During every summer and winter vacation, the entrances of Peking University and Tsinghua University in China are always crowded with visitors. Although it is not yet possible to fully open and share campus facilities with the public, a reasonable solution would be allocating part of the entrance space to create a shared public square, with trees planted and seating arranged. This would offer shade and rest areas for the young visitors and their parents who travel from across the country. Such improvements would not only benefit visitors and reduce security burdens, but also enhance the universities’ image and reflect their spirit of inclusiveness. Moreover, it would offer these prestigious institutions an opportunity to showcase their culture, disseminate knowledge, and promote enlightenment, while embodying the humanistic care and social commitment that universities should uphold.
Exploring the scale-effect of different land use types on the distribution pattern of urban park green space (PGS) at multiple grid scales would inform rational allocation and efficient collaborative construction of urban development land at different scales. Selecting 300-m, 500-m, 1, 000-m, and 2, 000-m grid scales, the research employed Create Fishnet tool in ArcGIS and Geodetector to construct a scale-effect analysis framework that revealed the scale-effects of different land use types on the distribution pattern of PGS at multiple grid scales in the main urban area of Nanjing, China in 2006, 2012, and 2017. Main research results are: 1) the overall distribution pattern of PGS showed the evolution characteristics from polarization to advancing quality and efficiency, while the trend gradually weakened with the increase of grid scale; 2) the scale-effect of other land use types on PGS increasingly enhanced—the larger the grid scale, the more obvious the synergistic or compressive effect; 3) the interactive scale-effects of different land use types gradually enhanced—the larger the grid scale, the more significant the overall factor interaction; and 4) at the 300-m grid scale, the major interaction factors were residential, transportation, industrial/manufacturing, water area, and administration/public services, which gradually changed to residential, water area, and administration/public services up to the 2, 000-m grid scale. The findings of this paper are expected to deepen the theory of the coupling between PGS and other land use types, as well as provide scientific support and a basis for efficient allocation, spatial layout optimization, and sustainable development of urban spaces.
The research on the impact of urban blue spaces on residents' mental health has attracted great attention from scholars internationally, and quantitative studies of the effects dominate the current academia. This study, on the basis of reviewing the theories of urban blue spaces and residents' mental health, conducted a meta-analysis of 47 key studies by systematically selecting and examining the literature from Web of Science, CNKI, and other databases. This paper analyzed the measuring indicators and research models among the literature and standardized the effect size of the research findings. The meta-analysis results include that: 1) the measurements of the characteristics of urban blue spaces are mainly conducted in space-based and individual-based dimensions; 2) residents' mental health is mainly measured from aspects of general mental health, positive psychology, and negative psychology; 3) the proximity of blue space has a significant positive effect in improving residents' general mental health and positive psychology; 4) the availability of blue space is significantly positively correlated with general mental health and positive psychology; 5) although there are studies confirming that factors such as blue space visibility, frequency of visit, and exposure types have an impact on mental health, the relevant studies are still limited; and 6) research on the effect of blue spaces on negative psychology is controversial, especially on mental disorders such as depression, and the findings among existing studies vary significantly. The results of this meta-analysis can provide guidelines for future research and the construction of healthy cities.
With the continuous advance of big data and artificial intelligence technologies, various data-driven machine learning algorithms have been widely applied in the studies of urban resilience, particularly in addressing the challenging issue of urban waterlogging. Currently, it is a pressing task to understand the influencing factors of waterlogging from the perspective of built environment, and provide guidance on dynamic monitoring and early alarm services. Focusing on Shenzhen, China, a typical high-density urbanized city, this research constructed a multifactorial dataset encompassing hydrological, meteorological, urban morphology, and waterlogging event data. Then, this research assessed and compared the performance of four mainstream machine learning models—LightGBM, RF, SVR, and BPDNN—in predicting urban waterlogging risks. The results showed that LightGBM had the best accuracy and robustness in predicting waterlogging depths and risk levels in urban areas. The research also employed interpretability algorithm—Shapley Additive Explanations (SHAP)—for decoupling analysis. The results indicated that hydro-meteorological factors (the total rainfall volume and the rainfall lasting time) and several architectural configuration factors (e.g., density of buildings, building congestion degree) are the main influencing factors. In addition, the percentage of water body is vital to waterlogging regulation and retention, especially exhibiting a significant mitigating effect when exceeding 2.5%. This research provides a new technical method for urban waterlogging prediction and reveals the influencing factors and intrinsic mechanisms from the perspective of built environment, which is of great significance for the enhancement of the resilience of high-density cities.
This study introduces a Landscape Information Modeling–Stable Diffusion (LIM–SD)-based digital workflow for ecological engineered landscaping (EEL) design, focusing on urban river wetlands. It explores how students from diverse academic backgrounds perform EEL tasks using the LIM–SD approach. A total of 30 participants, including industrial design postgraduates and landscape architecture undergraduates and postgraduates, completed the design tasks. The efficacy of their designs was assessed through expert evaluations on site appropriateness, aesthetics, spatial layout, and eco-engineering techniques of the design proposals, as well as the parametric simulation which calculated the vegetation coverage rate and proportion of riparian areas for each design. Moreover, evaluation of participants' subjective design experiences was conducted via questionnaires. Results indicated that landscape architecture postgraduates outperformed others applying ecological engineering principles. The study also elucidated discrepancies between LIM models and SD-generated renderings, as well as the uncertainty of SD-generated renderings, suggesting improvements are needed to align digital outputs with ecological design criteria.
Urban metabolism provides a robust framework for analyzing urban development and its impacts. However, several conceptual and operational shortcomings have constrained the application of urban metabolism in understanding the overall urban processes, limiting the transfer of its potential benefits to design and planning. This article systematically analysed the rationale of the current urban metabolism models, focusing on four prevailing shortcomings from a transdisciplinary perspective: 1) utilizing an isolated state approach, which treats urban systems as isolated from other ecosystems; 2) ignoring internal processes within urban systems, known as the black box paradox; 3) employing a linear material approach that focuses on the path of single materials; and 4) overlooking the material productivity of urban systems, where energy and materials entering the system are used to reproduce the urban material structure and generate goods and tradable products. While these issues have been identified individually in existing scientific literature, there is a lack of holistic solutions. This article proposes an enhanced urban metabolism analytical approach—the ecosystem approach applying "technomass"—to address these shortcomings and provide practical solutions in landscape architecture and planning disciplines for sustainable urban development.
The establishment and development of the Guangdong–Hong Kong–Macao Greater Bay Area have demonstrated the collaborative relationship of the globally regional reconstructing. As a major strategy of the Greater Bay Area, Northern Metropolis faces challenges in transboundary ecological collaborative management. Based on the analysis of the ecological issues and the complexity of the transboundary management in Shenzhen Bay, this article proposes a co-governed “Special Ecological Conservation Zone” under the context of “one country, two systems” by zoning areas with varied protection levels. The development and operation framework includes: establishing an independent Joint Work Group, building consensus and standardizing collaboration procedure, conducting hierarchical management and regulating development, leveraging and motivating knowledge and technological innovation, and strengthening community engagement and emphasizing shared benefits. By case-studying three sites along the Shenzhen Bay, this article also provides place-making strategies for different levels of protection zones. Exploring innovative collaboration models for Northern Metropolis, this article is expected to provide new solutions for the sustainable development of the Greater Bay Area and creative insights for global transboundary ecological collaborative management.
