Industrial structure upgrading (ISU) plays a critical role in reducing carbon emissions (CO₂ emissions); however, the existing literature lacks dynamic research on the relationship between the two. Based on provincial panel data from China between 2002 and 2021, this paper establishes a time-varying coefficient two-way fixed-effects model to empirically explore the dynamic effects of ISU on CO₂ emissions. The findings indicate that the overall impact of China’s ISU on CO₂ emissions demonstrates a dynamic tendency of initially promoting and subsequently inhibiting such emissions and, since 2016, ISU has had the ability to significantly reduce CO₂ emissions. This time-varying trend is highly related to the evolving direction and stage of the ISU. During the initial stage of ISU, dominated by industrialization, the promotional effect is dominant in terms of CO₂ emissions, but with the development of tertiary and emerging industries, its inhibitory effect is continuously enhanced and, eventually, ISU can significantly suppress CO₂ emissions. Further, regional heterogeneity analysis shows that in the eastern and western regions of China, ISU has always inhibited CO₂ emissions, while in the central and northeastern regions, ISU first promotes and then inhibits CO₂ emissions, which is similar to the overall pattern in China. Based on these findings, relevant policy suggestions are provided to promote sustainable economic and environmental development.

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.

In this paper, we propose a smoothing estimation method for censored quantile regression models. The method associates the convolutional smoothing estimation with the loss function, which is quadratically derivable and globally convex by using a non-negative kernel function. Thus, the parameters of the regression model can be computed by using the gradient-based iterative algorithm. We demonstrate the convergence speed and asymptotic properties of the smoothing estimation for large samples in high dimensions. Numerical simulations show that the smoothing estimation method for censored quantile regression models improves the estimation accuracy, computational speed, and robustness over the classical parameter estimation method. The simulation results also show that the parametric methods perform better than the KM method in estimating the distribution function of the censored variables. Even if there is an error setting in the distribution estimation, the smoothing estimation does not fluctuate too much.