In the context of the coordinated development of Beijing–Tianjin–Hebei, examining the spatial connections and network structure characteristics of the tourism economy holds substantial theoretical and practical significance. This study constructs a tourism economic quality evaluation index system from four dimensions and optimizes the tourism economic gravity model by incorporating time distance and weight factors to dynamically simulate the intensity of tourism economic spatial connection (ITESC). Based on the new perspective of relational data, social network analysis is employed to investigate the spatial correlation network characteristics and group structure changes in the tourism economy within the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). The results demonstrate a significant improvement in overall tourism economic quality from 2013 to 2022, with the average quality increasing from 0.128 in 2013 to 0.157 in 2022. Tourism economic spatial connections are prevalent among cities, exhibiting heterogeneous characteristics, characterized by density in the north and relatively sparse in the south. The network cohesion of ITESC strengthens, and the network structure evolves from a highly centralized to a multi-centralized configuration, with increasingly frequent cooperation and interaction in tourism between cities. Beijing, Tianjin, and Shijiazhuang occupy central positions in the network, exhibiting strong ITESC and a high degree of connectivity, generating a significant “trickle-down effect” on peripheral cities. Consequently, strengthening ITESC between peripheral cities and core areas, as well as enhancing the bidirectional spillover of tourism development between cities, is crucial for the future sustainable and coordinated development of tourism in the BTHUA.

Afforestation is an important way to effectively reduce carbon emissions from human activities and increase carbon sinks in forest ecosystems. It also plays an important role in climate change mitigation. Currently, few studies have examined the spatiotemporal dynamics of future afforestation areas, which are crucial for assessing future carbon sequestration in forest ecosystems. In order to obtain the dynamic distribution of potential afforestation land over time under future climate change scenarios in China, we utilized the random forest method in this study to calculate weights for the selected influencing factors on potential afforestation land, such as natural vegetation attributes and environmental factors. The “weight hierarchy approach” was used to calculate the afforestation quality index of different regions in different 5-year intervals from 2021 to 2060 and extract high-quality potential afforestation lands in each period. By dynamically analyzing the distribution and quality of potential afforestation land from 2021 to 2060, we can identify optimal afforestation sites for each period and formulate a progressive afforestation plan. This approach allows for a more accurate application of the FCS model to evaluate the dynamic changes in the carbon sequestration capacity of newly afforested land from 2021 to 2060. The results indicate that the average potential afforestation land area will reach 75 Mha from 2021 to 2060. In the northern region, afforestation areas are mainly distributed on both sides of the “Hu Line”, while in the southern region, they are primarily distributed in the Yunnan–Guizhou Plateau and some coastal provinces. By 2060, the potential calculated cumulative carbon storage of newly afforested lands was 11.68 Pg C, with a peak carbon sequestration rate during 2056–2060 of 0.166 Pg C per year. Incorporating information on the spatiotemporal dynamics of vegetation succession, climate production potential, and vegetation resilience while quantifying the weights of each influencing factor can enhance the accuracy of predictions for potential afforestation lands. The conclusions of this study can provide a reference for the formulation of future afforestation plans and the assessment of their carbon sequestration capacity.

Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV in the Yellow River Basin (YRB), a climate-sensitive and ecologically fragile area, by integrating climate change, land management, and ecological protection policies under various scenarios. To achieve this, we developed an EV assessment framework combining a scenario weight matrix, Markov chain, Patch-generating Land Use Simulation model, and exposure–sensitivity–adaptation. We further explored the spatiotemporal variations of EV and their potential socioeconomic impacts at the watershed scale. Our results show significant geospatial variations in future EV under the three scenarios, with the northern region of the upstream area being the most severely affected. Under the ecological conservation management scenario and historical trend scenario, the ecological environment of the basin improves, with a decrease in very high vulnerability areas by 4.45% and 3.08%, respectively, due to the protection and restoration of ecological land. Conversely, under the urban development and construction scenario, intensified climate change and increased land use artificialization exacerbate EV, with medium and high vulnerability areas increasing by 1.86% and 7.78%, respectively. The population in high and very high vulnerability areas is projected to constitute 32.75–33.68% and 34.59–39.21% of the YRB’s total population in 2040 and 2060, respectively, and may continue to grow. Overall, our scenario analysis effectively demonstrates the positive impact of ecological protection on reducing EV and the negative impact of urban expansion and economic development on increasing EV. Our work offers new insights into land resource allocation and the development of ecological restoration policies.

