The use of Principal Component Regression and Time Series Analysis to Predict Nitrous Oxide Emissions in Ghana

Abstract

The disturbing pace of emanation of Nitrous Oxide (N2O) into the atmosphere and its calamitous impact on the environment, as monitored by many governmental agencies and researchers has become a wellspring of worry for many nations and therefore needs due attention. The study deployed nitrous oxide emissions from the three sectors to the total nitrous oxide emissions in Ghana over the period 1990 to 2016. The sectors, energy sector, Agriculture Forestry and Other Land Use (AFOLU) and Waste sector were considered against the total N2O emissions. Principal Component Regression (PCR) was applied to the input variables for the reduction of its large size to a few principal components to explain the variations in the original dataset since there was the presence of multicollinearity. Autoregressive Integrated Moving Average (ARIMA) was used to develop models to predict the total N2O emissions and emissions from the sectors in Ghana. The appropriate models that fitted the data well were ARIMA (1,2,1) and ARIMA (1,1,2) based on information criteria (AIC, AICc and BIC). The ARIMA (1,2,1) model was found to be the most suitable model for predicting N2O emission from Energy sector and Waste sector. 70% Of the dataset was used for the analysis and the results from the forecasted values mimic the original dataset. It was revealed that the AFOLU sector is the predominant sector that significantly contribute the overall N2O emission in the atmosphere based on standardized coefficient. The model was adequate since its MAPE for AFOLU sector and the total N2O emissions were 2.95 and 2.68% respectively, meaning the model explained 97.05 and 97.32% respectively. The predicted values mimic the trend of the current situation at hand.