Prediction of carbon dioxide emissions using fuzzy linear regression model: a case of developed and developing countries
2014
Lazim Abdullah | Noor Dalina Khalid
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. However, linear regression model fails to describe the relationship due to the variables’ uncertainty and vague information. As to overcome this problem, fuzzy linear regression model has been proposed in explaining the relationship. This paper aims to predict CO2 emissions using possibilistic fuzzy linear regression model by employing data from two countries. The prediction on the efficiency of CO2 emissions for the United Kingdom (UK) and Malaysia was measured. The predictive models identified population and Gross Domestic Products as the most effective predictors for the UK and Malaysia respectively. The root mean square errors of the UK and Malaysia predictive models were 2.895 and 1010.117 respectively. It shows that the CO2 emissions predictors of the UK are more efficient than Malaysia. Instead of crisp deterministic regression coefficients, the fuzzy coefficients with middle and spread values of fuzzy linear regression equations offer new contribution to describe the relationship between CO2 emissions and the related economic variables.
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Este registro bibliográfico ha sido proporcionado por Universiti Putra Malaysia