding crowns through structural cues. To further improve the robustness of the proposed method under dense and cluttered conditions, the shortest-path optimization and stem-axis distance analysis techniques are proposed to further refine the individual tree extraction results. To evaluate the performance of the proposed method, the WHU-STree benchmark dataset is utilized for testing. Experimental results demonstrate that the proposed method achieves an average F1-score of 0.768 and coverage of 0.803, outperforming superpoint graph structure single-tree classification (SSSC) and nyström spectral clustering (NSC) methods by 17.4% and 43.0%, respectively. The comparison of visual individual tree segmentation results also indicates that the proposed framework offers a reliable solution for street tree detection in complex urban scenes and holds practical value for advancing smart city ecological management. Full article

Air pollutants and greenhouse gases share common sources, primarily originating from human activities such as energy utilization, thus presenting significant potential for synergistic control. Isolated consideration of solutions for either pollution mitigation or carbon reduction increases the unit cost of environmental governance and leads to inconsistencies and overlapping effects in policy measures. This study takes Chengdu, a low-carbon pilot city in China, as a case study. Based on clarifying the characteristics of regional air pollutant emissions and carbon emissions from energy consumption, it empirically investigates the synergistic variation in carbon emissions from diverse socioeconomic industries and multiple air pollutant emissions. The empirical results reveal the following: (1) during the research period, Chengdu’s air quality excellence rate demonstrated continuous improvement. Meanwhile, the carbon emissions from energy consumption exhibited a three-phase developmental pattern. The driving forces of growth had shifted from traditional high-energy-consuming industries to advanced manufacturing, urban basic energy demands, and energy extraction industries serving national strategies. (2) The synergistic reduction in carbon emissions with PM₁₀ and PM_2.5 reached relatively high levels from 2016 to 2019, followed by fluctuations due to the impact of the COVID-19 pandemic. The synergistic reduction between carbon emissions and SO₂ exhibited considerable volatility. The electrification trend in transportation significantly promoted the synergistic reduction in carbon emissions and NO₂ emissions. Due to the fact that O₃ is a secondary pollutant with complex sources, achieving synergistic governance with carbon emissions proved more challenging. As a result of technological limitations, the synergistic reduction in carbon emissions and CO gradually exhibited a trend of diminishing marginal effects. The synergistic reduction effects between industry-specific carbon emissions and overall air pollutant emissions can be divided into five categories: sustained high-efficiency, generally stable, fluctuating, sudden-decline, and persistently low. Full article

Innovation in the new energy industry serves not only as a key accelerator for the global green and low-carbon energy transition but also as a core driving force of the ongoing energy revolution. This study utilizes data on publications, patents, and the spatial distribution of representative innovation enterprises in the new energy industry of the Yangtze River Delta urban agglomeration from 2009 to 2023 to construct a multilayer science–technology–industry innovation network. Social network analysis is employed to examine its evolutionary dynamics and structural characteristics, and the Quadratic Assignment Procedure (QAP) is used to investigate the factors shaping intercity innovation linkages. The results reveal that the multilayer innovation network has continuously expanded in scale, gradually forming a multi-core radiative structure with Shanghai, Nanjing, and Hangzhou at the center. At the cohesive subgroup level, the scientific and technological layers exhibit clear hierarchical differentiation, where core cities tend to engage in strong mutual collaborations, while the industrial layer shows a hub-and-spoke pattern combining large, medium, and small cities. In terms of layer relationships, the centrality of the scientific layer increasingly surpasses that of the technological and industrial layers. Inter-layer degree correlations and overlaps also display a strengthening trend. Furthermore, differences in regional higher education scale, urban economic density, and geographic proximity are found to exert significant influences on scientific, technological, and industrial innovation linkages among cities. In response, this study recommends enhancing the leadership role of core cities, leveraging the bridging and intermediary functions of peripheral cities, and promoting application-driven cross-regional innovation collaboration, thereby building efficient science–technology–industry networks and enhancing intercity innovation linkages and the flow of innovation resources, and ultimately promoting the high-quality development of the regional new energy industry. Full article

