Accelerating the development of the digital economy is the way to build a modern industrial system and promote sustainable development. In order to accurately analyze the development status of China’s digital economy, this study introduced a text analysis method to construct an index of the digital economy and surveyed the digital economy based on the panel data of 278 Chinese cities from 2011 to 2019. Moran’s I index, the Dagum Gini coefficient, the kernel density and a Markov chain were used to reveal the space-time difference and dynamic change characteristics. Considering the impact of the spatial correlation and regional division on convergence, we compared the σ values and spatial σ values to study the convergence characteristics after grouping with the decision tree method. The research showed that the digital economy had greatly improved, but it showed a significant imbalance. The research on the regional division of cities according to their geographical distribution and grade showed that the development status of the digital economy was increasingly different, and there was no convergence feature. We chose continuous classification variables and used the decision tree method to divide cities into 10 groups to investigate the convergence. The results showed that the σ values and spatial σ values decreased significantly and showed convergence characteristics. The development of the digital economy showed convergence, indicating that the convergence was greatly affected by the geographical location and grouping basis. Overall, this study contributes to our understanding of the development status of the digital economy, and targeted policy recommendations were proposed to improve the level of digital economy development.

As the economy continues to grow, greenhouse gas emissions are increasing, and people are paying more attention to issues related to carbon emissions. The construction of information infrastructure has also become an important support for development in the new era. Therefore, to explore whether information infrastructure construction can reduce carbon emissions, this paper conducts a series of tests based on panel data from 30 provinces in China from 2010 to 2019. The empirical results show that, in addition to directly reducing carbon emissions, the construction of information infrastructure can also affect carbon emissions through technological innovation. According to the heterogeneity test, we find that the construction of information infrastructure has better emission reduction effects for the eastern region, provinces with a higher technological innovation level, and provinces with a higher carbon emission intensity. The results of this paper provide strong support for the mitigation of global warming.

Based on panel data from 30 provinces, cities, and autonomous regions from 2001 to 2019, this paper uses the nonlinear difference-in-difference ($DID$) method to estimate the distribution of causal effects of emissions trading policy on emission reduction in Chinese industrial enterprises, and examines the heterogeneity of the effects. The empirical results show that (1) the emissions trading policy has a significant effect on industrial $S{O}_2$ emissions reduction in China, where the reduction effect is larger in non-pilot areas than in pilot areas; (2) the policy effects are not proportional to the regional $S{O}_2$ emissions intensity, and the emissions trading policy is not more effective in regions with higher industrial $S{O}_2$ emissions intensities. One advantage of this paper is the use of nonlinear $DID$ to estimate the emissions reduction effect, which eliminates the bias problem caused by the strict linearity assumption of the classical $DID$ method. Another advantage is that the combination of the random forest method avoids the subjectivity in the selection of control variables and uses distribution effects for multilevel comparisons. This method improves the validity of estimating the effect of emissions trading policy and provides targeted policy suggestions for the effective promotion of system implementation, all of which have academic and application value.

In this paper, we study the D- and A-optimal assignment problems for regression models with experimental cost constraints. To solve these two problems, we propose two multiplicative algorithms for obtaining optimal designs and establishing extended D-optimal ($ED$-optimal) and A-optimal ($EA$-optimal) criteria. In addition, we give proof of the convergence of the $ED$-optimal algorithm and draw conjectures about some properties of the $EA$-optimal algorithm. Compared with the classical D- and A-optimal algorithms, the $ED$- and $EA$-optimal algorithms consider not only the accuracy of parameter estimation, but also the experimental cost constraint. The proposed methods work well in the digital example.