Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas.

Mining-induced ground fissures are the main type of geological disasters found on the Loess Plateau, China, and cause great impacts on the soil properties around ground fissures. However, little research has been conducted on the quantitative relationship between ground fissures and changes in soil properties. To address this, 40 ground fissures in the Yungang mining area, Datong City, Shanxi Province, China, were investigated, and changes in soil properties (soil organic matter, soil moisture, field capacity, bulk density, soil porosity, and grain compositions) were revealed by the difference in soil properties between the edge and contrast points around ground fissures. Redundancy analyses were used to illustrate the relationships between the value (Si_DV) and percentage (Si_DP) of the difference in soil properties between the edge and contrast points, as well as the ground fissures. The characteristics of ground fissures that had a significant correlation according to Pearson correlation analysis with Si_DP were selected and analyzed via multivariate linear fitting model, random forest model, and Back Propagation (BP) neural network model, respectively. Results show that soil organic matter, soil moisture content, bulk density, field capacity, and the content of clay at the edge points were significantly less than those at the contrast points; conversely, soil porosity at the edge points was significantly greater. The average percentage of the difference between the edge points and contrast points of ground fissures in these six properties was 15.27%, while soil moisture content showed the greatest change (20.65%). The Si_DP was significantly correlated with the width, slope, and vegetation coverage of ground fissures; however, the vegetation coverage was the determining factor. BP neural network model had the greatest performance in revealing the relationships between ground fissures and changes in soil properties. The model for soil organic matter had the highest accuracy (R² = 0.89), and all others were above 0.5. This research provides insights into the quantitative relationship between ground fissures and their impacts on soil physical properties, which can be used in conjunction with remote sensing images to rapidly assess soil erosion risks caused by mining on a large scale, given that soil physical properties are closely related to topsoil stability.

Cities are critical agents to promote carbon emission reduction, and are also a key part of China achieving carbon peaking by 2030 and carbon neutrality by 2060. This study used a time-varying difference-in-difference (DID) method to provide quasi-natural experimental evidence based on the data of 284 prefecture-level cities in China. We robustly found that the low-carbon city pilot (LCCP) policy has a significant effect on carbon emissions’ reduction. The carbon emissions of pilot cities were reduced by about 1.63 percentage points compared to non-pilot cities. In addition, this study generates several intriguing findings: (1) The carbon emission reduction effect of the LCCP is more significant for cities in the eastern areas and cities with high economic development. (2) The LCCP policy is sustainable and has a lagging effect. The carbon emissions of pilot areas with one lag period and two lag periods were reduced by 1.76% and 1.90%, respectively, which means that the LCCP led to greater carbon reductions over time. (3) We prove the existence of the mediating effect of electricity consumption. The LCCP policy reduced carbon emissions by 3.72% by affecting per capita electricity consumption. (4) Cities in a state of negative decoupling between carbon emissions and economic growth gradually transformed into a state of enhanced decoupling, which shows that the carbon emissions of low-carbon pilot cities were effectively controlled with the economic growth. The conclusion of this study evaluates the current achievements of the LCCP policy and provides an empirical reference for the further formulation of environmental policies.

In order to evaluate how residents choose savings and consumption behavior within the context of the income changes, which resulted from the COVID-19 pandemic, this paper constructed a conditional process model to study the impact of residents’ declining income on their low risk savings behavior and advanced consumption behavior from a cognitive perspective. We found that residents with declining income preferred low risk savings and advanced consumption behavior; residents with lower incomes, whose original income was higher, were more likely to be influenced by anxiety about perceived emergency supply shortages. The moderating role of social trust emerged only after supply shortage anxiety mediated the effect, and this moderation differed significantly based on construal level. This paper explored the impact of declining income and the emotional fluctuations that resulted from the COVID-19 epidemic on the differential choice of residents’ savings and consumption behavior. It can provide microeconomic guidance for similar emergencies in the future.

Ground deformation is one of the most common geological disasters arising in underground mining areas, and mining-induced environmental impacts have resulted in numerous concerns, especially the impacts on the surface vegetation. The evaluation of mining-induced impacts on vegetation is beneficial to revegetation in mining areas; however, the impacts of ground deformation have seldom been systematically evaluated and explained on long time scales despite the long-term existence of ground deformation in underground mining areas. To address this, in this study a vegetation-soil-climate (VSC) model was developed to evaluate the long-term impacts of ground deformation on vegetation, and to reveal its mechanism. The results revealed that the long-term impacts of ground deformation on vegetation result from the degradation of the vegetation and soil when ground deformation occurs, which thereby limits the growth and succession of plants after the ground deformation has occurred. The intensity of the long-term impacts is determined by the severity of the ground deformation, but the duration, climate factors, the substrate conditions of the vegetation and soil before the deformation, and the natural change coefficient of the vegetation and soil are also relevant. Furthermore, the characteristics of the long-term impacts on vegetation were analyzed, and a framework for implementing revegetation and suggestions for the supervision of revegetation in underground mining areas are presented based on the characteristics. The results of this study provide insights into the impacts of mining-induced ground deformation on vegetation on long time scales, considering the comprehensive interactions between the vegetation and other environmental factors, and provide theoretical support for revegetation in underground mining areas.