Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM 2.5 Concentration: A Provincial Panel Data Model Analysis of China

Haoran Zhao, Sen Guo and Huiru Zhao
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Haoran Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Sen Guo: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Huiru Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China

IJERPH, 2019, vol. 16, issue 16, 1-18

Abstract: With the rapid development of China’s economy, the environmental problems are becoming increasingly prominent, especially the PM 2.5 (particulate matter with diameter smaller than 2.5 μm) concentrations that have exerted adverse influences on human health. Considering the fact that PM 2.5 concentrations are mainly caused by anthropogenic activities, this paper selected economic growth, economic structure, urbanization, and the number of civil vehicles as the primary factors and then explored the nexus between those variables and PM 2.5 concentrations by employing a panel data model for 31 Chinese provinces. The estimated model showed that: (1) the coefficients of the variables for provinces located in North, Central, and East China were larger than that of other provinces; (2) GDP per capita made the largest contribution to PM 2.5 concentrations, while the number of civil vehicles made the least contribution; and (3) the higher the development level of a factor, the greater the contribution it makes to PM 2.5 concentrations. It was also found that a bi-directional Granger causal nexus exists between PM 2.5 concentrations and economic progress as well as between PM 2.5 concentrations and the urbanization process for all provinces. Policy recommendations were finally obtained through empirical discussions, which include that provincial governments should adjust the economic and industrial development patterns, restrict immigration to intensive urban areas, decrease the successful proportion of vehicle licenses, and promote electric vehicles as a substitute to petrol vehicles.

Keywords: PM 2.5 concentrations; GDP per capita; economic structure; urbanization rate; civil vehicles amount; panel data model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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