Can green finance policy mitigate corporate carbon risk? Insights from a double/debiased machine-learning approach

Despite extensive concern about the role of green finance in achieving carbon neutrality, limited proof exists on its micro-level impact on industrial corporate carbon risk. To address this gap, we treat China’s Green Finance Reform and Innovation Pilot Zone (GFRIPZ) as a quasi-natural experiment and utilize a double/debiased machine-learning (DDML) model on Chinese listed firms spanning 2014-2020. We notice that the establishment of the GFRIPZ significantly reduces industrial enterprises’ carbon risks, and this conclusion remains valid after undergoing a series of robustness and endogeneity tests. By conducting mechanism tests on the financial resource allocation effect (i.e., financing constraints and financing costs), technological innovation effect (i.e., green and non-green), and digital intelligence effect (i.e., digital technology and artificial intelligence), we find that the policy negatively affects industrial enterprises’ carbon risks by reinforcing financing constraints, promoting non-green technological progress, and improving firms’ digital intelligence levels. Moreover, the GFRIPZ exerts a more notable impact on mitigating carbon risks within non-state-owned enterprises, non-heavy-polluting enterprises, non-high-tech companies, and firms operating in low-competition industries. This study provides empirical support for improving China’s green finance policies to reduce industrial enterprises’ carbon risks.