The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model

Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to show low accuracy and lack of robustness. However, recurrent neural network (RNN) will have a serious gradient disappearance problem under long sequence training. This paper proposes a compound neural network model based on the attention mechanism to meet the needs of enterprise credit evaluation. The convolutional neural network (CNN) and the long short-term memory (LSTM) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. In the multimodel comparison experiment and three different enterprise data experiments, the CNN-LSTM-ATT model proposed in this paper is superior to the traditional models LR, RF, CNN, LSTM, and CNN-LSTM in most cases. The experimental results under multimodel comparison reflect the higher accuracy of the model, and the group test reflects the higher robustness of the model.

Precise Supervision of Enterprise Environmental Protection Behavior Based on Boolean Matrix Factorization under Low Carbon Background

Supervising the environmental protection behavior of enterprises is a key strategy to achieve “carbon peaking and carbon neutrality”. This research innovatively proposes the concept of precise supervision, aiming to implement differentiated supervision measures for different types of enterprises, and realize the precise supervision method of enterprise environmental protection, which is different from the traditional supervision mode. Firstly, this paper proposes a novel MEBF+ method based on the benchmark algorithm MEBF, and obtains MEBF++ after incorporating the model bias. Secondly, based on the dataset of environmental supervision and certification of listed Chinese companies, the accuracy and robustness of the proposed method are verified by using multiple evaluation indicators. Finally, based on the analysis of the experimental results, two precise supervision concepts, narrow and broad, are proposed under the low-carbon background. The results show that compared with the benchmark method, the accuracy of the proposed method has been improved to a large extent. In addition, the precise supervision proposed in this paper can help reduce the consumption of manpower and resources as well as unite the public to monitor the environmental protection behavior of enterprises.

The Conditional Configuration Path for Private Enterprises Participating in PPP: A Fuzzy-Set Qualitative Comparative Analysis

Public–private partnership (PPP) projects have been widely applied in infrastructure construction. Leveraging private capital is the key to promoting the high-quality development of PPP projects. This study examines the combined effect of seven factors determining private enterprises that participate in PPP and collects materials from 102 PPP sewage treatment projects to examine the causal configuration path of private enterprises participating in PPP (PEP3P) from an overall perspective by using necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA). The findings support the fact that any single antecedent condition is not a necessary condition for PEP3P and is instead the combined effect of different factors that commonly form the diversified causal configuration paths of PEP3P. There is an obvious asymmetry between the configuration paths of the high participation and low participation of private enterprises. The enterprise technology level (ETL) and doing business (DB) are important internal driving forces and give external traction for PEP3P, while the enterprise credit level (ECL) and project investment scale (PIS) are important factors that restrict private enterprises from participating in PPP. This research fills a theoretical gap for PEP3P and can be applied to developing strategies for attracting private enterprises to participate in PPP.