Industrial robots for a sustainable future: Uncovering the asymmetric effects of AI on ecological quality in G7 economies

Artificial intelligence is increasingly recognized for its potential to enhance ecological quality by streamlining production processes, reducing environmental emissions, and improving ecological monitoring systems. However, the influence of artificial intelligence on ecological quality is neither uniform across different stages of technological adoption nor consistent across national contexts. The central objective of this study is to investigate the asymmetric and stage-specific effects of artificial intelligence adoption on ecological quality within the Group of Seven (G7) economies over the period from January 2000 to December 2019. Employing a novel multivariate quantile-on-quantile regression framework, this research examines how varying intensities of artificial intelligence adoption impact different levels of ecological outcomes. The results indicate that artificial intelligence exerts a modest positive effect on ecological quality during early stages of adoption, a more substantial effect during transitional phases, and a significantly positive influence at advanced stages of integration. To address endogeneity concerns—particularly reverse causality and omitted variable bias—this study utilizes an instrumental variable multivariate quantile regression approach, using lagged values of artificial intelligence adoption as an instrument. The findings are validated through robustness checks using kernel regularized least squares and standard quantile regression techniques. The results also reveal considerable variation across countries, highlighting the necessity for country-specific and stage-aware policy interventions. Accordingly, the study offers detailed, actionable recommendations tailored to the adoption stage of each G7 member to maximize the ecological benefits of artificial intelligence. This research provides a rigorous, causally grounded analysis of how artificial intelligence can be harnessed to advance environmental sustainability in highly industrialized economies.