Heterogeneity in the data is a common issue arising in research. When data are heterogeneous, equal variation in the data to set up a model for the studied phenomena cannot be assumed. Ordinary least square regression does not consider the unequal variation which may provide inefficient estimation of the relationship between variables. On the contrary, quantile regression could efficiently tackle this problem by detecting the relationship between variables at different levels, and could be useful especially in applications where extreme values are important to consider, such as in environmental studies, where upper quantiles of pollution levels are critical from a public health perspective. The main purpose of this study is to model the relationship between CO₂, economic growth, and energy consumption by considering the heterogeneity problem for developed and developing countries and applying the quantile regression at different percentile values (0.05, 0.25, 0.50, 0.75, and 0.95) on panel data. The panel data consists of 29 countries from two different economic development groups: 17 developed versus 12 developing countries—over the period 1960–2008. Quantile regression (QR) results are then compared with those of the OLS model, resulting similar for developed and developing countries. In both cases, countries having lower GDP release less CO₂ emissions.