Principal Component Regression Analysis of CO2 Emission

Abstract

Principal component regression (PCR) model is developed, in this study, for predicting and forecasting the abundance of CO₂ emission which is the most important greenhouse gas in the atmosphere that contributes to global warming. The model was compared with supervised principal component regression (SPCR) model and was found to have more predictive power than it using the values of Akaike information criterion (AIC) and Swartz information criterion (SIC) of the models.

Keywords: Global warming, CO₂, Principal component regression (PCR), Supervised principal component regression (SPCR), Akaike information criterion (AIC) and Swartz information criterion (SIC)