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.

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.