Introduce: Urban street spatial quality, as an intervenable environmental factor from the perspective of public health, significantly affects residents' mental health and emotional wellbeing. Accurately identifying emotional hot spots in urban environment and exploring the mechanism of environmental features affecting emotions are crucial for improving residents' mental health level, promoting healthy urban planning and creating a sustainable urban environment.Methods: This study employed an interdisciplinary approach, utilizing street view images from Liwan District, Guangzhou, China. A Pyramid Scene Parsing Network (PSPNet) was applied to quantify 18 key environmental features, including the Green View Index (GVI), Space Openness (SO), Enclosure Index (EI), etc. By integrating an emotion dataset assessed by 40 experts, a random forest model was constructed to predict emotional responses to different street spaces. Emotional distribution maps were generated using ArcGIS Pro to identify emotional hotspots. Subsequently, SHAP (SHapley Additive exPlanations) analysis was conducted to explore how environmental features influence emotional responses.Results: The analysis revealed the following: (1) Positive emotions were significantly associated with areas of well-vegetated, while negative emotions were predominantly concentrated in industrial zones and narrow alleys. (2) GVI, sky-green ratio, EI, and SO had a notable impact on emotional responses. (3) The optimal range for the GVI (0.27–0.3) was found to maximize positive emotional valence. Beyond this range, further increases in the GVI did not result in significant emotional changes.Discussion: This study demonstrates the feasibility of predicting public emotional responses from street view images using machine learning. Optimizing green spaces and improving pedestrian environments can promote emotional health. To effectively balance the distribution of urban green spaces and maximize public health benefits, it is recommended that governments collaborate with communities, leveraging fiscal incentives and green infrastructure investments to promote equitable and sustainable development of green spaces. These findings play a crucial role in advancing both public health and environmental sustainability.

Soil erosion is a significant environmental challenge in Southwest China, influencing regional ecological security and sustainability. This study investigates the spatiotemporal evolution, driving mechanisms, and future projections of soil erosion in Southwest China, with a focus on the period from 2000 to 2023. The RUSLE model was used to analyze the spatiotemporal variation of soil erosion intensity over the 23-year period in Southwest China. The XGBoost and SHAP models were then employed to identify and interpret the driving factors behind soil erosion. These models revealed that precipitation, temperature, vegetation cover, and land use change were the primary drivers of soil erosion in the region. Finally, future soil erosion risks were projected for 2030, 2040, and 2050 under three climate scenarios (SSP119, SSP245, and SSP585) based on the CMIP6 climate model. The results suggest that (1) the analysis of soil erosion in Southwest China from 2000 to 2023 reveals a significant decline in soil erosion intensity, with a 58.16% reduction in average erosion intensity, from 4.23 t·ha⁻¹·yr⁻¹ in 2000 to 1.77 t·ha⁻¹·yr⁻¹ in 2020. The spatial distribution of erosion in 2023 showed that 90.9% of the region experienced slight erosion, with only 4.56% of the area facing moderate to severe erosion. (2) Natural factors, particularly elevation and precipitation, are the primary drivers of soil erosion. Regions with higher elevations and greater precipitation are more susceptible to soil erosion, particularly on steep slopes with shallow soil layers. Human activities, including GDP growth, land use patterns, and population density, also significantly influence soil erosion dynamics, with higher GDP levels and increased urbanization correlating with elevated erosion risks. The interaction between natural and socioeconomic factors demonstrates a complex relationship in soil erosion processes. (3) By 2050, soil erosion intensity in southwestern China is projected to increase overall, with the most significant increase occurring under the SSP585 scenario. The spatial distribution of soil erosion will largely maintain current patterns, with high-erosion areas concentrated in the northwest and low-erosion areas in the southeast. Areas experiencing mild erosion are expected to decrease, while moderately eroded regions will expand. Projection results suggest that increased precipitation and extreme weather events will lead to the most severe soil erosion in high-altitude regions, particularly in western Sichuan. Our historical assessments and future forecasts suggest vegetation conservation, rainfall monitoring, and restoration of western Sichuan in southwest China are critical for future erosion control and regional ecological security in southwest China.

Currently, many industrial cities in Northeast China are grappling with a surge in brownfields, population loss, and environmental degradation, largely driven by industrial decline. Consequently, brownfield regeneration has emerged as a critical pathway for exploring sustainable development in shrinking cities. This study investigates the regeneration potential of urban brownfields against the backdrop of population shrinkage and aging. Under the framework of Ecosystem Service Theory, this study adopts the Fulaerji District of Qiqihar City as a representative case, integrating its four key categories of ecosystem services into a three-dimensional “economic–social–environmental” evaluation framework. This approach facilitates the transformation of Ecosystem Service Theory from a conventional post-regeneration evaluation tool into a pre-regeneration framework for assessing potential and determining site prioritization. A tripartite evaluation system was constructed, integrating economic vitality (e.g., the population shrinkage index, the proportion of the elderly population, and transportation accessibility), social culture (e.g., the industrial heritage proximity index), and ecological regulation functions (e.g., proximity of green spaces and importance of ecosystem services). Leveraging multi-source geospatial data, land surveys, and field inspections, 12 candidate brownfield sites were identified. GIS spatial analysis and the Analytic Hierarchy Process (AHP) were employed to decipher their spatial distribution patterns and comprehensive potential grades. The findings reveal the following: (1) The evaluation results exhibit distinct spatial characteristics: high-potential sites, clustered near transport hubs and policy-supported zones, demonstrate multi-dimensional advantages, while low-potential sites, constrained by aging demographics, poor accessibility, and ecological vulnerability, are predominantly located on the urban periphery. (2) Correlation analysis between potential grades and various evaluation indicators, combined with a cross-comparison of population shrinkage levels and average values of other evaluation indicators across different potential grades, reveals that neither the population shrinkage index nor the elderly population proportion exerts a systematic negative impact on industrial brownfield regeneration potential. This finding indicates that within specific urban development contexts, brownfield regeneration potential is influenced more by structural factors such as locational conditions, policy support, and ecological service functions than by singular demographic trends. This research provides both theoretical foundation and decision-making support for differentiated brownfield regeneration and spatial governance in population-shrinking cities.