The counties have experienced urban expansion and landscape pattern fragmentation. As carriers of new urbanization, the balanced development between urban expansion and landscape connectivity in the counties needs to be emphasized. The uncontrolled expansion of land should be discouraged and planners need to clarify land use expansion patterns. Using Changsha County as the study area, the characteristics of the landscape pattern between 2000 and 2020 were analyzed. The morphological spatial pattern analysis and landscape connectivity method (CMSPACI), as well as the minimum cumulative resistance (MCR) model, was used to construct the ecological network. We also explored the most appropriate corridor width using the buffer zone to guide future land use planning and ecological network planning. The results show that based on CMSPACI the total area of ecological sources identified was 304.91 km², encompassing a large area of forest parks. The total length of the 25 ecological corridors identified by the MCR model was 431.97 km. Ecological sources and corridors are missing in the central region; so, their pattern was optimized using landscape connectivity and the absence of location as selection criteria. The optimized network indices showed significant improvement. The width of the ecological corridors should be controlled in order to be in the range of 30 m to 50 m to maximize the effect of the corridors on species dispersal and migration. Our proposed research framework for the construction and optimization of EN in Changsha County can provide ideas to balance the contradictions between urban expansion and landscape connectivity in Changsha County.

Green city areas are crucial in mitigating the Urban Heat Island Effect (UHI). However, the cooling effect of green city areas can be influenced by the surrounding complex urban spatial environment. This study focuses on Changsha, a subtropical city in China, where 40 green city areas were screened and analyzed. The study aims to quantify the specific impact of urban spatial morphology on the cooling effect of green city areas. Through statistical correlation and regression analysis, this study focused on six urban spatial morphology indicators: building density (BD), building floor area ratio (BFR), building volume density (BVD), building evenness index (BEI), building average height (BH), and building height standard deviation (BSD). The results indicate that the cooling effect of green city areas could be influenced by urban spatial morphology. Factors such as BD, BFR, BH, and BSD were found to be significantly correlated with the cooling effect of green city areas, with BH showing the strongest influence. BD and BFR were negatively correlated, while BH and BSD were positively correlated. The range values of BD, BFR, BH, and BSD were determined to achieve the optimal conditions for the cooling effect of green city areas. Additionally, the relative position of the green city areas in the neighboring urban areas affects the cooling effect of the green city areas. The cooling effect is most pronounced in the urban area situated to the south of the green city areas. These findings provide a solid foundation for urban planning around green city spaces and offer scientifically sound evidence for mitigating the UHI.

Introduction: Against the backdrop of a global shift toward greener cities, equitable access to urban green spaces has increasingly been recognized for its impact on residents’ well-being. However, most existing studies rely heavily on residential proximity, overlooking the role of individual activity space differences and their influence on perceived fairness and well-being.Methods: This study, grounded in environmental justice theory and the activity space perspective, constructs and tests a structural equation model integrating green space quality, green space exposure, perceived social benefits, perceived green justice, and subjective well-being. Based on 524 survey responses collected in Changsha, China, a multi-group structural equation modeling (Multi-Group SEM) approach was employed to compare path differences among near-, mid-, and far-distance activity space groups.Results and discussion: Results show that: (1) Urban green space quality significantly influences green space exposure and perceived social benefits, which in turn enhance residents’ perception of green justice and ultimately boost subjective well-being; (2) The pathways from “Green Space Quality → Green Space Exposure,” “Green Space Quality → Perceived Social Benefits,” and “Green Space Exposure → Perceived Green Justice” remain consistently significant across all activity groups, forming a stable core mechanism; (3) Green space exposure in the mid-distance group is more sensitive to economic conditions. By incorporating activity space segmentation, this study extends the micro-mechanism framework of environmental justice and subjective well-being. The findings provide empirical evidence for understanding the psychological impacts of green space equity on urban residents and offer theoretical support for precision-oriented green space planning and equity-focused policy interventions.

Against the backdrop of growing concerns over university students’ mental health worldwide, campus environments play a crucial role not only in shaping spatial experiences but also in influencing psychological well-being. However, the psychosocial mechanisms through which campus landscapes affect well-being remain insufficiently theorized. Drawing on survey data from 500 students across two Chinese universities, this study employs structural equation modeling (SEM) and interpretable machine learning techniques (XGBoost-SHAP) to systematically examine the interrelations among landscape perception, place attachment, perceived social acceptance, school belonging, and psychological well-being. The results reveal the following: (1) campus landscapes serve as the primary catalyst for fostering emotional identification (place attachment) and social connectedness (perceived social acceptance and school belonging), thereby indirectly influencing psychological well-being through these psychosocial pathways; (2) landscape perception emerges as the strongest predictor of well-being, followed by school belonging. Although behavioral variables such as the green space maintenance quality, visit frequency, and duration of stay contribute consistently, their predictive power remains comparatively limited; (3) significant nonlinear associations are observed between core variables and well-being. While the positive effects of landscape perception, place attachment, and school belonging exhibit diminishing returns beyond certain thresholds, high levels of perceived social acceptance continue to generate sustained improvements in well-being. This study advances environmental psychology by highlighting the central role of campus landscapes in promoting mental health and provides actionable strategies for campus planning. It advocates for the design of balanced, diverse, and socially engaging landscape environments to maximize psychological benefits.

As critical urban blue–green infrastructure, urban wetland parks serve as vital venues for visitors to obtain restorative experiences. However, existing studies primarily emphasize their ecological and economic benefits, with comparatively limited attention paid to their roles in promoting public mental health and enhancing well-being. Using Yanghu Wetland Park in Changsha as a case study, this research investigates how restorative environmental perception influences visitors’ well-being through two mediating variables: environmental preference and place attachment. A total of 251 valid responses were collected through field surveys and questionnaires. Structural equation modeling (SEM) was employed to empirically examine the relationships among the variables. This study enriches the theoretical framework of environmental psychology and urban landscape behavior research. It also provides evidence-based insights into the planning and design of urban wetland parks, contributing to the enhancement of public well-being and overall life satisfaction.

The COVID-19 pandemic has led to an unprecedented shift towards online learning, compelling university students worldwide to engage in self-directed learning within remote environments. Despite the increasing importance of online education, the factors driving students’ motivation for self-directed online learning, particularly those involving economic incentives, have not been thoroughly explored. This study aims to address this gap by analyzing large-scale data collected from 19,023 university students across China during the pandemic. Using mixed Bayesian networks and multigroup structural equation modeling, the study explores the complex relationships between personal characteristics, academic characteristics, the academic environment, and students’ motivation for self-directed online learning. The results reveal significant associations between online self-directed learning motivation and personal characteristics. such as gender and age, academic characteristics, such as education level and learning incentives, and the geographic location of the school within the academic environment. Moreover, the causal relationship between school location and online self-directed learning motivation varies by gender and educational level. This research not only provides new empirical support for the theoretical framework of online learning motivation but also contributes to the broader fields of educational psychology and online learning research.