Search Results (250)

Urban–rural integration realises the coordinated development and prosperity of urban and rural areas as a whole by optimising the allocation of resources and the flow of factors, and its connotations have been extended from a single dimension to multiple dimensions such as people, land and industry. The Beijing–Tianjin–Hebei Region has a typical “Core–Periphery Structure”, and this paper took the 187 county units within the region as the research object, taking into account indicators of development and coordination to construct an evaluation index system of urban–rural integration of the Beijing–Tianjin–Hebei region counties in the dimensions of “people–land–industry”. Global principal component analysis was used to measure the evolutionary pattern of the urban–rural integration level between 2005 and 2020, and its spatiotemporal drivers were analysed by using the Geographical and Temporal Weighted Regression model (GTWR). The results of the study show that (1) the level of urban–rural integration in the Beijing–Tianjin–Hebei region showed an increasing trend during the 15-year study period, the high-value areas of urban–rural integration were mainly distributed in Beijing and the Bohai Rim region in the eastern part of the Tianjin–Hebei region, and the level of urban–rural integration of the peri-urban county units of the city was better than that of the remote counties and cities as a whole. (2) In terms of spatial agglomeration, all dimensions were characterised by significant spatial agglomeration. The degree of agglomeration was categorised as urban–rural comprehensive integration (U-RCI) > urban–rural industry integration (U-RII) > urban–rural land integration (U-RLI) > urban–rural people integration (U-RPI). (3) In terms of spatial and temporal driving factors for urban–rural integration, the driving role of U-RPI, U-RLI and U-RII for U-RCI has gradually weakened during the past 15 years, and urban–rural integration in the counties shifted from a single role to a more central coordinated and multidimensional driving role. Full article

Anoplophora glabripennis (Asian longhorned beetle, ALB) (Motschulsky, 1854) is a local forest pest in China. Although the suitable area for this pest has some research history, it does not accurately predict the future distribution area of ALB. Accurate prediction of its suitable area can help control the harm caused by ALB more effectively. In this study, we applied the maximum entropy model to predict the suitable area for ALB. Moreover, the prediction results revealed that ALB is distributed mainly in northern, eastern, central, southern, southwestern, and northwestern China, and its high-fit areas are located mainly in northern, northwestern, and southwestern China. The average minimum temperature in September, precipitation seasonality (coefficient of variation), the average maximum temperature in April, and average precipitation in October had the greatest influence on ALB. The greatest distribution probabilities were observed at the September average minimum temperature of 16 °C, the precipitation seasonality (coefficient of variation) of 130%, the April average maximum temperature of 14 °C, and the October average precipitation of 30 mm. Furthermore, with climate change, the non-suitability area for the ALB will show a decreasing trend in the future. The intermediate suitability area will increase, while the low and high suitability areas will first increase and then decrease. Taken together, the potentially suitable areas for ALB in China include the Beijing–Tianjin–Hebei region and the Shanghai region in North China and East China, providing a deeper understanding of ALB control. Full article

Urban areas and their surrounding regions play a pivotal role in supporting population concentration, economic activities, and social interaction in modern society. However, the accelerated pace of urbanization and economic expansion has led to increasing ecological and spatial imbalances, posing significant challenges to sustainable urban development and human well-being. Therefore, China has implemented territorial spatial zoning policies aimed at guiding urban spatial structure transformation and improving ecological environmental quality (EEQ). This study employed the improved remote sensing ecological index to analyze the spatiotemporal dynamics and driving mechanisms of EEQ in Beijing from 2000 to 2020. The findings revealed a significant spatial pattern where the EEQ in both summer and winter decreased from the surrounding ecological conservation areas towards the central city. Notably, the overall EEQ was consistently higher in summer than in winter. Regarding the aggregation patterns of EEQ, the ecological conservation areas exhibited more favorable concentration distributions during both seasons, whereas the plain and urban areas displayed poorer aggregation characteristics. Overall, evapotranspiration was the dominant positive factor influencing EEQ across all spatial zones. These results provide a robust scientific basis for promoting sustainable development and informed spatial planning in metropolitan regions. Full article

