Analysis of Time—Varying Coefficients and Forecasting Effects Between Greenhouse Gas Emissions and Its Determinants in Thailand

This paper uses a dynamic linear model (DLM) to find the relationship between greenhouse gas emissions and its determinants in Thailand. From the Bayesian model averaging (BMA) analysis was found that only forest, GDP, and industrial are sufficient for further investigation and can explain best the GHG emission. To explain the correlation, the time-varying coefficients of forest and GDP strongly explain and change in the same direction with GHG emission. In contrast, intercept term and industrial variables significantly explain and change in the opposite direction with GHG emission. There are some significant events that affect time-varying coefficients such as climate policy and the economic crisis in 2009. Moreover, The DLM used to be a forecasting tool for the next 10 years. The time-varying coefficients from intercept, GDP, and industrial can have a potential effect on GHG emissions. Forest will potentially explain GHG emissions in a better direction. On the contrary, the coefficients from the intercept term are in opposite directions.