Abstract Daily human activities have caused severe impacts on global warming. Such human activities, in particular travel and freight transportation, generate massive emissions of greenhouse gases (GHGs), e.g., carbon dioxide (CO2). Hence, the aim of this study was to predict the amount of CO2 emissions from energy use in Thailand's transportation sector as well as related factors, thus providing a substantial benefit to determine policies for reducing GHG emissions and its impacts. In this study, 5 independent variables, namely the size of the population, gross domestic product (GDP), and the number of small, medium and large-sized registered vehicles, were considered in the forecasting of the CO2 amount released from transportation energy consumption using 4 techniques: log-linear regression, path analysis, time series, and curve estimation. According to the findings, the time series exemplified the minimum mean absolute percent error (MAPE=5.388), followed by the log-linear regression model (MAPE=6.379). The results, based on a path analysis model, indicated the significant effects of the large-sized registered vehicle numbers, GDP, and population on the amount of CO2 emissions. With the CO2 emission forecast, the maximum predicted value was 225.33 million tons by 2030 using curve estimation (cubic), and the minimum predicted value was 91.68 million tons using log-linear regression.