Estimating the effects of socioeconomic structure on CO2 emissions in China using an econometric analysis framework

This paper empirically explores the interactions between socioeconomic structures (i.e., energy consumption structure, economic structure, export and import structure, investment structure, and urban-rural structure) and CO2 emissions in China, using time series data for the period 1980–2015, and employing an econometric analysis framework. Using the Augmented Dickey Fuller (ADF) unit root test, we surveyed the stationarity of the selected variables, all of which were found to be stationary at the first difference. Based on Vector Error-Correction Model (VECM) model, Granger causality test was further employed in order to detect the causal relationship among the competing variables. The results show the presence of unidirectional causality running from energy consumption structure, and from export and import structure, to CO2 emissions. No causal link was found to exist in relation to the other two variables. An impulse response analysis and a variance decomposition analysis were subsequently utilized, the results of which demonstrated that the energy consumption structure and the investment structure do significantly affect CO2 emissions in the long run and that the energy consumption structure is the most significant variable. This paper contributes existing scholarly research into the relationship between socioeconomic structure and CO2 emissions, and our findings have significant implications for the government in the task of implementing policy measures in order to mitigate the growth of CO2 emissions.