Carbon emission is the focus of global climate change concerns. Population aging changes the level of labor structure, which directly affects the industry adjustment and will also have a long-term impact on carbon emissions. Uncovering the complex association among population aging, labor allocation, and CO₂ emission is crucial for developing effective policies for low-carbon and sustainable development in China. Therefore, this study aims to analyze whether population aging contributes to reducing carbon emission intensity by regulating labor allocation. Based on provincial panel data from 2000 to 2019, the Systematic Generalized Method of Moments (Systematic GMM) model and the Bias Corrected Least Squares Estimation with Nonsymmetric Dependence Structure (Bias Corrected LSDV) model are adopted in this study. The results show that nationwide as a whole, population aging objectively inhibits human capital accumulation and, to some extent, weakens its positive carbon emission reduction effect. Meanwhile, population aging helps to mitigate the increase in carbon emissions caused by the capital-labor endowment structure. Due to the dual impact of aging and population migration, the emission reduction effect of human capital accumulation is significant in the East. The brain drain in the central and western regions further inhibits the positive effect of regional human capital accumulation. Promoting the rationalization of population mobility nationwide, reducing the brain drain in less developed regions, and directing capital into technology-intensive industrial sectors are the core keys to achieving optimal labor allocation in an aging society. This will help China meet its carbon neutrality target on schedule.

To comprehend the intricate interaction between water resources and food security, it is critical to examine the hidden water footprint (WF) of food consumption and its underlying causes within specific nations or areas. This study investigates the changes in the quality and structure of food consumption in China’s urban and rural areas from 2000 to 2020. Following the calculation of the WF associated with food consumption for both urban and rural populations, this study uses ArcGIS 10.6 software to map the spatial configuration of the provincial per capita WF. Moreover, the random forest model is utilized to uncover the salient determinants influencing the WF of food consumption in urban and rural contexts. Quantitatively, rural populations have witnessed a more pronounced acceleration in their per capita food WF compared with urban entities, with a notable upswing in the proportion of meat and poultry consumption. Spatially, regions exhibiting elevated WF for urban populations have transitioned from the western zones toward the southeast and northeast, whereas a marked east–west dichotomy is evident in rural areas. In terms of drivers, for urban demographics, economic variables emerge as paramount determinants for food WF, while rural areas underscore the prominence of natural and technological parameters. The insights garnered from this investigation bear profound implications for facilitating balanced nutritional intake among China’s urban and rural populations, alleviating food-related water resource pressures, and optimizing water resource utilization.

Crop insurance is a crucial way to avoid disaster losses and to guarantee farmers’ basic production income in China and abroad. Securing agricultural production is a critical way to eradicate hunger and reduce poverty and an essential means to achieve the UN Sustainable Development Goals. How to pay out more quickly and fairly after a disaster has become an urgent issue for agricultural insurance. The standard domestic crop insurance rate is determined based on the statistical data of the entire administrative unit and ignores the spatial risk difference of disasters inside the administrative unit. Therefore, obtaining a pure premium based on crops inside the administrative unit is a key problem. Based on remote sensing data and insurance actuarial models, we studied and determined the fair premium rates to insure winter wheat at the farmer level in Heze, Shandong, China. Our study shows that remote sensing data can provide data security for determining a pure premium rate at the level of individual farms, and provide the primary reference for determining farmer-level crop insurance premium rates. The use of remote sensing for determining those rates can improve the customization of crop insurance and reduce farmers’ lower incomes due to exposure to natural disasters, improve farmers’ resilience to risk, and prevent a return to poverty due to disasters, ultimately reaching the UN Sustainable Development goals of eradicating hunger and reducing poverty.

The Yunnan snub-nosed monkey Rhinopithecus bieti is an endangered species distributed in a narrow strip of land extending from the north side of the Qinghai–Tibet Plateau of China. To better understanding the Yunnan snub-nosed monkey’s distribution and ecology, this study recorded the winter activity of the monkeys using infrared cameras and then employed MaxEnt modelling with six factors to analyse the habitat suitability for the species in the most critical season for its survival. The results showed that the main ecological factors affecting the habitat suitability of this species were aspect, altitude and distance from roads and rivers. The model identified low coverage of highly suitable (1.89 % of the entire reserve) and medium suitable (6.48 % of the entire reserve) habitats. The most suitable habitats were on the eastern and north-eastern slopes, at altitudes of 3100–3400 m a.s.l. The study revealed that the protection of primary forests, including the connectivity among areas of suitable habitats, is of great significance to the survival of this species.