Empowering the manufacturing industry with artificial intelligence: new quality productivity and sustainable development—an empirical study based on Chinese A-share companies - Cheng, Wenjing; Xu, Gongwen (Frontiers in Sustainability, 2025)
IntroductionArtificial Intelligence (AI) has become a key driving force for promoting the development of Manufacturing 4.0 and enhancing new quality productivity - an economic model that emphasizes high efficiency, innovation-driven growth, and sustainable development. Although the transformative potential of AI is increasingly recognized, how to effectively unleash its role in enhancing new quality productivity at the enterprise level remains a question that requires in-depth research. This study aims to investigate the impact of AI applications on the new quality productivity of Chinese manufacturing enterprises, explore its underlying mechanisms, and assess its heterogeneous effects and spatial spillover effects across different regions and industries.MethodsThis paper is based on the panel data of Chinese A-share listed manufacturing enterprises from 2015 to 2023, and employs multiple econometric models to analyze the relationship between AI and new quality productivity. The empirical strategies include: a benchmark regression model to estimate the basic impact; a spatial Durbin model (SDM) constructed under a 0-1 adjacency matrix and a geographical distance matrix to capture spatial spillover effects; a dynamic panel GMM model to address endogeneity and dynamic persistence issues; heterogeneity and mechanism analysis through group regression and mediation effect tests. The robustness of the results is ensured through a series of robustness tests and endogeneity tests.ResultsThe research findings indicate that AI has significantly enhanced the new quality productivity of manufacturing enterprises, and this conclusion remains robust under different model settings and endogeneity treatments. The mechanism analysis reveals that AI boosts new quality productivity through three pathways: promoting green innovation, alleviating financing constraints for enterprises, and optimizing the structure of human capital. Heterogeneity tests show that the promoting effect of AI is more pronounced in enterprises in the eastern region, non-heavy-polluting industries, and high-tech industries. Additionally, the spatial econometric results confirm that AI not only contributes to the improvement of local new quality productivity but also generates significant positive spatial spillover effects in surrounding areas.DiscussionThe above findings highlight the crucial role of artificial intelligence as a key engine driving the development of new quality productivity, especially demonstrating remarkable effectiveness in promoting the green transformation of manufacturing and the development of a circular economy. To fully unleash the potential of artificial intelligence in driving green productivity, policymakers should accelerate the construction of a “digital-green” collaborative innovation ecosystem. At the same time, the leading and radiating role of advanced regions should be fully leveraged to build cross-regional technology sharing platforms and data resource networks, forming a regional development pattern of “leading by points and advancing in coordination”, and comprehensively promoting the realization of new industrialization and the dual carbon goals.