Stochastic Modeling for Carbon Dioxide Emissions

Abstract

Time series analysis is an important research area that has been applied in a wide variety of fields particularly in environmental studies. ARIMA (Autoregressive Integrated Moving Average) is one of the most popular forecasting models over the past three decades. The aim of this research paper is to develop the forecasting model using Box-Jenkins methodology for analyzing and predicting the global atmospheric carbon dioxide emissions data taken from Mauna Lao Observatory for the period from May, 1974 to July, 2013. The developed ARIMA model takes into consideration for forecasting the global atmospheric carbon dioxide emissions for the upcoming months. The graphical comparison of the actual values and the predicted values of global atmospheric carbon dioxide emissions is presented.

Keywords:

• ARIMA
• Box-Jenkins methodology
• ACF
• BIC
• forecasting
• carbon dioxide emissions