Search Results (1,379)

Water is a valuable resource that needs to be managed carefully. Therefore, sustainable water consumption is a critical issue, mainly in urban areas, where tourism activities are of great importance. In mainland Portugal, this has become a crucial matter, particularly because of climate limitations and the recent growth of the tourism sector. To manage water resources effectively, we must analyze the primary factors driving consumption. Therefore, an econometric model to identify the main factors is a relevant tool for policy design. This paper tries to fill this gap. In the first stage, an ordinary least squares (OLS) model was implemented. In a second stage, the autocorrelation is evaluated, and a weighted geographical regression (GWR) model is used to improve the previous results. The models were implemented in the 278 municipalities of continental Portugal, considering the average situation of the last decade. The results were promising since the OLS model presented an R² of 0.42 identifying purchasing power, urban green spaces, tourism and education as the main explanatory factors for water consumption. The geographical weight regression presented an R² of 0.646 proving the importance of considering spatial context for the identification of these factors’ influence on water consumption. Full article

Spatiotemporal differentiation and coupled driving mechanisms of urban ecological resilience in transboundary composite economic belts remain an understudied niche within human–land coupling system research. Taking 29 prefecture-level and county-level units of the Huaihe River Eco-Economic Belt from 2014 to 2023 as research samples, this study constructs an ecological resilience evaluation framework tailored to the pollution disturbance characteristics of the Huaihe River Basin under a three-dimensional theoretical framework encompassing resistance, adaptability, and recoverability. The entropy weight method is adopted to calculate comprehensive ecological resilience values, while the geographically and temporally weighted regression (GTWR) model is applied to identify spatiotemporal heterogeneous correlations among multiple influencing factors. This paper further characterizes the spatiotemporal evolutionary patterns of urban ecological resilience across the study area and unpacks the coupled associative effects of natural, economic, and social driving factors. The empirical results reveal three key findings: (1) Temporally, the overall comprehensive ecological resilience of the study region rose from 0.318 to 0.416, with a total growth rate of 30.91%. Its evolutionary trajectory follows three successive phases: rapid growth, steady improvement, and slow saturation. Adaptability, which is predominantly boosted by anthropogenic environmental governance, constitutes the primary contributor to resilience growth. The range of urban resilience values narrowed by 9.97%, indicating continuous advancement in balanced regional development. (2) Spatially, ecological resilience presents a prominent core-periphery pattern, with high-resilience zones concentrated in mountainous southwestern areas and low-resilience zones distributed across northeastern plains. All low-resilience county-level units were eliminated by 2023. (3) In terms of driving associations, topographic relief and environmental governance investment maintain persistent positive correlations with ecological resilience, while per capita GDP acts as the core economic supportive factor. The proportion of secondary industry and population density exhibit significant negative correlations with resilience. The normalized difference vegetation index (NDVI) shifts from a negative correlation to a weak positive correlation alongside progressive ecological restoration, whereas river network variables exert negligible long-term associative impacts. Collectively, the spatiotemporally heterogeneous coupling of natural endowments, industrial-economic conditions, and social governance factors shapes the evolutionary patterns of regional ecological resilience. This study fills the research gap regarding long-timescale resilience driving mechanisms for transprovincial composite river basins covering five provinces. It identifies novel human–land coupling mechanisms, including the temporal reversal of vegetation’s ecological benefits and the dual stress imposed by industrial agglomeration and dense human settlements in plain regions. The quantitative outputs of this research can provide data-based references for differentiated coordinated ecological governance across the Huaihe Ecological Economic Belt. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

In the context of China’s carbon peaking and carbon neutrality goals, clarifying whether the Yellow River Basin can achieve a decoupling of economic growth from carbon emissions and sustain regional development through deep decarbonization is critical to both ecological protection and high-quality development of the region. This study integrates the Tapio decoupling model, the geographically and temporally weighted regression (GTWR) model, and an author-developed LEAP-YRB v5 macro-sectoral hybrid model to examine 95 prefecture-level cities from 2010 to 2022 and to project energy consumption and carbon emissions for the nine YRB provincial-level regions from 2022 to 2060. The results show that: (1) the urban decoupling status fluctuated among expansive coupling, strong decoupling, and weak decoupling, with weak decoupling becoming dominant and increasing to 62 cities in 2022; (2) per capita GDP and urbanization tended to increase the decoupling index and therefore inhibited decoupling, whereas more intensive construction-land use promoted decoupling, and industrial structure upgrading and green patents showed context-dependent effects; and (3) the basin cannot peak its emissions under the business-as-usual scenario, while the policy-driven scenario peaks at approximately 3.357 billion tons of CO₂ around 2030. Under the carbon-neutrality-oriented scenario, net emissions decline substantially to 825 million tons by 2060, indicating deep decarbonization but not full carbon neutrality. Full neutrality would require additional carbon sinks, cross-regional clean-electricity integration, stronger power-sector decarbonization, or negative-emission technologies beyond the endogenous measures represented in the model. Full article

