What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective

Ji, Xi; Yao, Yixin; Long, Xianling

doi:10.1016/j.enpol.2018.04.040

Cited by 186 publications

(84 citation statements)

References 68 publications

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“…Excessive urban population size will bring more resource consumption, housing demand, and travel demand, which indirectly aggravates haze. Thus, Us is expected to be positive for haze pollution [54][55][56].…”

Section: Variables Selection and Indicator Descriptionmentioning

confidence: 99%

Asymmetrically Spatial Effects of Urban Scale and Agglomeration on Haze Pollution in China

Fan

Yang

Liu

2019

IJERPH

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Rapid urbanization in China not only promotes the rapid expansion of urban population and economic agglomeration, but also causes the aggravation of haze pollution. In order to better clarify the asymmetric and nonlinear effects of urban scale and agglomeration on haze pollution, this paper quantitatively evaluates the spatial spillover effects of population size and economic agglomeration on haze pollution in 342 Chinese cities from 2001 to 2016 by using exploratory spatial data analysis (ESDA) and spatial econometric model. The results show the following: (1) During the research period, the distribution of urban scale, agglomeration, and haze pollution in China presented complex asymmetrical features, with the former two presenting a “core–periphery” distribution mode, while the latter having a tendency to spread around. In addition, under the influence of urban population size and economic agglomeration, haze pollution in Chinese cities presents significant spatial autocorrelation, with the agglomeration degrees showing a fluctuating upward trend during the study period. (2) Both urban scale and urban agglomeration have positive global spatiotemporal correlation with haze pollution. Local spatial correlation features are more obvious in China’s emerging urban agglomerations like Beijing–Tianjin–Hebei and Yangtze River Delta. (3) The spatial effects of haze pollution are better evaluated by spatial Durbin model (SDM) with spatial fixed effects, obtaining a coefficient of 0.416, indicating haze in neighboring cities affected each other and had significant spillover. By decomposing the effect of urban scale and agglomeration on haze as direct and indirect effects, the direct effect of urban population size and the indirect effect of urban economic agglomeration are found to be more prominent, reflecting that significant asymmetrical characteristics exist in the spatial effects of urban size and agglomeration on urban haze. (4) Among the control variables that affect China’s rapid urbanization, the level of urban economic development has a positive effect on haze pollution, while the high-level industrial structure and improved technical level can effectively reduce haze pollution. Continuous decline of haze concentration of Chinese cities in recent years has been indicating the spatial relationships between haze and urban size and agglomeration have a decoupling trend. The findings contribute to theory by emphasizing the spillover effect and spatial heterogeneities of geographical factors, and have implications for policy makers to deal with haze pollution reasonably and effectively.

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Section: Variables Selection and Indicator Descriptionmentioning

confidence: 99%

Asymmetrically Spatial Effects of Urban Scale and Agglomeration on Haze Pollution in China

Fan

Yang

Liu

2019

IJERPH

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“…Once this level of development is reached, emissions begin to decrease proportionately with additional increases in income [16]. This theoretical model explains why studies on the relationship between GDP and particulate matter emissions in developing countries show a positive relationship between these variables [17,18] and a negative relationship in developed countries [19].…”

Section: Literature Reviewmentioning

confidence: 89%

Efficiency of the EU Environmental Policy in Struggling with Fine Particulate Matter (PM2.5): How Agriculture Makes a Difference?

et al. 2019

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Fine particulate matter (PM2.5) emissions are an important global issue as air pollutants lead to approximately 7 million deaths per year (World Health Organization). In an attempt to combat this global threat, countries in the European Union (EU) allocate relatively large funds for environmental policies. The main goal of this paper is to assess the long-term efficiency of the EU countries’ environmental policy in reducing the pressure of particulates pollution on the natural environment. For this purpose, multilevel panel regression models based on seminal within–between specification are used. The models are run for a panel of 25 EU countries for the years 2004–2016. In the investigations, we tried to capture the effect of the share of utilized agricultural area (UAA) in non-urban areas of the analyzed countries, as it may potentially influence policy efficiency. It was found that environmental spending in all main categories (pollution abatement, biodiversity, R&D, and environmental protection) had a significant impact on decreasing pollution pressure; however, the policy was more efficient in countries which had a lower share of UAA in their non-urban areas. The study emphasized that the impact of “pollution abatement” expenditure may be underestimated in basic panel models.

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“…Cumulative primary energy consumption is selected as an independent variable because, as explained earlier in the introduction, energy consumption has been suggested as one of the most important influencing factors responsible for the level of PM 2.5 emissions in the literature [29][30][31][32][33][77][78][79]. While other factors such as urbanization, population, economic development, and industrialization are known to have an impact on the level of PM 2.5 emissions [80][81][82][83], it has been shown that energy consumption is closely related to them. A recent study [67] shows that the rapid urbanization in China has not only promoted economic development but also increased energy consumption and the problem of air pollution.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Trends of Fine Particulate Matter Exposure across 190 Countries: Analysis and Key Insights

2020

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Despite the fact that fine particulate matter (PM2.5) causes serious health issues, few studies have investigated the level and annual rate of PM2.5 change across a large number of countries. For a better understanding of the global trend of PM2.5, this study classified 190 countries into groups showing different trends of PM2.5 change during the 2000–2014 period by estimating the progress ratio (PR) from the experience curve (EC), with PM2.5 exposure (PME)–the population-weighted average annual concentration of PM2.5 to which a person is exposed—as the dependent variable and the cumulative energy consumption as the independent variable. The results showed a wide variation of PRs across countries: While the average PR for 190 countries was 96.5%, indicating only a moderate decreasing PME trend of 3.5% for each doubling of the cumulative energy consumption, a majority of 118 countries experienced a decreasing trend of PME with an average PR of 88.1%, and the remaining 72 countries displayed an increasing trend with an average PR of 110.4%. When two different types of EC, classical and kinked, were applied, the chances of possible improvement in the future PME could be suggested in the descending order as follows: (1) the 60 countries with an increasing classical slope; (2) the 12 countries with an increasing kinked slope; (3) the 75 countries with a decreasing classical slope; and (4) the 43 countries with a decreasing kinked slope. The reason is that both increasing classical and kinked slopes are more likely to be replaced by decreasing kinked slopes, while decreasing classical and kinked slopes are less likely to change in the future. Population size seems to play a role: A majority of 52%, or 38 out of the 72 countries with an increasing slope, had a population size of bigger than 10 million inhabitants. Many of these countries came from SSA, EAP, and LAC regions. By identifying different patterns of past trends based on the analysis of PME for individual countries, this study suggests a possible change of the future slope for different groups of countries.

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What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective

Cited by 186 publications

References 68 publications

Asymmetrically Spatial Effects of Urban Scale and Agglomeration on Haze Pollution in China

Asymmetrically Spatial Effects of Urban Scale and Agglomeration on Haze Pollution in China

Efficiency of the EU Environmental Policy in Struggling with Fine Particulate Matter (PM2.5): How Agriculture Makes a Difference?

Dynamic Trends of Fine Particulate Matter Exposure across 190 Countries: Analysis and Key Insights

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