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Empowering Industrial Chain Resilience through Artificial Intelligence: Evidence from Chinese Provincial Panel Data

Author(s)

Hang Yuan

Affiliation(s)

School of Economics and Management, Jiangxi Normal University, Nanchang, Jiangxi, China

Abstract

Amidst profound global changes exposing industrial chains to unprecedented disruption risks, this study investigates whether and how Artificial Intelligence (AI), a strategic General Purpose Technology (GPT), enhances Industrial Chain Resilience (ICR). Grounded in a framework integrating theories of technological paradigms and endogenous growth, we analyze balanced panel data from 30 Chinese provinces from 2013 to 2023. We employ a Two-Way Fixed Effects model and address endogeneity using a robust instrumental variable approach based on historical telecommunication data and a Bartik shift-share instrument. The findings reveal that AI exerts a significant, positive, and linear effect on ICR, suggesting current applications are in a phase of increasing returns. We further show that this empowerment operates through two core pathways: stimulating technological innovation and promoting industrial structure upgrading. Notably, AI's impact is context-dependent; while stronger in manufacturing-heavy and marketized regions, it demonstrates a higher marginal contribution in digitally lagging areas, revealing a significant "technological catch-up effect." These findings collectively establish AI as a critical endogenous driver for building a secure and resilient national industrial system.

Keywords

Artificial Intelligence; Industrial Chain Resilience; Technological Innovation; Industrial Structure Upgrading; Instrumental Variable; Digital Divide; Leapfrogging

References

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