The bond market is an important part of China’s capital market. However, defaults have become frequent in the bond market in recent years, and consequently, the default risk of Chinese credit bonds has become increasingly prominent. Therefore, the assessment of default risk is particularly important. In this paper, we utilize 31 indicators at the macroeconomic level and the corporate microlevel for the prediction of bond defaults, and we conduct principal component analysis to extract 10 principal components from them. We use the XGBoost algorithm to analyze the importance of variables and assess the credit debt default risk based on the XGBoost prediction model through the calculation of evaluation indicators such as the area under the ROC curve (AUC), accuracy, precision, recall, and F1‐score, in order to evaluate the classification prediction effect of the model. Finally, the grid search algorithm and k‐fold cross‐validation are used to optimize the parameters of the XGBoost model and determine the final classification prediction model. Existing research has focused on the selection of bond default risk prediction indicators and the application of XGBoost algorithm in default risk prediction. After optimization of the parameters, the optimized XGBoost algorithm is found to be more accurate than the original algorithm. The grid search and k‐fold cross‐validation algorithms are used to optimize the XGBoost model for predicting the default risk of credit bonds, resulting in higher accuracy of the proposed model. Our research results demonstrate that the optimized XGBoost model has a significantly improved prediction accuracy, compared to the original model, which is beneficial to improving the prediction effect for practical applications.

As the global economic development intensifies the plunder of resources and the environment, the constraints are becoming more and more obvious. Based on the background of the strategy for ecological conservation and high-quality development of the Yellow River Basin, this paper intends to construct a resource-environment-constrained economic growth drag effect model and a spatial Dubin model, and explore the economic growth drag effect and its spatial differences in the Yellow River Basin under the constraints of resources and environment. The study found that the total drag effects of the overall economic growth of the Yellow River Basin that were obtained by the classic panel model without spatial effects is significantly negative. This is consistent with the conclusion that the average total drag effects of 80 prefecture-level cities is negative. The total drag effects of the overall economic growth of the Yellow River Basin changes from unconstrained to medium-constrained after adding spatial constraints, indicating that the spatial correlation of factors will restrict economic growth. From the level of the Yellow River sub-catchment, the total drag effect of the direct effects of the upper, middle, and lower reaches of the Yellow River is consistent with the total drag effect of the total effect. It shows that the upper economic growth is strongly constrained by the local resources and environment, while the downstream is strongly constrained by the adjacent resources and the environment. The research results provide references for resolving the resources and environment constraints in the Yellow River Basin. It provides useful inspiration for promoting ecological protection and high-quality development strategies in the Yellow River Basin.

Purpose: Drawing on the notion of feature centrality and cue utilization theory, this study investigates when and how different green food attributes (core vs peripheral) affect consumers' purchase intentions for pro-environmental food. Design/methodology/approach: Two experiments were conducted to examine the influence of green core and peripheral attributes, as well as to explain the psychological mechanism by which green attributes affect consumers' purchase intentions. Additionally, the moderating effect of the depth of price discounts was explored in this context. Findings: The results indicate that consumers' purchase intentions are higher for food with green core attributes than for those with green peripheral attributes. Furthermore, green food attributes affect consumers' purchase intentions through perceived naturalness and perceived environmental friendliness. Finally, the results show that green food core attributes are more likely to stimulate consumers' purchase intentions at low and moderate price discounts, while green food peripheral attributes are more effective in increasing purchase intentions at deep price discounts. Originality/value: This study contributes to the literature on green consumption by examining when and how green core and peripheral attributes influence consumers' purchase intentions for pro-environmental food. This study also advances this body of knowledge by highlighting the moderating effect of the depth of price discounts.