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Analysis of Regional Differences in Energy-Related PM2.5 Emissions in China: Influencing Factors and Mitigation Countermeasures
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.
Analysis of Regional Differences in Energy-Related PM2.5 Emissions in China: Influencing Factors and Mitigation Countermeasures
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.
Analysis of Regional Differences in Energy-Related PM2.5 Emissions in China: Influencing Factors and Mitigation Countermeasures
Hui Wang (author) / Guangxing Ji (author) / Jisheng Xia (author)
2019
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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