Urban vitality, intricately connected to urban morphology, has long been a cornerstone of urban planning and design. The accelerated pace of urbanization has created abundant living and working spaces, but it has also brought about a series of issues such as traffic congestion, environmental pollution, insufficient public spaces, and uneven urban development, leading to a decline in urban vitality. The spatial distribution patterns of urban vitality and their influencing factors are diverse and vary across different cities, necessitating a multidimensional exploration of the relationship between urban vitality and the built environment. Utilizing the central urban area of Beijing as a case study, this research leverages multi-source urban spatial data to delineate the spatial characteristics of social, economic, cultural, and comprehensive vitality. Furthermore, a comprehensive set of built-environment indicators is developed across five dimensions to analyze their correlation with urban vitality. The results indicate: (1) There is a significant spatial clustering of various vitality types in Beijing, with a pronounced correlation between high-density population aggregation and vigorous economic activities. (2) Subdistricts exhibiting high social vitality display an “n”-shaped distribution around the Second Ring Road. In contrast, those with high economic vitality are concentrated along Chang’an Street and various district commercial centers. High cultural vitality subdistricts are distributed in a northwest–southeast trajectory from the Fourth Ring Road to the Second Ring Road, and subdistricts with high comprehensive vitality exhibit a concentric distribution radiating outwards from the center. (3) Social vitality is most closely related to comprehensive vitality, and the various vitality types in Beijing’s central urban area develop relatively evenly. (4) The built environment significantly affects all types of urban vitality. Factors such as floor area ratio, POI density, POI mixing degree, and intersection density are fundamental to enhancing urban vitality, whereas the greening rate somewhat inhibits it. (5) Future spatial planning should utilize the radiating effect of high-vitality subdistricts to optimize population distribution, enhance POI mixing, increase metro station density, and strengthen critical urban structures for synergistic economic and cultural development. This study provides a foundation and promotion strategies for optimizing the layout and enhancing vitality at the subdistrict scale within Beijing’s central urban area.

Climate change and human expansion are primary drivers of ecological degradation in metropolitan areas, underscoring the necessity of examining the complex interplay between environmental factors and ecological quality. This study investigates the spatial–temporal evolution of ecological quality within the Beijing Metropolitan Area (BMA) from 2000 to 2020 and proposes a comprehensive assessment framework integrating machine learning techniques and spatial heterogeneity analyses. Ecological quality is quantitatively evaluated using the Remote Sensing Ecological Index (RSEI), leveraging MODIS imagery, climate data, land use patterns, and soil characteristics. Spatial clustering patterns of ecological quality are identified through RSEI calculations and spatial autocorrelation analyses, while future trends are projected utilizing the coefficient of variation, Sen and Mann–Kendall methods, and the Hurst index. The XGBoost algorithm elucidates the multifaceted driving mechanisms, and geographically weighted regression (GWR) quantifies the spatial variability of these drivers. The application of XGBoost reveals nonlinear relationships among ecological drivers, and GWR enhances spatially explicit interpretations of these relationships. Results indicate an overall improvement in ecological quality, with the RSEI rising from 0.428 in 2000 to 0.480 in 2020, corresponding to an annual average increase of approximately 0.55%. Notable spatial variability exists, with ecological quality consistently higher in the Taihang Mountains relative to lower-altitude plains. Current ecological protection policies have effectively mitigated ecological degradation in approximately 32.35% of the study area; however, significant environmental pressures persist in urban–rural transition zones and plain regions. Topography and soil properties emerge as dominant influencing factors, while climate indirectly influences ecological quality by shaping vegetation coverage. Human activities predominantly exert negative impacts within urban expansion zones. This research offers a robust quantitative framework for regional ecological conservation, providing critical insights to inform sustainable development and environmental policy-making.

Urban Heat Island (UHI) effect is a significant concern in cities, increasing environmental and living conditions challenges. Urban green spaces are essential in mitigating UHI effects by regulating temperature. However, most studies do not adequately consider the multidimensional characteristics of green spaces in their cooling effects on the UHI. To address this gap, this study systematically investigates the cooling effects of green space by examining global and local mitigation mechanisms, with Guangzhou as a case study. Land surface temperature (LST) data were retrieved using the Mono-Window algorithm to analyze spatial variations in UHI intensity. Green space was further evaluated in terms of distribution, quality, and morphology, providing a robust evaluation. A Random Forest (RF) regression model, combined with SHapley Additive exPlanations (SHAP), was used to quantify the global contributions of UGS indicators to cooling effects. A Multiscale Geographically Weighted Regression (MGWR) model was employed to examine spatial heterogeneity. Results show an increase in high-temperature zones in Guangzhou from 2019 to 2024, particularly in Panyu and Nansha, indicating intensified UHI effects. Northern forested areas exhibited strong cooling effects, with Cooling Effect Index (CEI) values above 0.45. The distance to green space (DGS) was found to be the primary factor influencing cooling, accounting for 75.3% of the variance in CEI in 2024. The MGWR model confirmed that DGS has a negative correlation with LST, indicating that closer proximity to green spaces is associated with lower temperatures. This study provides evidence for optimizing green space planning and thermal environment management in Guangzhou and other megacities.

Amid global environmental degradation, understanding the spatiotemporal dynamics and trade-offs of ecosystem services (ESs) under varying land-use scenarios is critical for advancing the sustainable development of social–ecological systems. This study analyzed the Chaohu Lake Basin (CLB), focusing on four scenarios: natural development (ND), economic priority (ED), ecological protection (EP), and sustainable development (SD). Using the PLUS model and multi-objective genetic algorithm (MOGA), land-use changes for 2030 were simulated, and their effects on ESs were assessed quantitatively and qualitatively. The ND scenario led to significant declines in cropland (3.73%) and forest areas (0.18%), primarily due to construction land expansion. The EP scenario curbed construction land growth, promoted ecosystem recovery, and slightly increased cropland by 0.05%. The SD scenario achieved a balance between ecological and economic goals, maintaining relative stability in ES provision. Between 2010 and 2020, construction land expansion, mainly concentrated in central Hefei City, led to a marked decline in habitat quality (HQ) and landscape aesthetics (LA), whereas water yield (WY) and soil retention (SR) improved. K-means clustering analysis identified seven ecosystem service bundles (ESBs), revealing significant spatial heterogeneity. Bundles 4 through 7, concentrated in mountainous and water regions, offered high biodiversity maintenance and ecological regulation. In contrast, critical ES areas in the ND and ED scenarios faced significant encroachment, resulting in diminished ecological functions. The SD scenario effectively mitigated these impacts, maintaining stable ES provision and ESB distribution. This study highlights the profound effects of different land-use scenarios on ESs, offering insights into sustainable planning and ecological restoration strategies in the CLB and comparable regions.

The Grand Canal serves as a vital water transportation route, a UNESCO World Cultural Heritage site, and an ecological corridor. It is currently undergoing coordinated transformation through infrastructure development, heritage preservation, and ecological restoration. However, existing research has primarily focused on either cultural heritage conservation or localized ecological issues, with limited attention to the spatial relationship between landscape patterns and ecological quality along the entire corridor. To address this gap, this study examines eight sections of the Grand Canal and develops a gradient analysis framework based on equidistant buffer zones. The framework integrates the Remote Sensing Ecological Index (RSEI) with landscape pattern indices to assess ecological responses across spatial gradients. A Multi-scale Geographically Weighted Regression (MGWR) model is applied to reveal the spatially heterogeneous effects of landscape patterns on ecological quality. From 2013 to 2023, landscape patterns showed a trend toward increasing agglomeration and regularity. This is indicated by a rise in the Aggregation Index (AI) from 91.24 to 91.38 and declines in both patch density (PD) from 8.45 to 8.20 and Landscape Shape Index (LSI) from 199.74 to 196.72. During the same period, ecological quality slightly declined, with RSEI decreasing from 0.66 to 0.57. The effects of PD and Shannon’s Diversity Index (SHDI) on ecological quality varied across canal sections. In highly urbanized areas such as the Tonghui River, these indices were positively correlated with ecological quality, whereas in less urbanized areas like the Huitong River, negative correlations were observed. Overall, the strength of these correlations tended to weaken with increasing buffer distance. This study provides a scientific foundation for the integrated development of ecological protection and spatial planning along the Grand Canal and offers theoretical insights for the refined management of other major inland waterways.

Assessment of cultural ecosystem services (CESs) is a key component in advancing the sustainable development of urban ecosystems. Mapping the spatial distribution of CESs provides spatially explicit insights for urban landscape planning. However, most assessments lack regional adaptability, particularly in cities with pronounced environmental and cultural heterogeneity. To address this gap, this study focused on the central urban area of Lhasa, using communities as units to develop a tailored CES assessment framework. The framework integrated the MaxEnt model with multi-source indicators to analyze the spatial distribution of five CES categories and their relationships with environmental variables. Spatial statistics and classification at community level informed the CES spatial optimization strategies. Results indicated that high-value CES areas were predominantly concentrated in the old city cluster, typified by Barkhor and Jibenggang subdistricts, following an east–west spatial pattern along the Lhasa River. Distance to tourist spot contributed 78.3% to cultural heritage, 86.1% to spirit and religion, and 42.2% to ecotourism and aesthetic services, making it the most influential environmental variable. At the community level, CESs exhibited a distinct spatial gradient, with higher values in the central area and lower values in the eastern and western peripheries. For the ecotourism and aesthetic category, 61.47% of the community area was classified as low service, whereas only 1.48% and 7.33% were identified as excellent and high. Moreover, communities within subdistricts such as Barkhor and Zhaxi demonstrated excellent service across four CES categories, with notably lower performance in the health category. This study presents a quantitative and adaptable framework and planning guidance to support the sustainable development of CESs in cities with similar characteristics.