Co-Evolution of Artificial Intelligence and Green Technological Innovation: A Computational Mapping and Diagnostic Framework

Artificial intelligence (AI) is increasingly recognised for its transformative implications for sustainability transitions. Yet little is known about how AI co-evolves with green technological innovation systems and whether institutional adaptation keeps pace with technological diffusion. This study maps 3357 peer-reviewed publications between 2003 and 2025 using transformer-based topic modelling and cross-model triangulation to characterise structural evolution across enabling technologies, sectoral applications, and governance domains. Results reveal a reproducible triadic configuration consistent with innovation-system layering, alongside pronounced asymmetry in growth trajectories. While AI-enabled application domains (e.g., energy systems, waste and circularity, agriculture, and urban mobility) exhibit sustained expansion and increasing specialisation, governance and institutional strands demonstrate thinner density, greater model sensitivity, and delayed acceleration. These patterns are consistent with an asynchronous relationship between technological capability and institutional oversight, echoing the innovation-regulation lag observed in other technology-diffusion processes. The findings contribute to technological change literature by providing systematic evidence on the thematic structure and evolution of AI-enabled sustainability research and by proposing a co-evolution diagnostic framework for monitoring alignment between technological expansion and governance attention.