The carbon-labeling system is able to quantify the level of greenhouse gas emissions of goods throughout their life cycle, including production, delivery, and consumption. With the proposal of carbon peak and carbon neutrality goals, the carbon-labeling system has an inevitable impact on production by companies and the purchase behavior of consumers. This paper constructs a theoretical model of the influencing mechanism on consumer willingness to purchase carbon-labeled products by utilizing the theory of consumption values. Through a survey and analysis of a sample of 347 Chinese university students, a regression analysis is applied to explore their willingness to consume carbon-labeled products and the corresponding influencing factors. The results show that (1) despite relatively low public awareness of the carbon-labeling system, the willingness to purchase carbon-labeled products is strong; (2) functional value, emotional value, and epistemic value can positively influence customer willingness to purchase carbon-labelled products; and (3) there is a significant difference in the willingness to purchase carbon-labelled products in terms of age and no significant difference in terms of gender, income, occupation, and education level. Based on the findings, some recommendations are made to help companies adopt appropriate strategies to trigger consumers’ purchase intentions and gain a market advantage in carbon-labeling scenarios.

In recent years, many retailers sell their products through not only offline but also online platforms. The sales of perishable goods on e-commerce platforms recorded phenomenal growth in 2020. However, some retailers are overconfident and order more products than the optimal ordering quantity, resulting in great losses due to product decay. In this paper, we apply the newsvendor model to analyze the impacts of overconfident behavior on the retailer’s optimal pricing and order quantity decisions and profit. Our model provides the overconfident retailer with a feasible and effective method to adjust optimal ordering and pricing decisions. Through numerical studies, we examine the retailer’s optimal decisions under the scenarios of complete rationality, over-estimation, and over-precision. We find that the over-estimation retailer always orders more products than the optimal order quantity, and the over-precision retailer always orders fewer products than the optimal order quantity. Under some conditions, overconfidence hurts the retailer’s revenue to a large extent. Therefore, it is beneficial for the overconfident retailer to adjust its order quantity according to our research findings.

Intelligent vehicles refer to a new generation of vehicles with automatic driving functions that is gradually becoming an intelligent mobile space and application terminal by carrying advanced sensors and other devices and using new technologies, such as artificial intelligence. Firstly, the traditional autoregressive intelligent vehicle sales prediction model based on historical sales is established. Secondly, the public opinion data and online search index data are selected to establish a sales prediction model based on online public opinion and online search index. Then, we consider the influence of KOL (Key Opinion Leader), a sales prediction model based on KOL online public opinion andonline search index is established. Finally, the model is further optimized by using the deep learning algorithm LSTM (Long Short-Term Memory network), and the LSTM sales prediction model based on KOL online public opinion and online search index is established. The results show that the consideration of the online public opinion and search index can improve the prediction accuracy of intelligent vehicle sales, and the public opinion of KOL plays a greater role in improving the prediction accuracy of sales than that of the general public. Deep learning algorithms can further improve the prediction accuracy of intelligent vehicle sales.

Selecting the right partner is a key factor for the successful construction of the strategic alliance of prefabricated construction enterprises in China. Based on the summarization of domestic and foreign studies, combined with the characteristics of the strategic alliance of prefabricated construction enterprises, the paper has constructed an evaluation indicator system of the partner selection of the strategic alliance of prefabricated construction enterprises in China. The paper also has conducted an empirical study on the evaluation model of partner selection of the strategic alliance of prefabricated construction enterprises in China by using the method Entropy Weight-TOPSIS. The research results show that the five most influential second-level indicators are commercial housing sales capability, property management capability, commitment of capital, commitment of talents, and product innovation capability. The model constructed in the paper can comprehensively evaluate and select the strategic alliance partners of prefabricated construction enterprises in China.