Search Results (22,684)

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

The spatiotemporal evolution of ecosystem services and the elucidation of their driving mechanisms constitute a central scientific issue in territorial spatial optimization and regional sustainable development. Taking Gansu Province, a core area of the ecological security barrier in northwestern China, as the study area, this study integrates land-use, natural geographic, and socioeconomic data from 2000 to 2020. Using a land-use transfer matrix, the InVEST model, the Geographical Detector, and the PLUS model, we constructed a comprehensive analytical framework that combines historical evolution analysis, spatial differentiation identification, and multi-scenario simulation and prediction. The framework was used to systematically reveal the spatiotemporal dynamics of four core ecosystem services, namely carbon storage (CS), water yield (WY), habitat quality (HQ), and soil retention service (SDR), and to analyze their natural and socioeconomic driving mechanisms, while also simulating land-use change and ecosystem-service responses under the natural development, ecological protection, and urban expansion scenarios in 2030. The results show that, from 2000 to 2020, land use in Gansu Province was dominated by grassland (average proportion: 33.34%) and unused land (average proportion: 41.35%). Urban land expanded from 660.52 km² to 2227.36 km², with its share increasing from 0.15% to 0.50%, mainly through the conversion of cropland and grassland. Ecosystem services exhibited marked spatial differentiation: CS increased from east to west; WY showed an increasing pattern from northwest to southeast; HQ was lower in the central and southeastern regions and higher in the western and southern regions; and SDR was dominated by low-value areas in the northwest (average proportion: 84.81%). Driving-mechanism analysis indicated that slope was the core natural factor affecting CS, HQ, and SDR (q = 0.18–0.45), while mean annual precipitation dominated the variation in WY (q = 0.31–0.35). The influence of socioeconomic factors such as GDP increased gradually over time, showing an evolutionary trend from natural dominance to coordinated natural–socioeconomic regulation. Multi-scenario simulation further showed that, under the ecological protection scenario, grassland area increased significantly (+0.60%), the proportions of medium-value CS zones and high-value WY zones increased, and ecosystem services were optimized overall; under the urban expansion scenario, cropland and urban land expanded (+0.87% and +0.23%, respectively), imposing potential pressure on part of the ecosystem-service functions. These findings provide a scientific basis for optimizing territorial spatial planning, strengthening the ecological security barrier, and promoting regional sustainable development in Gansu Province. The methodological framework also offers a broadly applicable reference for ecologically sensitive arid and semi-arid regions in northwestern China. Full article

Air pollution caused by fine particulate matter (PM_2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM_2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM_2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO₂, SO₂), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R² = 0.77 (RMSE = 24.75 µg m⁻³), XGBoost R² = 0.76 (RMSE = 26.32 µg m⁻³), CatBoost R² = 0.73 (RMSE = 30.39 µg m⁻³), and LightGBM R² = 0.70 (RMSE = 32.75 µg m⁻³). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R² = 0.77; RMSE = 23.37 µg m⁻³). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO₂ were the most influential predictors of PM_2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article

Accelerated urbanization in Ulaanbaatar, Mongolia, has driven substantial changes in Land Use and Land Cover (LULC), threatening sustainable urban ecosystems. This study investigates historical LULC dynamics (2000–2021) and simulates future expansion scenarios through 2050 using a hybrid Machine Learning (ML) and Cellular Automata-Artificial Neural Network (CA-ANN) approach. Multi-temporal classification was performed using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Both classifiers demonstrated high and comparable accuracy; SVM achieved an average Kappa coefficient of 0.8939 while RF achieved 0.8917, a marginal difference that should be interpreted with caution. Change detection analysis revealed a continuous expansion of built-up areas at the expense of dense forest and grassland, a trend driven largely by accessibility factors. Future projections indicate that even as the rate of urbanization may slow, encroachment on green spaces will persist without policy intervention. This research presents a replicable methodological workflow for monitoring urban sprawl and provides evidence to inform sustainable land management and reforestation strategies in rapidly developing urban regions. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