Digital Twins are becoming central enablers of Europe’s digital and green transitions, yet their data-intensive and autonomous nature exposes them to one of the most complex regulatory environments in the world. This article presents a comprehensive scoping review of how six principal European digital laws—the General Data Protection Regulation, Data Governance Act, Data Act, Artificial Intelligence Act, NIS2 Directive, and Cyber Resilience Act—jointly govern the design, deployment, and operation of Digital Twin systems. Building on the PRISMA-ScR methodology, the study constructs a Unified Digital Twin Compliance Framework (UDTCF) that consolidates overlapping obligations across data governance, privacy, cybersecurity, transparency, interoperability, and ethical responsibility. The framework is operationalised through a Digital Twin Compliance Evaluation Matrix (DTCEM) that enables qualitative assessment of compliance maturity in research and innovation projects. Applying these tools to representative European cases in Smart Cities, Industrial Manufacturing, Transportation, and Energy Systems reveals strong convergence in data governance, security, and interoperability, but also persistent gaps in the transparency, explainability, and accountability of AI-driven components. The findings demonstrate that European digital legislation forms a coherent yet fragmented ecosystem that increasingly requires integration through compliance-by-design methodologies. The article concludes that Digital Twins can act not only as regulated technologies but also as compliance infrastructures themselves, embedding legal, ethical, and technical safeguards that reinforce Europe’s vision for trustworthy, resilient, and human-centric digital transformation. Full article

This study develops a GIS-based, heritage-sensitive urban lighting framework for Niğde, Türkiye, integrating Sentinel-2 MSI Level-2A imagery (10 m), ASTER DEM, and municipal cadastral data. Five spatial criteria—land cover, parks, protected heritage assets, population distribution, and government institutions—were classified through supervised mapping, visibility analysis, and architectural integrity assessment. All layers were standardized and combined using a weighted-overlay approach, supported by sensitivity testing across three weighting scenarios to ensure model robustness. Priority zones are concentrated in the historic core, where cultural landmarks, central parks, and high-density residential areas overlap. Peripheral agricultural and rural zones exhibited minimal lighting needs. Field verification and expert consultation demonstrated 82% correspondence between modeled and observed priority and visibility patterns, while a structured nighttime audit and ecological checklist provided additional empirical grounding for lighting sufficiency, glare risks, and biodiversity considerations. Results emphasize context-specific lighting that strengthens cultural identity, improves pedestrian comfort and nighttime legibility, and reduces unnecessary energy use and light pollution. This approach offers a replicable workflow aligned with CIE 150:2017 and IES RP-8-18 guidance. Future work may incorporate dynamic population mobility, AHP-based weighting, and adaptive smart-lighting systems to scale the methodology across similar medium-sized heritage cities seeking balanced aesthetic, cultural, and ecological nighttime environments. Full article

Hyperspectral image (HSI) classification is often challenged by cross-scene domain shifts and limited target annotations. Existing approaches relying on class-agnostic moment matching or confidence-based pseudo-labeling tend to blur decision boundaries, propagate noise, and struggle with spectral overlap and class imbalance. We propose Spectral Prototype Attention Domain Adaptation (SPADA), a framework that integrates an attention-guided spectral–spatial backbone with dual prototype banks and distance-based posterior modeling. SPADA performs global and class-conditional alignment through source supervision, kernel-based distribution matching, and prototype coupling, followed by diversity-aware active adaptation and confidence-calibrated refinement via prior-adjusted self-training. Across multiple cross-scene benchmarks in urban and inter-city scenarios, SPADA consistently outperforms strong baselines in overall accuracy, average accuracy, and Cohen’s $\kappa$, achieving clear gains on classes affected by spectral overlap or imbalance and maintaining low variance across runs, demonstrating robust and stable domain transfer. Full article