Net primary productivity (NPP) is a core diagnostic variable of terrestrial carbon cycling, yet consistent annual NPP simulation remains challenging in alpine heterogeneous regions where topography, hydrothermal gradients, vegetation structure, and nutrient constraints interact. Remote-sensing products such as MODIS provide valuable observational constraints, whereas light-use-efficiency models such as CASA retain process transparency and scenario transfer capability. This study develops a CASA-based, MODIS-constrained framework for annual NPP simulation over the Gannan Plateau. The framework preserves a locally parameterized CASA baseline and adds a geographically weighted regression (GWR) residual-alignment layer trained on CASA–MODIS residuals during 2005–2013. The fitted correction relationship was then applied to the 2014–2020 temporal transfer period and evaluated in a 2030s SSP scenario transfer experiment. MODIS was treated as the correction target rather than ground truth, and GLASS was adopted as an independent product-level benchmark. During 2014–2020, the GWR-corrected product showed improved pooled pixel-level agreement with the MODIS-constrained target relative to parameter-localized CASA, with R² increasing from 0.438 to 0.708 and RMSE decreasing from 91.4 to 68.7 g C m⁻² yr⁻¹. Residual Moran’s I also decreased, indicating weaker residual spatial organization after correction. Product-level comparison with GLASS showed a moderate, directionally consistent improvement relative to uncorrected CASA, although this comparison was not interpreted as ground-truth validation. The 2030s scenario transfer experiment indicated that the correction layer changed the spatial expression of NPP divergence among SSP pathways. Overall, the proposed framework provides a process-model-preserving and observation-constrained approach for improving agreement between annual NPP estimates and the MODIS-constrained target in alpine heterogeneous regions, while its applicability remains subject to product uncertainty, spatial dependence, and future nonstationarity. Full article

Urban noise complaints reflect not only perceived acoustic disturbance, but also complaint behaviour shaped by thermal conditions and the built environment. Using Sanya, China, as a case study, this study integrated Landsat-derived land surface temperature data with the spatial distribution of noise complaints to examine how thermal environment and urban contextual factors jointly influence complaint patterns. An interpretable modeling framework combining eXtreme Gradient Boosting (XGBoost) and Multiscale Geographically Weighted Regression (MGWR) was employed to assess the associations of urban heat island intensity (UHI), road density, point of interest (POI) count, and population density on complaint occurrence and intensity, while also generating spatial predictions of complaint distribution. The results revealed a weak but statistically significant spatial association between the thermal environment and noise complaints, with urban heat island intensity showing nonlinear and spatially heterogeneous associations with complaint counts. POI count emerged as the strongest global predictor, while road density, population density, and thermal environment exhibited substantial spatial heterogeneity in their associations with complaint patterns. The integrated model outperformed both individual models alone, achieving the highest prediction accuracy. Overall, the findings suggest that urban noise complaint patterns reflect not only perceived acoustic disturbance, but also context-dependent social perception and reporting behaviour, providing empirical support for more place-sensitive and people-centered urban noise governance. Full article

Climate and human activity shape cropland dynamics, while their cascading interactions remain an intricate black box blocking mechanistic understanding of land system shifts. Taking Jilin Province as a case, we developed an integrated framework combining OPGD (optimal parameter geographic detector), SEM (structural equation modeling) and GWR (geographically weighted regression) to quantify multi-scale cascading mechanisms of 13 environmental factors and cropland. Cropland conversion showed distinct spatial disparities: western plains saw mainly grassland reclamation, central urban plains experienced cropland occupation by construction, and eastern mountain areas had forest-to-cropland expansion. Over 2010–2019, cropland decreased by 1.39% (1257.7 ha) for construction, compensated by conversions from 4.99% grassland (4518.8 ha) and 3.01% forestland (2720.2 ha). Climate dominated cropland variations through indirect human-mediated pathways, with path coefficients of 0.664 for Climate → Human and 0.571 for Human → Cropland; climate exerted direct driving effects on grass–cropland transition in western ecologically fragile plains. Greenhouse gases, evapotranspiration, humidity, temperature and leaf area index controlled overall cropland variations. Central and western croplands were dominated by topography, whereas precipitation, population and GDP determined eastern cropland dynamics. This framework differentiated direct and indirect driving pathways of cropland evolution, guiding policy formulation for regional grain security. Full article

