The spatial and temporal characteristics of land use carbon emissions are relevant to the sustainable use of land resources. Although spatial and temporal studies have been conducted on land use carbon emissions, the spatial correlation of land use carbon emissions at the city level still requires further research. Here, we estimated the distribution of carbon emissions at the city level in Shandong Peninsula urban agglomeration in spatial and temporal terms based on land use remote sensing data and fossil energy consumption data during 2000–2019. The results showed that the land use change in the 16 cities in the study area was the conversion of cropland to construction land. Carbon emissions from land use had an upward trend for all 16 cities overall during the period of 2000–2019, but the incremental carbon emissions trended downward after 2010. Among them, Jinan and Qingdao had higher carbon emissions than other cities. In addition, we also found that land use carbon emissions at the city level were characterized by stochasticity, while per capita carbon emissions displayed geospatial aggregation. Among them, Yantai displayed a spatial pattern of high–high clustering of carbon emissions, while Jining presented a spatial pattern of low–low clustering in terms of land-average carbon emissions and carbon emissions per capita during 2000–2019. The results of the study are important for guiding the achievement of urban carbon emission reduction and carbon neutrality targets at the city level.

The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought.

Changes to land use carbon emissions (LUCEs) have become significant contributors to increasingly severe climate issues. Land use change is one of the crucial factors that affect carbon emissions. Alpine meadows regions are sensitive to climate change and human activities. However, current research on LUCEs mainly focuses on analyzing present land use status and spatial patterns. To reveal and forecast future LUCEs in the alpine region, the Upper Yellow River of Gannan (UYRG) was used as a case study. Based on the land use data from 1990 to 2020, we used the multi-scenario PLUS model to predict the land use types in 2030 and analyzed the spatial and temporal dynamic trends of LUCEs from 1990 to 2030. The results showed a strong correlation between the predicted and actual land use types, with a Kappa value of 0.93, indicating the applicability of the PLUS model in predicting land use in the UYRG. Over the study period, construction land expanded, while woodland and grassland diminished. Carbon emissions (CEs) increased by 516.4% from −200,541.43 Mg CO₂e in 1990 to 835,054.08 Mg CO₂e in 2020, with construction land being the main contributor. In the Natural Development scenario for 2030, construction land expanded most rapidly, resulting in the highest LUCEs. In the Ecological Protection scenario, woodland and grassland expanded, while construction land decreased, leading to an expansion in carbon sinks. In the Cropland Protection scenario, cropland expanded, with CEs falling between the other two scenarios. These findings lay a theoretical groundwork for formulating policies addressing LUCEs in alpine meadows, providing valuable insights for further studies.

Educational tourism serves as a vital avenue for enhancing students’ practical skills and knowledge acquisition. As key components of educational tourism, the spatial distribution and accessibility of educational tourism bases significantly influence the effectiveness of study tour programs. Here, we employed stepwise regression analysis and geographically weighted regression (GWR) to analyze the spatial distribution characteristics, accessibility, and driving factors of educational tourism bases along the Yellow River region. The results indicate that museums accounted for the greatest proportion at 25.14% of the nine types of educational tourism bases. The educational tourism bases show a pattern of “dense in the southeast and sparse in the northwest.” Furthermore, they demonstrate clustered distributions centered around provincial capitals. The key factors influencing the distribution of educational tourism bases, ranked from highest to lowest based on the absolute value of the GWR coefficient, are as follows: 4A and above scenic spots > urbanization rate > internet broadband users > length of classified roads > education expenditure. The accessibility of educational tourism bases is basically consistent in terms of time and spatial distance, both showing better accessibility in the southeast than in the northwest. However, where Sichuan Province and Gansu Province meet, time accessibility is better, while distance accessibility is worse. These findings provide valuable insights for promoting the sustainable development of educational tourism in the Yellow River region.

Changes in land use types in alpine meadow areas have significant impacts on the ecological environment in alpine areas. Exploring land use change is crucial for land use management and optimization in alpine regions. Thus, it is necessary to analyze land use evolution and its drivers in alpine meadow regions from a production–living–ecology space (PLES) perspective by using remote sensing data. We first constructed the PLES evaluation system for Gannan. Then, we analyzed the spatial and temporal evolution characteristics and coupling degree of PLES in the study area. Finally, the driving factors affecting PLES were explored with geodetector. The conclusions of the study reveal that the distribution of productive and ecological spaces is large and concentrated, while the distribution of living spaces is more decentralized. The PLES was mainly concentrated in the area above 2500 m but below 4000 m and with a slope of 40° or less. During the study period, the area of production space showed a decreasing trend, while the areas of living and ecological space both showed increasing trends, primarily occurring at the expense of production space. DEM and GDP were the main factors affecting the distribution of PLES. The coupling level and the degree of coupling coordination were relatively stable in general, showing a pattern of “high in the east and low in the west”. The study provides technical support and a theoretical basis for the future planning of land space and ecological environment optimization in the alpine meadow regions.