Search Results (10,550)

Based on the characteristics of cities as organic entities, this paper constructs a five-dimensional evaluation framework encompassing economy, industry, society, population, and space. A three-stage process of “fuzzy comprehensive evaluation—bi-level K-means clustering—state stability correction” is adopted to identify the development stages and spatiotemporal evolution of 286 Chinese cities from 2008 to 2023. The study finds that China’s urban development has shifted from “universal growth” to “divergent evolution,” exhibiting multiple characteristics such as the decline in the initial-stage cities and differentiation in the growth stage. Significant regional spatial differentiation is observed, with notable development gaps among the eastern, central, western, and northeastern regions, as well as between the northern and southern regions. Furthermore, most urban agglomerations exhibit a “mature center–lagging periphery” structure. Full article

Background/Objectives: Although urban greening interventions are increasingly implemented to improve livability, environmental quality, and adaptation capacity in cities, their evaluation still predominantly relies on physical outputs rather than validated, resident-centered outcomes. This study examined whether the five-item attachment dimension of the Urban Green Attachment Scale (UGAS) can reliably indicate the social integration of newly introduced greenery in an SDG 11-oriented evaluation context. The present adaptation of the UGAS captures the perceived importance of the planting, its contribution to well-being, anticipated loss, willingness to protect it, and aesthetic appreciation. Methods: Data were collected through two independent face-to-face surveys conducted among residents of the same housing estate shortly after a greening intervention in May 2025 (n = 150) and September 2025 (n = 191). The first sample was used for exploratory factor analysis (EFA) and the second for confirmatory factor analysis (CFA). Reliability was assessed using Cronbach’s α and McDonald’s ω; inter-item associations were estimated using Kendall’s tau-b; and construct validity was examined through known-groups comparisons with theoretically relevant appraisals and stewardship-related indicators. Results: The adapted UGAS demonstrated high internal consistency, low floor and ceiling effects, and moderate to strong inter-item associations. Exploratory factor analysis supported a unidimensional solution with high loadings and 65.7% explained variance, and confirmatory factor analysis corroborated this structure after minor, theory-guided localized refinements. Higher UGAS scores were consistently observed among residents who reported stronger calming and home-related effects, perceived healthier local conditions, expressed willingness to help care for the plants, and demonstrated a readiness to cooperate in improving the area. Conclusions: The results support the five-item UGAS attachment score as a compact, psychometrically adequate measure of residents’ attachment to newly planted urban greenery. Rather than replacing official SDG indicators, the UGAS can complement them at the project level by determining if urban greening becomes socially meaningful and accepted and if it supports stewardship. In this sense, UGAS offers municipalities a practical tool for linking physical greening outputs with resident-centered outcomes relevant to inclusive public spaces, participatory urban development, and the long-term social durability of urban greening interventions. Full article

Chitosan (CS) was employed to modify coconut shell activated carbon (CAC) to fabricate a composite adsorbent for wastewater treatment. By integrating the functional groups of CS with the high specific surface area of CAC through chemical modification, the resulting CS-AC composite exhibited significantly enhanced adsorption performance toward hexavalent chromium (Cr(VI)) in aqueous solutions. The effects of key parameters, including adsorbent dosage, initial Cr(VI) concentration, contact time, temperature, and solution pH on the adsorption efficiency were systematically investigated. Under optimal conditions, the CS-AC composite achieved a Cr(VI) removal efficiency of up to 99.04%. Kinetic and isotherm modeling revealed that the adsorption process followed the pseudo-second-order kinetic model and was well described by the Langmuir isotherm. Regeneration studies conducted over five consecutive adsorption-desorption cycles demonstrated that the composite retained a high removal efficiency of 98.10%, indicating excellent reusability. These findings suggest that the CS-AC composite is a promising and effective adsorbent for the removal of Cr(VI) from contaminated water. Full article

