Climate change mitigation has become an urgent global priority, particularly for OECD countries that face mounting pressure to reconcile sustained economic growth with significant reductions in carbon emissions. Against this backdrop, this study aims to examine the roles of eco-innovation, resource efficiency, and geopolitical risk in shaping environmental quality across OECD nations from 1990 to 2021. To achieve this, the study employed a comprehensive methodological framework that integrates Method of Moments Quantile Regression (MM-QREG) to capture distributional heterogeneity across the 10th–90th CO₂ emission quantiles, with machine learning approaches, including Artificial Neural Networks (ANN) and Gradient Boosting Decision Trees (GBDT), to validate potential nonlinear relationships and ensure robustness. The empirical findings reveal that eco-innovation consistently exerts a negative and statistically significant effect on CO₂ emissions across all quantiles, highlighting its stable mitigation role, while resource efficiency further reduces emissions and exhibits a synergistic effect with eco-innovation, particularly at lower emission levels. In contrast, geopolitical risk does not directly influence CO₂ emissions but weakens the emission-reducing impact of eco-innovation. Additionally, GDP and urbanization are found to increase CO₂ emissions, with GDP effects being more pronounced at lower quantiles. These findings suggest that policymakers in OECD countries should prioritize strengthening eco-innovation systems, improving resource efficiency, and addressing geopolitical uncertainties, while promoting development pathways that effectively decouple economic growth from carbon emissions to achieve long-term climate mitigation and sustainable development goals.
Wang et al. (Thu,) studied this question.