PM_2.5 mitigation in China: Socioeconomic determinants of concentrations and differential control policies

Elucidating the key impact factors on PM_2.5 concentrations is crucial to formulate effective mitigation policies. In this study, we employed an extended Stochastic Impacts by Regression on Population Affluence and Technology (STIRPAT) model to identify the socioeconomic determinants of PM_2.5 concentrations for 12 different regions and across China. The evaluation was based on a balanced panel dataset integrating long-term satellite-derived PM_2.5 concentrations and socio-economic data in China from 1999 to 2011. Empirical results indicate that the influencing factors can be ranked in descending order of importance as: proportion of secondary sector of the economy, GDP per capita, urbanization, population, energy intensity, and proportion of tertiary sector. Proportion of secondary sector is the greatest contribution to increasing PM_2.5 concentrations, especially for heavily polluted regions. GDP per capita is secondary in importance, and its impact is weakened by the existence of an EKC relationship between GDP per capita and PM_2.5 concentrations. Therefore, PM_2.5 pollution is an economic development mode problem, rather than a general economic development problem. The impact of urbanization varies across regions; while promoting urbanization will be conducive to decreased PM_2.5 concentrations in Northwest China and Northeast China, it will contribute to increased PM_2.5 concentrations in other regions. Population and energy intensity are significant in most regions, but neither are decisive factors because of the small absolute value of their coefficients. Finally, different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets.