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.

Greenway is an important linear public space that meets the diverse needs of the public. With the increasing popularity of greenway construction, the study of different greenway usage behavior in different socio-economic areas is of great value to the detailed design and construction of greenway in the future. Using the theory of environment-behavior studies (EBS), this study selected representative urban greenways and suburban greenways in Beijing, China, and conducted a questionnaire survey. Descriptive statistics and the chi-squared test are used to quantitatively analyze and summarize the behavior of greenway users. It is found that user gender, educational level, and residence (i.e., permanent resident or visitor), as well as season of use, are highly similar for urban greenways and suburban greenways in Beijing. However, due to a close relationship with urban location, modified by temporal, spatial, and personal factors, different behavioral characteristics are evident as follows: (1) Urban greenways are most closely related to daily life, work and education of urban residents, with short travel distances, short single use time, high frequency of use, high social and cultural value, wide distribution of age groups and wide distribution of time periods of use. (2) Suburban greenways are an important choice for residents’ outdoor activities on weekends and holidays. It is mainly used for ecological protection and sightseeing, supplemented by sports and fitness functions. It has the characteristics of low use frequency, high income level, wide distribution of time and distance, mainly used by young and middle-aged people, and used for a single time of more than 1 hour. Natural scenery along the trail is the most important attraction factor, and waterfront space and walking space are the main use behavior characteristics.

Forest therapy bases are essential ecological recreational locations to promote mental and physical health while at the same time allowing people to enjoy and appreciate the forest. The article took Japan, where the development of forest therapy is in a mature stage, as the research object. Using multi-data and the methodologies of Kernel Density Analysis in ArcGIS and GeoDetector, the spatial distribution characteristics of Japan’s forest therapy bases were investigated, as well as the influencing factors. The results reveal that the spatial distribution of forest therapy bases in Japan is unbalanced, with an aggregated distribution in economically developed and densely populated areas. The spatial density of natural landscape resources, Gross Domestic Product (GDP), the spatial density of population, distance from three major metropolitan areas, rail transit ridership, and spatial density of tourism resources are factors influencing the distribution of forest therapy bases in Japan. The factors interact with each other, forming the spatial distribution pattern. Among these factors, GDP has the greatest explanatory power for the spatial distribution of forest therapy bases in Japan, followed by the distance from Japan’s three major metropolitan areas and spatial density of tourism resources, while the spatial density of population, spatial density of natural landscape resources, and rail transit ridership have a relatively weaker influence on forest therapy bases in Japan. The findings provide some insight into the macroscopic layout of forest therapy bases in various regions of different countries, demonstrating that excellent transportation facilities and good natural resources are the fundamental considerations for the location of forest therapy bases and that densely populated urban areas with a strong economic foundation are key areas for the development of forest therapy bases. Additionally, to take advantage of industrial agglomeration and synergize regional development, considerations for merging with existing resources, such as national parks, forest parks, and recreation forests, should be made.

The Qinghai–Tibetan Plateau (QTP) has the largest area of natural grassland in China, and continuous grassland degradation poses a serious threat to regional ecological security and sustainable resource management. It is essential to comprehensively evaluate the cost–benefit differences and drivers of grassland degradation across various zones to enhance sustainable management practices. This study presents a zonal management framework for the ecological restoration of degraded grasslands based on cost–benefit analysis, specifically applied to Qinghai in the Northeastern QTP. The results indicate: (1) Although the overall NDVI of grasslands shows an upward trend, some areas still exhibit significant degradation. (2) Cost–benefit analysis can divide degraded grasslands into four types of Ecological Management Zones (EMZs): high-cost–high-benefit zone, high-cost–low-benefit zone, low-cost–low-benefit zone, and low-cost–high-benefit zone. (3) The driving factors of grassland degradation show significant differences in different EMZs. Based on these research findings, differentiated spatial planning and management strategies for grassland ecological restoration were developed for each EMZ. This study not only provides a scientific methodology for grassland ecological restoration but also offers important insights for the sustainable management of grassland resources in the QTP and other ecologically sensitive areas.