Digital transformation solutions of entrepreneurial SMEs based on an information error-driven T-spherical fuzzy cloud algorithm

Evaluation mechanism of sustainable development level for the cities in the Yangtze River Economic Belt under the dual-carbon policy

Low carbon sustainable development (LCSD) has become an inevitable choice, for which China has put forward a "dual-carbon" policy. The purpose of this study is to capture the interaction between the environment and the economy in the context of this goal, thus evaluating LCSD level from a systematic perspective. This paper proposes a super slack based measurement (SBM) model with a non-equal weight structure to assess the LCSD level. Firstly, a maximum influence minimum redundancy (MIMR) index selection algorithm is designed to establish input and output index systems, which avoids redundancy indexes. Secondly, the objective function of the original super SBM employs an equal weight structure, which leads results inadequately reflect the research preferences. Therefore, the weights of indexes are introduced to form an improved super SBM. Finally, 40 cities along the Yangtze River Economic Belt (YREB) are selected for empirical analysis. Results show that (1) the LCSD level of YRBE decreases from downstream to upstream to midstream; (2) Jiangsu, Zhejiang, and Sichuan provinces have higher LCSD levels, while Hunan and Jiangxi provinces have lower levels; and (3) up to 2021, there are 32 effective cities and 8 ineffective cities. The research implies that balancing the economy-environment relationship is crucial for higher efficiency. The LCSD evaluation method not only reflects the coordination level between the economy and the environment, but also integrates the research preference into the results, providing decision support for the government to formulate carbon reduction policies and allocate resources.

An interaction and feedback mechanism-based group decision-making for emergency medical supplies supplier selection using T-spherical fuzzy information

Selecting a supplier for emergency medical supplies during disasters can be considered a typical multiple attribute group decision-making (MAGDM) problem. MAGDM is an intriguing common problem that is rife with ambiguity and uncertainty. It becomes much more challenging when governments and medical care enterprises adjust their priorities in response to the escalating problems and the effectiveness of the actions taken in different countries. As decision-making problems become increasingly complicated nowadays, a growing number of experts are likely to use T-spherical fuzzy sets (T-SFSs) rather than exact numbers. T-SFS is a novel extension of fuzzy sets that can fully convey ambiguous and complicated information in MAGDM. The objective of this paper is to propose a MAGDM methodology based on interaction and feedback mechanism (IFM) and T-SFS theory. In it, we first introduce T-SF partitioned Bonferroni mean (T-SFPBM) and T-SF weighted partitioned Bonferroni mean (T-SFWPBM) operators to fuse the evaluation information provided by experts. Then, an IFM is designed to achieve a consensus between multiple experts. In the meantime, we also find the weights of experts by using T-SF information. Furthermore, in light of the combination of IFM and T-SFWPBM operator, an MAGDM algorithm is designed. Finally, an example of supplier selection for emergency medical supplies is provided to demonstrate the viability of the suggested approach. The influence of parameters on decision results and comparative analysis with the existing methods confirmed the reliability and accuracy of the suggested approach.

Evaluation of the Social Effects of Enterprise Carbon Accounts Based on Variable Weight CFPR Fuzzy VIKOR

The carbon account is a digital path for an enterprise to achieve low-carbon transformation and high-quality sustainable development under the 'dual carbon' strategy. The carbon account has a good social effect while generating economic benefits. An evaluation index system of the social effects of enterprise carbon accounts has been established, including the concepts of energy conservation and carbon reduction, contributions, technological innovation, and customer trust. In view of the difficulty of quantifying the evaluation indicators of the social effects of enterprise carbon accounts and the requirement of effect equalization, a variable-weight CFPR fuzzy VIKOR evaluation model was constructed. Compared with the traditional fuzzy VIKOR model, the variable-weight CFPR fuzzy VIKOR model can solve the problem of quantifying indicators and realize the balance between indicators. This method can better compare and analyze the social effects of each enterprise's carbon accounts and provides a basis for overall carbon account construction and digging improvement space.

High-Discrimination Comparison Algorithm for the Comprehensive Evaluation of Innovation Ability in Colleges and Universities under Uncertain Information

The innovation ability of students is one of the most important objectives that need to be cultivated in colleges and universities. The comprehensive evaluation of innovation ability discussed in the study can be divided into two stages: the first stage can be called preliminary evaluation and its main target is to identify students with innovative potential; in the second stage, the target objects found in the previous stage will be evaluated quantitatively and ranked. However, it is always difficult to quantitatively evaluate the innovation ability by using traditional algorithms. Based on the above analysis, the study proposes an algorithm to quantitatively evaluate the innovation ability with the help of management thought and fuzzy mathematics. Data are the basis of evaluation, and the accuracy of the data directly determines the quality of the algorithm; the data structure of the incompletely probabilistic fuzzy set is proposed in the study; the data structure can fully consider the fuzziness of the problem and the hesitation in the decision-making process; it can save the original detailed data to the maximum extent. Certainly, certain information may be lost or only the value range can be determined; there are usually some unknowns in the evaluation data, and the consistency optimization model is proposed for solving the problem. Usually, there are certain contradictions among the evaluation data; the definition of the consistency degree is proposed in the study; the consistency can be verified in time after all the unknowns are obtained, and the automatic adjustment module will be activated immediately if the value of the consistency degree exceeds the warning threshold. Finally, after verifying the data consistency, the solution can be obtained by solving the optimization model. Several experiments have been carried out to verify the effectiveness and high-discrimination ability of the algorithm proposed in the study; meanwhile, the superiority of the algorithm is further verified through comparisons with other outstanding algorithms.

Digital Transformation and Financial Risk Prediction of Listed Companies

Digitalization is a revolution, a frontal battleground in the new global competitive landscape, and a long-distance race for which all employees must be prepared, and organizations must actively embrace the resulting changes. The article begins by analyzing three characteristics of digital transformation and enterprise growth: the heterogeneity of digital transformation's impact on enterprise growth and the process by which digital transformation influences enterprise growth. In addition, this article develops a convolutional neural network-based financial early warning model to aid businesses' digital transformation initiatives.