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• A framework is proposed to integrate machine learning, ecological footprints and PLUS models. • Coupling of multiple data sources with machine learning algorithms improves classification accuracy. • The random forest algorithm is optimal for comparison accuracy. • Improved ecological footprint models to improve the accuracy of ECC. • Restoration of forest land with the highest ECC potential. Ecological carrying capacity is key to evaluating sustainable development capacity, and land use change has varying degrees of impact on ecosystems. This study explores the ecological carrying capacity based on land use change, which is important for the conservation and scientific and rational use of ecosystems. Therefore, in this study, three machine learning classification 3algorithms, namely support vector machine, random forest and artificial neural network, were selected for accuracy comparison and analysis based on multi-source data, using Southwest Guangxi Karst-Beibu Gulf as the study area. The Patch-generating Land Use Simulation Model is used to dissect the spatial and temporal changes in ecological carrying capaciyt from 1990 to 2040 and future trends. The results showed that (1) Coupling multiple sources of data and machine learning classification algorithms can ultimately improve land use classification accuracy. The RF is compared and found to be the optimal classification algorithm for this study. (2) The area of arable and grassland gradually decreases between 1990 and 2040, mainly encroached upon by build land; the area of forested land gradually recovers, the watershed fluctuates up and down, and the area of build land gradually increases. (3) The ecological carrying capacity from 1990 to 2040 shows a trend of decreasing and then increasing, and the area of low and medium–low decreases from 2020 to 2040, with arable land and forest land having a stronger potential to increase ecological carrying capacity. Nanning City, Qinzhou City and Chongzuo City are severely damaged. The framework provides an effective tool for exploring future ecological carrying capacity to support the ecological conservation and future sustainable development of the region. [ABSTRACT FROM AUTHOR]

• Explore the comprehensive evaluation of multi-risk sources ecological risk in complex ecosystem. • Multi-model based multi-perspective analysis of ecological risks. • The OWA-GIS method overcomes the shortcomings of the traditional single evaluation method. The transitional space is a complex ecosystem, which has a certain impact on the survival and development of human beings. However, with the aggravation of resource utilization and the acceleration of urbanization, the ecological environment is more damaged and the ecological risk is increased. Therefore, it is necessary to establish a reasonable and scientific ecological risk assessment system. In order to reveal the comprehensive ecological risk situation in the transitional space of Guangxi, China, the representative southwest Guizhou-Beibu Gulf region was used as the study area. This research was based on multi-source data and DPSIR model, using a non-dimensional quantitative model for data standardization, and using subjective and objective evaluation methods to assign weights to 18 indicators. Finally, the OWA-GIS method was used to conduct a comprehensive ecological risk assessment of the region under different decision risks. The results show that: (1) The risk level was at high risk in karst areas and coastal areas of Beibu Gulf as well as areas with intensive human activities. The forest areas in Baise show low-risk levels under different risk perspectives. (2) Vulnerability-resources-disasters affect each other's ecological risks. The Chongzuo Karst area, Nanning urban area, Beihai and Fangchenggang have higher-risk levels. The comprehensive ecological risk presents a transitional distribution from a low-risk in the northwest mountainous area to a high- risk in the southeast coastal area. (3) The decision attitude ranges from optimistic to pessimistic, with a gradual increase in risk level as the risk factor increases. The central and southern regions are higher than those in the north, and the areas with better socio-economic development and frequent human activities have higher risk levels. In reality, decision makers must adjust the decision risk coefficients according to different decision goals to obtain reasonable decision results. [ABSTRACT FROM AUTHOR]

• Exploring the change of ESV pattern under "Production-Living-Ecological Space". • Analysis of ESV distribution characteristics in multi-characteristic regions. • Spatial autocorrelation analysis reveals the regional ESV spatial aggregation state. • A standard deviation ellipse inscribes the regional ESV center of gravity migration. • Natural, economic, and social variables all have a role in regional ESV development. In order to investigate the changing patterns and influencing factors of ecosystem service values (ESV) provided by the "production-life-ecological space" (PLES) in the karst-Beibu Gulf of southwest Guangxi under the geographical characteristics of the mountain-river-sea transition zone, this study selected land use data at four time points from 1990 to 2020, clarified the characteristics of regional ESV evolution through spatial autocorrelation analysis and standard deviation ellipse, and explored its formation mechanism with the help of geographic probes. The results showed that: (1) The overall spatial variation of the total ESV in the study area was obvious, with "high in the southwest and low in the northeast" and a decreasing trend in general. (2) The ESV in PLES perspective showed positive spatial autocorrelation overall and exhibited obvious spatial aggregation characteristics. Their center of gravity as a whole shifts to the southeast, the ellipse area keeps expanding, and the center of gravity positions all remain in Nanning City. (3) ESV evolution is closely related to natural, economic, and social factors, with rocky desertification area and Normalized Difference Vegetation Index (NDVI) having the most significant spatial differentiation effect on ESV provided by production space and ecological space, average altitude contributing more to ESV provided by living space, and interaction between various elements improving ESV differentiation from the perspective of PLES. The results of the study provide a scientific basis for optimizing regional territorial spatial planning and improving regional ESV. [ABSTRACT FROM AUTHOR]