Impact of macroeconomic factors on ozone precursor emissions in China

In recent years, ground-level ozone pollution is becoming increasingly severe in China. Long-term exposure to such an environment will threaten public health. Here, a Logarithmic Mean Divisia Index (LMDI) model was used to estimate the driving forces of VOCs and NOx, the two most important precursors of surface ozone. The LMDI model can decompose macroeconomic indicators, including per capita gross (PCG), energy intensity (EI), energy structure (ES), and pollutant emission intensity (EP), which can affect precursor emissions. Results indicate that PCG was the primary promoting factor of precursors, while EI and EP suppressed the precursor emissions. That is, the macroeconomic factors can affect precursor emissions, and then affect ozone concentrations. To demonstrate this, we used the random forest model to analyze the relationships between macroeconomic factors and ozone concentrations, together with meteorological elements. We found macroeconomic factors can improve the predictive performance of the Random Forest. The result revealed that it was feasible to restrain precursor emissions through macro-control, and then to adjust ozone concentrations appropriately. • The LMDI model is used to explore the impact of macroeconomic factors on NOx and VOCs emissions. • The Random Forest model is used to analyze the relationships between LMDI factors and ozone concentrations. • The result of Random Forest model proves that the LMDI factors have a certain relationship with ozone concentration. • It is feasible to control ozone concentration by controlling the emission of precursors through macro-control. • Both ozone pollution and the influence of LMDI factors are regional, policy formulation should take it into consideration.