The counties have experienced urban expansion and landscape pattern fragmentation. As carriers of new urbanization, the balanced development between urban expansion and landscape connectivity in the counties needs to be emphasized. The uncontrolled expansion of land should be discouraged and planners need to clarify land use expansion patterns. Using Changsha County as the study area, the characteristics of the landscape pattern between 2000 and 2020 were analyzed. The morphological spatial pattern analysis and landscape connectivity method (CMSPACI), as well as the minimum cumulative resistance (MCR) model, was used to construct the ecological network. We also explored the most appropriate corridor width using the buffer zone to guide future land use planning and ecological network planning. The results show that based on CMSPACI the total area of ecological sources identified was 304.91 km², encompassing a large area of forest parks. The total length of the 25 ecological corridors identified by the MCR model was 431.97 km. Ecological sources and corridors are missing in the central region; so, their pattern was optimized using landscape connectivity and the absence of location as selection criteria. The optimized network indices showed significant improvement. The width of the ecological corridors should be controlled in order to be in the range of 30 m to 50 m to maximize the effect of the corridors on species dispersal and migration. Our proposed research framework for the construction and optimization of EN in Changsha County can provide ideas to balance the contradictions between urban expansion and landscape connectivity in Changsha County.

To achieve sustainable urban development, it is essential to gain insight into the spatial and temporal differentiation characteristics and the driving mechanisms of the urban thermal environment (UTE). As urbanization continues to accelerate, human activity and landscape configuration and composition interact to complicate the UTE. However, the differences in UTE-driven mechanisms at different stages of urbanization remain unclear. In this study, the UTE of Shenyang was measured quantitatively by using the land surface temperature (LST). The spatial and temporal differentiation characteristics were chronologically studied using the standard deviation ellipse (SDE) and hotspot analysis (Getis–Ord Gi*). Then, the relationship between human activities, landscape composition and landscape configuration and LST was explored in a hierarchical manner by applying the geographical detector. The results show that the UTE in Shenyang continues to deteriorate with rapid urbanization, with significant spatial and temporal differentiation characteristics. The class-level landscape configuration is more important than that at the landscape level when studying UTE-driven mechanisms. At the class level, the increased area and abundance of cropland can effectively reduce LST, while those of impervious surfaces can increase LST. At the landscape level, LST is mainly influenced by landscape composition and human activities. Due to rapid urbanization, the nonlinear relationship between most drivers and LST shifts to near-linear. In the later stage of urbanization, more attention needs to be paid to the effect of the interaction of drivers on LST. At the class level, the interaction between landscape configuration indices for impervious surfaces, cropland and water significantly influenced LST. At the landscape level, the interactions among the normalized difference building index (NDBI) and other selected factors are significant. The findings of this study can contribute to the development of urban planning strategies to optimize the UTE for different stages of urbanization.

Human activities pose significant impacts on ecosystem services (ESs) in mining areas, which will continually increase over time and space. However, the mechanism of ES change on spatiotemporal scales post-disturbance remains unclear, especially in the context of global climate change. Here, we conducted a global literature review on the impact of two of the most frequent disturbance factors (mining and restoration) on 27 different ESs, intending to synthesize the impacts of human disturbance on ESs in mining areas via a meta-analysis, and analyze the spatiotemporal variability of ESs after disturbance. We screened 3204 disturbance studies published on the Web of Science between 1950 and 2020 and reviewed 340 in detail. The results of independence test showed that human disturbance had a significant impact on ESs in the mining areas (p < 0.001). The impacts (positive and/or negative) caused by mining and restoration differed considerably among ESs (even on the same ESs). Additionally, spatiotemporal scales of human disturbance were significantly related to spatiotemporal scales of ES change (p < 0.001). We found that the positive and negative impacts of disturbances on ESs may be interconversion under specific spatiotemporal conditions. This seems to be associated with spatiotemporal variability, such as the temporal lag, spatial spillover, and cumulative spatiotemporal effects. Climate changes can lead to further spatiotemporal variability, which highlights the importance of understanding the changes in ESs post-disturbance on spatiotemporal scales. Our research presents recommendations for coping with the twofold pressure of climate change and spatiotemporal variability, to understand how ESs respond to human disturbance at spatiotemporal scales in the future, and manage disturbances to promote sustainable development in mining areas.

The COVID-19 pandemic has led to an unprecedented shift towards online learning, compelling university students worldwide to engage in self-directed learning within remote environments. Despite the increasing importance of online education, the factors driving students’ motivation for self-directed online learning, particularly those involving economic incentives, have not been thoroughly explored. This study aims to address this gap by analyzing large-scale data collected from 19,023 university students across China during the pandemic. Using mixed Bayesian networks and multigroup structural equation modeling, the study explores the complex relationships between personal characteristics, academic characteristics, the academic environment, and students’ motivation for self-directed online learning. The results reveal significant associations between online self-directed learning motivation and personal characteristics. such as gender and age, academic characteristics, such as education level and learning incentives, and the geographic location of the school within the academic environment. Moreover, the causal relationship between school location and online self-directed learning motivation varies by gender and educational level. This research not only provides new empirical support for the theoretical framework of online learning motivation but also contributes to the broader fields of educational psychology and online learning research.