It is highly valuable to analyze and assess the landscape ecological risk of nature reserves to prevent and resolve ecological risks, as well as to effectively protect and maintain the sustainable development of nature reserves. Taking the forest landscape of the Lushan National Nature Reserve as its study object, this study performed grid processing for the nature reserve and classified forest landscape types using the Forest Resource Inventory Database in 2019. A landscape ecological index model was constructed to evaluate the ecological risk. Global and local Moran index values were used to reveal the autocorrelations for ecological risk. The geodetector method was used to comprehensively analyze the effects of natural and human factors on ecological risk. The results showed that, in general, the ecological risk level of the nature reserve was relatively low, as the proportion of the lowest-, lower-, and medium-risk areas to the total forestry land area accounted for 91.03%. The ecological risk ranking of each functional zone, from high to low, was in the order of the experimental zone, the buffer zone, and the core zone. The ecological risk levels of different forest landscape types were closely related to their area, spatial distribution, and succession stage, as well as human factors, such as the proximity to roads and settlements, etc. The forest landscape with the highest ecological risk was the Cunninghamia lanceolata (Lamb.) Hook. forest, and the forest landscape with the lowest ecological risk was other forestry land. Ecological risk had a positive spatial correlation and tended to be aggregated in space, demonstrating coupling with the proximity to roads and settlements. The ecological risk was affected by both human and natural factors, among which human factors played a dominant role. The proximity to roads and settlements, the relative humidity, and the temperature were the main driving factors. The interaction of pairwise factors had a stronger influence than that of single factors. Therefore, controlling the intensity of human activities and enhancing the coordination between humans and nature are beneficial for alleviating the ecological risks in the forest landscapes of nature reserves.

Land use change has been one of the common problems in the context of urbanization in China. Social economy and land use interact with each other, and it is especially important for human society to adhere to sustainable development, and to deal with the contradictory relationship between the social–economic needs and land use change. The objectives of this study are: (1) Obtain time-series land-use classification data and its spatial distribution in Nanchang City; (2) Identify the characteristics and driving force of spatial–temporal land use changes in Nanchang City from 2000 to 2020; (3) Discuss the relationship between the urban expansion and social economy in Nanchang City. The results show that the spatial distribution of land use in Nanchang City has changed significantly from 2000 to 2020, and the largest area of land-use type in Nanchang City has been cropland. The cropland has continuously declined, and the urban area has increased significantly. A lot of cropland has been transformed into urban areas, and land use degree in Nanchang City has significantly increased. The spatial pattern of land use has greatly changed, and the city spatial pattern has become more aggregated, while the spatial distribution of cropland, forest and grassland has become more fragmented. Moreover, there has been an obvious correlation between social-economic development and the level of land use, and GDP has been the main driver of land use change. The central urban area of Nanchang city has been the main hotspot of land use change.

Carbon emissions (CEs) are one of the most important factors causing global warming. The development of social economy and the acceleration of the urbanization process leads to increasing CEs, especially in China. Therefore, it has become an international community consensus to control the growth of CEs and mitigate global warming. Understanding the changing patterns and driving forces of CEs are important prerequisites for formulating policies that target the reduction of CEs in response to global warming. This study developed an improved logarithmic mean Divisia index (Spatial-LMDI) to explore the urban form and socio-economic driving forces of CEs in China. Comparing with previous studies, this study is unique in the way of applying spatial landscape index to LMDI decomposition analysis. The results show that population, per capita GDP, investment intensity and urban expansion are the top driving forces of CEs growth from 1995 to 2019. Investment efficiency, technology level, and aggregation are the most important factors in terms of restraining the growth of CEs. To achieve the goal of energy saving, CEs reduction and climate change mitigation, we proposed that strategies should be formulated as follows: improving efficiency of energy investment, optimizing the spatial distribution of construction land aggregation, and rationalizing distribution of industries.

The Chinese government has set ambitious carbon peak and carbon neutrality goals for 2030 and 2060. To achieve these targets, China will need to adopt stricter carbon emission policies in the future. Due to China’s vast territory, there are significant disparities in economic development levels and carbon dioxide emissions (CEs) among its provinces. Therefore, equitable carbon reduction policies are crucial for regional economic development. This study calculates CEs from energy consumption in 30 Chinese provinces using energy balance tables and CEs factors. Based on this, the Dagum decomposition Gini index is used to quantitatively assess CEs inequality between the primary, secondary, and tertiary industries in China’s central, eastern, and western regions. The results show that over the past 25 years, China’s CEs have increased from 3.17 billion tons to 13.32 billion tons, with an average annual growth rate of 6.17%. Additionally, CEs inequality in China has decreased by 3.96%. Moreover, CEs inequality in the eastern provinces has decreased the most (57.65%), followed by the central and western regions. Furthermore, CEs inequality in the secondary industry has expanded in all three regions, while inequality in the tertiary industry remained smaller due to technological and geographical constraints.

Fine-scale habitat data for alpine musk deer (Moschus chrysogaster) remain scarce across the Qinghai–Tibet Plateau, limiting evidence-based conservation. We deployed 221 camera stations at approximately 10 km grid spacing in Baqing County, Nagqu, Tibet, from August to November 2023, and analyzed 1004 independent detections (30 min threshold) with single-season occupancy models that account for imperfect detection, complemented by logistic and negative binomial models and circular activity analyses. Musk deer were detected at 123 of 221 stations (55.66%). Occupancy was negatively associated with elevation (β = −0.70, 95% CI: −1.02 to −0.38) and positively associated with slope (β = 0.58, 95% CI: 0.24–0.92), while detection probability increased with survey effort; the normalized difference vegetation index (NDVI) and distances to roads, settlements, and water showed weak and unstable support across all frameworks. Activity was broadly cathemeral with a moderate diurnal tendency (Rayleigh test, p = 0.001), and activity timing differed among months and between stations near versus far from roads and settlements. These results provide the first occupancy-based evidence of alpine musk deer habitat use on the central Qinghai–Tibet Plateau and identify relatively low-elevation, steep terrain as priorities for conservation planning.