Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

Microfinance institutions (MFIs) have attracted great attention, due to their significant role in poverty reduction. Given the features of MFIs, this paper proposes a novel hybrid model of soft set theory, and an improved order preference by similarity to ideal solution (HMSIT) to evaluate the sustainability of MFIs, considering accounting ratios, corporate governance factors, and macro-environmental factors, from a cross-country perspective. This setting enables the examination of the role of macro-environmental factors in the sustainability of MFIs. For this purpose, soft set theory is adopted to select optimal criteria. An improved order preference by similarity to ideal solution method, in which the weight of each criterion is determined by soft set theory, is proposed to rank the sustainability of MFIs. This algorithm enables HMSIT to make full use of various types of information. The case study uses cross-country samples. Results indicate that macro-environmental factors are significant in evaluating the sustainability of MFIs from a cross-country perspective. Particularly, they can play a key role in distinguishing MFIs with low sustainability. The results also indicate that HMSIT has strong robustness. Ranked results, produced from the proposed HMSIT are reliable enough to provide some managerial suggestions for MFIs and help stakeholders make decisions.

This work presents a novel soft ensemble model (ANSEM) for financial distress prediction with different sample sizes. It integrates qualitative classifiers (expert system method, ES) and quantitative classifiers (convolutional neural network, CNN) based on the uni‐int decision making method of soft set theory (UI). We introduce internet searches indices as new variables for financial distress prediction. By constructing a soft set representation of each classifier and then using the optimal decision on soft sets to identify the financial status of firms, ANSEM inherits advantages of ES, CNN, and UI. Empirical experiments with the real data set of Chinese listed firms demonstrate that the proposed ANSEM has superior predicting performance for financial distress on accuracy and stability with different sample sizes. Further discussions also show that internet searches indices can offer additional information to improve predicting performance.

Respiratory infectious diseases (RID) are the major form of infectious diseases in China, and are highly susceptible to climatic conditions. Current research mainly focuses on the impact of weather on RID, but there is a lack of research on the effect of El Niño–Southern Oscillation (ENSO) on RID. Therefore, this paper uses the system generalized method of moments (SYS-GMM) and the data of 31 provinces in China from 2007 to 2018 to construct a dynamic panel model to empirically test the causality between ENSO and RID morbidity. Moreover, this paper considers the moderating effects of per capita disposable income and average years of education on this causality. The results show that ENSO can positively and significantly impact RID morbidity, which is 5.842% higher during El Niño years than normal years. In addition, per capita disposable income and average years of education can effectively weaken the relationship between ENSO and RID morbidity. Thus, this paper is of great significance for improving the RID early climate warning system in China and effectively controlling the spread of RID.

Analyzing the impact of El Nilo Southern Oscillation (ENSO) on the number of tourists is essential in realizing the sustainable development of natural scenic spots. From the current research results, research on the effects of ENSO on tourism focuses on the impact of the formation of the natural environment. However, there is a lack of ENSO-related research on the number of people arriving at natural attractions. This paper considers the adjustment effects of personal disposable income, per capita GDP, and population size and constructs a new framework of ENSO’s influence on tourism. This paper builds a system GMM (Gaussian Mixture Model) and analyzes the impact of ENSO on tourist flow by using Google Trend data (big data technology) to obtain annual passenger flow data of 48 natural scenic spots in the United States (mainly national parks and national forests). The empirical results show that the increase in ENSO has led to a significant decrease in visitors to natural attractions in the United States. Moreover, the increase in personal disposable income, per capita GDP, and population size can weaken the relationship between ENSO and the number of tourists. This research expands and enriches the theoretical perspective of ENSO and outdoor tourism.