Driven by both natural and anthropogenic factors, farmland abandonment and recultivation constitute complex and widespread global phenomena that impact the ecological environment and society. In the Inner Mongolia Yellow River Basin (IMYRB), a critical tension lies between agricultural production and ecological conservation, characterized by dynamic bidirectional transitions that hold significant implications for the harmony of human–nature relations and the advancement of ecological civilization. With the development of remote sensing, it has become possible to rapidly and accurately extract farmland changes and monitor its vegetation restoration status. However, mapping abandoned farmland presents significant challenges due to its scattered and heterogeneous distribution across diverse landscapes. Furthermore, subjectivity in questionnaire-based data collection compromises the precision of farmland abandonment monitoring. This study aims to extract crop phenological metrics, map farmland abandonment, and recultivation dynamics in the IMYRB and assess post-transition vegetation changes. We used Landsat time-series data to detect the land-use changes and vegetation responses in the IMYRB. The Farmland Abandonment and Recultivation Extraction Index (FAREI) was developed using crop phenology spectral features. Key crop-specific phenological indicators, including sprout, peak, and wilting stages, were extracted from annual MODIS NDVI data for 2020. Based on these key nodes, the Landsat data from 1999 to 2022 was employed to map farmland abandonment and recultivation. Vegetation recovery trajectories were further analyzed by the Mann–Kendall test and the Theil–Sen estimator. The results showed rewarding accuracy for farmland conversion mapping, with overall precision exceeding 79%. Driven by ecological restoration programs, rural labor migration, and soil salinization, two distinct phases of farmland abandonment were identified, 87.9 kha during 2002–2004 and 105.14 kha during 2016–2019, representing an approximate 19.6% increase. Additionally, the post-2016 surge in farmland recultivation was primarily linked to national food security policies and localized soil amelioration initiatives. Vegetation restoration trends indicate significant greening over the past two decades, with particularly significant increases observed between 2011 and 2022. In the future, more attention should be paid to the trade-off between ecological protection and food security. Overall, this study developed a novel method for monitoring farmland dynamics, offering critical insights to inform adaptive ecosystem management and advance ecological conservation and sustainable development in ecologically fragile semi-arid regions.

The Chinese economy has shifted towards a stage of high-quality development, and promoting high-quality development of tourism is of great significance for its sustainable development. Based on the new development concept, using Geographic Information System (GIS) spatial analysis, Moran’s I Index, and the Geographically Weighted Regression (GWR) model, this paper analyzes the spatial–temporal evolution of tourism development in 31 provinces in China during 2010–2022. The findings are as follows: (1) the overall tourism development level of China from 2010 to 2022 was at a medium-low level, showing a spatial distribution pattern of “higher in southeast and lower in northwest”; (2) from the perspective of the Quality–Quantity relationship, the quality of tourism development in China has gradually improved, the regional gap between the east and the west and the internal gap among western regions/provinces have gradually expanded; and (3) tourism innovation and internationalization level are the main influencing factors of high-quality tourism development in China’s provinces, providing practical decision-making references for advancing the high-quality development of tourism.

Afforestation is a critical nature-based strategy for enhancing ecological resilience and supporting cleaner land-use systems. This study presents a spatially explicit modeling framework to evaluate the long-term impacts of potential afforestation amendments on two key ecosystem services—soil conservation and carbon sequestration—across China’s major shelterbelt program areas under the SSP245 scenario (2020–2070). Using a zonal approach, we integrated Random Forest models, Bayesian belief networks, and Geodetector analysis to identify region-specific afforestation suitability and quantify ecological service gains across eight national shelterbelt program zones. The results reveal pronounced spatial heterogeneity in ecosystem service improvements. (1) High-quality potential afforestation lands, totaling approximately 2.33 × 10⁵ km², are primarily concentrated near the Hu Line (a geographical boundary that divides China into two distinct climatic regions), with the shelterbelt program for upper and middle reaches of Yangtze River accounting for 45.94%. (2) Based on the amended annual afforestation target of 0.47 × 10⁵ km², the adjusted land use projections indicate a significant increase in forest cover. By 2070, the afforestation program for Taihang Mountain exhibits the most significant improvements, with a 47.56% increase in soil conservation and a 10.15% increase in carbon sequestration. (3) Optimization areas differ across zones, with the Taihang mountain area (99.2%) and Pearl river area (70.1%) achieving the highest improvements in soil and carbon services, respectively. These findings provide robust scientific support for data-driven, region-specific afforestation planning under future land-use change scenarios.