This study investigates how economic growth, financial integration, social integration, and knowledge management shape CO2 emissions in Saudi Arabia using quarterly data from 1995Q1 to 2024Q4. It applies kernel-regularized quantile regression to capture nonlinear and state-dependent effects across the conditional distribution of emissions without imposing restrictive parametric assumptions, while regularization mitigates overfitting and multicollinearity. The results reveal strong distributional heterogeneity. Economic growth is emission-augmenting and is strongest at the lower tail, weaker around the median, and positive again in the upper tail. Financial integration reduces emissions across quantiles, most strongly under low-emission states, while social integration is mostly near-neutral beyond the lower tail. Knowledge management increases emissions throughout, and quantile Granger causality is concentrated in the upper quantiles, indicating stronger predictive linkages when emissions are high. Based on these findings, this study proposes precise, quantile-specific policy guidelines across the distribution.
Kriym et al. (Sat,) studied this question.
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