Jobs–housing balance is a critical concern in urban planning and sustainable economic development. Urban rail transit, as a key determinant of employment and residential location decisions, plays a pivotal role in shaping jobs–housing dynamics. Beijing, the first Chinese city to develop a subway system, offers a comprehensive rail network, making it an ideal case for exploring the effects of transit density on jobs–housing balance. This study utilizes medium-scale panel data from Beijing (2009–2022) and employs a fixed-effects model to systematically examine the impact of rail transit station density on jobs–housing balance and its underlying mechanisms. The results indicate that increasing transit station density tends to aggravate jobs–housing separation overall, with pronounced effects in central and outer suburban areas but negligible effects in near suburban areas. Mechanism analysis reveals two primary pathways: (1) improved accessibility draws employment toward transit-rich areas, reinforcing the attractiveness of central districts; (2) rising housing prices elevate residential thresholds, pushing lower-income populations toward outer suburbs. While enhanced transit density improves commuting convenience, it does not effectively reduce jobs–housing separation. These findings offer important policy implications for optimizing transit planning, improving jobs–housing alignment, and promoting sustainable urban development. Full article

The near-surface air temperature lapse rate (SATLR) is a crucial parameter in climate, hydrology, and ecology research conducted in mountainous regions. However, existing research has difficulty characterizing its dynamic changes on an hourly scale. Obtaining data with high spatiotemporal resolution in complex terrains using existing methods poses challenges. This study introduces a hierarchical method for estimating SATLR at high spatiotemporal resolutions based on Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) land surface temperature (LST) data and machine learning techniques. Based on reconstructed FY-4A AGRI LST data, this study downscales the 4 km resolution data to a 1 km resolution using machine learning. It then estimates the spatial distribution of near-surface air temperature (SAT) and normalized near-surface air temperature (nSAT) by integrating station observations. Subsequently, high spatiotemporal resolution SATLRs are estimated, and their spatial and temporal distribution characteristics in the Beijing–Tianjin–Hebei mountainous region are analyzed. The results indicate that the SATLR exhibits a predominant distribution of 2~6 °C/km annually across the study area. However, in specific regions such as Taihang Mountains in the southwest, Damajun Mountain in the northwest, and certain areas of central Beijing City, the SATLR exceeds 6 °C/km in depth. Conversely, in Chengde City in the northeast and Huapiling in Damajun Mountain in the northwest, the SATLR is shallower than 2 °C/km. Seasonally, the average SATLR displays significant variation, with 3~5 °C/km being prevalent in spring, summer, and autumn, and 2~4 °C/km in winter. Moreover, the diurnal SATLR patterns from the second to fifth altitude grades exhibit consistency throughout the year and across seasons, albeit with varying overall values at different altitudes. Notably, the SATLR of the first altitude grade demonstrates stability within a day at lower elevations. Full article

Tongzhou District is the urban sub-center of Beijing, and the importance of groundwater resources is increasingly prominent. Based on groundwater level data from 1980 to 2020 and water usage data from various sectors in Tongzhou District between 2011 and 2020, this paper utilizes continuous wavelet transform (CWT), geostatistical models, and grey relational analysis (GRA) to explore the spatiotemporal evolution patterns and influencing factors of groundwater levels in Tongzhou District. The study reveals that the groundwater level evolution in Tongzhou District exhibits two primary cycles, and it predicts that the groundwater level at Liyuan Station will decrease and eventually rebound. From 1980 to 2020, the overall trend of groundwater levels in Tongzhou District showed a decline. However, the groundwater levels in the central and southern regions exhibited an upward trend from 2000 to 2020. The groundwater level is mainly influenced by spatial structural factors, with minimal impact from external random factors. Domestic water consumption, water usage in the tertiary sector, and industrial water usage have the greatest impact on groundwater levels, attributed to the rapid growth of the population and regional economy. Agricultural water usage has the least grey relational grade, which is related to changes in agricultural development planning in the study area, as well as reductions in the area of crop planting and the actual utilization area of facility agriculture. Full article

In rapidly urbanizing areas, there is a notable aggregation of vitality in high-density urban environments, accompanied by an increasing discrepancy between the supply and demand of urban green space (UGS). This study presented an integrated framework comprising a model for UGS supply-demand coupling coordination and a measure of urban vitality. Using downtown Beijing as a case study, the Gini coefficient assessed UGS supply-demand disparities across different vitality types. The study examined how UGS supply and demand factors interact with urban vitality, revealing the impact of UGS supply-demand imbalances on various dimensions of vitality and the UGS mismatches experienced by different vitality groups. The study showed that: (1) 63.29% of central Beijing’s areas had low UGS supply-demand coordination, with 39.23% experiencing UGS mismatches; (2) UGS supply and demand were significantly correlated with urban vitality spatial distribution; (3) these factors significantly impacted urban comprehensive vitality; (4) and there were notable UGS distribution disparities among vitality groups, with economic vitality group perceiving the greatest inequity (Gini = 0.311), followed by social vitality (Gini = 0.289) and cultural vitality group (Gini = 0.247). These findings offer valuable insights for a more refined assessment and enhancement of UGS, aiming to achieve balanced, high-quality, and sustainable urban development. Full article

