Driving <scp>ESG</scp> Excellence: Analysing China's Green Finance Policy With Double/Debiased Machine Learning

ABSTRACT The study explores the effect of China's Green Financial Reform and Innovation (GFRI) pilot zone policy on corporate ESG performance from 2012 to 2021. Utilising a double/debiased machine learning (DDML) model, we find that the policy enhances ESG performance by mitigating financial constraints, improving environmental practices, and reducing short‐sighted management behaviour in Chinese corporates. Heterogeneity analysis reveals varied effects based on resource endowments, geographic locations, ownership properties and life cycles. This study underscores the significance of the GFRI policy in advancing China's dual‐carbon goals. It further illustrates the potential of this policy to serve as a model for similar initiatives globally, offering empirical evidence that supports environmental governance and remediation efforts at both regional and corporate levels.