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<p>Using panel data from 30 Chinese provinces for the period 2012–2023, this study systematically examines the mechanisms, nonlinear characteristics, and spatial heterogeneity of artificial intelligence’s impact on the resilience of manufacturing industrial chains. The results indicate that AI exerts a significant and robust direct positive effect on industrial chain resilience. Furthermore, AI indirectly enhances resilience by promoting regional economic development. The urbanization rate positively moderates this relationship, with a higher urbanization level amplifying AI’s enabling effect. A threshold analysis reveals that the influence of AI exhibits nonlinear characteristics based on the development level of data elements; beyond a certain threshold, its positive effect displays a pattern of “marginal increase.” Heterogeneity analysis shows that AI’s enabling effect varies regionally, being strongest in the east, followed by the west, and least pronounced in the central region. Moreover, this effect intensifies with higher levels of supply chain resilience, suggesting a “Matthew effect” whereby stronger chains benefit more. This study provides theoretical and empirical insights into how digital technologies enhance industrial resilience and offers policy implications for designing differentiated and coordinated AI promotion strategies.</p>

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Exploring the relationship between artificial intelligence and resilience in manufacturing industrial chains: mechanisms, effects and empirical evidence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月6日

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  • Sirui Liu,
  • Yang Fu,
  • Hanqi Song,
  • Ping Han

摘要

Using panel data from 30 Chinese provinces for the period 2012–2023, this study systematically examines the mechanisms, nonlinear characteristics, and spatial heterogeneity of artificial intelligence’s impact on the resilience of manufacturing industrial chains. The results indicate that AI exerts a significant and robust direct positive effect on industrial chain resilience. Furthermore, AI indirectly enhances resilience by promoting regional economic development. The urbanization rate positively moderates this relationship, with a higher urbanization level amplifying AI’s enabling effect. A threshold analysis reveals that the influence of AI exhibits nonlinear characteristics based on the development level of data elements; beyond a certain threshold, its positive effect displays a pattern of “marginal increase.” Heterogeneity analysis shows that AI’s enabling effect varies regionally, being strongest in the east, followed by the west, and least pronounced in the central region. Moreover, this effect intensifies with higher levels of supply chain resilience, suggesting a “Matthew effect” whereby stronger chains benefit more. This study provides theoretical and empirical insights into how digital technologies enhance industrial resilience and offers policy implications for designing differentiated and coordinated AI promotion strategies.