Rapid urbanization poses a threat to various ecosystem services. Beijing has undergone extensive infrastructure development in recent years. The study aims to extract land surface temperature (LST) and land use cover (LUC) data from satellite imagery, identify urban heat island (UHI) areas in Beijing, and determine the correlation between LST, LUC, NDVI, and BUI. It will also investigate the relationship between UHI and built/unbuilt areas, evaluate thermal comfort in Beijing using UTFVI, and assess the ecological quality of different land use types using the Ecological Evaluation Index (EEI). The results can inform urban planning and management in rapidly urbanizing and climate-changing regions. Changes in LUC and other activities affect the distribution of LST. For the study years (2005–2020), the estimated mean LST in Beijing was 24.72 °C, 27.07 °C, 26.22 °C, and 27.03 °C, respectively. A significant positive correlation (r = 0.96 p > 0.005) was found between LST and urban areas with other infrastructures. Geographically weighted regression (GWR) outperformed with Adj R² > 0.74, suggesting that the extent of an urban heat island (UHI) is strongly dependent on the settlements, LUC composition, size, and terrain of surrounding communities. Urban hotspots in the city were identified and validated using Google Earth imagery. The Ecological Evaluation Index (EEI) value was relatively low compared to other ecosystem-related units. EEI showed a continuous increase of six percent in the most negative categories, indicating an unstable environment. This study concludes that urbanization affects the city’s environment, and study findings would help to regulate the urban ecosystem in Beijing.

Urban green spaces are pivotal to enhancing carbon sinks and advancing carbon neutrality goals, yet the structural complexity of green space units often leads to scale mismatches and weak spatial responsiveness in current assessments. This study develops an integrated evaluation framework that combines landscape spatial unit typologies with life-cycle-based carbon flux modeling. We defined 22 landscape spatial unit types based on two-dimensional surface cover and three-dimensional vegetation structure, including waterbodies and vertical greening. A life-cycle carbon model was developed with indicators covering unit carbon sink, unit carbon emission, unit net carbon sink efficiency, and time to carbon balance. Taking Luhe Park in Nanjing as a case study, the carbon sink efficiency indicators were quantified for 108 units over a 50-year cycle. Results indicate that multilayer vegetation structures, high green coverage, and moderate-to-high planting density markedly enhance carbon sink efficiency, whereas extensive built surfaces and high impervious ratios suppress it. K-means clustering classified the spatial units into four types with emphasis on efficiency-driven, structural optimization, functional compatibility, and imbalance compensation, respectively, revealing a clear gradient tied to spatial configuration. To translate diagnosis into design, we report 95% confidence intervals of key structural factors as actionable thresholds. These ranges inform targeted interventions such as maintaining continuity and multilayer structure in high-efficiency areas, modest structural upgrades with native drought-tolerant plants, edge greening with permeable pavements in open spaces, and streamlined vertical systems linked to adjacent high-sink ground units. The framework delivers spatially explicit, life-cycle-aware evidence to support low-carbon planning and design of urban green spaces.

Urban parks are important components of the urban green space system, providing residents with a variety of leisure options. The design’s focal point is the spatial layout of the different use scenarios within these parks. Previous studies have largely concentrated on the scene and macro layout of urban public spaces but have not thoroughly investigated the digital characteristics and corresponding parametric methods for the layout of different internal use scenes in urban green spaces. This research selected 18 urban parks from various global regions as case samples and categorized eight typical park scene space types based on common activity scene requirements in park design using AutoCAD to identify vector boundaries in each sample. To examine the digital characteristics of these scene space types, a quantitative index system was established, including spatial density and scale indicators, spatial connection relationship indicators, and spatial unit morphology indicators. The analysis of these indicators across the samples shows that the number of scene spaces is positively correlated with the total park area, while natural experience scenes constitute the largest proportion of urban parks. Different scene types exhibit distinct spatial layouts; for example, circulation spaces demonstrate high connectivity due to their role in directing visitor flow. Some spaces exhibit a more fixed scale and size, while others vary considerably. Finally, this research develops a parametric design framework using the Grasshopper platform. By taking a park in Nanjing as a case study, this paper illustrates how to utilize digital layout features to generate scene space layouts, offering insights into intelligent generative design. This approach provides a structured method to enhance urban park design through the application of digital and parametric tools, contributing to the broader field of urban park design.

The characterization and mapping of urban landscape spatial form are critical for advancing sustainable planning and informed environmental management. From a morphometric perspective, this study introduces a novel, data-driven framework for typo-morphological analysis. First, morphological cells (MCs) are defined as objectively and universally applicable spatial units for morphometric investigation. Second, by integrating a multi-dimensional cognition of full-scale morphological and associated landscape elements, we construct a set of 48 spatial form indicators and attach them to morphological cells, enabling a precise description of each unit. Third, a Gaussian mixture model (GMM) is employed to cluster the metrical information within the spatially lagged context derived from the topological structure of the morphological cells, resulting in the delineation of distinct typo-morphological zones (TMZs). We then adopt Ward’s algorithm to establish a hierarchical relationship among identified urban landscape types. Using Wuxi City, China, as a case study, our results demonstrate the effectiveness of the proposed framework in capturing the heterogeneity and underlying connotation of urban landscape spatial characteristics. Building upon the unsupervised clustering results, we further apply the classification and regression tree (CART) to provide a supervised interpretation of the key spatial form conditions driving typological decisions. It facilitates the systematic identification of the components and formative mechanisms of spatial form. The findings contribute a scalable, reproducible, and interpretable typo-morphometric approach for analyzing urban landscape spatial characteristics, thereby providing a robust quantitative foundation for integrated decision-making in landscape planning, socio-ecological assessment, and urban design practices. More broadly, the study carries both applied and theoretical significance for advancing refined urban governance and fostering interdisciplinary research related to urban sustainable development.