Improving water use efficiency (WUE) is a crucial approach to addressing agricultural water scarcity and promoting the sustainability of agricultural ecosystems. Although extensive research has been conducted on the factors associated with cropland WUE, the relationship between cropland fragmentation and WUE remains poorly understood. This study focused on northern China and employed an integrated “size–shape–configuration” framework to measure cropland fragmentation from 2005 to 2020. A geographically weighted regression (GWR) model was then applied to analyze the spatially varying relationships between cropland fragmentation and WUE. The results showed that over half of northern China experienced intensified cropland fragmentation. Noticeable spatial heterogeneity was observed, with relatively low fragmentation in the North China Plain and the Northeast China Plain and higher levels in the topographically complex northwestern region. Throughout the study period, the size-based fragmentation index (FI_size) consistently exhibited higher mean values than the shape-based (FI_shape) and configuration-based (FI_config) indices. The mean cropland WUE across the 1027 analysis units increased from 0.962 to 1.071, although 220 units—mainly distributed in the three northeastern provinces—experienced a decline. Among the analysis units with statistically significant local coefficients, FI_config was predominantly negatively associated with WUE, whereas FI_size and FI_shape were predominantly positively associated with WUE, with the positive associations mainly concentrated in the North China Plain. Moreover, FI_config exhibited larger absolute local coefficient magnitudes and greater spatial variability than the other two fragmentation dimensions. Environmental factors also showed distinct spatial associations with WUE, with annual precipitation and NDVI being predominantly positively associated with WUE, whereas soil erodibility and slope were mainly negatively associated. These findings highlight the need for region-specific management of cropland landscape patterns. This study provides preliminary quantitative evidence of the relationships between cropland fragmentation and WUE, offering valuable insights for improving WUE and promoting sustainable agricultural management. Full article

Background/Objectives: Childhood obesity is prevalent in the United States, and although food environments (FEs) may be related to child weight, findings remain inconsistent. This study evaluated neighborhood and home FEs’ associations with children’s weight and explored interactive effects of the neighborhood and home FEs on child weight. We hypothesized that children exposed to less healthy neighborhood and home FEs would have higher weight. Methods: This secondary analysis leveraged baseline data from caregiver–child dyads (n = 115) enrolled in an obesity intervention trial (HABITS). The Modified Retail Food Environment Index (mRFEI) was calculated as the proportion of healthy versus unhealthy food vendors within a 4-mile buffer of participants’ homes using food retailer data from business directories and geographic information systems software. Caregivers reported fruit, vegetable, and sugary beverage availability at home. Child weight was measured and used to determine weight-for-age z-scores (WAZs). Linear regression models were used to examine neighborhood FE, home FE, and their interactions as predictors of child WAZs. Results: Participants were majority non-Hispanic Black (77%) with household incomes of <$30,000/year (73%). Children were 3.0 ± 1.0 years old. Child WAZs were negatively associated with home fruit availability (β = −0.07, 95% CI: −0.14 to −0.01, p = 0.02), with no associations observed for the neighborhood FE or other home FE factors and no interaction between neighborhood and home FEs on weight. Conclusions: There were no significant associations between neighborhood FEs and weight among young children, while within the home FE, only the availability of fruit was associated with child weight. Full article

Integrating machine learning with spectral data provides an effective and cost-efficient scheme for estimating cadmium (Cd) contents in soils compared with labor-intensive laboratory analyses. However, conventional machine learning–based spectral estimation methods often yield unsatisfactory performance because they rely on an unrealistic assumption that the functional relationships across geographically distinct subregions are homogeneous, thereby reducing the accuracy and stability of soil Cd estimation. To address this issue, a geospatial information-guided XGBoost (GIGS) model was developed, in which a spatial weighting module was incorporated into the XGBoost framework to account for spatial heterogeneity in functional relationships among sampling locations. The spatial heterogeneity of each soil spectral sample was quantified and assigned a corresponding spatial weight, which was subsequently incorporated during model calibration. The optimal principal component spectra (PCS) derived from spectral data were used as model inputs, with measured soil Cd content as the dependent variable, thereby establishing a robust spectral estimation model for soil Cd. Results indicated that the GIGS model exhibited satisfactory performance in the spectral estimation of soil Cd content, with R², RMSE, and RPIQ values of 0.78, 0.04, and 2.02, respectively. Compared to commonly used spectral estimation models (e.g., XGBoost, random forest, support vector regression), the GIGS model achieved a maximum performance improvement of approximately 27.87% and a minimum improvement of approximately 16.42% (with reference to the R² value). Five PCS of soil spectral data were extracted as predictors for the model. PCS-1 was found to be closely associated with iron oxides, while PCS-2 primarily reflects the spectral characteristics of clay minerals. The spectral bands at 600 nm and 815 nm contribute most strongly to PCS-3, which is linked to soil organic matter and indirectly reflects the soil Cd status. PCS-4 and PCS-5 represent mixed spectral information derived from materials associated with soil Cd. An integrated framework combining the GIGS model and PCS data developed in this study provides an accurate and reliable tool for spectral estimation and mapping of soil Cd, thereby supporting cost-effective soil management and environmental sustainability worldwide. Full article

