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 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.

In the context of digital transformation, Chinese small and medium sized enterprises (SMEs) face significant challenges and opportunities in adapting to market dynamics and technological advancements. This study investigates the impact of coopetition strategy on the core competencies of SMEs, with a particular focus on marketing, technological, and integrative competencies. Data were collected from a sample of 300 SMEs in Anhui Province through an online survey, and reliability and validity were tested using SPSS and AMOS. The results indicate that dependency and trust significantly enhance the effectiveness of coopetition strategy from an external perspective, while managerial ambidexterity and strategic intent are critical internal factors driving the successful implementation of coopetition strategies. Both external and internal factors positively impact the core competencies of SMEs. Additionally, environmental uncertainty moderates the relationship between coopetition strategy and core competencies, underscoring the need for flexibility and adaptability in dynamic market environments. The findings suggest that SMEs can better integrate internal and external resources, optimize resource allocation, and improve operational efficiency through coopetition strategy, thereby enhancing their core competencies. This study provides valuable insights and practical guidance for policymakers and business practitioners aiming to support the digital transformation of SMEs.

Using generative artificial intelligence systems in the classroom for law case analysis teaching can enhance the efficiency and accuracy of knowledge delivery. They can create interactive learning environments that are appropriate, immersive, integrated, and evocative, guiding students to conduct case analysis from interdisciplinary and cross-cultural perspectives. This teaching method not only increases students’ interest and participation in learning but also helps cultivate their interdisciplinary thinking and global vision. However, the application of generative artificial intelligence systems in legal education also faces some challenges and issues. If students excessively rely on these systems, their ability to think independently, make judgments, and innovate may be weakened, leading to over-trust in machines and reinforcement of value biases. To address these challenges and issues, legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit. This will enable students to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.