To comprehensively assess global sustainable energy consumption from the perspectives of economic development and environmental conservation, we propose a multidimensional structure that includes two-dimensional and three-dimensional networks. Based on the proposed methods, we empirically investigate 35 countries of the International Energy Agency. It is found that: 1) Regarding economic income, Asia ranks first, followed by America, Oceania, Europe and Africa. 2) As for sustainable development, the best-performing region is Asia, followed by Africa and Europe, Oceania and America. 3) The regions with greater development potential in energy consumption are America, Oceania, Africa and Europe, while Asia is less efficient. In consideration of the above findings, we give some recommendations for different regions to improve their energy consumption efficiency, such as countries in Asia can focus on innovative high-tech industries while countries in Europe can enhance economic vitality with market integration.

Power and energy balancing technology is at the core of smart grids. Expert subjective judgments play a critical role in the balancing control process. However, due to the subjectivity, ambiguity, and large-scale nature of the assessment data, traditional models struggle to effectively model and analyze such information. To address the large-scale nature of the data, we introduced long short-term memory (LSTM) to develop the LSTM-Transformer model. Considering the uncertainty and ambiguity of the data, we further constructed the hesitation-fuzzy-based LSTM-Transformer (HF-LSTM-Transformer) model. This is a novel deep learning method capable of handling complex subjective and fuzzy information in power balance control decision making. By incorporating the hesitant fuzzy set, the HF-LSTM-Transformer enhances the LSTM-Transformer’s ability to process uncertainty. In addition, we designed the forward and backward propagation processes, parameter update procedures, and an optimization algorithm to enhance the classification prediction accuracy of the new model. Finally, we provided two smart grid examples from both comparative and application perspectives to fully demonstrate the robustness and accuracy of the classification results obtained by the proposed method.

With the respect to the key factors, namely the psychological state of individuals and organizations, psychological capital (PsyCap) is widely used in various fields, such as management decisions and organizational behavior. To fully show the related studies and their knowledge development and mapping path, in this paper, we examine 2,786 papers about the PsyCap related research from 1970 to 2021. Based on the bibliometric analysis and main path demonstration (the tools are Cite-Space and Pajke, respectively), we derive some conclusions as follows: (1) the publication number about the PsyCap study is growing rapidly and it is a highly cross-cutting research topic. (2) The main authors come from Australia, the United States, and China, and also are the core researchers. (3) Refinement and measurement in the PsyCap study are constant and hot topics. (4) Stress, performance and well-being issues among students, health care workers and corporate employees are core research themes, and team organization, creativity, innovation, and COVID-19 are hot topics in this field. The bibliometric analysis are quantitatively analyzed to provide scholars with a more comprehensive insight into PsyCap research. The main path demonstration helps scholars to understand the main lines and key nodes of development in the field of psychological capital.

In the case of insufficient quantitative data, qualitative evaluation information is very important for investment decision-making. However, if it is completely based on qualitative evaluation information, the results may be subjective. In response to this problem, this paper proposes a method, namely, probabilistic hesitant fuzzy cross-efficiency evaluation (PHFCEE) method, based on probabilistic hesitant fuzzy theory, cross-efficiency data envelopment analysis (CEDEA). This method uses probabilistic hesitant fuzzy sets to collect qualitative evaluation information and then uses the cross-efficiency DEA model to fuse quantitative data and qualitative information. And finally, an investment portfolio is built based on the cross-efficiency value and its variance. In addition, this article gives the specific operating steps of the PHFCEE method and uses the construction of a portfolio of 10 stocks in the China CSI 300 Index as an example to illustrate the effectiveness of this method.