High-quality development is essential for China’s modernization. The in-depth implementation of the new development philosophy has become crucial for promoting China’s development in the context of “domestic and international” double-cycle development. This study constructs an evaluation index based on the new development philosophy, measures the level of China’s high-quality development majorly from 2005 to 2020, dynamically examines the spatial and temporal pattern of China’s high-quality development on a multi-level spatial scale, and explores its influence mechanism with the help of the obstacle degree model. The results show that: (1) China’s high-quality development level has increased as a whole, with the high-quality development index rising from 0.056 in 2005 to 0.092 in 2020, with an average annual growth rate of 3.36% and an overall development pattern of “high level in the east and fast growth rate in the west”. (2) Spatial correlation, China’s high-quality development shows a significant positive correlation, with cities with higher levels of high-quality development concentrated in the eastern coastal region, the Pearl River Delta region, and the Beijing-Tianjin-Hebei region, and those with lower levels of high-quality development clustered in the western region. (3) In terms of dynamic evolution, China’s high-quality development level shows a small rightward shift, the polarization of high-quality development level is weakening, and the rightward trailing situation has been alleviated. (4) Obstacles to identifying the factors affecting China’s high-quality development include the amount of imports, exports, and the number of foreign direct investment contract projects. From the criterion level, openness and innovation are the biggest obstacles to high-quality development, and the obstacle degree of each criterion level shows significant spatial differentiation characteristics in the research period. The results can provide a scientific basis for China’s path to upgrading and building a modern socialist country.

As a large province with forest resources, assessing the value of forest ecosystem services in Yunnan is of great significance to maintain the sustainable development of Yunnan’s economy. Based on the latest survey data of Yunnan Province, i.e., the forest resources type II survey data, and in accordance with the Specification for Forest Ecosystem Service Function Assessment (GB/T 38582-2020), the value of forest ecosystem service function of 16 cities (prefectures) in Yunnan was assessed, and the ridge regression method was used to study the main factors affecting the value differences among cities (prefectures). The results show that: (1) The value of forest ecosystem services in Yunnan is 982.926×10⁹ yuan yr^-1, of which the value of carbon fixation and oxygen release is the largest. (2) The top four cities (prefectures) in terms of value of services are Pu’er City > Chuxiong Prefecture > Diqing Prefecture > Dali Prefecture; the bottom four cities (prefectures) are Kunming City > Yuxi City > Dehong Prefecture > Zhaotong City; (3) The main factors affecting the value of the service function of each city (prefecture) are forested land area, forest cover, GDP and population density. The findings of this study provided a reference for the sustainable development of the ecological environment in the prefectures and cities of Yunnan Province.

Habitat quality plays a crucial role in enhancing the regional ecological environment and safeguarding biodiversity, with topography being a key element influencing the structure and function of ecosystem services. This research aims to assess habitat suitability across various topographic gradients, analyze the spatial heterogeneity of habitat quality between 2000 and 2020, and explore the relationship between influencing factors and habitat quality. InVEST model is used to evaluate the spatial-temporal evolution characteristics of habitat quality in Zhaotong City, focusing on how topographic gradients impact habitat quality distribution. The study also delves into the factors affecting habitat quality in Zhaotong City, including land use patterns, elevation, slope, average annual temperature and other variables. The results indicate three key aspects of this system. (1) During the study period, the land use types were mainly forest land, farmland and grassland, with construction land experiencing the most significant increase. (2) From 2000 to 2020, the quality areas of high and medium habitats in Zhaotong City decreased, while the quality areas of medium-high, medium-low and low habitats increased. (3) The study revealed a notable topographic gradient effect on habitat quality, with the primary driver shifting from GDP to land use type and subsequently to average annual precipitation in Zhaotong City. The transfer between different grades of habitat quality mainly presents the characteristics of “descending” transfer, with medium-low quality habitats typically found on medium-low topographic gradients and medium-high quality habitats on medium-high gradients. Cross-detection results show that land use type exhibited the strongest correlation with other influencing factors. Therefore, this study can provide a scientific basis for policy makers to protect biodiversity, enhance ecosystem services and promote regional economic development.

