The impact of green credit on ESG performance of Chinese manufacturing firms: the mediating role of knowledge diversity and artificial intelligence application

Purpose Based on institutional theory, resource-based view, organizational cognitive theory, and behavioral economics, this study aims to reveal the mediating mechanism of knowledge diversity (KD) and artificial intelligence (AI) application between green credit (GC) and corporate environmental, social, and governance (ESG) performance. It provides a new theoretical framework for enhancing corporate ESG performance by facilitating the absorption of external pressures and internal knowledge and technological capabilities, as well as a multidimensional theoretical basis for the subsequent optimization of green finance policies and the formulation of corporate sustainable development strategies. Design/methodology/approach The authors select data from A-share manufacturing listed companies in the Shanghai and Shenzhen Stock Exchanges in China from 2011 to 2022 as the sample and employ a two-way fixed effects model for analysis. Findings The study shows that GC significantly enhances firms’ ESG performance, but the effect is inconsistent across the ESG dimensions. In addition, GC contributes to ESG performance by increasing the level of corporate KD and AI application. Originality/value The mechanisms by which GC affects corporate ESG performance have not been fully explored. This study reveals the mediating role of KD and AI applications between GC and ESG performance, provides a theoretical framework for GC to enhance ESG performance, and offers new insights into how firms use financial resources, knowledge resources, and AI technologies to promote sustainable development.