With the acceleration of climate change and urbanisation, Chinese cities are facing increasingly severe flood risks. To address this challenge, China began implementing its sponge city policy in 2013, leveraging low-impact development, green infrastructure construction, and integrated water resource management to enhance urban resilience to floods and improve water security. This study utilises the Web of Science database as a reference, retrieving 201 relevant literature sources. From these, 61 studies closely related to China’s sponge city policy and urban flood management were selected. CiteSpace was employed to conduct keyword co-occurrence and temporal evolution analyses, comprehensively outlining the research hotspots and developmental trajectory of this field. The results indicate that research content has gradually shifted from early engineering-based flood control models to multi-objective, interdisciplinary comprehensive management, encompassing flood risk assessment, policy implementation mechanisms, integration of green infrastructure, and economic feasibility analysis. Based on this, this paper constructs an analytical framework incorporating technical, environmental, institutional, and social dimensions to integrate existing research findings, while identifying gaps in cross-scale coordination, smart management, and public participation. The research conclusions can provide valuable references for future policy optimisation and urban sustainable development.

Achieving carbon neutrality has become one of the core objectives in contemporary urban development and sustainable growth, underscoring the importance of clarifying the relationship between urban green space landscape metrics and plant carbon sequestration. While existing research confirms the significant role of the structure and pattern of green spaces in carbon sequestration, systematic understanding of their relationship at the local scale within diverse built environments remains limited. To address this, this study objectively categorises five types of built environments using K-means clustering and conducts in-depth analysis on four representative areas. Employing the CatBoost machine learning model and the Shapley Additive Propensity (SHAP) method, we highlighted the influence of green space pattern characteristics on net prmary productivity (NPP) across different built environments. The findings are as follows: (1) Green Coverage Ratio (GCR) exhibits the highest contribution among all explanatory variables across different built environments. In low-intensity built environments, it contributes 74% to the overall explanation, showing a stable association between higher green space proportion and higher carbon sink levels. (2) In high-intensity built environments, limited green spaces exhibit a pronounced “spatial compensation effect” through morphological optimisation and enhanced spatial connectivity. In medium-intensity built environments, they demonstrate a “moderate positive effect,” with peak carbon sequestration efficiency occurring when GCR ranges from 0.25 to 0.75, aggregation index (AI) from 94 to 98, and splitting index (SI) from 1.2 to 1.4. (3) Significant interactions exist among green space landscape metrics, with moderately connected and moderately complex spatial structures enhancing carbon sink efficiency. This study reveals the differentiated impact by which green space landscape metrics influence carbon sink effects under varying urban built environments, providing scientific basis for optimising urban green space systems and low-carbon spatial planning.

During the Qing Dynasty in China, Fujian, the region with the highest forest coverage in the country, experienced frequent commercial transactions of forestland. Throughout these transactions, local residents produced many valuable folk historical documents. These documents, represented by transaction agreements, not only record the aesthetic sophistication of society’s approach to important information in land transactions but also reflect this through their layout design. This study uses surviving Qing Dynasty rural mountain and forestland transaction agreements from Yongtai County in Fujian as sample references. Modern Machine Learning-related research methods explore the layout design preferences of these historical documents. Research reveals that the layout design of forestland transaction agreements varied across different historical periods of the Qing Dynasty and among various clans. These hidden layout features may indirectly reveal the historical reasons behind cultural sophistication differences and provide practical reference value for modern graphic layout design and historical document restoration.

The maritime defence settlements of the Ming Dynasty are a key component of China’s military cultural heritage. This study examines the three coastal defence sectors of Fujian by establishing a three-tier evaluation framework utilising GIS spatial analysis and the Analytic Hierarchy Process (AHP) for quantitative assessment. The findings reveal that the synergy between military outposts significantly enhances overall defence effectiveness, while the independent defence capability of each stronghold is crucial for withstanding external threats. A comprehensive evaluation further indicates that the Fujian central coastal defence sector, characterized by its robust economy and densely distributed fortifications, demonstrates the highest level of defensive performance. By systematically quantifying the defensive performance of Fujian’s maritime defence settlements, this study develops an evaluation model that provides a scientific basis and decision support for value assessment, sustainable conservation, and adaptive reuse of this category of military cultural heritage.

Rapid urbanisation has exacerbated habitat fragmentation and degradation, necessitating urgent improvements to urban habitat quality. However, most current studies lack an analysis of spatial differences in local ecological quality, particularly a systematic exploration of how different urban spatial characteristics drive such differences. Based on this, we use Shanghai as an example, employing the InVEST model to assess habitat quality, and utilise CatBoost machine learning models and the SHAP model to reveal the specific spatial distribution characteristics of the habitat quality spatial differences from a morphological perspective, as well as its response to changes in urban spatial form factors. The results indicate that (1) urban habitat quality exhibits significant spatial differences, with quality differences persisting even within regions of the same habitat grade, demonstrating complex spatial characteristics; (2) density-related indicators such as building density and population density have a significant negative impact on the habitat quality spatial difference value, while configuration-related indicators such as the water ratio and Normalised Difference Vegetation Index have a significant positive effect, with Population Density contributing the most among all variables (20.74%); and (3) the variables exhibit significant nonlinearity and threshold effects. For example, when building density exceeds 0.05, the positive impact becomes a negative impact. The interactions between variables further reveal the multi-dimensional coupling mechanisms underlying habitat quality performance. This study contributes to a deeper understanding of the spatial differences of urban habitat quality, providing scientific support for urban ecological zoning management, the optimised allocation of green space resources, and differentiated spatial governance while offering methodological and decision-making references for the construction of high-quality ecological cities.

Urban expansion will squeeze the green space system and cause ecological fragmentation. The question of how to expand cities more scientifically and build eco-cities has become an important topic of sustainable urban construction. This paper takes Shanghai as a research case. A deep neural network combined with an attention mechanism model measures the comprehensive level of the built environment and green space pattern of urbanization and quantitatively analyzes the coordinated relationship between the two using the coupled degree of coordination model. Subsequently, the K-Means clustering model was used for spatial clustering to determine the governance and construction directions for different spatial areas and was, finally, combined with the LightGBM model plus SHAP to analyze the importance and threshold effect of the indicators on the degree of coupled coordination. The results of the study show that (1) the core area of the city shows a high state of coordination, indicating that Shanghai has a better green space construction in the central city, but the periphery shows different imbalances; (2) three different kinds of areas are identified, and different governance measures as well as the direction of urbanization are proposed according to the characteristics of the different areas; and (3) this study finds that the structural indicators of the built environment, such as Average Compactness, Weighted Average Height, and Land Use Diversity, have a significant influence on the coupling coordination degree and have different response thresholds. The results of the study provide theoretical support for regional governance and suggestions for the direction of urban expansion for sustainable urbanization.