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 aim of this article is to develop Cook’s distance measures for assessing the influence of both atypical curves and observations under varying coefficient model with functional responses. Our Cook’s distance measures include Cook’s distances for deleting multiple curves and for deleting multiple grid points, and their scaled Cook’s distances. We systematically investigate some theoretical properties of these diagnostic measures. Simulation studies are conducted to evaluate the finite sample properties of these Cook’s distances under different scenarios. A real diffusion tensor tract dataset is analyzed to illustrate the use of our diagnostic measures.

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.

In this article, for the generalized linear models (GLMs) with “working” covariance matrix and adaptive designs, we develop the asymptotic properties of maximum quasi-likelihood estimators (MQLEs) under some mild regular conditions. The existence of MQLEs in quasi-likelihood equation, the rate of convergence and asymptotic normality of MQLEs are presented. The results are illustrated by Monte-Carlo simulations.