The long-run average growth rates of per capita carbon dioxide emissions and GDP per capita are positively correlated, though the rate of emissions intensity reduction varies widely across countries. The conventional approach to investigating these relationships involves panel regression models of the levels of the variables, which are plagued by unit root and cointegration issues as well as the difficulty of identifying time effects. In this paper, we adopt a new representation of the data in terms of long-run growth rates, which allows us to test multiple hypotheses about the drivers of per capita emissions of pollutants in a single framework. It avoids the econometric issues associated with previous approaches and allows us to exploit the differences in growth performance across countries. We also apply our new approach to sulfur emissions. The results show that scale, environmental Kuznets, convergence, and, for sulfur, time effects are important in explaining emissions growth. Though the elasticity of emissions with respect to income declines with increased income, for carbon the effect of growth is monotonic. For sulfur, most of our specifications find an in sample turning point, but for our preferred specification the turning point is three times mean income. We also found that the Green Solow Model convergence effect is more important than GDP growth or the EKC effect in explaining sulfur emissions but that the latter is true for carbon emissions.