In developing countries, promoting sustainable income growth for farmers is a major priority. This study constructs an evaluation index system for the whole agricultural industry chain from the perspective of synergy among the innovation chain, supply chain, value chain, and capital chain. It also empirically tests the enabling mechanisms and spatial effects of the whole agricultural industry chain on farmers’ income. The entropy value method was used to measure the development level of the whole agricultural industry chain. Two-way fixed effects, mediation effects, and spatial Durbin models were applied to investigate the impacts, mechanisms, and spatial characteristics of the whole agricultural industry chain on farmers’ income. The whole agricultural industry chain significantly promotes farmers’ income growth, with the expansion of the non-agricultural employment scale and the improvement of urbanization levels serving as the main pathways through which the whole agricultural industry chain drives increases in farmers’ income. The heterogeneity analysis reveals that the innovation chain and capital chain contribute the most prominent marginal effects; the effect intensity of the whole agricultural industry chain on farmers’ income presents a spatial gradient pattern of “Central > Western > Eastern”; and its income-increasing effect is more noticeable for middle- and low-income farmers, demonstrating significant pro-poor characteristics. Further analysis indicates that the whole agricultural industry chain exerts a significant positive spatial spillover effect on farmers’ income. Therefore, it is essential to optimize the layout of the whole agricultural industry chain, smooth the transmission channels of non-agricultural employment and urbanization, and enhance the benefit linkage mechanism targeting middle- and low-income farmers. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) and the Yangtze River Delta (YRD) are key economic growth poles in China, playing a critical role in driving national economic development and facilitating international exchanges in commerce, culture, and ecology. However, rapid urbanization and industrialization have exerted considerable pressure on regional environments. In this study, we first assessed the dynamics of carbon storage (CS) and habitat quality (HQ) in the GBA and the YRD from 2000 to 2020 using the InVEST model and ArcGIS software, systematically analyzing their spatiotemporal changes and underlying driving mechanisms. Subsequently, we employed the PLUS model to predict land use changes by 2030 and evaluate their potential impacts on CS and HQ. The results indicate that: (1) Both regions have experienced increases in construction land and declines in cropland. (2) Between 2000 and 2020, CS in the GBA decreased by 33.65 × 10⁶ t and HQ declined by 0.0833, whereas in the YRD, CS decreased by 15.35 × 10⁶ t and HQ dropped by 0.0504. (3) By 2030, CS in the GBA is projected to decline further by 4.08%, with HQ decreasing to 0.4777, while in the YRD, CS is expected to fall by 2.71% and HQ decrease to 0.4115. (4) The spatial differentiation of CS and HQ in the GBA is primarily driven by anthropogenic processes, whereas in the YRD it is mainly constrained by natural factors such as topography. This study highlights the importance of understanding the spatiotemporal dynamics of CS and HQ, which can help enhance ecosystem service functions, mitigate the impacts of climate change, and provide a scientific basis for regional sustainable development. Full article

This study investigates the separation performance of transformer oil–water mixtures using gravity separation and chemical demulsification. The synthetic emulsion had an initial oil concentration (C₀) of approximately 246,000 mg/L. For gravity separation, the effects of compartment volume ratio, influent flow rate, initial water level, and oil discharge strategy were systematically evaluated. Under optimal conditions (volume ratio 2:1:1, flow rate 0.0055 L/s, initial water level 5 cm), the effluent oil concentration was reduced to as low as 0.020 mg/L, corresponding to a removal efficiency higher than 99.99%. For chemical demulsification, polyaluminum chloride (PAC), polyferric sulfate (PFS), polyacrylamide (PAM), and an organosilicon polyether demulsifier (MCL-D) were tested. The effects of pH, dosage, and temperature on demulsification efficiency (DE) and dehydration rate (DR) were investigated. Under optimal conditions (pH 3–5, dosage 300 mg/L, temperature 50 °C), MCL-D achieved the best performance, with a DE of 95.09% and a DR of 99.50%. Overall, gravity separation is effective for removing free and dispersed oil with low operational cost, whereas chemical demulsification is more suitable for treating stable emulsified oil. The combination of these two methods provides an efficient strategy for the treatment of transformer oil-containing wastewater. Full article

