Recently, the relationship between carbon dioxide emission (CO2), aggregate energy consumption (EC) and economic growth (GDP) has been widely studied by many researchers using different approaches but the results were conflicting. Such controversy may due to the efficiency of the applied statistical approaches and using different dataset. The main objective of this experimental study is to examine the relationship between CO2, EC, and GDP using different data transformation forms (natural logarithm versus inverse form) in reducing the heteroscedasticity in panel data. The panel data consist of 29 countries from two different economic levels of countries, 17 developed versus 12 developing countries. The data spanning from 1960 to 2008. A panel data approach is applied and estimations based on three models. First of all, the estimations are conducted by constructing three different models; First model is estimated by using the original data without any transformation, while the second and third model use the natural logarithm (Log) and inverse form to transform the data. Those two transformation forms are applied to reduce the heteroscedasticity problem. The main findings show a strong relationship between the three variables. The model with inverse function transformation is superior to the other two models using original data and Log transformation, as it has the highest R-2 which illustrates that more than 84% of CO2 emission can be explained by GDP and EC. Since EC and GDP are influential on the CO2 emissions, higher EC and lower GDP may lead to environmental problems such as air and water pollution. Therefore, prevention action should be taken to minimize the environmental degradation.