To balance ecological protection and urban development, a land use simulation model that couples an ecological network (EN) and multiple scenarios was developed based on the PLUS model. The simulation of land use in the Qiantang River Basin in 2030 successfully demonstrates the usefulness of the EN-PLUS model. In this model, conventional ecological constraints (nature reserves and water areas) and three different EN levels were taken as restricted conversion areas during the simulation. Then, four ecological constraints were coupled with four simulation scenarios: business as usual (BAU), rapid urban development (RUD), ecological protection (EP), and urban- and ecology-balanced (UEB). Information from the analysis of model simulation results can be used to reduce the potential damage to a range of land cover types. However, this protective effect is not obvious under the RUD scenario due to the impact of significant human disturbance. Furthermore, although EP is the scenario with the least ecological damage at the whole watershed scale, this is not the case for all subbasins. This indicates the existence of a landscape scale effect. Therefore, the best development scenario should be selected by comprehensively weighing the scale effect and the ecological characteristics of each subbasin.

Forest walking is a popular, healthy, and light outdoor activity. The potential comprehensive relationships between the vertical structures, thermal comfort, negative air ions (NAI), and human physiological stress in forest walking spaces have not been determined. We performed an experiment in the Baishuihe National Nature Reserve, Sichuan Province, China. Thirty-two college students recruited as subjects completed a forest walk (approximately one kilometer) on the same trail divided into three vertical structure type subsections, namely: A (dense herb and shrub layers with a sparse tree layer), B (dense tree, herb and shrub layers), and C (dense tree and herb layers with a sparse shrub layer). When the subjects passed preset environmental measurement points, staff measured climatic indexes (air temperature, relative humidity, wind velocity, surface temperature and global radiation) and NAI levels, and these data were input into the Rayman model to form a comprehensive thermal comfort index, the physiologically equivalent temperature (PET). PET and NAI differences and dynamic data among the subsections were analyzed. The subjects’ brain waves, heart rates (HRs), and walking speed (S) were digitally recorded. We selected brain wave θ, γ and β-high/α rates, neuroemotional indexes (stress and relaxation) and HR as physiological indicators, and S as an auxiliary indicator. The correlations between PET and NAI with physiological and auxiliary indexes were analyzed. Forest type C showed the lowest PETs and highest NAIs along with the most stable dynamic changes. PET was negatively correlated with HR and positively correlated with γ (12 channels). NAI was positively correlated with S and relaxation and negatively correlated with γ (two channels) and the β-high/α ratio (five channels). These comprehensive relationships suggest that dense tree, sparse shrub, and high-coverage herb layers combined with optimal temporal conditions (before noon or after a light rain) form the best thermal comfort and NAI conditions conducive to reducing human physiological pressures during summer daytime forest walking. These results provide theoretical references for forest walking and spatial regulation.

An urban agglomeration is a highly developed spatial area formed by integrated cities. While previous studies have analyzed green space allocation at the provincial and city scales, there is insufficient information on green space allocation in urban agglomerations. For this research, a database of green spaces in eight urban agglomerations (133 cities) in China from 2002 to 2019 was constructed to better understand the equity of green space distribution among land resources. A green space equity index (GEI) was established based on the Theil index and combined with GeoDetector to analyze the differences in urban agglomeration green spaces. The main conclusions are as follows: The sum of the GEI of China’s urban agglomerations has increased significantly, rising from 3.74 in 2002 to 6.34 in 2019. The GEI value for each of the eight urban agglomerations was kept under 0.01. Polarized development has occurred within urban agglomeration cities, and the allocation of green space in megacities is relatively weak, especially in the more economically developed Yangtze River Delta and Guanzhong urban agglomerations. The average temperature, humidity, and precipitation have dominant influences in determining the GEI values. This paper provides a new perspective on the management and allocation of urban agglomeration green spaces.

Quantifying the emotional impact of street greening during the full-leaf seasons in spring, summer, and fall is important for well-being-focused urban construction. Current emotional perception models usually focus on the influence of objects identified through semantic segmentation of street view images and lack explanation. Therefore, interpretability models that quantify street greening’s emotional effects are needed. This study aims to measure and explain the influence of street greening on emotions to help urban planners make decisions. This would improve the living environment, foster positive emotions, and help residents recover from negative emotions. In Hangzhou, China, we used the Baidu Map API to obtain street view images when plants were in the full-leaf state. Semantic segmentation was used to separate plant parts from street view images, enabling the calculation of the Green View Index, Plant Level Diversity, Plant Color Richness, and Tree–Sky View Factor. We created a dataset specifically designed for the purpose of emotional perception, including four distinct categories: pleasure, relaxation, boredom, and anxiety. This dataset was generated through a combination of machine learning algorithms and human evaluation. Scores range from 1 to 5, with higher values indicating stronger emotions and lower values indicating less intense ones. The random forest model and Shapley Additive Explanation (SHAP) algorithm were employed to identify the key indicators that affect emotions. Emotions were most affected by the Plant Level Diversity and Green View Index. These indicators and emotions have an intricate non-linear relationship. Specifically, a higher Green View Index (often indicating the presence of 20–35 fully grown trees within a 200 m range in street view images) and a greater Plant Level Diversity significantly promoted positive emotional responses. Our study provided local planning departments with support for well-being-focused urban planning and renewal decisions. Based on our research, we recommend the following actions: (1) increase the amount of visible green in areas with a low Green View Index; (2) plant seasonal and flowering plants like camellia, ginkgo, and goldenrain trees to enhance the diversity and colors; (3) trim plants in areas with low safety perception to improve visibility; (4) introduce evergreen plants like cinnamomum camphor, osmanthus, and pine.