Active school travel (AST) is an important source of daily physical activity for children, yet its environmental correlates remain mixed and may be more complex than conventional linear models suggest. This study examines the associations of school-travel distance, built environment, socio-demographic attributes, and natural environment with children’s AST in Wuhan, China. Using 2020 travel survey data for children aged 6–15 and an XGBoost–SHAP framework, the study evaluates relative importance, nonlinear association patterns, and pairwise interactions among key variables. The results show that school-travel distance is the dominant correlate of AST, accounting for 44.07% of total model importance. Built-environment variables collectively contribute 36.24%, substantially exceeding socio-demographic attributes, while terrain also shows a non-negligible role. Several built-environment variables, including transit station density, road density, intersection density, land-use entropy, and POI density, display clear nonlinear patterns and directional shifts. Distance and age further condition the associations of other variables with AST, indicating substantial heterogeneity across distance bands and developmental stages. Overall, the findings suggest that AST should not be understood through average main effects alone, and that more age-sensitive, distance-sensitive, and context-specific planning strategies are needed to support child-friendly school travel. Full article

This research examines how the relationship between cities and rural areas has evolved in light of the profound transformation affecting rural areas of high landscape value, which has been driven by the expansion opportunities granted to the real estate sector by urban planning regulations. The role of the landscape dimension in interpreting the relationship between territorial wealth and landscape value is considered, based on the convergence of two complementary disciplinary perspectives on territory: land planning and valuation science. Against this backdrop, and with a view to containing the progressive contamination of rural and agricultural heritage by the real estate sector, this study proposes a structured observation, valuation, interpretation, and regulatory tool to support the development of territorial planning in areas significantly characterized in terms of rural landscape value. The proposed tool is based on evidence regarding the phenomenon of building expansion in the agricultural territory of a municipality in southeastern Sicily, where favorable conditions for the development of the building sector exist, such as the vastness of the municipal territory and extensive farming as the mainstay of agricultural activity. This wider sub-regional area has also received attention due to the over-tourism phenomenon that has occurred in its cities of art. The evaluation approach experienced is a value-based representation of the evolution of this process over three observation periods: 2000, 2007, and 2012, relating the quantitative observation of the building expansion to the connected qualitative impact on rural landscape. It is the result of coordinating a large set of data in a hierarchical model of indices that converge to construct a synthetic index of rural landscape resilience. This achievement is based on the linguistic progression of “lexicon”, “semantics”, “syntax”, and “pragmatics”, each of which robustly supports “observation”, “valuation”, “interpretation”, and “planning”, respectively. The final stage is based on the convergence of explanatory indices, which are developed by coordinating evidence and assessments (factual and value judgements). This stage enables the proposal of a constraints system that supports a modus vivendi between the interests of the real estate sector and the values of the rural landscape in such a rich and fragile area. Full article

As a form of living heritage, Historic Urban Landscapes (HULs) have long been limited by the static perspectives and reductionist tendencies of conventional conservation and research approaches. Although the geological and archaeological concept of “stratification” offers a methodological basis for understanding the diachronic evolution of heritage, its unidimensional temporal lens fails to capture the inherent complexity and systemic nature of historic urban landscapes. To address this gap, this study proposes a “multidimensional stratification” theoretical framework through theoretical critique and paradigm reconstruction. The framework introduces innovations at the ontological, epistemological, and methodological levels, positing that the evolution of historic urban landscapes emerges from the nonlinear interaction and dynamic interweaving of four core dimensions: time, space, society, and value. It further systematizes five intrinsic attributes of such landscapes: authenticity, integrity, continuity, adaptability, and dynamism. Building on this foundation, the paper constructs a systematic analytical pathway—elements–processes–patterns–modes–drivers–characteristics—that enables dynamic analysis from micro-level identification to macro-level generalization, offering a scalable tool for HUL conservation and regeneration. To demonstrate the framework’s applicability, the historic urban area of Shenyang—a nationally designated historical and cultural city—is selected as a case study. Its urban landscape comprises four core districts: the Shengjing City District, the South Manchuria Railway Concession District, the Commercial Port District, and the Tiexi Industrial District, representing historical strata from the Qing dynasty capital, modern colonial planning, commercial opening, to industrial heritage. Using the multidimensional stratification approach, this study elucidates the spatial complexity, temporal nonlinearity, social dynamism, and value pluralism embedded in Shenyang’s historic urban area. Corresponding conservation strategies grounded in holism, dynamism, and differentiation are proposed. The research not only advances the theoretical understanding of HUL but also provides a novel paradigm—integrating holistic, dynamic, and operational perspectives—for the conservation, renewal, and regenerative practice of historic urban landscapes worldwide. Full article

