Prediction of Carbon Dioxide Emissions Using Two Linear Regression-based Models: A Comparative Analysis

Chee San Choi, Lazim Abdullah

Abstract


Carbon dioxide (CO2) emissions have been continuously escalating in recent years. The escalating trend is consistent with the current economic activities and other uncertain variables such as demand and supply in businesses and energy needs.  Linear model is one of the most commonly used methods to explain the relationship between CO2 emissions and the related economic variables. The conventional linear regression model has a disadvantage in describing the relationships due to the variables’ uncertainty and vague information. To address this problem, the fuzzy linear regression model has been proposed for explaining the relationships. However, the performance of the two linear models for predicting CO2 emissions is not immediately known. This paper presents a comparative study of conventional linear regression model and linear regression with fuzzy numbers model for predicting CO2 emissions in Malaysia. Twenty five years data from 1981 to 2005 of CO2 emissions, fuel mix, transportation, gross domestic product, and population have been used to develop the model of possibilistic fuzzy linear regression (PFLR) and multiple linear regression (MLR).  The criteria of performance evaluation are calculated for estimating and comparing the performances of PFLR and MLR models. The performance comparison of PFLR and MLR models due to mean absolute percentage errors, root mean squared error criteria; indicate that MLR performed better on CO2 emissions prediction.  A considerable further work needs to be done to determine the flexibility of fuzzy numbers in enhancing the performance of PFLR against the MLR.

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