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.

Compared with the traditional agri-food supply chain (AFSC) whose only goal is to maximize economic benefits, the sustainable agri-food supply chain (SAFSC) starts to attract more attention. Typical challenges faced by SAFSC development are unfair pricing of produce, yield uncertainty caused by adverse weather, as well as conflict and cooperation between stakeholders and sustainable activities of SAFSC. In this paper, we establish a two-echelon decision-making model consisting of a loss-averse farmer and a loss-neutral company. A guaranteed price mechanism is contrived to mitigate the effects of uncertain procurement price on the farmers’ profit. It is found that this mechanism can improve the sustainable investment level but fails to reach the optimal level of the SAFSC system. Thus, a risk–reward contract taking into account the weather index (temperature) and the degree of loss aversion is designed. Results show that this contract can settle the distortion of the sustainable investment level and effectively motivate farmers to participate in the sustainable agricultural practice. Furthermore, we derive the conditions on the contract parameters under which both the company and the farmer are motivated to exert efforts to stand by sustainable agricultural practice.

This paper deals with robust quasi approximate optimal solutions for a nonsmooth semi-infinite optimization problems with uncertainty data. By virtue of the epigraphs of the conjugates of the constraint functions, we first introduce a robust type closed convex constraint qualification. Then, by using the robust type closed convex constraint qualification and robust optimization technique, we obtain some necessary and sufficient optimality conditions for robust quasi approximate optimal solution and exact optimal solution of this nonsmooth uncertain semi-infinite optimization problem. Moreover, the obtained results in this paper are applied to a nonsmooth uncertain optimization problem with cone constraints.

This paper provides some new results on robust approximate optimal solutions of a fractional semi-infinite optimization problem under uncertainty data in the constraint functions. By employing conjugate analysis and robust optimization approach (worst-case approach), we obtain some necessary and sufficient optimality conditions for robust approximate optimal solutions of such a fractional semi-infinite optimization problem. In addition, we state a mixed type approximate dual problem to the reference problem and obtain some robust duality properties between them. The results obtained in this paper improve the corresponding results in the literature.

With consumers increasing in environmental awareness, manufacturers have integrated green R&D into their strategies, aiming to grasp the green market. However, manufacturers may be too bullish on the market potential of green products and maintain an optimistic attitude toward green R&D. Despite having an optimistic attitude, manufacturers often have no demand information advantage over downstream retailers due to their position in the supply chain, away from the market. It is worth exploring what impact optimistic green R&D in a sustainable supply chain with demand information asymmetry will have. Previous studies have not managed to reveal this. In this study, a stylized model is introduced to explore this question. The main findings are as follows: (1) optimistic green R&D increases the feasibility of the retailer sharing demand information, which facilitates information communication in the sustainable supply chain; (2) in most cases, optimistic green R&D does not bring higher profits for the manufacturer, yet is likely to allow the retailer to earn more, thereby resulting in a loss–win outcome; and (3) depending on the green R&D efficiency of the manufacturer and the consumer’s environmental awareness, optimistic green R&D may not generate higher environmental benefits.