Hai-Li Zhang

Urban trees are important biological carbon pools in cities, yet their standing carbon stocks and the associated methodological uncertainty regarding their evaluation often vary among functional spaces. This study assessed 2442 individual trees representing 148 species in 116 standard 20 m × 20 m plots in the core urbanized area of Qingdao, China. Individual-tree carbon stocks were estimated from diameter at breast height, tree height, wood basic density, and allometric equations and were analyzed across urban functional units (UFUs), DBH classes, and species. Under the baseline Chave model, the sampled trees stored 153.337 Mg C, equivalent to 562.237 Mg CO₂, with a mean carbon density of 33.047 Mg C ha⁻¹. Residential districts contributed the highest total carbon stock, whereas public affairs service districts had the highest carbon density. Trees with DBH ≥ 40 cm accounted for only 3.93% of individuals but contributed 29.50% of the total carbon stock, and the top 20 species contributed approximately 79.0%. Alternative China-specific equations increased total estimates by 9.38–11.24% but covered only 10 of 148 species and 21.38% of trees at most; the UFU ranking and the main size- and species-contribution patterns remained unchanged. A root-to-shoot parameter sensitivity analysis likewise affected absolute estimates but not relative patterns. These findings support conserving mature trees, maintaining medium-sized trees as future carbon reserves, and targeting greening improvements to low-carbon-density functional spaces while recognizing the uncertainty inherent in absolute carbon-stock estimates.

Urban street greenery plays a crucial role in enhancing biodiversity, environmental quality, and human well-being. However, how different street greening strategies shape urban plant diversity across functional urban contexts remains insufficiently understood. Taking Jinan, a rapidly urbanizing city in China, as a case study, this research investigates the spatial patterns, compositional differences, and driving mechanisms of plant diversity between Green Streets (GS) and Non-Green Streets (NGS) across various Urban Functional Units (UFUs). A 1 km × 1 km grid was used to delineate UFUs, combined with field-based plant surveys, linear regression analyses, and the public space assessment framework of Sustainable Development Goal (SDG) 11.7.1. Results indicate that plant diversity is strongly dependent on urban functional types, with higher species richness observed in residential and recreation/leisure districts, and lower levels in industrial, commercial, and transportation districts. The ecological effects of GS exhibit clear context dependence, being more pronounced in residential, educational, and public service areas, but limited in commercial and industrial zones. NGS recorded a significantly higher total number of plant species (346) than GS (116), with NGS dominated by native spontaneous species and GS characterized by introduced cultivated plants, reflecting the filtering effects of different management intensities. Management variables, particularly watering (positive) and fertilization frequency (negative), is primarily positively associated with plant diversity in GS, whereas diversity in NGS is more closely associated with socio-economic and spatial factors such as UFU area and housing prices. Furthermore, the current SDG 11.7.1 indicator emphasizes the quantity and accessibility of public spaces but insufficiently captures their ecological quality. This study highlights the need to integrate biodiversity and vegetation structural complexity into public space assessments, providing scientific support for quality-oriented urban green infrastructure planning and sustainable urban development.

Urban green spaces (UGS) play a critical role in enhancing urban resilience and livability by delivering a wide range of ecosystem services. Understanding residents’ preferences for urban green space ecosystem services (UGES) and their underlying drivers is essential for advancing human-centered and equitable UGS management. Existing studies have largely emphasized socio-demographic and contextual factors, while the role of personal values as deeper psychological drivers of UGES preferences remains insufficiently examined. Values are deeply rooted beliefs that shape cognition and behavior and, as such, provide a powerful theoretical lens for interpreting human–ecosystem interactions. Based on 1080 responses from a nationwide online survey, this study investigates how value orientations influence residents’ preferences for UGES in China. The results show that personal values have significantly greater explanatory power for UGES preferences than demographic variables. Specifically, self-transcendence is positively associated with preferences for regulating and cultural services, whereas self-enhancement is positively associated with preferences for provisioning services. These patterns indicate that prosocial and egoistic value orientations underpin preferences for ecosystem services characterized by public versus individual benefits, respectively, suggesting that UGES preferences represent external expressions of individuals’ underlying value structures. The opposing value dimensions of conservation and openness to change both exert positive effects on preferences across all three UGES categories, indicating that UGES preferences are jointly shaped by pluralistic value systems. In the context of intergenerational value change and evolving societal expectations, UGS planning should therefore prioritize diversity and inclusivity. Overall, this study demonstrates the central role of value orientations in shaping residents’ UGES preferences and highlights the potential of integrating value-based perspectives to enhance preference-sensitive management and value-oriented governance of UGS.

