Configurational pathways of artificial intelligence in driving regional green innovation: A dynamic QCA approach

Exploring the role of artificial intelligence (AI) in regional green innovation is vital for green economic transition. Sufficient insights into the causal complexity and temporal evolution of this process are lacking in the existing literature. To address this gap, an extended technology-organization-environment framework is developed, and dynamic qualitative comparative analysis (QCA) is used to identify multiple equifinal pathways and their evolutionary patterns. The analysis yields three core findings. First, AI is a key antecedent of high-level green innovation but not a necessary condition. A holistic approach that aligns technological factors with organizational and environmental elements is required to achieve high-level green innovation. Second, four distinct configurational pathways are identified. The AI technology development-led pathway relies on internal technological capabilities and human capital. The AI technology application-driven pathway prioritizes technology diffusion alongside human capital and service infrastructure. The AI technology development and institutional regulation synergistic pathway combines technological reserves with institutional pressure. The synergistic pathway for AI technology development and application integrates dual technological drivers with financial and service empowerment. Third, the explanatory consistency of the AI technology application-driven pathway exhibits a continuous upward trend during the observation period. The underlying causal logic across all pathways maintains robust temporal stability. This study expands the configurational framework for AI-enabled green innovation and provides empirical guidance for designing differentiated and dynamically adaptive regional green innovation policies.