Understanding how different population groups interact with urban environments is essential for analyzing spatial dynamics and informing urban planning, especially in cities experiencing high visitor pressure. This study presents a methodological framework for the spatial and temporal delineation of urban areas based on user-generated location-based data. By collecting nearly 1 million Google Maps reviews in the municipality of Donostia-San Sebastián, we identify and classify user profiles based on their spatiotemporal behavior. First, we collect points of interest (POIs) and associated reviews, including profile identifiers and timestamps. Then, we perform user-level webscraping to reconstruct review histories, enabling us to infer the predominant geographical origin of each user. Users are classified as residents or tourists using both spatial prevalence and temporal activity patterns. The resulting data is aggregated onto a hexagonal grid for geostatistical analysis. Using the Getis-Ord Gi* statistic and Mann-Kendall trend tests, we identify hotspots and long-term trends of activity for different population segments. Additionally, we propose novel indicators such as predominant periods of activity and diversity of geographical origin per cell to characterize heterogeneous patterns of urban use. Our results reveal distinct behavioral patterns, highlighting a more evenly distributed use of urban space by residents, with spatially overlapping yet temporally offset activities across central areas where tourists tend to concentrate their interactions. This spatiotemporal concentration is intensified as the tourists’ origin becomes more distant, suggesting that proximity shapes urban engagement. The proposed methodology offers a replicable strategy for urban analysis using publicly accessible user-generated data and contributes to the understanding of sociospatial dynamics in tourism-intensive cities. Full article

Understanding spatial characteristics of urban building systems is critical for unraveling urban building stock growth patterns, addressing housing vacancy challenges, and advancing urban carbon neutrality. However, existing research on built environment stocks and housing vacancy spatial distribution remains limited, particularly in underdeveloped cross-river cities—where rapid urbanization often prioritizes scale expansion over demand matching, leading to unresolved issues of resource waste and environmental pressure. This study integrated material stocks analysis (MSA) and geographical information system (GIS) to uncover the spatial patterns of urban building material stocks and housing vacancy at a high spatial resolution for Nanchang, China—a typical underdeveloped cross-river city facing the “scale expansion trap” in its urbanization across the Ganjiang River. Results show that (1) Nanchang’s building stock exhibits a “butterfly-shaped” spatial pattern centered on the Ganjiang River, with simultaneous horizontal expansion (40-fold urban area growth since 1949) and vertical growth (super high-rises in new west-bank districts), reflecting aggressive cross-river scale expansion; (2) the total building material stock reached 1034 Mt (204 t/cap) in 2021, with over 85% accumulated post-2000—coinciding with large-scale cross-river development. Vacant buildings locked in 405 Mt of materials (39.17%), which is a direct consequence of the “scale expansion trap” where construction outpaced actual demand; (3) total embodied carbon emissions from building materials amounted to 264 Mt, with 104 Mt (39.39%) attributed to vacant stocks. This “vacant carbon lock-in” stems from mismatched urban construction and actual demand in the process of cross-river scale expansion; (4) spatially, high-value clusters of material stocks and carbon emissions overlapped at two cores (old town and Honggutan CBD), while housing vacancy rates were significantly higher in the urban periphery and Ganjiang’s west bank—the primary areas of cross-river scale expansion—than in the old town and east bank. These findings empirically demonstrate how the “scale expansion trap” in cross-river urbanization drives building stock vacancy and carbon lock-in. These findings also offer data-driven strategies for optimizing urban resource allocation, reducing housing vacancy, and promoting low-carbon transitions, especially for other underdeveloped cross-river cities globally. Full article

We present a site-agnostic workflow to identify Fireline Tactical Support Points (FTSPs) and corridors following wildfire where spectral-change proxies (dNBR, RdNBR, and dNDVI) are paired pre/post-fire and co-registered on a 20 m grid together with a 72 h rainfall accumulation layer, which is treated as an operational feasibility and safety overlay, complementing access and terrain. Applied to the Vesuvius National Park (Italy) wildfire episode of August 2025, the pipeline yields suitability/susceptibility surfaces, ranked factors, and corridor candidates, with estimated successes including coherent prioritization within high-severity mosaics, improved continuity toward existing access routes, and reduced overlap with mapped sensitive areas at like-for-like suitability. Low-carbon staging is retained as a design safeguard, while detailed greenhouse-gas accounting is intentionally deferred to future, fleet-resolved multi-criteria analyses. The approach enables rapid, repeatable decision support and is relevant to SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). Full article