Urban green spaces (UGSs), including parks, forests, and community gardens, play a critical role in enhancing public health and well-being by providing essential ecosystem services such as improving air quality, reducing surface temperatures, and mitigating harmful substances. As urbanization accelerates, especially in rapidly growing cities like Jinan, China, the demand for UGSs is intensifying, necessitating careful urban planning to balance development and environmental protection. While previous studies have often focused on city-level green coverage, this study shifts the analytical focus from UGS as a whole to urban functional units (UFUs), allowing for a more detailed examination of how green space is distributed across different land use types. We investigate UGS changes in Jinan over the past two decades and assess the influence of socio-economic factors—such as housing prices, land use types, and building age—on UGS distribution within UFUs. Remote sensing technology was employed to analyze the spatiotemporal dynamics of UGS and its correlation with these variables. Our findings reveal a significant shift in UGS distribution, with parks and leisure areas becoming primary drivers of UGS expansion. This study also highlights the growing influence of economic factors, particularly housing prices, on UGS distribution in more affluent UFUs. Additionally, while UGS in Jinan has generally expanded, challenges remain in balancing green space with urban expansion, especially in commercial and residential UFUs. This paper contributes to a more nuanced understanding of UGS distribution by integrating the UFU framework and identifying socio-economic drivers—including housing prices, construction age, and land use type—that shape green space patterns in Jinan. Our findings demonstrate that the spatial pattern of UGS in Jinan mirrors socio-economic and land use disparities observed in other global cities, highlighting both the universality of these patterns and the need for targeted planning in rapidly urbanizing contexts.

A thorough understanding of the consequences of urbanization can be significantly advanced by examining urban environmental dynamics at high spatial and temporal resolutions. This study evaluates eco-environmental quality and investigates the underlying drivers of urban greening within the functional units of Haikou, a tropical coastal city located on Hainan Island, China, using advanced techniques from Landsat and Google Earth imagery. Ecological index and land use change analyses were conducted using Landsat 5 (TM) imagery for 2010 and Landsat 8 (OLI) imagery for 2020. In addition, Google Earth imagery was used to interpret the driving factors influencing urban functional units (UFUs) in 2010 and 2020. Spatial and temporal environmental changes were quantitatively assessed. Multi-spectral Landsat 8 data at a 30 m resolution were used to construct a remote sensing ecological index (RSEI) to assess Haikou’s ecological condition. Land use impacts on eco-environmental quality were evaluated through RSEI values from 2010 to 2020, showing that eco-environmental quality improved over time, revealing a gradual improvement over time. Land use across 190 UFUs from 2010 to 2020 was categorized into five types: trees and shrubs, herbs, built-up areas, sandy lands, and water bodies. The primary drivers of greening percentage in each UFU were identified as housing prices, maintenance duration, and construction age. The most significant changes in land cover type were observed in the herb areas. Similarly, maintenance duration emerged as the most influential factor driving changes in urban green space (UGS). In conclusion, this study offers valuable insights for future urban planning and improvements in eco-environmental quality in Haikou, Hainan Island, China.

