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As a critical ecological security barrier, Yunnan Province has significantly reduced forest fragmentation through ecological restoration programs in recent years. However, the optimization process of the forest landscape and the most effective ecological restoration projects remain unclear. Our study combined land use data with 13 driving factors, including meteorological and socioeconomic data, to analyze the spatial distribution, temporal dynamics, and key ecological restoration programs of forest fragmentation using dynamic and static indexes, morphological spatial pattern analysis, boosted regression tree models, and partitioned statistical methods. We found that over the past 30 years, FF has significantly decreased. Fragmentation was higher before 2000 but has steadily declined, with eastern regions more fragmented than western areas. Forest landscapes have transitioned from degradation to recovery, with core forest areas expanding by 6997.72 km2. Afforestation was the main driver, adding 238,109.21 km2 of forest cover, while reforestation contributed 17,254.47 km2, improving patch size and connectivity. Regionally, the southwest has lower fragmentation due to ample rainfall and reforestation efforts, while central and northeastern areas face higher fragmentation from drought, human activities, and urban expansion. Our study offers a scientific basis for formulating effective ecological restoration policies.

In the topographically and climatically diverse region of Yunnan, clarifying the driving mechanisms and threshold effects of factors influencing forest net primary productivity (NPP) is crucial for managing forest carbon sinks. In this study, we established a comparative analysis framework for the stable forest (SF) and the changing forest (CF). Yunnan was then divided into five subregions based on topography and climate. Using a random forest model and the SHAP method, we systematically analyzed drivers of spatiotemporal NPP variations. The results show that CF exhibits greater NPP variation and spatial heterogeneity than SF and maintains stronger trend persistence. At the provincial scale, elevation and precipitation are the main drivers of NPP in SF and CF, respectively, while at the subregional scale, dominant factors differ and include solar radiation, temperature, and forest age, indicating clear scale‐dependent effects. The explanatory power of multiple‐factor interactions for NPP variations is generally higher than that of single factors, with precipitation showing particularly strong synergistic effects. Human activities also have a pronounced impact on NPP in CF. Key driving factors exhibit nonlinear threshold responses, with NPP in SF most suitable under temperature (14°C–20°C), precipitation (100–125 mm), elevation (~2000 m), and forest age (50–70 years), whereas CF shows broader response ranges, reflecting greater adaptability. This study highlights the nonlinear responses and threshold characteristics of forest NPP to multiple driving factors in complex mountainous environments. It also emphasizes the importance of interactions and scale effects in ecological modeling. We first classified the forests in Yunnan Province into stable forest and change forest regions, and then further divided the province into five sub‐regions based on topography and climate. Based on this classification, we systematically analyzed the spatiotemporal variation of forest net primary productivity (NPP) across these regions. Moreover, by employing the Random Forest model and SHAP analysis, we identified the key driving factors behind the NPP changes in different sub‐regions.

Changes in terrestrial ecosystem carbon storage (CS) affect the global carbon cycle, thereby influencing global climate change. Land use/land cover (LULC) shifts are key drivers of CS changes, making it crucial to predict their impact on CS for low‐carbon development. Most studies model future LULC by adjusting change proportions, leading to overly subjective simulations. We integrated the Integrated Valuation of Ecosystem Services and Trade‐offs (InVEST) model, the Patch‐generating Land Use Simulation (PLUS) model, and the Land Use Harmonization 2 (LUH2) dataset to simulate future LULC in Yunnan under different SSP‐RCP scenarios of climate and economic development. Within the new PLUS‐InVEST‐LUH2 framework, we systematically analyzed LULC alterations and their effects on CS from 1980 to 2040. Results demonstrated that: (1) Forestland had the highest CS, whereas built‐up land and water showed minimal levels. Western areas boast higher CS, while the east has lower. From 1980 to 2020, CS continuously decreased by 29.55 Tg. In the wake of population increase and economic advancement, the area of built‐up land expanded by 2.75 times. Built‐up land encroaches on other land categories and is a key cause of the reduction in CS. (2) From 2020 to 2040, mainly due to an increase in forestland, CS rose to 3934.65 Tg under the SSP1‐2.6 scenario, whereas under the SSP2‐4.5 scenario, primarily due to a reduction in forestland and grassland areas, CS declined to 3800.86 Tg. (3) Forestland is the primary contributor to CS, whereas the ongoing enlargement of built‐up land is causing a sustained decline in CS. Scenario simulations indicate that future LULC changes under different scenarios will have a significant impact on CS in Yunnan. Under a green sustainable development pathway, Yunnan can exhibit significant carbon sink potential. Overall, this research offers a scientific reference for optimizing land management and sustainable development in Yunnan, aiding China's “double carbon” goals. We integrated the InVEST model, PLUS model, and LUH2 dataset to simulate future LULC patterns under different SSP‐RCP scenarios. Within the framework, we took Yunnan Province as the study area and systematically analyzed the LULC alterations and their effects on CS from 1980 to 2040.

Blue and green spaces are well-known for their benefits in improving urban thermal environments. However, the optimal configuration of green, blue, and grey spaces (GBGSs) for the physical and mental health of urban residents remains unclear. Therefore, we employed land surface temperature (LST), near-surface air temperature (SAT), and Humidex to analyze the optimal configuration of GBGS. The results indicated the following: (1) The spatial distribution of Perceptual Urban Thermal Environments (PTEs) is consistent with that of Surface Urban Thermal Environments (STEs). However, the temperature of most perceptual indicators is lower than the daytime LST and higher than the SAT. (2) Blue spaces have higher cooling efficiency than green spaces. (3) The coverage of grey space is less than 40%, at least 35% for green space, and blue space covers between 15% and 25%, which is the optimal configuration to balance the thermal environment. Moreover, increasing blue space and simplifying green spaces is recommended where grey space coverage is below 30%. In areas with 30–40% grey space, enhancing the complexity and fragmentation of blue space boundaries is more effective. Maintaining at least 30% blue space and optimizing green space aggregation improves cooling efficiency where grey space coverage is over 40%. This study provides the scientific foundation for configuration of GBGSs in urban development and renovations.