Reassessing the Role of Green Tax Reforms and Institutional Quality in Enhancing Environmental Performance: A Bayesian Quantile Regression Analysis

In response to the global challenge of climate change, many countries are increasingly adopting policy instruments designed to reduce greenhouse gas emissions, with green taxes and institutional reforms serving as two central strategies. However, the relationship between these instruments and CO2 emissions remains inconclusive, particularly with respect to nonlinear effects and differences in policy effectiveness across countries. This study applies Bayesian quantile regression (BQR) and Bayesian linear regression (BLR) to explore both the individual and interactive impacts of environmental taxes (GTAX) and institutional quality (IQ) on per capita CO2 emissions. By using cross‐national data that reflect a range of development levels and pollution intensities, the study examines these relationships across different quantiles of CO2 emissions. The results reveal that GTAX exhibits a nonlinear influence: It significantly reduces emissions at lower quantiles but has a weaker or even reversed effect at higher quantiles—suggesting that the effectiveness of green taxation is context‐dependent. IQ consistently shows a negative effect on emissions, confirming its essential role in environmental governance. Importantly, the interaction term (GTAX × IQ) underscores a threshold effect: At lower quantiles (Q0.05–Q0.30), it has a positive coefficient with high probability, implying potential unintended consequences when taxes are imposed in low‐pollution but institutionally strong settings. From Q0.35 onwards, however, the interaction effect turns significantly negative, indicating the combined reforms are more effective in highly polluted countries. Compared with BLR, the BQR model provides a more nuanced and accurate depiction of how policy impacts vary across the emission distribution. Based on these findings, we propose tailored policy implications for different groups of countries.