Carbon emission reduction has become a worldwide concern on account of global sustainability issues. Many existing studies have focused on the various socioeconomic influencing factors of carbon dioxide (CO₂) emissions and the corresponding transmission mechanisms, while very few models have unified the scale effect, structure effect, and technique effect in the context of China. This paper attempted to analyze the impact of economic growth, industrial transition, and energy intensity on CO₂ emissions in China by constructing an autoregressive distributed lag (ARDL) model. The results showed that there are long-term cointegration relationships between the three factors mentioned above and CO₂ emissions. There is an inverted U-shaped relationship between economic growth and CO₂ emissions, which not only verifies the environmental Kuznets curve (EKC) hypothesis, but also upholds the scale effect. In addition, the proportion of added value of secondary industry and energy intensity has significant positive impacts on CO₂ emissions. On one hand, this confirms the structure effect and technique effect; on the other hand, it implies that the reduction effect is the dominant effect in the case of China, instead of the rebound effect. This paper is expected to make a valuable contribution to research in the field of sustainable development by providing both theoretical support and implementation of path choice for CO₂ reduction in China.

The optimization of the sampling scheme is particularly important in order to achieve sustainability in quality management. This paper discusses the problem of optimizing the design of metrological sampling scheme when the mean is a random variable. Assuming that the prior distribution of product means is known, a Bayesian posterior probability is calculated by using the likelihood function with decision variables to measure the sampling risk, and the expected cost of the sampling scheme is calculated based on the protection of producer and user risk in combination with the Taguchi quality loss function. The influence of model parameters on the selection of the optimal sampling scheme is investigated through sensitivity analysis. The model constructed in this paper solves the problem of sampling design in the case of food processing enterprises, quantifies the quality loss of products in the sampling process, facilitates sustainable quality management of enterprises, and has important theoretical significance and application value for sustainable business management of food processing enterprises.