Human activity intensity has become an increasingly significant driver of changes in ecosystem services, and understanding their spatiotemporal relationships is essential for watershed ecological governance. Taking the Taihu Lake Basin as the study area, this research utilizes multi-source geospatial data from 2003 to 2023. The InVEST model is employed to assess four key ecosystem services: water yield, soil conservation, carbon storage, and habitat quality. A human activity intensity (HAI) index is constructed using the entropy-weight method, while bivariate spatial autocorrelation and geographically weighted regression are applied to disentangle the spatiotemporal evolution, spatial associations, and local variations in the associations between HAI and ecosystem services. The results indicate that (1) HAI has continuously intensified, with high-intensity areas undergoing an approximately 9.5-fold expansion and exhibiting a distinct “high in the east, low in the west” pattern; (2) carbon storage and habitat quality show persistent declines, whereas water yield and soil conservation exhibit fluctuating upward trends, with an overall “high in the southwest, low in the northeast” distribution; (3) HAI is significantly positively correlated with water yield and negatively correlated with carbon storage, soil conservation, and habitat quality, with 2018 identified as a critical turning point; and (4) the associations exhibit notable spatial non-stationarity, with the proportion of significant grid cells following a hierarchical order of habitat quality > water yield > carbon storage > soil conservation. Zones with strong regression coefficients occur only as localized patches, while the majority of the basin is characterized by low-to-moderate driving intensity. This study further reveals three core mechanisms—land-use path dependence, policy-induced threshold responses, and spatial spillover associations—providing a scientific basis for differentiated ecological management and sustainable development in the Taihu Lake Basin. Full article

Innovation-oriented productive forces are increasingly concentrated in cities, but the multiscale mechanisms through which the built environment shapes these forces remain insufficiently understood. This study develops a spatial analytical framework linking firm-level new quality productive forces (NQPF) to fine-grained urban spatial structures. Using 89 A-share listed firms in the Xiamen–Zhangzhou–Quanzhou (XZQ) urban agglomeration, we first construct an entropy-weighted NQPF index from eleven financial indicators related to R&D human capital, advanced capital stock, intangible assets, and operational efficiency. Kernel density estimation is then used to transform discrete firm-level NQPF values into a continuous 600 m × 600 m grid surface as the dependent variable. On the explanatory side, 27 built-environment variables are organized into an integrated indicator system covering urban form, natural conditions, jobs–housing structure, and service infrastructures. We combine cross-validated recursive feature elimination (RFE-CV) with multiscale geographically weighted regression (MGWR) to construct two model specifications: a 7-variable parsimonious subset and a 14-variable highest-performing subset. This dual-subset design allows us to distinguish core structural drivers from more context-dependent spatial mechanisms. The results reveal three mechanisms. First, ecological adaptation reflects the scale-dependent enabling and constraining effects of infrastructure and natural-foundation variables. Second, structural coordination shows that mature cores may experience crowding-related suppression when functional and institutional resources become spatially mismatched. Third, boundary activation indicates that transport, public-service, and leisure-related facilities can activate peripheral and cross-jurisdictional interface zones when supported by network connectivity and institutional coordination. By coupling variable-specific bandwidths with local coefficients, this study advances the analysis of spatial heterogeneity and provides evidence for differentiated, innovation-oriented urban regeneration. Full article