The Three-River Source Region is an important ecological security barrier in China. Revealing the spatiotemporal evolution characteristics of its landscape types and ecological risks is of great significance for promoting ecological restoration and landscape pattern optimization in the Three-River Source Region. Selecting the Three-River Source Region for a case study and applying the land-use data from four periods (the 1990, 2000, 2010, and 2020), we constructed a landscape ecological risk assessment model for the region based on the landscape pattern index. We then quantitatively assessed the ecological risks and determined the characteristics of their spatial-temporal evolution. The results showed that: (1) The overall landscape ecological risk in the Three- River Source Region tended to decrease from northwest to southeast, and the distribution of landscape ecological risk was closely related to the natural plateau zones and the changes in land cover. (2) From 1990 to 2020, the areas covered by grasslands, water bodies, croplands, and construction land in the Three-River Source Region increased, while the areas of woodlands and unused land decreased. The spatial-temporal changes in the ecological landscape risk were consistent with the characteristics of the changes in the landscape types. The areas categorized as highest, higher, medium, lower and lowest risk areas, while highest and higher risk areas decreased by 9.76%, medium risk areas increased by 1.03%, lower risk areas increased by 8.99%, and lowest risk areas decreased by 0.26%, respectively. (3) Overall, the Three-River Source Region was dominated by very low to medium ecological risk, the areas of which accounted for more than 70% of the entire study area. Overall ecological risks are decreasing, and there is positive spatial autocorrelation of landscape ecological risks in adjacent evaluation units.

Urbanization has resulted in growing ecological pressures on cities, necessitating assessments of urban ecological quality. Long-term characterization of regional dynamics and drivers is critical for environmental management. This study proposes an enhanced ecological quality model (MRSEI) incorporating vegetation cover and EVI rather than just NDVI. The MRSEI model was applied to analyse ecological quality in Yulin City during 2000-2018 using Landsat TM/OLI data on Google Earth Engine. Geographic detectors also quantified anthropogenic and environmental influences on the study area. The results are summarized as follows: (1) MRSEI showed an average correlation coefficient of 0.840 with other indices, demonstrating higher representativeness than individual components. The principal component analysis indicated a 12.88% increase in explained variance. MRSEI also exhibited significantly improved identification of roads, villages, and unused lands over RSEI, better matching ground conditions, and suitability for regional ecological assessment. (2) During 2000-2020, the average MRSEI in Yulin City was 0.481, peaking at 0.518 in 2018, indicating general ecological improvement over time. Spatially, conditions were better in the southeast than northwest. While 38.81% of the area showed significant improvement, 10.15% exhibited significant deterioration, concentrated in western Dingbian and Jingbian counties, highlighting areas requiring enhanced protection. (3) Ecological conditions in Yulin City remained stable over time. High-high clusters were concentrated in eastern counties (Qingjian, Wubao, Jia, Fugu) and central lower-altitude areas near Yokoyama and Zizhou. Low-low clusters predominated in the northern Yuyang desert and high-altitude western Dingbian regions. (4) Enhanced vegetation cover had the greatest influence in improving Yulin’s ecological quality. Rainfall was the most impactful environmental driver, while precipitation and land use change interactions showed the strongest combined effects. In contrast, air quality had minimal explanatory power in Yulin City. (5) The MRSEI model significantly impacts the ecological assessment of urban areas, thereby enhancing urban ecological monitoring accuracy. Moreover, our analysis demonstrates applicability to watershed regions, facilitating comprehensive regional ecological assessment and monitoring.

Street greening is a popular topic in urban design research. Traditionally, assessments for urban greening levels using Normalised Difference Vegetation Index (NDVI) from satellite remote sensing images, often overlooking street greening from a human-scale perspective. This study combined spatial syntax, machine learning techniques, streetscape images, and remote sensing data to comprehensively assess thoroughly analyse street greening levels in Chengdu’s Fourth Ring Road. Additionally, by integrating accessibility analysis with Green View Index (GVI), this study identified areas that should be prioritised for street greening interventions. The results indicate that: (1) Streets in the western and southern regions of Chengdu City’s Fourth Ring Road possessed higher GVI. (2) There is a significant difference in the overall distributions of GVI and NDVI, particularly in the central and eastern regions. (3) Streets with “high commuting and walking accessibility (low GVI) overlapped in the area east of Shuncheng Avenue. The methodology presented in this study can serve as a reference for human-scale street greening in Chengdu and other cities.