In a national park master plan, functional zoning plays a key role in developing differentiated zoning controls that achieve multiple park construction objectives. In this study, a geographical attribute code and basic zoning elements are developed for the proposed “Ailaoshan-Wuliangshan” National Park, followed by the development of spatial multi-criteria sets and weight sets to determine the suitability of the land. Next, we use a clustering algorithm and conflict unit prioritization to allocate space for multi-target units to get the preliminary zoning schemes, and then identify stable units and unstable units through sensitivity analysis. Ultimately, the functional zoning of the National Park was determined. According to the results, the proposed “Ailaoshan-Wuliangshan” National Park can be divided into nine types of 164 landscape units; the highest land suitability values of each zone showed the traits of differentiation and aggregation in spatial distribution; there are 97 stable units and 67 unstable units; approximately 62.83% and 37.17% of the total park area can be divided into core conservation area (primary sensitive area and secondary sensitive area) and general control area (ecological activity area and ecological control area). By implementing a comprehensive assessment and decision-making process, the defined functional zones are precise and simple to recognize on the ground, and they adhere to the area proportions needed by national standards. Furthermore, the functional zoning is clustered, which avoids the fragmentation of the zoning results causing difficulties in management, and serves as a point of reference for the functional zoning approaches used in other proposed national parks in China.

The demarcation of national park boundaries is crucial for comprehensive planning, effective management, and maintaining the integrity of ecosystems and biodiversity. This research uses the proposed ‘Ailaoshan–Wuliangshan’ National Park (AWNP) in Yunnan Province, China, as the study area and adheres to the principles of systematic conservation planning (SCP). It employs the Marxan 2.43, MaxEnt 3.4.4, and InVEST 3.14.2 models to predict suitable distribution areas for key endangered species within the AWNP, identifies core ecological source areas, priority conservation areas, and conservation gaps, and constructs a double boundary protection framework. The study’s findings indicate that the potentially suitable habitats for the major rare and endangered species, as predicted by the MaxEnt model, are predominantly located in the Ailaoshan and Wuliangshan areas, with a smaller portion distributed in the Konglonghe area. The InVEST model assessment of habitat quality revealed that the total area of the core ecological source areas is 4775.26 km², accounting for 35.34% of the total study area. The Marxan model identified a total area of 1064.22 km² as priority conservation areas, constituting 7.90% of the total study area. Additionally, it revealed conservation gaps of 302.1 km², which represent 2.20% of the total area. Ultimately, by integrating biodiversity conservation and ecosystem services, the boundaries of the AWNP were optimized into a double boundary delineation model: the inner boundary, characterized by rigid control, spans an area of 1076.20 km², while the outer boundary, characterized by elastic management, covers an area of 3056.92 km². Corresponding management recommendations are proposed for the different areas. The double boundary delineation method proposed in this study can, to a certain extent, reconcile the conflict between biodiversity conservation and resource utilization, providing an appropriate reference for the demarcation and dynamic management of national park boundaries in China.

Urban green spaces (UGSs) play an important role in improving urban ecological envi-ronments and social well-being. In this study, we selected Chengdu, China as the research area. By integrating urban big data with spatial autocorrelation analysis, we identified UGS distributions across different years and their spatial clustering patterns. Furthermore, eleven factors influencing UGS distribution were systematically selected from both city-scale and neighborhood-scale perspectives. The main factors influencing the kernel density of UGS in Chengdu were explored using geographical detectors, and the results showed that the quantity and area of UGS in Chengdu gradually increased from 2012–2021 through an unbalanced spatial distribution. All influencing factors had an interac-tive relationship with the UGS kernel density. As the UGS kernel density increased, transportation hub had the greatest impact, the influence of air pollution and urban pre-cipitation on the density of UGS increased, and the nighttime light index decreased. By categorizing the influencing factors into natural environmental and human activity di-mensions, the analysis revealed that human activities and natural environment served as primary drivers of UGS density before and after 2018, respectively. Furthermore, their ef-fects on UGS gradually expanded from the urban core to encompass the entire metropoli-tan area over time. By exploring the relationship between the evolution of UGS spatial patterns and changes in the urban ecological environment and social development using a quantifiable method, this study provides references and guidelines for the future plan-ning of cities to achieve high-quality development.