Poyang Lake represents China’s largest freshwater wetland. The wetland landscape has undergone substantial changes driven by climate change and intensive human activities. Nevertheless, long-term classified analyses of wetland evolution and quantitative assessments of its driving factors remain scarce in the region. Based on 21 Landsat images from 2000 to 2020, this study systematically examined the spatiotemporal dynamics of the wetland landscape. Analyses incorporated land-use dynamic degree, landscape metrics, transfer matrices, and standard deviational ellipses, with key driving forces identified via Pearson correlation and structural equation modeling. Results indicate a 3029.63 km² reduction in wetland area, exhibiting contrasting trends between natural and artificial wetlands. The wetland centroid shifted 7.4 km southwestward. Connectivity of lake increased and fragmentation declined, whereas paddy field fragmentation intensified. Wetland evolution was predominantly driven by socioeconomic factors, whereas climate primarily influenced natural wetlands. The study elucidates the coupled effects of anthropogenic and natural factors, offering insights into wetland restoration and ecological security in the middle and lower Yangtze River. The findings suggest prioritizing natural wetland connectivity, controlling wetland-to-non-wetland conversion, and incorporating long-term remote-sensing monitoring into regional wetland restoration planning. Full article

Global urbanization is shifting from incremental expansion to stock optimization, and old residential communities have become important spatial units for low-carbon transition. However, in existing built environments, traditional process-based inventory methods face practical constraints, including missing original drawings, complex site conditions, and severe vegetation obstruction. As a result, systematic accounting of buildings, landscapes, and natural carbon sinks remains difficult. This study integrates life cycle assessment (LCA), BIM reverse modeling, 3D point clouds, DesignBuilder simulation, inventory-based accounting, and i-Tree Eco to construct a life cycle carbon emission accounting framework for old residential communities. The framework links current-condition data reconstruction, quantity take-off, operational energy simulation, landscape inventory accounting, and vegetation carbon sequestration assessment. It is applied to Nanyuan Xincun in Hefei to quantify the community-scale carbon source–sink structure. The results show that Nanyuan Xincun presents a clear operation-led emission pattern, with the operation and maintenance phase accounting for 82.52% of total positive emissions. Within architectural engineering, operation and maintenance accounts for 82.91%, while material production accounts for 13.28%. Landscape engineering shows a more mixed structure, with operation and maintenance accounting for 52.95% and material production accounting for 36.49%. Vegetation carbon sequestration analysis shows that mature trees and shrubs are the main ecological carbon assets. Annual sequestration reaches 16.95 t-CO₂e/a, and trees and shrubs contribute 92.85% of total vegetation carbon storage. Under current vegetation conditions, annual sequestration is equivalent to 32.99% of annual landscape operation emissions, indicating considerable ecological compensation potential. Based on these findings, this study proposes four optimization pathways: operational energy reduction, low-carbon material substitution, construction and demolition waste recycling, and mature tree protection. These pathways provide data support for refined carbon management and low-carbon renewal in existing communities. Full article

The rapid expansion of subway systems has led to significant carbon emissions during station construction, yet a systematic and interpretable framework for evaluating low-carbon performance across different construction methods remains underdeveloped. To address this gap, this study proposes a comprehensive evaluation model that integrates a pressure–state–response (PSR) framework with an entropy-weighted TOPSIS method. A multi-dimensional indicator system comprising 17 indicators was established, covering material and energy consumption (pressure), environmental carbon states (state), and management responses (response). The entropy weight method was employed to determine objective indicator weights, and the TOPSIS method was used to rank the overall low-carbon performance of different construction schemes. An empirical study of a subway station in Guangzhou, China, was conducted to compare three construction methods: open-cut, top-down cover excavation, and reverse cover excavation. The results demonstrate that the reverse cover excavation method achieves the highest low-carbon performance. Electricity consumption and concrete-related emissions were identified as the most influential factors, while obstacle analysis revealed key constraints for carbon reduction. The proposed PSR–entropy–TOPSIS framework offers a transparent, data-driven decision-support tool for optimizing construction schemes, contributing to the sustainable development goals of urban rail transit projects. Full article

