Time-Varying Impacts of Robust Determinants on Greenhouse Gas Emissions: Panel Data Evidence
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Understanding the key drivers of greenhouse gas (GHG) emissions is crucial for designing effective and adaptable climate policies, particularly given the complex interplay among structural, institutional, and energy-related factors. This study examines the time-varying impacts of key determinants of GHG emissions across 29 countries from 1993 to 2018, with an emphasis on the shadow economy, energy security risks, and geopolitical volatility. The analysis follows a four-step framework: countries are classified using principal component analysis (PCA) and K-means clustering, robust covariates are selected via Bayesian Model Averaging (BMA), and their impacts are estimated with time-varying coefficient panel models. Model robustness is evaluated through grouped cross-validation, confirming the superior performance of the time-varying random effects (tvRE) specification. The results reveal that the shadow economy and energy security risk exert more dynamic and substantial impacts in the Higher-income group, while their effects are comparatively muted in the Lower-income group. Geopolitical risk, however, shows limited explanatory power for emissions in both contexts. This study provides a novel empirical framework for capturing the dynamic influences of emissions drivers and contributes actionable insights toward achieving sustainable development goals.
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