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AI-enabled industrial policy and knowledge spillovers: Evidence from China’s pilot cities

Year: 2025

Type: article

Abstract: • Leverages a national quasi-natural experiment on industrial-financial cooperation in China to assess economic and innovation impacts. • Identifies dual mediation mechanisms through efficiencies of explicit and tacit knowledge transfer. • Demonstrates AI's moderating effect in amplifying policy-induced knowledge spillovers. • Reveals heterogeneity in effects by firm size, technology intensity, and regional innovation readiness. • Offers policy insights for AI-driven industrial strategies tailored to regional and sector-specific contexts. This study examines the impact of China's industrial-financial cooperation policy on knowledge spillovers among high-tech enterprises, leveraging the "National Industrial and Financial Cooperation Pilot Cities" initiative as a quasi-natural experiment. The empirical results indicate that the policy significantly enhances both explicit and tacit knowledge flows by alleviating financial constraints and fostering industrial collaboration. Moreover, artificial intelligence (AI) serves as a positive moderating factor by strengthening firms' capabilities in knowledge acquisition, assimilation, and innovation application. Heterogeneity analysis shows that while the central region exhibits slightly higher regression coefficients, the eastern region demonstrates greater statistical robustness and institutional readiness, making it more responsive to the policy. The policy's effect is particularly pronounced in high-tech industries and among larger firms with stronger absorptive capacities. These findings contribute to the literature by introducing a dual-path mediation framework for explicit and tacit knowledge flows and by positioning AI as a technological enabler that amplifies policy-driven knowledge spillovers. Additionally, the study offers practical implications, recommending that policymakers adopt differentiated strategies based on regional, sectoral, and firm-specific characteristics while promoting AI adoption to further maximize innovation outcomes.

Source: Finance Research Open

DOI: 10.1016/j.finr.2025.100040

Authors: Yuanjia Li, Liangmou Gao

Institution: Dongbei University of Finance and Economics

Language: English

Topic: Innovation Policy and R&D

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Decent work and economic growth

Open Access status: gold

Estimated APC paid: $1,000