Urban energy systems are expected to undergo a rapid transition towards smart, sustainable, and resilient infrastructures. Within this transformation, the interaction between smart buildings and energy grids plays a critical role in shaping future urban energy solutions. Smart building–grid interaction strategies facilitate the bidirectional energy flow between buildings and urban energy systems and support the integration of renewable energy sources (RESs) into cities’ energy systems through advanced control systems, sensing technologies, and digital infrastructures. However, the adoption of these solutions remains complex due to fragmented key performance indicators (KPIs) and the diversity of enabling technologies, and it requires accurate performance-driven design and operation. Despite recent advancements, the management and evaluation of the interaction of smart buildings and urban energy systems remain challenging due to overlapping and fragmented KPIs as well as the complexity of enabling technologies. Therefore, this study aims to review the recently published research works and provide a holistic taxonomy of KPIs and enabling technologies for such interplay between smart buildings and urban energy systems to achieve the goal of sustainable energy transition in cities. The study identifies and categorizes several existing KPIs across sustainability dimensions, including technical, environmental, economic, and social, covering the KPIs to measure the performance of smart building–urban energy systems from a sustainability-aware lens, offering an integrative framework for assessing urban energy resilience and efficiency. Additionally, the study contributes to classifying the enabling technologies for smart building and urban energy system interaction and discusses the interdependencies among such technology clusters. The findings contribute to ongoing urban energy transitions by promoting systemic approaches to planning, performance evaluation, and decision-making for sustainable and equitable urban energy futures. This contributes to the sustainability of the building and energy sectors at the urban scale by promoting and helping multi-dimensional performance assessment and informed decision-making. Full article

Access to safe water and sanitation remains a pressing challenge in Sub-Saharan Africa. Rapid urbanisation, fragile governance, and increasing climate hazards continue to undermine the sustainability of WASH (Water, Sanitation and Hygiene) services. This study examines whether Mozambique’s normative and institutional framework effectively supports sustainable urban WASH service delivery in Beira, the country’s second-largest city. Combining a critical policy review with six semi-structured interviews involving institutional actors and community leaders, the research employs a qualitative, phenomenological design to explore the interaction between national frameworks and local practices. Findings reveal five interrelated dimensions shaping sustainability: governance coordination, infrastructure robustness and maintenance, community participation, climate resilience, and financial viability. Although post-disaster investments and recent policy reforms have led to improvements, significant challenges persist. These include overlapping institutional mandates, underdeveloped preventive maintenance systems, limited recognition and support for community-led initiatives, fragmented climate adaptation efforts, and strong dependence on external funding. The study also reveals how historical legacies, particularly colonial-era governance structures, continue to shape water and sanitation delivery. By integrating policy analysis with local perspectives, the paper contributes to debates on WASH sustainability in African cities, particularly in climate-vulnerable secondary urban centres. It highlights the need for systemic reforms that clarify institutional roles, institutionalise maintenance practices, formalise community engagement, embed nature-based adaptation strategies, and strengthen financial transparency. These changes are essential if Beira, and similar cities across sub-Saharan Africa, are to achieve Sustainable Development Goal 6 under mounting climate pressure. Full article

Traffic calming zones (TCZs) are increasingly being implemented in urban areas to enhance road safety, reduce vehicle speeds, and support sustainable mobility. However, their impact on public transport (PT) operations, particularly bus services, remains underexplored. This study examines the impact of classifying streets into TCZs on bus transport performance in Poland’s ten largest cities. Geospatial analysis and a custom R algorithm delineated areas suitable for TCZs based on road class and administrative category. GTFS data were analysed for almost 1000 bus lines to evaluate the overlap of their routes with TCZs. The findings reveal that in several cities, a significant portion of bus operations would run through TCZs, with the average route segment affected notably by city and zone classification methods. Differences in TCZ size and shape across cities were also statistically significant. This study concludes that although TCZs contribute to safer and more liveable urban environments, their influence on bus speeds, which can lead to changes in fuel or energy consumption, and route design must be carefully managed. Strategic planning is essential to find a balance between the benefits of traffic calming and the operational efficiency of PT. These insights offer valuable guidance for integrating TCZs into sustainable urban transport policy without compromising PT performance. Full article