Sepsis is a life-threatening condition driven by dysregulated immune responses, resulting in organ dysfunction and high mortality rates. Identifying key genes and pathways involved in sepsis progression is crucial for improving diagnostic and therapeutic strategies. This study analyzed transcriptomic data from 49 samples (37 septic patients across days 0, 1, and 8, and 12 healthy controls) using weighted gene co-expression network analysis (WGCNA) and multi-algorithm feature selection approaches. Differential expression analysis, pathway enrichment, and network analyses were employed to uncover potential biomarkers and molecular mechanisms. WGCNA identified modules such as MEbrown4 and MEblack, which strongly correlated with sepsis progression (r > 0.7, p < 0.01). Differential expression analysis highlighted up-regulated genes like CD177 and down-regulated genes like LOC440311. KEGG analysis revealed significant pathways including neuroactive ligand–receptor interaction, PI3K-Akt signaling, and MAPK signaling. Gene ontology analysis showed involvement in immune-related processes such as complement activation and antigen binding. Protein–protein interaction network analysis identified hub genes such as TNFSF10, IGLL5, BCL2L1, and SNCA. Feature selection methods (random forest, LASSO regression, SVM-RFE) consistently identified top predictors like TMCC2, TNFSF10, and PLVAP. Receiver operating characteristic (ROC) analysis demonstrated high predictive accuracy for sepsis progression, with AUC values of 0.973 (TMCC2), 0.969 (TNFSF10), and 0.897 (PLVAP). Correlation analysis linked key genes such as TNFSF10, GUCD1, and PLVAP to pathways involved in immune response, cell death, and inflammation. This integrative transcriptomic analysis identifies critical gene modules, pathways, and biomarkers associated with sepsis progression. Key genes like TNFSF10, TMCC2, and PLVAP genes show strong diagnostic potential, providing novel insights into sepsis pathogenesis and offering promising targets for future therapeutic interventions. Among these, TNFSF10 and PLVAP are known to encode secreted proteins, suggesting their potential as circulating biomarkers. This enhances their translational relevance in clinical diagnostics.

Ecosystem services (ES) are key benefits that humans derive from natural ecosystems, including provisioning, regulating, and cultural services. As urbanization accelerates globally, urban green spaces (UGS), increasingly recognized for their role in improving environmental quality and enhancing human well-being, provide essential ES that help mitigate the effects of urbanization. However, the factors influencing public preferences for these services, particularly environmental knowledge and religiosity, remain underexplored. This study seeks to bridge this gap by examining how environmental knowledge and religiosity shape public preferences for ecosystem services, with a particular focus on regulating services (e.g., air quality improvement, carbon sequestration) and cultural services (e.g., outdoor recreation, aesthetic enjoyment). A survey of 1236 respondents conducted in China reveals that both environmental knowledge and religiosity significantly enhance preferences for regulating services, especially in relation to air quality improvement (M = 4.33) and carbon sequestration (M = 4.26). Furthermore, higher education levels correlate with stronger preferences for ecosystem services, and coastal residents exhibit greater preferences for these services compared with inland residents. This study emphasizes that disseminating environmental knowledge through education and religious practices can significantly enhance public awareness of ecosystem services and foster greater support for green infrastructure investments. Policy recommendations include the adoption of targeted communication strategies in urban green space planning to enhance public engagement and support.

Green space is essential in urban areas to maintain, and improve human well-being. To better understand the impact of environmental and socioeconomic changes on the sustainability of tropical urban green space landscapes, work is needed to explore the patterns of plant diversity and its drivers in urban green spaces. We explore urban floristic diversity patterns based on samples from 826 urban functional units located in five cities in the tropical coastal region of China. Field surveys were conducted to obtain data on plant diversity, land use types, socioeconomic characteristics, and environmental characteristics within these units. Plant diversity within the cities varied significantly among land-use types, with high-rise housing, parks, and universities exhibiting higher diversity. The diversity of cultivated plant species and the regional economy showed a significant positive correlation (β coefficient = 0.15, p-value < 0.05), while an increase in the diversity of spontaneously growing plant species and native species was linked to building age (β coefficient = 0.12, p-value < 0.01; β coefficient = 0.13, p-value < 0.01). Management also affected plant species diversity, with watering and maintenance frequency having a positive association. Urban plant diversity patterns result from a combination of multiple factors, and different drivers affect native vs. non-native plant diversity Socio-economic factors are the primary driver of urban plant diversity patterns, as space management and maintenance determine what can grow in different areas. This study has important practical significance for policymakers and managers in developing and managing urban green spaces more sustainably.