This paper proposes semiparametric estimation of the relationship between carbon dioxide (CO2) emissions and economic activities for a panel of 81 countries observed over the period 1991-2015. The observed differentiated behaviors by country reveal strong heterogeneity as well as different trends across countries and years. This is the motivation behind using a mixed fixed- and random-coefficients panel data model to estimate this relationship. Following Lee and Wand (2016a), the authors apply a mean field variational Bayes approximation to estimate a log model with structural breaks between CO2 emissions per capita and gross domestic product (GDP) per capita including control covariates such as energy intensity and use, energy consumption, population density, urbanization and trade. Results reveal a strong “CO2 emissions - GDP elasticity”, close to one, confirming the increasing but complex link between these two variables. The use of this methodology enriches the estimates of climate change models underlining a large diversity of responses across variables and countries.