In the last decade, as cities increasingly seek sustainable development pathways within the cultural and creative economy, cultural-creative events have gained prominence as strategic instruments for urban transformation. Among them, city design weeks have emerged as complex service systems that connect creative industries, urban governance, and tourism development. This research aims to understand how cultural-creative events (represented by design weeks) facilitate sustainable tourism development from a service design perspective. Adopting a qualitative comparative research design, the study examines 30 design weeks selected through a cross-validated process with the World Design Weeks global network and UNESCO City of Design network. Data from 2020 to 2025 is collected primarily through expert interviews, official reports, and media materials in relation to the United Nations Sustainable Development Goals (SDGs). Grounded in the service design perspective, four Service Design Levels are summarized into 17 assessment dimensions, and experts applied Likert scale to evaluate the relative service intensity of each case. Through cross-case analysis, the findings reveal four distinct models of design weeks, reflecting different configurations of service intensity and strategic orientation. The study contributes theoretically by extending service design theory to cultural-creative tourism research, and practically by providing guidance for the organizers of cultural-creative events seeking to support sustainable city tourism development. Future research may incorporate quantitative impact assessments to further refine these models. Full article

Blue space, as an important natural and social composite feature system in cities, not only provides supporting, regulating, and provisioning services, but also plays a key role in human well-being, recreational experience, and urban sustainable development. The blue space cultural ecosystem service (CES) has gradually attracted the attention of academia in recent years, but there is a lack of systematic integration research in related fields. Therefore, it is necessary to conduct a comprehensive analysis of current studies to clarify how, and to what extent, blue spaces influence CESs. This study adopts a PRISMA-based systematic search combined with qualitative synthesis, aiming to review the research status of CES and its developmental trajectory within blue space studies, and to identify future research trends and critical gaps. A total of 52 studies meeting the inclusion criteria were finally selected through database screening. The research innovatively divides the evolution of blue space CES into three stages (2012–2017/2018–2022/2023–2025), revealing a shift in research focus from single value identification to complex policy support. Secondly, through the mapping of six typical blue space types (such as rivers and wetlands) and 10 CES indicators, combined with a Pearson correlation heatmap, it provides quantitative insights into the coupling mechanisms between indicators, such as the significant synergy between spiritual and educational values. Methodologically, it systematically discriminates between the application boundaries of monetary valuation based on the contingent valuation method and non-monetary valuation represented by social media big data and PPGIS, pointing out that technological progress is driving the evaluation toward high dynamics and refinement. Finally, the study points out current bottlenecks such as uneven geographical distribution and insufficient planning transformation, emphasizing that future research should use artificial intelligence to improve data processing accuracy and transform blue space CESs from “invisible welfare” into “explicit policy assets” to guide sustainable urban renewal and healthy space design. Full article

Conceptual ambiguity in People–Place Relationships (PPR) research limits consistent operationalization and cross-context comparability, particularly in under-represented cultural settings. This study develops an integrated, context-sensitive framework for assessing PPR in residential environments and empirically examines its measurement structure. The framework is applied in Damascus as a pilot context to assess its structural validity, internal consistency, and applicability. The methodological approach comprised two stages: conceptual development and empirical validation. First, two rounds of case-study analysis derived from a prior systematic literature review synthesized environmental (social and urban) and relational (cognitive, affective, attachment) dimensions into a coherent framework. Second, the framework was operationalized and tested using survey data from 1610 residents across Damascus districts. Six first-order indices and one composite PPR index were constructed and evaluated using exploratory factor analysis and Cronbach’s alpha with item–total correlation analysis. Results demonstrate a stable multidimensional structure that integrates evaluative environmental conditions with relational processes, moving beyond emotion-dominant interpretations of attachment. The framework advances existing approaches by linking theoretical constructs to empirically tested measurement dimensions. While further validation in diverse contexts is required, the results indicate that the model provides a coherent and adaptable basis for assessing residential PPR in socio-culturally complex urban environments. Full article

The establishment of Pilot Free Trade Zones (PFTZs) is a key strategic measure in China’s new-era reform agenda, aimed at comprehensively advancing opening-up and fostering high-quality economic development. This paper aims to investigate whether and how the establishment of PFTZs promotes the development of new quality productive forces (NQPFs) in China. Taking the PFTZs policy as a quasi-natural experiment, this paper employs panel data from 270 prefecture-level cities spanning 2010 to 2022 and adopts a multi-period difference-in-differences (DID) model to identify the causal effects of PFTZs. The results show that establishing PFTZs significantly enhances the development of NQPFs. Heterogeneity analysis revealed that this promoting effect is more significant in cities with higher administrative status and in inland regions. Mechanism analyses reveal that PFTZs facilitate the development of NQPFs primarily by raising external economic openness and fostering industrial agglomeration. Furthermore, the positive impact of PFTZs is strengthened by higher levels of urban logistics development. This study offers theoretical and empirical evidence on how strengthening institutional development can facilitate high-quality and sustainable economic development. Full article

