This paper studies the regional differences, dynamic evolution and influencing factors of regional carbon emission intensity (CEI) in 262 cities and 5 regional urban agglomerations (UAs) in China. The Dagum Gini coefficient is used to analyze the intra-regional and inter-regional differences in carbon emissions, and the temporal evolution of the absolute differences of CEI among regions is analyzed by means of kernel density estimation (KDE). The paper provides an in-depth study on the spatial difference and temporal evolution of CEI in Chinese cities and major strategic regions. Through Moran index and LISA's test, the spatial correlation of carbon emission in prefecture-level cities is tested, and its spatial agglomeration characteristics are described. It is found that China's CEI is decreasing year by year, presenting a spatial pattern of plow in the south but high in the northq. Based on the calculation of carbon emission intensity at the urban level, this paper conducts LDMI factor decomposition research on carbon emission intensity at the national and key regions, and analyzes the impact of the impact factors on carbon emission intensity. The research results provide a path for China's green development at the city level and urban agglomeration level, and a theoretical support for different regions and cities to introduce emission and carbon reduction policies.

Motivated by the Bagging Partial Least Squares (Bagging PLS) and Principal Component Analysis (PCA) algorithms, a novel approach known as Principal Model Analysis (PMA) method is introduced in this paper. In the proposed PMA algorithm, the PCA and the Bagging PLS are combined. In this method, multiple PLS models are trained on sub-training sets, derived from the training set using the random sampling with replacement approach. The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix. The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix. Subsequently, the proposed PMA method is compared with other traditional dimension reduction methods, such as PLS, Bagging PLS, Linear discriminant analysis (LDA) and PLS-LDA. Experimental results on six public datasets demonstrate that our proposed method consistently outperforms other approaches in terms of classification performance and exhibits greater stability. Additionally, it is employed in the application of financial statement fraud identification. PMA and other five algorithms are utilized to financial statement fraud which concerned by the academic community, and the results indicate that the classification of PMA surpassed that of the other methods.

In the background of the green transformation of the economy and society, the ESG performance of enterprises has been paid more and more attention in the investment decision-making. However, previous studies have inadequately explored how the ESG performance affects corporate financing costs. Based on the information asymmetry theory, this paper analyzes the impact mechanism of ESG performance on corporate financing costs. Then, taking 1044 A-share listed companies in 2016–2020 as a sample, through the sorting and analysis of ESG report disclosure and rating data, the company's ESG performance indicators are obtained, and an empirical model is built to test the relationship between ESG performance and corporate financing costs. This paper constructs a panel regression model using ESG rating data and corporate financial data and finds that in the overall sample, the higher the ESG performance, the lower the equity financing cost; The higher the ESG performance, the lower the debt financing cost. In addition, it also discussed the moderating effect of enterprise scale and media attention on the impact of ESG performance on enterprise financing costs. The empirical results show that the influence of company size on ESG performance on financing costs has a moderating effect and a positive moderating effect.

This paper examines the data of A-share listed companies in China from 2002 to 2017, drawing on the theory of equal opportunity and market rules in M&A transactions. This paper investigates the correlation between changes in tender offer policy and M&A tendencies and performance. The findings suggest that following the policy shift and the adoption of market rules, companies that secure an exemption from the mandatory tender offer obligation not only exhibit stronger M&A tendencies but also improved long-term M&A performance. This indicates that market rules are more suitable for China and contribute to enhancing the efficiency of the M&A market. The paper also presents evidence of a moderating effect, demonstrating that exemptions from the mandatory tender offer obligation positively influence the relationship between policy change and M&A performance. Lastly, this paper finds that state-owned and large-scale firms tend to exhibit a higher degree of M&A tendencies.

This study considers a nonlinear grey Bernoulli forecasting model with conformable fractional-order accumulation, abbreviated as CFNGBM$(1, 1, \lambda)$, to study the gross regional product in the Cheng-Yu area. The new model contains three nonlinear parameters, the power exponent $\gamma$, the conformable fractional-order $\alpha$ and the background value $\lambda$, which increase the adjustability and flexibility of the CFNGBM$(1, 1, \lambda)$ model. Nonlinear parameters are determined by the moth flame optimization algorithm, which minimizes the mean absolute prediction percentage error. The CFNGBM$(1, 1, \lambda)$ model is applied to the gross regional product of 16 cities in the Cheng-Yu area, which are Chongqing, Chengdu, Mianyang, Leshan, Zigong, Deyang, Meishan, Luzhou, Suining, Neijiang, Nanchong, Guang'an, Yibin, Ya'an, Dazhou and Ziyang. With data from 2013 to 2021, several grey models are established and results show that the new model has higher accuracy in most cases.

This paper considers sales mode selection issues for a two-echelon supply chain of perishable products involving a supplier and a retailer. Consumers' purchase desire is assumed to be positive correlation with the freshness of the product. First, the traditional sales mode is examined, in which the supplier and the retailer play a Stackelberg game. We propose a three-layer decision model for this situation, and obtain dynamic pricing strategies and selling cycle length. It is shown that the retailer has little motivation to order many perishable products so as to avoid a long selling cycle length. Second, the commission-charge mode is analyzed, in which the retailer declares its decision first. In this mode, we demonstrate that the perishable product will be on sale during the whole shelf life under a certain condition. The correlation between the sales price of each stage and the remaining shelf-life length is analyzed. Third, the superiority analysis for the two sales modes is conducted. We show the relation between the selling cycle lengths of the two modes. By our analysis, it is shown that both the supplier and the retailer gain more profits when the commission-charge mode is adopted and the commission rate locates in a certain open interval. Finally, a numerical illustration is presented to visualize the discussed models, and some supplements are made for the acquired conclusions by the illustration.

The probabilistic hesitant fuzzy multi-attribute group decision-making method introduces probability and hesitation into decision-making problems at the same time, which can improve the reliability and accuracy of decision-making results, and has become a research hotspots in recent years. However, there are still many problems, such as overly complex calculations and difficulty in obtaining probability data. Based on these, the paper proposes a multi-attribute group decision-making model based on probability hesitant fuzzy soft sets. Firstly, the definition of probabilistic hesitant fuzzy soft set is given. Then, based on soft set theory and probabilistic hesitant fuzzy set, the similarity measure of probabilistic hesitant fuzzy soft set is proposed, and the two measures are further combined. Finally, it is applied to the construction of multi-attribute group decision-making model, and the effectiveness and rationality of the model are verified by an example. The example shows that the new similarity calculation formula and algorithm model in this paper have higher accuracy, and the calculation process is more simple, it provides a feasible method for multi-attribute group decision making problems.