Clarifying how ecosystem services (ESs) change over time and space, and how their trade-offs and synergies evolve, is essential for regional ecological protection and high-quality development. Using Shandong Province as a case study, this research quantified carbon storage (CS), water yield (WY), soil conservation (SC), and habitat quality (HQ) with the InVEST model. GeoDetector, geographically weighted regression (GWR), XGBoost-SHAP, Spearman’s rank correlation, bivariate spatial autocorrelation, and spatial overlay analysis were then combined to examine ES patterns, driving mechanisms, and interaction relationships. The main findings are as follows. (1) During 2000–2020, the most evident land-use changes occurred in cropland, grassland, built-up land, and water bodies. (2) The dominant drivers varied markedly among services: CS and HQ were mainly shaped by land-use type and human activity, WY was chiefly controlled by precipitation, and SC was most sensitive to topographic conditions. Factor interactions were generally stronger than single-factor effects, with two-factor enhancement being the prevailing interaction type. (3) ES trade-off/synergy relationships were relatively stable through time. A strong synergy persisted between CS and HQ, whereas CS and SC exhibited a moderate synergistic relationship. By contrast, WY showed evident trade-offs with both HQ and CS, with the WY–HQ trade-off being particularly pronounced. (4) Spatial overlay results showed that the overall ES synergy level remained low. Low-synergy areas accounted for 69.23–70.94% of the study area across the study period. Although strong-trade-off areas expanded overall, high-synergy areas remained limited, indicating considerable room to improve the coordinated provision of ESs in Shandong Province. Full article

Automated detection of lunar impact craters from digital elevation model (DEM) data is important for lunar geological analysis, landing-site selection, and crater catalog updating. However, this task remains challenging because lunar craters exhibit large scale variations, weak or degraded rims, ambiguous boundaries, and complex topographic backgrounds. In addition, large-scale lunar remote sensing applications require detection models to achieve a reasonable balance among accuracy, model complexity, and inference efficiency. To address these challenges, this study proposes FPW-YOLO11n, a frequency-perception crater detection method developed based on YOLO11n. First, a Frequency-Directional Attention Module (FDA-Module) is introduced into the shallow stage of the backbone. This module combines frequency-aware channel attention and direction-aware spatial attention to enhance the representation of crater rim structures, elevation variations, and directional topographic cues in DEM data. Second, a C2PSA-LRSA module is designed by embedding Local Region Self-Attention into the C2PSA framework, thereby improving local contextual feature interaction while reducing the excessive cost associated with global self-attention. Third, Inner-WIoU is adopted to replace the original CIoU loss in YOLO11n. By combining the auxiliary-box mechanism of Inner-IoU with the sample-quality-aware weighting strategy of WIoU, Inner-WIoU provides a more flexible bounding-box regression objective for craters with weak rims, scale variations, and uncertain boundaries. A DEM-based lunar crater dataset was constructed from the Moon LRO LOLA–SELENE Kaguya TC DEM Merge 60N60S 59m product and the Robbins lunar crater catalog, covering the non-polar region from 60° S to 60° N and containing 4760 image tiles. Under the random data-splitting strategy, FPW-YOLO11n achieves 78.3% Precision, 66.2% Recall, 75.1% mAP@0.5, and 50.2% mAP@0.5:0.95, outperforming the YOLO11n baseline by 1.2, 2.0, 1.6, and 4.0 percentage points, respectively. Additional experiments based on geographically disjoint data splitting further show that the proposed method consistently performs better than YOLO11n on DEM data, indicating that the proposed structural improvements remain effective under a more rigorous spatially independent evaluation setting. Although the computational cost increases from 6.3 to 24.0 GFLOPs, FPW-YOLO11n maintains a compact parameter size of 2.59 M and a high inference speed, demonstrating an improved accuracy–efficiency trade-off for lunar crater detection from DEM data. Full article

Quantifying mountain forest carbon stocks and elucidating their spatially heterogeneous driving mechanisms are both critical for terrestrial carbon management under the global carbon neutrality agenda. Conventional single-source remote sensing approaches can neither fully exploit multi-source data synergies nor adequately resolve spatial heterogeneity in complex terrains. This study develops an integrated framework combining multi-source remote sensing classification, InVEST-based carbon estimation, and multiscale geographically weighted regression (MGWR) and applies it to Zixi County, a subtropical mountainous bamboo-abundant region in southeastern China. Sentinel-2 imagery, PlanetScope data, and DEM derivatives were fused with an optimized Random Forest classifier, achieving an overall accuracy of 0.8565 (Kappa = 0.7065). Carbon stocks were then estimated via the InVEST model. MGWR analysis (adjusted R² = 0.930, AICc = 594.032) substantially outperformed the global OLS model (adjusted R² = 0.795, AICc = 1717.450), confirming strong spatial non-stationarity across all drivers. Canopy density exhibited the strongest positive local effect (coefficient range: 0.343–0.768); slope position showed predominantly negative regulation with localized positive reversals (−0.778 to 0.270); elevation displayed a broad-scale positive gradient (0.133–0.140); and total vegetation cover exhibited bidirectional effects (−0.134 to 0.208) with pronounced east–west divergence. This framework not only provides a robust methodological reference for carbon stock assessment in complex mountain landscapes but also supports targeted forest management and carbon sequestration strategies through spatially explicit driver identification. Full article