AI-Driven Sustainability in EU Nations: A Quantile Regression Analysis of AI Innovations, Human Capital, and Carbon Productivity

This study investigates the heterogeneous effects of artificial intelligence (AI) innovations and human capital on carbon productivity in the European Union (EU), focusing on the mediating roles of tertiary education and data literacy. Using an unbalanced panel dataset covering 27 EU countries from 2000 to 2023, the analysis employs quantile regression to capture distributional differences across the conditional distribution of carbon productivity. The results reveal significant non-linearities: AI innovations positively affect carbon productivity at the lower quantile (Q25), but the effect turns negative at higher quantiles (Q75 and Q90), suggesting diminishing returns. Similarly, tertiary education enhances carbon productivity only at lower quantiles, while the interaction term between AI and education becomes strongly positive at the upper end of the distribution, indicating a complementary relationship in high-performing contexts. Conversely, the interaction between AI and data literacy remains negative and significant up to the 75th percentile, implying that digital skills alone may not be sufficient to translate technological progress into environmental efficiency. These findings underscore the importance of tailored green-digital policy strategies that simultaneously foster technological advancement and strengthen human capital foundations, particularly in economies aiming to boost carbon productivity as part of their climate commitments.