Urban resilience plays a critical role in sustainable regional development. This is particularly so for ecologically vulnerable urban agglomerations undergoing rapid urbanization. This study examines the spatiotemporal development and driving mechanisms of urban resilience in the Chengdu–Chongqing Urban Agglomeration (CCUA) via the perspective of ecological risk. Using panel data from 16 prefecture-level cities during 2010–2023, this study constructs ecological risk and urban resilience indices were constructed based on the entropy weight–TOPSIS method. The coupling coordination degree model was applied to analyze the interactive dynamics between the two subsystems, and a two-way fixed effects panel model was employed to identify the impact of ecological risk on urban resilience and its moderating mechanisms. The results show that urban resilience experienced a foundational stabilization phase followed by gradual improvement, while ecological risk underwent a three-stage transformation characterized by accumulation, stabilization, and decline. The coupling degree between ecological risk and urban resilience remained moderately high, indicating structural tension within the regional system. Econometric analysis indicates that ecological risk significantly suppresses urban resilience. Infrastructure development has a positive direct effect on resilience. However, it negatively moderates the marginal impact of ecological risk, indicating a nonlinear and conditional risk–resilience relationship. Full article

Nasal tumors in dogs are most frequently found in long-nosed breeds aged 10 to 15 years, especially among urban dogs. This case report describes a dog with recurrent bloody nasal discharge. Diagnostic tests, including laboratory analysis, CT, MRI, and histopathology, confirmed the diagnosis of nasal squamous cell carcinoma. The dog was treated with surgical resection and chemotherapy over 21 weeks. At the 21-week follow-up, nasal discharge had returned, prompting external carotid artery ligation, which reopened the nasopharynx. Full article

Equitable access to rehabilitation services is essential for individuals living with a disability, yet geographic disparities in outpatient rehabilitation care remain understudied. This study examined spatial accessibility to outpatient physical and occupational therapy services across Texas to identify regional inequities and inform workforce and policy planning. A descriptive cross-sectional geospatial analysis was conducted using outpatient clinic location data from the Texas Health and Human Services database (2022) and population data from the 2020 U.S. Census. Clinic addresses were verified and geocoded. Accessibility was measured using an origin–destination cost matrix to estimate the travel time to the nearest clinic, and the two-step floating catchment area (2SFCA) method to calculate an accessibility index. Spatial clustering of access was assessed using the Getis-Ord Gi* statistic to identify hot and cold spots. The analysis included 2255 outpatient rehabilitation clinics across 6896 census tracts. Travel times varied substantially, with rural areas experiencing the longest travel burdens. The 2SFCA analysis revealed pronounced disparities, with low-accessibility clusters concentrated in rural and border regions and high-accessibility clusters in urban metropolitan areas. These findings demonstrate persistent geographic disparities in outpatient rehabilitation access across Texas, suggesting the need for targeted workforce placement, transportation investment, and policy interventions to improve equitable access. Full article

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km²) annual mean concentrations of key pollutants (PM_2.5, PM₁₀ and NO₂) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM_2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km² grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure. Full article

Managing soil heavy metal pollution is pivotal for the sustainable development of the Beijing–Tianjin–Hebei (BTH) urban agglomeration. This study integrated geostatistical methods, Principal Component Analysis, and Positive Matrix Factorization (PMF) to characterize “source–sink” dynamics across diverse Main Functional Zones. Results revealed distinct pollution landscapes: Key Development Zones exhibited high-risk accumulation driven by multi-source superposition, while Ecological-restricted Zones, despite overall low pollution levels, faced significant anomalous enrichment of Cadmium (Cd). Source apportionment confirmed that this spatial differentiation stems from the coexistence of “in situ accumulation” and “source–sink misalignment” mechanisms. The former is driven by high-intensity industrial agglomeration, whereas the latter is governed by cross-boundary atmospheric transport and the topographic blocking of emissions from the plains. This research demonstrates for the first time the joint shaping effect of national spatial planning and natural geographical processes on regional pollution patterns. Accordingly, a precise management framework incorporating source reduction, cross-boundary synergy, and spatial reorganization is proposed, providing a new paradigm for addressing environmental risks caused by unbalanced development in rapidly urbanizing regions. Full article