Research background: With the rapid development of new‑generation digital technologies, including big data, blockchain, and artificial intelligence (AI), the deep integration of AI into traditional industries has induced unprecedented economic changes. The development of AI technologies requires complementarity between capital and highly skilled employees, and the optimization of enterprise human capital structures warrants investigation; therefore, AI has significant potential to improve employees’ human capital and enhance enterprise productivity. This study reveals the economic consequences of AI, examines the internal logic of optimizing enterprises’ labour structures amid AI’s rise, and explores how the application of AI affects enterprise productivity at the enterprise level, as well as the role of upgrading human capital in this process. Purpose of the article: This study aims to explore how AI application impacts enterprise productivity, as well as the mechanism by which such an impact is achieved through the enhancement of human capital. To begin with, we integrate advanced academic research to explore the theoretical relationships among AI applications, human capital upgrading, and enterprise productivity, thereby clarifying the inherent drivers and practical methods for boosting enterprise productivity. Subsequently, the study employs a dataset of 5,167 listed companies from the Shanghai and Shenzhen A‑share markets to investigate how the adoption of AI affects corporate productivity through the lens of human capital enhancement. Furthermore, we offer strategic recommendations to adjust the workforce configuration and boost corporate efficiency by improving human capital quality and promoting AI applications. Methods: Using panel data from 3,646 Chinese A‑share listed companies for the period 2011–2024, the study applies machine learning methods to generate an AI dictionary and investigates the relationships among enterprise AI application, human capital upgrading, and productivity. Findings & value added: Theoretical exploration indicates that AI applications will increase demand for high‑skilled labour and crowd out some low‑skilled labour to optimize the human capital structure, thereby improving enterprise productivity. A mechanism test reveals that AI applications enhance enterprise productivity by upgrading human capital. Heterogeneity analysis suggests that the impact of AI application on productivity is more significant for non‑state‑owned, small‑ and medium‑sized, and non‑technology‑intensive enterprises. This research applies machine learning methods to generate an AI dictionary from a text‑analysis perspective and constructs AI application indicators at the micro‑enterprise level. At the same time, from the perspective of human capital upgrading, the study analyzes the impact of AI applications on enterprise productivity and provides a more reasonable explanation for the improvement of enterprise production efficiency in the AI era.

This paper uses a bottom-up national energy technology model to study the optimization of China’s energy transformation pathway. The model clarifies specific action plans for China’s energy transformation pathway from 2020 to 2060, total carbon emissions, industry emission reduction responsibilities, and other dimensions. The results show that: (1) The proportion of renewable energy consumption in China’s entire energy system from 2020 to 2060 will gradually exceed that of fossil energy under ideal circumstances, and the energy system will transition from traditional fossil energy to renewable energy. Meanwhile, the proportion of low-carbon energy sources, such as renewable energy, in primary energy demand will jump from 15.9% in 2020 to over 80% by 2060. (2) China’s CO₂ emissions will be approximately 3 billion tons, 2 billion tons, and 1 billion tons under three different socio-economic development scenarios of low, medium, and high speed in 2060. At that time, China will still need to absorb CO₂ through carbon sinks in forests, oceans, and wetlands. (3) The electricity industry has the highest CO₂ emissions compared to other industries. The electricity industry must bear significant responsibility for carbon reduction in future energy transformation and economic development.

Existing research has focused chiefly on the impact of artificial intelligence (AI) on economic growth. This study developed an AI dictionary using machine learning methods. Based on data from 3646 Shanghai- and Shenzhen-listed A-share companies from 2011 to 2022 and a panel mediation effect model, the relationships between AI application, human capital structure adjustment, and corporate ESG performance were examined. Theoretical research suggests that when corporates adopt AI, demand for high-skilled labor will increase while some low-skilled positions will be replaced. This leads to optimization of the human capital structure, which in turn improves corporate ESG performance. The results of the mechanism examination show that enhancing corporate ESG performance through AI use is achieved by modifying the human capital structure. Analysis of heterogeneity finds that for non-state-owned, large-sized, and non-technology-intensive corporates, the impact of AI applications on corporate ESG performance is more pronounced. This research further deepens the understanding of AI’s role in the corporate governance process at the micro-corporate level and offers suggestions to promote the development of AI technology.

Research background: With the deep integration of artificial intelligence (AI) into industrial development, AI technologies have reshaped cross-regional collaboration models, broken geographical and information barriers, and reconstructed organizational logic along industrial chains. The wide application of AI has enhanced real-time interaction, resource-matching and risk-sharing capabilities between upstream and downstream enterprises, providing a new driving force for enterprises to carry out cross-regional investment layouts. However, existing studies pay less attention to the micro-mechanism of how AI affects cross-regional investment from the perspective of industrial chain synergy, lacking empirical evidence based on Chinese enterprises. Purpose of the article: This study aims to explore whether and how AI application affects enterprises’ cross-regional investment decisions, and reveals the positive moderating role of industrial chain synergy in this relationship. On this basis, this paper clarifies the internal mechanism of digital empowerment to promote cross-regional allocation of factors, and provides decision-making guidance for enterprises to optimize investment layout and governments to formulate regional coordinated development policies. Methods: Using panel data of 4,580 Chinese A-share listed companies from 2011–2023, this paper constructs an enterprise-level AI application indicator through machine-learning-based text analysis. Industrial chain synergy is measured from upstream–downstream collaboration, factor matching and value-chain connection dimensions. Baseline fixed-effect regressions, moderation-effect tests, multiple robustness checks and instrumental-variable estimation (IV-2SLS) are adopted to empirically test the impact and mechanism of AI application on cross-regional investment. Findings & value added: The results show that AI application is positively associated with enterprises’ cross-regional investment, and industrial chain synergy plays a positive moderating role. Baseline estimation documents that higher AI application intensity correlates with expanded cross-regional investment scale. AI reduces information asymmetry and transaction costs, strengthens upstream-downstream supporting relations, and improves cross-regional factor matching efficiency, which facilitates firms’ investment expansion. This paper expands the research perspective of AI’s micro-economic consequences to the field of cross-regional investment, reveals the conditional relationship of “AI application → industrial chain synergy → cross-regional investment”, and provides new micro-level empirical evidence for digital-driven regional coordinated development.