Modeling GHG Emissions Across Selected South Asian Countries Using a Unit Log‐Log Quantile Mixed Model

This study examines greenhouse gas (GHG) emissions in South Asian countries by analyzing both their proportion relative to global emissions and total emissions, along with key country‐level determinants. To address the bounded (0‐1) and correlated nature of the GHG proportion, this study proposes a new Bayesian Unit Log‐Log Quantile Mixed Model (ULLQMM), and its performance was evaluated through Monte Carlo simulation. The ULLQMM is applied to investigate the associations between GHG proportion and time, population size, log(GDP), urban population growth, and renewable energy share in selected South Asian countries. Results show that time, population size, and urban population growth are positively associated with the proportion of GHG emissions, whereas renewable energy share and log(GDP) are negatively associated with the proportion of GHG emissions. Additionally, the association between total GHG emissions and key determinants: time, population size, log(GDP), urban population growth, and renewable energy share was analyzed using a Bayesian general linear mixed model. The results show that time, population size, and log(GDP) are positively associated with total GHG emissions, whereas renewable energy share and urban population growth are negatively associated. These findings underscore the critical role of demographic, economic, and energy‐related factors in shaping GHG emissions and demonstrate the potential of renewable energy expansion to reduce GHG emissions in South Asia.