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<p>As the “Artificial Intelligence Plus” strategic initiative continues to deepen, the circumstances under which and how artificial intelligence (AI) can enhance the resilience of corporate supply chains are rapidly drawing academic attention. This paper, utilizing data from 4,144&#xa0;A-share listed companies in China and relevant data from prefecture-level cities spanning from 2016 to 2023 as samples, employs the double machine learning (DML) method with random forest regression as the DML learner to investigate the relationship mechanism between government-level AI policies and corporate supply chain resilience. The results reveal that AI pilot policies can elevate the level of corporate supply chain resilience (with an average increase of 0.0177 units in supply chain resilience in pilot regions, and a 95% confidence interval of [0.0074–0.0281]). This enhancement is primarily achieved by strengthening enterprises’ absorptive capacity, resource integration capability, and innovation ability, while the digital foundation and capital investment of regions and enterprises further amplify this positive impact. Meanwhile, the policy’s influence exhibits significant heterogeneity, with more pronounced effects in eastern regions, central cities, technology-intensive industries, and state-owned enterprises. When facing external shocks, AI policies can mitigate the adverse impacts caused by such shocks, and this mitigating effect is more significant in the later stages of the shock. Additionally, these policies can drive the improvement of supply chain resilience in non-pilot regions through spatial spillover effects. The conclusions of this study offer practical references for optimizing supply chain management and enhancing supply chain resilience through AI policies, as well as valuable insights for relevant policy formulation and corporate strategic decision-making.</p>

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The impact of china’s artificial intelligence pilot policies on enterprise supply chain resilience

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月7日

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  • Guangbin Cheng,
  • Hongshuai Zhang

摘要

As the “Artificial Intelligence Plus” strategic initiative continues to deepen, the circumstances under which and how artificial intelligence (AI) can enhance the resilience of corporate supply chains are rapidly drawing academic attention. This paper, utilizing data from 4,144 A-share listed companies in China and relevant data from prefecture-level cities spanning from 2016 to 2023 as samples, employs the double machine learning (DML) method with random forest regression as the DML learner to investigate the relationship mechanism between government-level AI policies and corporate supply chain resilience. The results reveal that AI pilot policies can elevate the level of corporate supply chain resilience (with an average increase of 0.0177 units in supply chain resilience in pilot regions, and a 95% confidence interval of [0.0074–0.0281]). This enhancement is primarily achieved by strengthening enterprises’ absorptive capacity, resource integration capability, and innovation ability, while the digital foundation and capital investment of regions and enterprises further amplify this positive impact. Meanwhile, the policy’s influence exhibits significant heterogeneity, with more pronounced effects in eastern regions, central cities, technology-intensive industries, and state-owned enterprises. When facing external shocks, AI policies can mitigate the adverse impacts caused by such shocks, and this mitigating effect is more significant in the later stages of the shock. Additionally, these policies can drive the improvement of supply chain resilience in non-pilot regions through spatial spillover effects. The conclusions of this study offer practical references for optimizing supply chain management and enhancing supply chain resilience through AI policies, as well as valuable insights for relevant policy formulation and corporate strategic decision-making.