Urban green space (GS) equity is crucial to achieving environmental justice. From the environmental justice perspective, this study focuses on the equity of GS in residential areas of urban disadvantaged groups, quantitatively assessing and comparing the fairness of GS usage between urban villages (UVs) and formal residential quarters (RQs). Using data on green space area, NDVI, and FVC, this study analyzes GS conditions across different buffer distances within the central urban area of Beijing. Statistical methods, including the Theil index, were employed to evaluate the equity of per capita green space, vegetation coverage, and vegetation conditions. Our findings reveal distinct spatial distribution patterns of internal and external GS characteristics between UVs and RQs. Additionally, while the internal GS equity in UVs is generally lower than in RQs, FVC equity demonstrates the opposite trend. Finally, intra-group inequity in both UVs and RQs is the dominant factor contributing to overall GS disparities in residential areas. This study establishes a comprehensive evaluation framework for analyzing GS availability, NDVI, and FVC equity in two types of residential communities. It provides a valuable reference for subsequent GS equity assessments and offers actionable recommendations for policymakers to prioritize improving GS equity in certain residential areas. By addressing gaps in environmental justice theory regarding urban GS, this study proposes a pragmatic and effective approach to enhancing GS equity in large, rapidly developing cities. Full article

Understanding the contribution of macroinvertebrate diversity indices to community stability in urban rivers is critical for developing more effective strategies to manage and conserve the ecological health of urban rivers and to maintain sustainable regional economic and social development. However, knowledge regarding the relationship between environmental factors, multidimensional biodiversity, and community stability in urban rivers remains limited. In this study, we investigated the relationships among macroinvertebrate multidimensional diversity, secondary productivity-to-biomass ratio (SP/B), and average variation degree (AVD) in a typical urban river—the North Canal River basin in Beijing—to identify which biodiversity metric best indicates community stability. Macroinvertebrates were extensively sampled from September to October 2020 in the North Canal River basin (BYH), a typical urban river in Beijing. We comparatively analyzed the spatial variation in different types of diversity—species diversity (SD), functional diversity (FD), and phylogenetic diversity (PD)—as well as SP/B and AVD between the upstream and midstream–downstream reaches of the river under varying degrees of urbanization and human disturbance. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to assess the relationships among multidimensional diversity, SP/B, and AVD. The results showed that upstream environmental factors and diversity indices together explained 52.9% and 52.0% of the variance in SP/B and AVD, respectively, while midstream–downstream factors explained 65.9% and 84.2%, respectively. These findings suggest that both SP/B and AVD are suitable indicators for examining the relationships between macroinvertebrate community stability, diversity indices, and environmental factors in the BYH. In the upstream region, total phosphorus (TP), FD, and PD were more indicative of SP/B in the central urban area, while SD and PD were more indicative of AVD. In contrast, in the midstream–downstream suburban areas, dissolved oxygen (DO), SD, and PD were more indicative of SP/B, while FD and PD were more indicative of AVD. These findings demonstrate that PD is a stronger indicator of both SP/B and AVD under varying anthropogenic disturbances and environmental conditions. The PLS-SEM results also indicated differences in the specific effects of FD and SD on community stability across the upstream and midstream–downstream sections, as well as differences in the direct effects of environmental factors such as TP and DO. These results suggest that PD is more sensitive than FD and SD in detecting the impacts of anthropogenic disturbances and environmental fluctuations on macroinvertebrate community stability in urban rivers. Our study provides evidence that PD outperforms FD and SD in predicting macroinvertebrate community stability in urban river ecosystems and that the combined use of SP/B and AVD better reveals the complex interactions between biodiversity and environmental factors influencing community stability. This combination can thus enhance our understanding of how biodiversity affects macroinvertebrate community stability in urban rivers. Full article

