Analysis of Regional Differences in Energy-Related PM2.5 Emissions in China: Influencing Factors and Mitigation Countermeasures

Abstract

China’s rapid economic development has resulted in a series of serious environmental pollution problems, such as atmospheric particulate pollution. However, the socioeconomic factors affecting energy-related PM2.5 emissions are indistinct. Therefore, this study first explored the change in PM2.5 emissions over time in China from 1995 to 2012. Then the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model was adopted for quantitatively revealing the mechanisms of various factors on energy-related PM2.5 emissions. Finally, the Environmental Kuznets Curve (EKC) hypothesis was adopted to examine whether an EKC relationship between affluence and energy-related PM2.5 emissions is present from a multiscale perspective. The results showed that energy-related PM2.5 emissions in most regions showed an increasing trend over the study period. The influences of the increase in population, energy intensity, and energy use mix on energy-related PM2.5 emissions were positive and heterogeneous, and population scale was the major driving force of energy-related PM2.5 emissions. The effects of the increase in the urbanization level and the proportion of tertiary industry increased value to GDP on energy-related PM2.5 emissions varied from area to area. An inverse U-shape EKC relationship for energy-related PM2.5 emissions was not verified except for eastern China. The conclusions are valuable for reducing PM2.5 emissions without affecting China’s economic development.

Impact of Socio-Economic Factors and Energy Mix on PM2.5 Concentration: An Empirical Analysis of Next-11 Countries

“…The Primary factor of air pollution is the concentration of PM2.5 that reduces visibility and generates further pollution through chemical reactions as it can suspend in the air (W. Zhu, Wang, and Zhang 2019). Studies show that long-term exposure to the air contaminated by PM2.5 can cause serious illness, respiratory problems, cardiovascular disease, damage immune system, and increased risk of premature death (Hui Wang, Ji, and Xia 2019). The concentration of PM2.5 is being considered as the main factor for increased mortality, reducing the life expectancy and greater mortality contributor than AIDs and HIV (Diao et al, 2020;Fang et al, 2020;Sarkodie et al, 2019).…”

“…Empirical studies exhibited the main driving factors of this deadly concentration are urbanization (Fang et al 2020;Tao et al 2020;Z. Zhu, Fu, and Liu 2020), income level and economic growth (Ouyang et al 2019;Sarkodie et al 2019;Hui Usman & Chughtai Impact of Socio-Economic Factors and Energy . .…”

“…. Wang, Ji, and Xia 2019), trade openness (Haikun Wang et al 2017), industrialization and energy consumption (Liu et al 2019;Reddington et al 2019;Hui Wang, Ji, and Xia 2019;Zhao, Chen, and Zhao 2019). The structure of energy consumption is the leading cause of PM2.5 concentration (Chen et al 2018;Okedere et al 2021).…”

“…The empirical findings on this impurity are scant and very recently researchers have shifted toward econometric methods to find the driving factors of its concentration. The study has developed a panel data methodology using the rationale of the Environment Kuznets Curve (EKC) that explains the nonlinear relation between economic growth and environmental quality (Hui Wang, Ji, and Xia 2019;Zafar, Saud, and Hou 2019). EKC hypothesis posits that at an early stage of economic growth, the resources are being extracted more than generated which leads to degrade the environmental quality, but later the environmental quality starts to improve due to the environmental consciousness and increased intention on social wellbeing (Ben Amar 2021; Dinda 2004).…”

“…The pioneering study was conducted in 1991 by Grossman et al, (1991) to validate the claimed U-shaped curved relation between income and environmental degradation. Later on, it has been analyzed by taking the different proxies of income and environmental quality but considering the PM2.5 in the EKC framework with empirical panel data analysis is understudied yet (Hui Wang, Ji, and Xia 2019). The study ought to offer very useful implications.…”