Purpose: The purpose of this study is to elaborate the theoretical mechanism of rural credit input affecting the urban–rural income gap from the perspective of labor transfer, and use a dynamic panel mediation model to test the transmission mechanism of rural credit input affecting the urban–rural income gap through labor transfer, so as to provide an empirical basis for narrowing the urban–rural income gap in China.Design/methodology/approach: This paper constructs a mechanism analysis framework for rural credit input affecting the urban–rural income gap. From the perspective of resource allocation and labor transfer, the authors expound the transmission path of rural credit input to the urban–rural income gap and analyze the theoretical mechanism of rural credit input that affects the urban–rural income gap through labor transfer. Based on this, this paper uses the dynamic panel mediation model to test the effect relationship between rural credit input, labor transfer and urban–rural income gap in 31 provinces of China from 2009 to 2018.Findings: In theory, increasing rural credit input can ease the financial constraints on the development of “agriculture, rural areas and farmers” and provide capital accumulation for the development of rural non-agricultural industries. The development of rural non-agricultural industries can provide more jobs for rural surplus labor, thereby increasing the labor rate of return in rural areas, and ultimately conducive to narrowing the urban–rural income gap. Further, increasing rural credit input can improve the development level of rural non-agricultural industries, thereby promoting the transfer of agricultural labor. At the same time, rural credit input based on the intermediary variable of labor transfer has a significant inhibitory effect on the urban–rural income gap.Research limitations/implications: This study mainly focuses on the relationship between rural credit input, labor transfer and urban–rural income gap, so it is impossible to use micro-level data to further verify the impact of rural credit input on labor transfer. At the same time, the collection of indicators of rural credit investment in the China Financial Yearbook only started in 2009, which limited the number of samples to a certain extent.Practical implications: This paper assumes that the economy is mainly composed of urban and rural economic sectors. Therefore, labor can flow freely between urban and rural areas. However, in the near future, China's rural secondary and tertiary industries may develop rapidly, especially with the in-depth implementation of rural revitalization strategy, it is very important to pay attention to the current situation of rural industrial structure and incorporate the factors such as rural industrial structure into the existing model.Social implications: This study attempts to provide a new perspective and inspiration for rural credit input, the optimal allocation of labor force and narrowing the urban–rural income gap under China's rural revitalization strategy.Originality/value: Based on the analysis framework of neoclassical economic theory, this paper uses the constant elasticity of substitution production function to establish an urban–rural two-sector nested model that includes credit supply variables and analyzes the mechanism of rural credit input affecting the urban–rural income gap through labor transfer.

Research background: With the rapid development of digital technologies such as artificial intelligence (AI), big data, cloud computing, and blockchain, the deep integration of digital technology and traditional industries offers unprecedented opportunities and has become an important technological force driving the development of enterprise environmental, social, and governance (ESG) performance. Digital transformation increases the complementarity between the production and operation processes of enterprises and highly skilled labour, specifically manifested in the adaptive change of the human capital structure of enterprises in labour relations. Meanwhile, transforming enterprises’ human capital structure also affects ESG performance. Purpose of the article: This study investigates the effects of digital transformation on enterprise ESG performance and its underlying mechanisms of human capital upgrading. First, it combines cutting-edge literature to analyse the theoretical mechanism between digital transformation, human capital upgrading, and enterprise ESG performance, revealing the internal driving factors and implementation paths for improving enterprise ESG performance. Second, we use the data of 3928 listed enterprises in the Shanghai and Shenzhen A-share markets from 2009–2022 to analyse the impact of digital transformation on enterprise ESG performance from the perspective of human capital upgrading as well as the mechanism by which digital transformation affects enterprise ESG performance through human capital upgrading. Third, we provide policy recommendations for optimising the labour structure and enhancing enterprise ESG performance from the perspective of optimising human capital and digital technology applications. Methods: Based on data from 3928 listed enterprises in Chinese A-share markets from 2009-2022, we investigate the effects of digital transformation on enterprise ESG performance and the mechanisms of human capital upgrading. The panel mediation effect model tests the relationships between digital transformation, human capital upgrading, and enterprise ESG performance. Findings & value added: In theory, digital transformation is beneficial for optimising human capital structure, accelerating green technological innovation, and improving management efficiency, thus improving enterprise ESG performance. Mechanism testing shows that digital transformation enhances enterprise ESG performance through human capital upgrading. Heterogeneity analysis shows that the impact of digital transformation on ESG performance is more significant in enterprises that are capital- and technical-intensive, and located in the eastern and central regions. This study examines the microeconomic consequences of digital transformation, contributing to a deeper understanding of the theoretical logic inherent in enterprises’ optimisation of their labour structure. It also provides empirical evidence and decision support for developing industrial talent policies.

This study investigates the existence of the pollution halo hypothesis in China. To realize the goals, an urban green development efficiency measuring framework was established applying SBM directional distance function and Malmquist-Luenberger index. Using linear panel model and nonlinear panel model and data of 284 cities in China from 2008 to 2019, we have found that the pollution halo hypothesis does exist in China because FDI increases urban green development efficiency. The mechanism analysis of introducing environmental regulation indicates that the impact of FDI on urban green development efficiency presents a ‘U-shaped’ feature. The implementation of strict environmental regulation can significantly increase the positive impact of FDI on the urban green development efficiency. In addition, the analysis of regional heterogeneity has shown that when the environmental regulation intensity exceeds the turning point, FDI in eastern and central China plays a significant positive role in urban green development efficiency, while that in western China is inhibitory effect. Our empirical results have suggested the Chinese government should further utilize the positive role of FDI in urban green development, including gradually and orderly implementing strict environmental protection regulatory policies, and implementing green energy conservation transformation projects for energy intensive industries.

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.