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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]

• Spatiotemporal evolution and destruction degree of wetlands from 1990 to 2018. • Comparison and Selection of Four Intelligent Algorithms for Land Cover Classification. • CA-Markov, support vector machine algorithm, wetland evolution prediction in 2018–2035. Wetlands are an important transitional ecosystem, and they play an important role in maintaining ecological balance. However, human activities and climate change have led to a decrease in wetlands. Therefore, to explore the degree of damage and assess the future trends of Guangxi wetlands, this study used the Google Earth Engine (GEE) cloud platform, and the support vector machine algorithm was selected for comparison and analysis to simulate the accuracy of land cover. GIS was used to analyse the evolution and degree of damage of nearly 30 Guangxi wetlands. The geographic detector model was used to explore the driving mechanism, and finally, the CA-Markov model and multi-scenario simulation were used to predict the wetland evolution from 2018 to 2035 to reveal the future direction of development. The results showed that (1) From 1990 to 2018, paddy fields accounted for the largest proportion of wetlands in Guangxi. In the past 30 years, the total area of wetlands in Guangxi has been degraded, with a total decrease of 983.33 km2. (2) The degree of wetland damage results showed that the total damaged area was greater than the total restored area, The wetland damage in Nanning city was the most serious, with an area difference of 503.22 km2 between the damaged and restored areas. (3) The analysis of the driving mechanism of wetland damage showed that distance from cities and towns, average precipitation and population density were the main driving factors. (4) The spatial distribution of natural development and economic construction in 2035 will be slightly damaged; additionally, the spatial distribution of ecological protection will expand as a whole. From 2025 to 2035, wetlands will be basically stable under Natural Development Scenario (NDS), degraded each year under the Economic Construction Scenario (ECS), and steadily increased each year under Ecological Protection Scenario (EPS). [ABSTRACT FROM AUTHOR]

• Compared with intelligent classification algorithms of remote sensing, the algorithm with the highest accuracy was selected. • Explore the impact of land use change on ecosystem service value. • Analyze implications for SDGs reporting and regional sustainable development. Ecosystem services provide a variety of benefits for mankind, and their sustainable use plays an important role in achieving the Sustainable Development Goals (SDGs). This study takes Guangxi as the research object and compares multiple intelligent classification algorithms for land cover based on multi-source data. Selects Random Forest (RF) classification algorithms for land cover classification, analyses their temporal and spatial changes, and the extracted results produce SDG 15.1.1, 15.1.2, 15.1.4 indicators, and assess the degree of completion of Guangxi's SDGs 15.2 indicators. On this basis, the equivalent factor method was used to evaluate the change of ecosystem value caused by land use change, and the direct impact of land use change on SDG15.9 was evaluated. The results indicate that:(1) From 1990 to 2020, forest was the most important land use mode in Guangxi, and the most serious wetland shrinkage was in Beihai and Nanning; The overall trend of land use changes is that wetlands, forests, and grasslands have decreased, dry land and construction land have increased, and bare land has remained basically remained unchanged. (2) From 1990 to 2020, the SDG 15.1.1 indicator showed a slow rise and then a rapid decline. The SDG 15.1.2 indicator showed a continuous downward trend. The SDG 15.1.4 indicator showed a steady upward trend. Guangxi failed to achieve the target of SDG15.2 by 2020. (3) From 1990 to 2020, the overall ecosystem service value in Guangxi showed a decreasing trend, and the one-way ecosystem service value in Guangxi was dominated by hydrological regulation and climate regulation. The ecosystem service value showed a spatial pattern of "low in the central region and high in the surrounding areas." (4) Grassland to forest and dryland to forest were the main types of ecological service income; Forest conversion to dryland is the most important type of ecological service loss. (5) As a whole, only dryland sensitivity index less than 0 during 2010–2015 had a negative impact on Ecosystem services values (ESV), while other time and land use type sensitivity index were all greater than 0 had a positive impact on ESV. [ABSTRACT FROM AUTHOR]