Assessing grassland cultural ecosystem services supply and demand for promoting the sustainable realization of grassland cultural values

How does high temperature weather affect tourists' nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China

Natural landscapes are crucial resources for enhancing visitor experiences in ecotourism destinations. Previous research indicates that high temperatures may impact tourists' perception of landscapes and emotions. Still, the potential value of natural landscape perception in regulating tourists' emotions under high-temperature conditions remains unclear. In this study, we employed machine learning models such as LSTM-CNN, Hrnet, and XGBoost, combined with hotspot analysis and SHAP methods, to compare and reveal the potential impacts of natural landscape elements on tourists' emotions under different temperature conditions. The results indicate: (1) Emotion prediction and spatial analysis reveal a significant increase in the proportion of negative emotions under high-temperature conditions, reaching 30.1%, with negative emotion hotspots concentrated in the downtown area, whereas, under non-high temperature conditions, negative emotions accounted for 14.1%, with a more uniform spatial distribution. (2) Under non-high temperature conditions, the four most influential factors on tourists' emotions were Color complexity (0.73), Visual entropy (0.71), Greenness (0.68), and Aquatic rate (0.6). In contrast, under high-temperature conditions, the most influential factors were Greenness (0.6), Openness (0.56), Visual entropy (0.55), and Color complexity (0.55). (3) Compared to non-high temperature conditions, high temperatures enhanced the positive effects of environmental perception on emotions, with Greenness (0.94), Color complexity (0.84), and Enclosure (0.71) showing stable positive impacts. Additionally, aquatic elements under high-temperature conditions had a significant emotional regulation effect (contribution of 1.05), effectively improving the overall visitor experience. This study provides a data foundation for optimizing natural landscapes in ecotourism destinations, integrating the advantages of various machine learning methods, and proposing a framework for data collection, comparison, and evaluation of natural landscape perception under different temperature conditions. It thoroughly explores the potential of natural landscapes to enhance visitor experiences under various temperature conditions and provides sustainable planning recommendations for the sustainable conservation of natural ecosystems and ecotourism.

How does multiscale greenspace exposure affect human health? Evidence from urban parks in the central city of Beijing

While the health benefits of exposure to urban greenspace have been widely discussed at different spatial scales, the comprehensive health effects of multiscale greenspace exposure are far from understood. There is a lack of quantified evidence when conducting cost-effective greenspace management practices for promoting human health and well-being. This study proposed a conceptual model that links objective and subjective greenspace exposure metrics at different spatial scales with self-rated health of residents. The model attempted to deconstruct and explore the associations between multiscale greenspace exposure and human health, and a cross-sectional study was conducted to examine the model. Taking urban parks in the central city of Beijing as case study area, the objective greenspace exposure metrics at both the site and subdistrict scales were spatial explicitly assessed, and the subjective exposure metrics and self-rated health status of 1017 respondents were obtained through questionnaire survey and spatial positioning. The results of multiple regression analyses and path analyses suggested that greenspace exposure metrics at both site and subdistrict scales were significantly associated with the respondents' self-rated health status, with the exposure metrics at the site scale being more important than those at the subdistrict scale in affecting human health. The contribution of urban parks to self-rated physical and mental health of respondents varied across spatial scales. Specifically, the aesthetic value of urban parks at site scale contributed the most to mental health by promoting respondents' resting behaviors in urban parks, and the density of urban parks at subdistrict scale had the most significant effects on self-rated physical health by increasing the usage frequency of urban parks. Findings of this study can contribute to understanding the complex associations between urban greenspace and human health from a multiscale perspective, and are also expected to provide quantified information for health-oriented urban greenspace planning and management practices.