What cause large regional differences in PM2.5 pollutions in China? Evidence from quantile regression model. (10th February 2018)

• Record Type: Journal Article
• Authors: Xu, Bin; Lin, Boqiang
• Is Part Of: Journal of cleaner production. Volume 174(2018)
• Page Start: 447
• Page End: 461
• Publication Date: 2018-02-10
• Subjects: PM2.5 pollution -- Quantile regression approach -- STIRPAT model; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2017.11.008
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: China has become one of the most heavily polluted countries in the world with China's air pollution attracting claims from some international media of its spread to neighboring countries such as Korea and Japan, in order to bring to the concern of the international community. PM2.5 (fine particles) pollution is one of the main sources of China's air pollution. The detrimental effect PM2.5 pollution poses on health of residents and its hindrances to transportation has attracted the attention of many scholars who have conducted a wide range of investigations on PM2.5 pollution. However, in spite of the avalanche of research on PM2.5 pollution in China, majority of the existing studies in terms of methodology has usually investigated air pollution using the averaging method. In fact, the data distribution of socio-economic variables is often non-normal, with the tail having hidden important information. In order to overcome the shortcomings of existing research, this paper uses a quantile regression approach to explore the main driving forces of the difference in PM2.5 pollution under high, medium and low emission levels. The results show that the effect of economic growth on PM2.5 pollution in the upper 90th quantile provinces is the highest in all the quantile provinces due to the differences in fixed–asset investment and export trade. The impacts of energy efficiency in the 75th–90th and upper 90th quantile provinces are stronger than those in the lower 10th, 10th–25th, 25th–50th, and 50th–75th quantile provinces because of a big differences in research and development (R&D) funding and R&D personnel investment. The case of industrialization was similar on account of the differences in the industrial and building sectors. However, the empirical evidence showed that influences of urbanization in the 25th–50th and 50th–75th quantile provinces were lower than those in the other quantile provinces owing to the differences in motor vehicle and real estate industry. Thus, the heterogeneous effects of these driving forces on the different quantile provinces should be taken into consideration when discussing the mitigation of PM2.5 pollution in China. Highlights: We explore the driving forces of PM2.5 pollution in China. The effect of economic growth in the upper 90th quantile provinces is the highest. The impact of urbanization in the 25th−50th and 50th−75th quantile provinces is lower.