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.

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.

Lately, there is a progressive assimilation of sustainable and green development principles into the collective conscience of individuals. Companies have received considerable attention from all sectors of life when it comes to the environment, society and governance (ESG). This study uses a bidirectional fixed effects model to investigate the influence and the mechanism of green innovation on company ESG information, using a research sample composed of data from the A-share listed companies in China spanning the period from 2011 to 2021. The findings indicated that green innovation exerted a substantial positive influence on ESG information disclosure, and the effect was more substantial, especially in mature and declining companies. Financing constraints and analysts’ attention played a mediating role between green innovation and ESG information disclosure. The results of heterogeneity analysis showed that green innovation played a more significant role in promoting ESG information disclosure among state-owned companies, large-scale companies, manufacturing companies and heavy pollution companies. Furthermore, implementing green development policies had facilitated the reinforcement of the promotion impact of ESG information disclosure through green innovation. Additionally, the instrumental variable method was employed to conduct a robustness test. This study enhances the understanding of the theoretical framework about green innovation and the disclosure of ESG information, and offers valuable insights for advancing the sustainable development of companies.

This study aims to quantitatively analyze the equity of social service space in urban parks in China, in order to explore the equity issues faced by different social groups in accessing urban park services. The research background focuses on the importance of urban parks as social service spaces, particularly in improving residents’ quality of life and well-being. Through a comprehensive literature review, the study examines the social service functions of urban parks, the relationship between parks and social psychology, and the theoretical framework of equity. The study employs quantitative research methods, collects data on urban park usage and resident satisfaction, and defines relevant analysis variables. The data analysis section reveals the basic characteristics of park service space usage and resident well-being index through descriptive statistical methods. Subsequently, quantitative analysis is conducted to evaluate the current status of equity in urban park service space and explore the key factors influencing equity. The study reveals a significant correlation between social psychological factors, resident well-being index, and equity in park service space. Finally, the research conclusion emphasizes the importance of improving equity in social service space in urban parks and provides specific policy recommendations. At the same time, the study acknowledges its limitations and suggests future research directions. This study provides insights for urban planners and policymakers on how to enhance equity in urban park services and offers important strategic guidance for improving overall well-being of urban residents.

This study investigates the link between debt and political alignment in international relations between the People’s Republic of China (PRC) and African nations. Using recorded roll-call votes on United Nations General Assembly (UNGA) resolutions, we explore whether PRC investment in sovereign debt influences the voting behaviour of loan recipient countries. We compile voting data for African countries from 2000 to 2020 to calculate an annual voting affinity score as a proxy for political alignment. Concurrently, data on Chinese public and publicly guaranteed (PPG) loans to African governments are collected. A Two-Stage Least-Squares analysis is employed, using the ratio of Chinese PPG debt to GDP as an instrument to address endogeneity. Results reveal a negative impact of Chinese lending on African political support, while trade, foreign direct investment (FDI), and Chinese GDP positively influence political alignment. In high debt-risk African countries, interest rates have a negative impact, whereas loan maturity shows a positive effect. These findings suggest that Chinese loans, particularly under commercial terms, may have strained bilateral relations due to debt sustainability concerns. Nevertheless, the positive impacts of trade and FDI may enhance international relations, highlighting the limitations of China’s loan diplomacy in fostering long-term strategic alignment in Africa.

Artificial intelligence has transformed teachers’ teaching models. This article explores the application of artificial intelligence in basic education in Macao middle schools. This study adopts case analysis in qualitative research, using a total of eight cases from the innovative technology education platform of the Macau education and Youth Development Bureau. These data illustrate how Macao’s artificial intelligence technology promotes teaching innovation in basic education. These eight cases are closely related to the application of artificial intelligence in basic education in Macao. The survey results show that Macao’s education policy has a positive effect on teaching innovation in artificial intelligence education. In teaching practice, the school also cooperates with the government’s policy. The application of AI technology in teaching, students’ learning styles, changes in teachers’ roles, and new needs for teacher training are all influential.