A pressing challenge of modern wireless networks is ensuring stable radio coverage inside buildings, where radio signal propagation is significantly complicated by the influence of building structures. Reinforced concrete walls, floor slabs, internal partitions, and energy-efficient windows with metallized coatings create substantial obstacles to the propagation of electromagnetic waves, causing reflection, absorption, and scattering. As a result, areas with weakened coverage are formed inside buildings, leading to deterioration in mobile communication quality and reduced data transmission rates. This study presents an experimental investigation of the received signal strength of mobile operators inside a multi-storey residential complex. An analysis was conducted to evaluate the impact of building height, architectural features, and construction materials on radio signal propagation. In addition, the frequency bands used in 4G LTE and 5G networks by mobile operators were examined. It was found that LTE networks mainly operate in the 1.8–2.1 GHz frequency range, whereas 5G networks operate in the n77 band (3.6–3.7 GHz), which provides higher data throughput but is characterized by greater signal attenuation when propagating inside buildings. To address this issue, a Distributed Antenna System (DAS) based on GPON technology was implemented in the studied building. The placement of antenna equipment on the roof enabled the efficient reception of the signal from the base station and its subsequent distribution inside the building through an internal antenna network. The measurement results demonstrated that the deployment of a GPON-based DAS significantly improves the received signal level and ensures more uniform radio coverage inside indoor environments. The obtained results confirm that the use of distributed antenna systems is an effective solution for compensating signal losses caused by the shielding effect of building structures and can significantly improve the quality of mobile communications in dense urban environments. The results show that the RSRP level in indoor environments without DAS decreases to approximately −100 to −110 dBm, while after deployment of the GPON-based DAS, it improves to −45 to −75 dBm. This corresponds to a signal gain of up to 40–50 dB, ensuring stable connectivity and significantly improved data transmission performance. Full article

Vision–language models (VLMs) hold considerable potential for interpreting large-scale remote sensing (RS) archives, which are critical for applications such as environmental monitoring, disaster response, and urban planning. However, general-purpose VLMs primarily target optical imagery and often underperform on RS tasks, while existing RS-specific VLMs still struggle with fine-grained understanding. To address these limitations, we propose GeoPilot, a tool-augmented multimodal assistant tailored for RS scenarios. GeoPilot interprets user instructions, autonomously determines whether to invoke external tools, and synthesizes their outputs to generate precise responses. A key capability of our approach is its ability to process both optical and Synthetic Aperture Radar (SAR) imagery, supporting representative tasks such as visual grounding, object detection, segmentation, and cross-domain reasoning. To support this setting, we construct a novel large-scale RS instruction dataset that jointly supports optical and SAR imagery together with explicit tool use reasoning traces, addressing the critical challenge of task-specific data scarcity. We also introduce GeoPilotBench, a benchmark for cross-domain, multi-task dialogue and tool-aware evaluation in RS, and use it to assess GeoPilot across representative tasks. Experimental results show that GeoPilot achieves strong task planning accuracy (92.6% overall planning accuracy) and competitive performance on VQA, SAR understanding, and referring object detection. End-to-end evaluation further confirms that GeoPilot’s learned tool policy introduces only limited overhead compared to standalone tool execution, demonstrating its practical value as a tool-augmented RS assistant. Full article

Psychological constructs such as anxiety, depression, fatalism subscales—fatalism, divine control, luck, helplessness, and internality—play an important role in shaping mental health outcomes in the United States (US). Although several studies have explored how specific variables correlate with these constructs, less is known about how sociodemographic and experiential factors interact to shape multidimensional fatalism, including the subscales of fatalism, divine control, luck, helplessness and internality. This study addresses the gap by using Conditional Inference Trees (CITs) to explore how interactions among variables are associated with these constructs. Using the Conditional Inference Tree (CIT) analyses, we examined how Adverse Childhood Experience (ACE), age, gender, race, education, and urbanicity are associated with depression, anxiety, and fatalism subscales. Our analyses revealed that ACE and age were the most significant variables associated with depression and anxiety, with higher ACE scores associated with higher levels of both depression and anxiety. For multidimensional fatalism, age, race, gender, and urbanicity were key variables, although their effects varied across subscales. Overall, these findings highlight the importance of considering interaction effects when examining mental health outcomes and fatalistic belief systems. CIT analysis provides a useful explanatory framework for identifying complex patterns of association between early life adversity, sociodemographic factors and psychological constructs. Full article