AI and green credit: A new catalyst for green innovation in Chinese enterprises

Research background: China has invested heavily in special credit funds to promote green transformation in enterprises. While green loans have financial characteristics, their pricing is not fully market-driven. This unique environmental regulation has a significant impact on the behavior of enterprises in green innovation, and the rapid integration of artificial intelligence (AI) adds complexity to the process. Purpose of the article: This study aims to empirically investigate whether China's green credit policy, as a unique environmental regulatory instrument, has led to the "Porter Effect". The study examines the impact of the green credit policy on firms' green innovation in two different periods (2007–2012 and 2012–2020), while also assessing the heterogeneous impact on different types of firms. Particular attention is paid to how the integration of artificial intelligence (AI) and fintech has influenced the impact of the policy on corporate green innovation, especially by changing the transmission mechanisms related to operational and agency costs. Methods: The Causal Forest method is applied to observational data from 1,510 listed companies in China between 2007 and 2020. This approach integrates the Neyman-Rubin framework with classical econometric techniques and machine learning to capture complex causal relationships and analyze the long-term effects of policy interventions over time, overcoming the limitations of dealing with nonexperimental data. Findings & value added: The role of green credit policy in stimulating green innovation in enterprises is quite limited. However, the application of AI technology appears to play a significant role in amplifying the effects of green credit. The study suggests that while the classic "Porter hypothesis" may not be fully applicable in terms of corporate operating costs and innovation outcomes, the interplay of green credit policy and AI technology does indeed help reduce agency costs.