Revealing dynamic impacts of socioeconomic factors on air pollution changes in Guangdong Province, China

• Published in: The Science of the total environment Vol. 699; p. 134178
• Main Authors: Xu, Xinli, Huang, Guohe, Liu, Lirong, Guan, Yuru, Zhai, Mengyu, Li, Yongping
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 10.01.2020
• Subjects: Air pollutant equivalent; Ghosh-supply IO model; Leontief-demand IO model; Structural decomposition analysis; Structural decomposition analysis; Ghosh-supply IO model; Leontief-demand IO model; Air pollutant equivalent
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2019.134178

[Display omitted] •A dynamic regional air pollution analysis (DRA) model is developed.•Various air pollutants are integrated according to their environmental hazards.•Impacts of demand and supply on regional air pollution are different.•Primary input structure is more significant than final demand structure. The rapid development of cities leads to the frequent occurrence of air pollution incidents, which seriously hinders urban sustainability. This study develops a dynamic regional air pollution analysis (DRA) model to explore the mechanism of air pollutant emission changes. Specifically, the emissions differences among various sectors are distinguished by multi-angle accounting (MAA) method, and sectors' evolutionary trajectories are described by sector evolution analysis (SEA). Through combining emission deconstruction analysis (EDA) and structural decomposition analysis (SDA), key emission patterns and decisive socioeconomic factors are identified. The empirical results indicate that different sectors play different roles in the urban emission system and differentiated regulation policies should be formulated according to their characteristics. Changes of demand and supply patterns can result in the fluctuation in regional air pollutant emissions. Exports and worker's reward are the most significant contributors to air pollution on demand and supply sides, accounting for more than 54.3% and 44.0% of total emissions, respectively. The final demand level and the primary input level are the two biggest drivers of the emission increase, while emission intensity is the most crucial factor that offsets the emission growth. Also, there are significant differences in demand and supply structure. The contribution of primary input structure to emission reduction was more significant than that of final demand structure, which contributed 14.6% in 2015. The findings in this study will provide reliable information for developing more comprehensive and effective mitigation policies.