Yijie Bian

The high carbon emissions and pollution of China’s thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a “U”-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs.

The development of new energy vehicles is an important path to promote the realization of the dual carbon target. However, the complex and changeable external macro environment, large capital investment required in the early stage of new energy vehicle projects, and unstable returns resulting in high financial risks for new energy vehicle enterprises. Furthermore, factors such as information asymmetry, moral hazard and the uncertainty of the external macro environment make the dynamic characteristics of financial risk transmission between enterprises in the new energy vehicle supply chain more prominent. Existing studies on risk transmission have mainly focuses on theoretical exploration and qualitative research, while the quantitative research is lacking. This study empirically analyzed the financial risk transmission effect between the new energy vehicles chain enterprises based on the GARCH–time-varying copula–CoVaR model. It selected 8 upstream enterprises, 2 midstream enterprises and 8 downstream enterprises; constructed 36 financial risk transmission chains, and selected the stock returns of sample companies from 2017 to 2021 as research data. The results indicated that: (1) the financial risk between upstream and downstream enterprises in the supply chain of new energy vehicles had a bidirectional conduction effect, and the conduction effect was heterogeneous; (2) compared with midstream and downstream enterprises, financial risk was more conductive between upstream and midstream enterprises; and (3) the financial risk transmission effect between enterprises had time-varying characteristics, which affected by the external macro environment and the degree of cooperation between enterprises.

The rapid development of artificial intelligence technology demands higher requirements for employment and talent training. The integration of industry and education is an important way to solve the mismatch between industrial demand and talent supply. Therefore, this study starts from the perspective of the integration of industry and education. We collect recruitment texts from the perspective of industry and mine the specific requirements of the artificial intelligence post system through the LDA topic model and the combination of Word2Vec and K-means. We then conduct expert consultations and adjust the selected indicators from the perspective of education. Finally, we construct a four-dimensional vocational ability grade evaluation index system, including basic vocational skills of artificial intelligence, database, network skills, algorithm and design skills, and research and practice skills. The intuitionistic fuzzy analytic hierarchy process, which can eliminate the subjective uncertainty of experts in the scoring process, is applied to calculate the index weights. We find that the weight of algorithm and design skill is the highest, which is an important criterion for artificial intelligence professional ability evaluation. Among the second-level indicators, practical indicators such as team spirit, innovation ability, and communication ability are the focus of investigation from the perspective of industry, while in education, the cultivation of knowledge and skills such as programming ability, applied mathematics ability, data structures, and algorithms are more important.

In this paper, we propose a novel neural network for named entity recognition, which is improved in two aspects. On the one hand, our model uses a parallel BiLSTM structure to generate character-level word representations. By inputting character sequences of words into several independent and parallel BiLSTMs, we can obtain word representations from different representation subspaces, because the parameters of these BiLSTMs are randomly initialized. This method can enhance the expression abilities of character-level word representations. On the other hand, we use a two-layer BiLSTM with gating mechanism to model sentences. Since the features extracted by each layer in a multi-layer LSTM from texts contain different types of information, we use the gating mechanism to assign appropriate weights to the outputs of each layer, and take the weighted sum of these outputs as the final output for named entity recognition. Our model only changes the structure, does not need any feature engineering or external knowledge source, which is a complete end-to-end NER model. We used the CoNLL-2003 English and German datasets to evaluate our model and got better results compared with baseline models.

The development of the new energy vehicle industry is necessary for its advantages of saving energy and reducing greenhouse-gas emissions. However, the industry is currently facing risks with regard to, for example, technology, market, and the policy. Most existing studies of industry risk focused on analyzing and evaluating risk factors and summarizing and interpreting risk phenomena. In this study, systematic classification and quantitative analysis for the risk of the new energy vehicle industry were investigated, in which the entropy weight method and cloud model were combined to evaluate and quantify the industry risk. The formation mechanism of the industry risk from endogenous and exogenous perspectives was analyzed to screen out risk-evaluation factors. Combining the expert-investigation and fuzzy-statistics methods, a risk-evaluation index system with six primary indicators and twenty-four secondary indicators was constructed. On the basis of the entropy weight-cloud model, the risk of the new energy automobile industry of Jiangsu province in July 2019 was evaluated. Results indicated that the impact of exogenous risk on the industry was greater than that of endogenous risk, and industry risk was higher than medium risk, which was close to a higher medium level. A series of suggestions are given for preventing industry risk, such as improving the industry’s own ability to resist risk and building the industry’s soft environment.

With the deepening of information technology, in an increasingly competitive market, more and more enterprises seek to import ERP system to achieve lower operating costs and improve efficiency and customer satisfaction. But in the ERP system, there is random and blindness on importing mode choosing. The three models are analyzed and "Principal agent" is established by using game theory. Joint importing is proved to be the best model for the ERP system importing. Scientific and theoretical guidance is given to the ERP systems importing decision-making.