This longitudinal study applies decade-spanning socioeconomic indicators (2013–2022) from the Beijing–Tianjin–Hebei urban agglomeration. An integrated analytical framework was developed, merging the super-efficiency slack-based measurement (SBM) methodology with entropic weighting techniques to quantify tourism efficiency and economic development. Subsequent phases employed a multi-method analytical cascade: coupling coordination assessment modeling for system interaction analysis, standard deviation ellipses for spatial dispersion characterization, and Markovian transition matrices for temporal pattern identification. The investigation concludes with evolutionary trajectory projections using gray system forecasting GM(1,1) modeling. The analytical findings reveal the following patterns: (1) Within the Beijing–Tianjin–Hebei metropolitan cluster, tourism efficiency demonstrates a consistent upward trajectory, manifesting spatial differentiation characteristics characterized by a dual-core structure centered on Tianjin and Baoding, with higher values observed in northwestern areas compared to southeastern regions. Concurrently, regional disparities exhibit progressive convergence over temporal progression. (2) The level of economic development in the Beijing–Tianjin–Hebei city cluster has been rising steadily, demonstrating a geospatial distribution of ‘central concentration with peripheral attenuation, with the north-east being better than the southwest’, and the gap between the regional differences has become broader over time. (3) The coupling between tourism efficiency and the level of economic development in the Beijing–Tianjin–Hebei city cluster has generally improved, with Beijing and Tianjin predominantly in a coordinated regime, and some cities in Hebei Province about to shift from dysfunctional to coordinated, and, spatially, the coupling and coordination in northern sectors demonstrate superior performance compared to southern counterparts nationally. (4) The coupling coordination degree of the Beijing–Tianjin–Hebei city cluster in the next eight years is predicted by the gray GM(1,1) prediction model and the overall continuation of the growth trend of the Beijing–Tianjin–Hebei city cluster over the past ten years, thus verifying the importance of the regional integrated policy frameworks in the system integration of the Beijing–Tianjin–Hebei metropolitan system. Full article

Identifying the spatiotemporal variations in and driving factors of trade-offs and synergies among ESs in the plain area forms a critical foundation for the effective management of ecosystems and regulation. It is also crucial for effectively distributing the management of natural assets and the formulation of effective ecological policy. This research utilized correlation analysis, GWR and OPGD to examine the trade-offs and synergies among Net Primary Production, Soil Carbon, Water Conservation, and Habitat Quality in the Beijing Plain from 2001 to 2020. The results revealed that from 2001 to 2020, HQ and SC showed a declining trend, while NPP and WC exhibited an increasing trend. The trade-off intensities among NPP-SC, NPP-WC, and WC-HQ increased, whereas the trade-off intensities among NPP-HQ, SC-HQ, and SC-WC decreased. High-synergy areas for NPP-HQ, SC-HQ, and SC-WC were focused in the central urban area, with scattered distribution in the southeast and northwest. NPP-SC displayed a fragmented spatial distribution with significant variations. The spatiotemporal distributions of NPP-WC and WC-HQ were highly similar, both exhibiting strong synergy. However, NPP-WC demonstrated strong trade-offs in the northern plain area but weaker trade-offs elsewhere, while WC-HQ exhibited strong trade-offs outside the central urban area. The kind of land use was the primary element affecting the trade-off intensities of NPP-HQ, SC-HQ, and WC-HQ. NDVI and precipitation significantly influenced NPP-SC. The key factors influencing the spatial variation in NPP-WC were the land use type, temperature, and precipitation. Temperature was the primary determinant affecting SC-WC. The trade-off intensity among ESs is not determined by a single factor but is driven by the interactions between services or shared influencing factors, exhibiting high spatial heterogeneity. These findings provide valuable guidance for developing strategies for land-use planning and ecological restoration. Full article

Residential green space (RGS), as a frequently visited green space by residents, is the main space for daily activities and interactions, and its quality directly affects residents’ physical and mental health. Although many studies have revealed the impact of green space characteristics on health, research on the relationship between its environmental elements and health is still insufficient. This study selected five types of residential area in the central urban area of Beijing for investigation, collecting people’s green space perception, usage, and self-rated health information, and, using stepwise regression analysis, exploring the impact of RGS environmental factors on residents’ self-rated health under different vegetation cover rates. The results suggest the following: (1) Residents’ perception and usage of RGS characteristics are closely related to their self-rated health status, but the impact of environmental factors varies depending on vegetation coverage. (2) Maximizing natural features and cultural symbols is crucial for residential areas with high greenery. In residential areas with moderate vegetation, priority should be given to enhancing path elements, maintenance and shelter. For residential areas with low greenery cover, efforts should focus on strengthening fitness facilities and improving shelter to promote people’s health. (3) The impact of activity duration on usage behavior is most significant. These findings contribute to a more comprehensive understanding of the significance of RGS quality in urban residential areas. They also provide a reference for the optimization and management of the living environment and support the sustainable development of community environments. Full article

