The low-carbon economy is the major objective of China’s economy, and its goal is to achieve sustainable economic development. The study enriches the literature on the relationship between digital Chinese yuan (E-CNY), low-carbon economy, AI trust concerns, and security intrusion. The rapid growth of Artificial Intelligence (AI) offered more ways to achieve a low-carbon economy. The digital Chinese yuan (E-CNY), based on the AI technique, has shown its nature and valid low-carbon characteristics in pilot cities of China, it will assume important responsibilities and become the key link. However, trust concerns about AI techniques result in a limitation of the scope and extent of E-CNY usage. The study conducts in-depth research from the perspective of AI trust concerns, explores the influence of E-CNY on the low-carbon economy, and discusses the moderating and mediating mechanisms of AI trust concerns in this process. The empirical data results showed that E-CNY positively affects China’s low-carbon economy, and AI trust concerns moderate the positive impact. When consumers with higher AI trust concerns use E-CNY, their feeling of security intrusion is also higher. It affects the growth of trading volume and scope of E-CNY usage. Still, it reduces the utility of China’s low-carbon economy. This study provides valuable management inspiration for China’s low-carbon economy.

This study aimed to analyze the effect of training programs on entrepreneurial self-efficacy (ESE) and the Optimism of micro, small, and medium enterprises (MSMEs). The research was conducted at Babakan Madang MSMEs, Bogor Regency, assisted by Human Resources Education and Training Center (P2SDM) under the Community Service Institution (LPPM) at IPB University (IPB). The sample size was set at 100 SMEs with a purposive sampling method. Data was obtained by distributing questionnaires and analyzed using Structural Equation Modeling (SEM). The results of the study were as follows: 1) Reactions in the training program did not affect the ESE of MSME actors, 2) Learning in the training program affected the ESE of MSME actors, 3) Behavior in the training program did not affect the ESE of MSME actors, 4) Results in the training program does not affect the ESE of MSME actors, and 5) ESE affects the Optimism of MSME actors. The effect of ESE on the Optimism of MSME actors is greater than the effect of learning in training programs on the Optimism of MSME owners.

Sustainable development (SD) is an approach that aims to meet the needs of the present generation without compromising the ability of future generations to meet their own needs. Education for sustainable development (ESD) is a key component in achieving this goal, as it equips young people with the knowledge, skills, and values needed to make sustainable decisions. This study investigated how preschool teachers in Saudi Arabia understood (SD) and the state of (ESD) practices. A survey was used to collect data from 230 Saudi preschool teachers. The findings revealed that 90% of teachers lacked awareness regarding SD. The overall evaluation of ESD practices among participants indicated a weak subpar status, with an average score of 2.49 out of 4. Notably, in ascending order, the following three dimensions had weak mean scores: the content aspect (2.38) had the lowest score, followed by the practice aspect (2.54) and the competencies aspect (2.58). Meanwhile, the values aspect (2.63) had an average outcome. Analysing the mean scores of ESD practices based on teachers’ qualifications and school types revealed significant differences, although no variations were observed based on experience. The primary obstacle to ESD implementation in pre-schools was the lack of awareness regarding SD/ESD. The study underscores the significance of expanding teacher training to promote ESD effectively in pre-school settings. The results highlight the need for professional development opportunities to improve ESD implementation in classrooms, educate Saudi preschool teachers about SD, and create instructional materials that align with the principles of ESD.

 This research aims to empirically examine the role of learning organization practices in enhancing sustainable organizational performance, utilizing knowledge management and innovation capability as mediating variables. The study was conducted in public IT companies across China, which is a vital sector for driving innovation and economic growth. A mixed-methods approach was employed, with quantitative methods accounting for 70% and qualitative methods for 30% of the research. Purposive sampling was utilized to distribute questionnaires to 546 employees from 10 public IT companies. Statistical analysis was conducted using Structural Equation Modeling (SEM). The findings indicate that learning organization practices significantly influence knowledge management practices (β = 0.785, p < 0.001) and innovation capability (β = 0.405, p < 0.001). Furthermore, knowledge management practices positively contribute to sustainable organizational performance (β = 0.541, p < 0.001), while innovation capability also has a positive effect (β = 0.143, p < 0.001). Moreover, knowledge management practices partially mediate the relationship between learning organization practices and sustainable performance, with a total effect of 0.788 (p < 0.001). The mediating role of innovation capability is also significant, with a total effect of 0.422 (p = 0.045). The study further includes qualitative in-depth interviews with 20 managers from 10 IT companies across five regions in China: East, South, West, North, and Central. Senior managers were selected through a stratified sampling method to ensure comprehensive representation by including both the largest and smallest companies in each region. These findings underscore the critical role of learning organizations in promoting sustainability through effective knowledge management and innovation capabilities within the IT sector.

Amidst the COVID-19 pandemic, the imperative of physical distancing has underscored the necessity for telemedicine solutions. Traditionally, telemedicine systems have operated synchronously, requiring scheduled appointments. This study introduces an innovative telemedicine system integrating Artificial Intelligence (AI) to enable asynchronous communication between physicians and patients, eliminating the need for appointments and providing round-the-clock access from any location. The AI-Telemedicine system was developed utilizing Google Sheets and Google Forms. Patients can receive dietary recommendations from the AI acting as the physician and submit self-reports through the system. Physicians have access to patients’ submitted reports and can adjust AI settings to tailor recommendations accordingly. The AI-Telemedicine system for patients requiring daily dietary recommendations has been successfully developed, meeting all nine system requirements. System privacy and security are ensured through user account access controls within Google Sheets. This AI-Telemedicine system facilitates seamless communication between physicians and patients in situations requiring physical distancing, eliminating the need for appointments. Patients have round-the-clock access to the system, with AI serving as a physician surrogate whenever necessary. This system serves as a potential model for future telemedicine solutions.