Statistical Modelling of CO2 Emissions in Malaysia and Thailand

Abstract

Carbon dioxide (CO2) emissions is an environmental problem which leads to Earth’s greenhouse effect. Much concerns with carbon dioxide emissions centered around the growing threat of global warming and climate  change. This paper, however, presents a simple model development using multiple regression with interactions for estimating carbon dioxide emissions in Malaysia and Thailand. Five indicators over the period 1971-2006, namely  energy use, GDP per capita, population density, combustible renewables and waste, and CO2 intensity are used in the analysis. Progressive model selections using forward selection, backward elimination and stepwise regression are used to remove insignificant variables, with possible interactions. Model selection techniques are compared against the performance of eight criteria model selection process. Global test, Coefficient test, Wald test and Goodnessof-fit test are carried out to ensure that the best regression model is selected for further analysis. A numerical illustration is included to enhance the understanding of the whole process in obtaining the final best model.