Artificial-intelligence-driven Green Credit Regulation and Corporate Cross-Regional Entrepreneurship under Supply Chain Resilience: Evidence from China

With the widespread application of artificial intelligence technology, green financial regulation has enhanced the precision of credit support, creating opportunities to strengthen supply chain integration and optimise capital allocation efficiency. This study manually gathers subsidiary data of China’s listed industrial enterprises from 2007 to 2023 and employs the difference-in-differences (DID) method to empirically examine the impact of the Green Credit Guidelines (GCGs) on cross-regional entrepreneurship of industrial enterprises. It reveals that the GCGs significantly inhibit cross-regional entrepreneurship by heavy-polluting enterprises. The mechanism analysis indicates that the GCGs primarily impact the level of cross-regional entrepreneurship by strengthening credit financing constraints, promoting environmental information disclosure, and altering entrepreneurs’ personal traits. Additionally, the green credit regulation has a significant impact on samples featuring non-state-owned enterprises, executives with environmental backgrounds, regions with higher marketisation, and industries with more competition. Moreover, it curbs ‘pollution-driven’ cross-regional entrepreneurship and stimulates green entrepreneurial activities. However, the policy does not promote green innovation in these enterprises, displaying a lack of synergy with green subsidy policies. This study extends the existing literature on green credit regulation and provides policy insights into stimulating the vitality of green entrepreneurship and innovation.