China’s population is aging rapidly, with a large proportion of elderly individuals “aging in place”. In central areas of large cities, the amount of community and home-based elderly care services provided by the government and for-profit organizations are insufficient to meet the demands of these “aging in place” elderly. Taking the core area of Beijing as the spatial scope, this empirical study collects the demand on services of the main types of elderly residents in community and home-based dwelling through questionnaires (n = 242) and employs a mixed-methods approach for analysis. Descriptive statistics and exploratory factor analysis are used to determine the categories and levels of those demands, and machine learning methods (random forest regression model) are used to calculate the importance of various influencing factors (features of the elderly and subdistricts’ built environment) on them. It is shown that elderly residents have a higher demand for psychological and physical condition maintenance services (mean = 3.40), and a lower demand for reconciliation and rights defense services (mean = 3.08). The results also show that the built environment factors are very important for the elderly on choosing demands, especially mean distance of CECSs (community elderly care stations) to downtown landmarks and main roads in subdistricts, and characteristics of CECS. The elderly’s own features also have a relatively important impact, especially their living arrangements, caregivers, and occupations before retirement. This study applies machine learning techniques to sociological survey analysis, helping to understand the intensity of elderly people’s demand for various community and home-based elderly care services. It provides a reference for the allocation of such service resources. Full article

Understanding the spatiotemporal dynamics of short-duration heavy rainfall (SDHR) is critical for urban flood management. This study applies the K-shape clustering algorithm to classify 105 SDHR events in Beijing (2009–2021) using hourly rainfall data. Compared to K-means and DTW, K-shape prioritizes temporal shape alignment, crucial for capturing phase-shifted rainfall patterns. Three clusters emerged: (1) localized moderate-intensity events (13.3% of events) peaking at noon (11:00–14:00 LST) in western/southeastern regions, with weak burstiness (44.3% stations peak within 0–1 h) and moderate spatial variability (Cv = 1.08); (2) highly variable, intense urban rainfall (47.6% of events) characterized by rapid burstiness (72.5% stations peak within 0–1 h) and extreme spatial heterogeneity (Cv = 1.21), concentrated in central urban areas with peak intensities >130 mm/h; (3) prolonged heavy rainfall (39.1% of events) lasting >6 h, featuring significant accumulation (mean > 50 mm/day) in northeastern plains. The framework identifies high-risk zones (e.g., Cluster 2’s urban flash floods) and informs adaptive drainage design (e.g., prolonged resilience for Cluster 3). This study highlights the necessity of combining statistical metrics with domain expertise for robust SDHR classification and provides insights for urban flood management, emphasizing targeted strategies for different rainfall patterns. Full article

Under the context of global climate change, floods are one of the major challenges facing urban development. Based on resilience theory, this study proposed an evaluation method to accurately assess the resilience of urban flooding prevention and control systems (FPCs), integrating both attribute resilience (AR) and functional resilience (FR). First, the method organized FPC attributes from the perspective of the waterlogging generation and elimination processes using foundational data from the study area, and it established a resilience indicator system. The Entropy Weight Method (EWM) was applied to calculate indicator weights, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to calculate indicator values, ultimately deriving the attribute resilience (AR). Subsequently, functional performance during actual operations was evaluated using scenario simulation based on hydrodynamic model results, and the FR was determined. Finally, spatial correlation analysis of the AR and FR was conducted to identify areas with weak resilience. This study developed an evaluation method that considers both system attributes and functional performance using the central urban area of Beijing as a case study to assess flood resilience. The results indicated that the most influential factors affecting the AR of the FPC are the green space percentage (GSP), average slope, and drainage capacity (DC), with their weights calculated as 0.17, 0.137, and 0.205, respectively. Among resistance, absorption, and recovery, absorption had the greatest influence, with a weight of 0.447. The Moran’s I indices for the AR and FR were 0.66 and 0.49, respectively, indicating spatial clustering, although the clustering locations differed. There was spatial correlation between the AR and FR, enabling more precise identification of areas with high and low flood resilience. However, the trends of the AR and FR were not entirely consistent across different types of sub-districts due to differences in evaluation methods and the influence of various indicators. Full article