This study uses a Time-Varying Parameter Stochastic Volatility Vector Autoregression (TVP-SV-VAR) model to conduct an empirical analysis of the dynamic effects of China’s stock market volatility on the agricultural loan market and its channels. The results show that the relationship between stock market and agricultural loan market volatility is time varying and is always positive. The investor sentiment is a major conduit through which the effect takes place. This time-varying effect and transmission mechanism are most apparent between 2011 and 2017 and have since waned and stabilized. These have significant implications for the stable and orderly development of the agricultural loan market, highlighting the importance of the sound financial market system and timely policy, better market monitoring and early warning system and the formation of a mature and sound agricultural credit mechanism.

In the fast-paced modern society, enhancing employees’ professional qualities through training has become crucial for enterprise development. However, training satisfaction remains under-studied, particularly in specialized sectors such as the coal industry. Purpose: This study aims to investigate the impact of personal characteristics, organizational characteristics, and training design on training satisfaction, utilizing Baldwin and Ford’s transfer of training model as the theoretical framework. The study identifies how these factors influence training satisfaction and provides actionable insights for improving training effectiveness in China’s coal industry. Design/Methodology/Approach: A cross-sectional design that allowed the study to capture data at one point in time from a large sample of employees was employed to conduct an online survey involving 251 employees from the Huaibei Mining Group in Anhui Province, China. The survey was administered over three months, capturing a diverse sample with nearly equal gender distribution (51% male, 49% female) and a majority aged between 21 and 40. The participants represented various educational backgrounds, with 52.19% holding an undergraduate degree and most occupying entry-level positions (74.9%), providing a broad workforce representation. Findings: The research indicated that personal traits were the chief predictor of training satisfaction, showing a beta coefficient of 0.585 (95% CI: [0.423, 0.747]). Linear regression modeling indicates that training satisfaction is strongly related to organizational attributes (β = 0.276 with a confidence interval of 95% [0.109, 0.443]). In contrast, training design did not appear to be a strong predictor (β = 0.094, 95% CI: [−0.012, 0.200]). Employee training satisfaction was the principal outcome measure, measured with a 5-point Likert scale. The independent variables covered personal characteristics, organizational characteristics, and training design, all measured through validated items taken from former research. The consistency of the questionnaire from the inside was strong, as Cronbach’s alpha values stood between 0.891 and 0.936. We completed statistical testing using SPSS 27.0, complemented by multiple linear regression, to study the interactions between the variables. Practical implications: This research contributes to the literature by emphasizing the necessity for context-specific training approaches within the coal industry. It highlights the importance of considering personal and organizational characteristics when designing training programs to enhance employee satisfaction. The study suggests further exploration of the multifaceted factors influencing training satisfaction, reinforcing the relevance of Baldwin and Ford’s theoretical model in understanding training effectiveness. Ultimately, the findings provide valuable insights for organizations seeking to improve training outcomes and foster a more engaged workforce. Conclusion: The study concluded that personal and organizational characteristics significantly impact employee training satisfaction in the coal industry, with personal characteristics being the strongest predictor. The beta coefficient for personal characteristics was 0.585, indicating a strong positive relationship. Organizational characteristics also had a positive effect, with a beta coefficient of 0.276. However, training design did not show a significant impact on training satisfaction. These findings highlight the need for coal companies to focus on personal and organizational factors when designing training programs to enhance satisfaction and improve training outcomes.

In the current context of multicultural collision, online information is impacting traditional gender values. To analyze the changes in gender role attitudes and gender awareness among Chinese Generation Z college students under the influence of various social factors, the study focuses on Generation Z college students and explores the impact of cultural, media, educational, and family factors on gender role attitudes and gender awareness among Chinese Generation Z college students through questionnaire surveys and quantitative analysis methods. The research results show that Generation Z college students exhibit extremely favorable gender perspectives, with the proportion of bisexual gender roles approaching 38%, surpassing the number of students with traditional understanding of single sex gender roles. At the same time, in school gender awareness education, research has found that the proportion of bisexual gender roles is the highest among students who accept open mindedness, at 46.6%. In family gender awareness education, students who receive parental gender awareness sharing education have the highest proportion of bisexual gender roles, accounting for 48.5%. Therefore, the current gender education for the new Generation of students in China needs to abandon traditional avoidance-based teaching methods and adopt an open and supportive attitude to guide students’ gender values.

As China’s urbanisation continues, the building area is expanding, of which the occupancy of rural residential buildings is also very large. However, most rural buildings have poor thermal performance. This paper analyses the energy-saving potential of green facades for rural buildings in China by simulating typical buildings with different types of facades in rural China. The simulation results show that indirect green façades can achieve good energy savings. Buildings with four types of facades: red brick, rubble, hollow brick, and concrete achieve energy savings of 18.39%, 17.85%, 14.47%, and 11.52%, respectively, after retrofitting with green facades.

With the vigorous development of international trade and the in-depth advancement of economic globalization, China is facing the increasingly serious problem of invasive alien species, which poses a major threat to China’s ecological environment, economic development and human health. At present, although China has a comprehensive institutional norms in the prevention and control of invasion of alien species, but in the face of the challenge of invasion of alien species, China is still facing problems such as insufficient legal basis and imperfect specific legal system. Based on this understanding, this paper discusses the prevention and control of invasive alien species legal regulation, that although in recent years China has made certain achievements in the field of prevention and control of invasive alien species, but still faces a number of problems to be solved, should promote the relevant legislative amendments, and combined with the experience of developed countries to summarize the perfect.

With the advent of the big data era, the amount of various types of data is growing exponentially. Technologies such as big data, cloud computing, and artificial intelligence have achieved unprecedented development speed, and countries, regions, and multiple fields have included big data technology in their key development strategies. Big data technology has been widely applied in various aspects of society and has achieved significant results. Using data to speak, analyze, manage, make decisions, and innovate has become the development direction of various fields in society. Taxation is the main form of China’s fiscal revenue, playing an important role in improving the national economic structure and regulating income distribution, and is the fundamental guarantee for promoting social development. Re examining the tax administration of tax authorities in the context of big data can achieve efficient and reasonable application of big data technology in tax administration, and better serve tax administration. Big data technology has the characteristics of scale, diversity, and speed. The effect of tax big data on tax collection and management is becoming increasingly prominent, gradually forming a new tax collection and management system driven by tax big data. The key research content of this article is how to organically combine big data technology with tax management, how to fully leverage the advantages of big data, and how to solve the problems of insufficient application of big data technology, lack of data security guarantee, and shortage of big data application talents in tax authorities when applying big data to tax management.