Does Artificial Intelligence Enable or Constrain Green Innovation Resilience in New Energy Firms? Evidence From a Supply Chain Systems Perspective

ABSTRACT Against the backdrop of the global energy transition and multiple overlapping external shocks, green innovation in new energy firms has frequently been disrupted, making the development and enhancement of green innovation resilience a key issue in the low‐carbon energy transition. Drawing on systems theory and supply chain management theory, this study uses Chinese A‐share listed new energy firms from 2009 to 2023 as the research sample to systematically examine the impact of artificial intelligence (AI) on their green innovation resilience. The results show that AI significantly enhances the green innovation resilience of new energy firms. Improved supply chain agility and reduced supply–demand coordination costs in the supply chain serve as the main mechanisms, whereas supply chain risk perception weakens the positive effect of AI on green innovation resilience. The effect of AI on the green innovation resilience of new energy firms varies across ownership structures and green production intentions. Further analysis reveals that the effect of AI on green innovation resilience follows a U‐shaped threshold pattern contingent on the level of human–AI collaboration. This study extends the analysis of the channels through which green innovation resilience is shaped in new energy firms from a supply chain perspective, broadens the application of dynamic capability theory to the supply chain context and identifies a nonlinear boundary condition for human–AI complementarity in AI empowerment research. These findings provide valuable insights for new energy firms seeking to deploy AI in a differentiated manner and build sustainable green innovation systems.