Environmental Kuznets curve for PM2.5 emissions in Beijing, China: What role can natural gas consumption play?

Dong, Kangyin; Sun, Renjin; Dong, Cong; Lĭ, Hui; Zeng, Xiangang; Ni, Guohua

doi:10.1016/j.ecolind.2018.05.045

Cited by 76 publications

(24 citation statements)

References 45 publications

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“…The coefficients of lnPGRP and (lnPGRP) 2 were significantly positive and negative, respectively, indicating the existence of an inverted U-shaped environmental Kuznets curve (EKC) of PM 2.5 concentration in the study area. With the increase of the per capita income level, the study area experienced a process of pollution first and then treatment, resulting in the PM 2.5 concentration first increasing and then decreasing, which is consistent with some existing research [51][52][53]. Although most cities in the study area except for Beijing, Tianjin, and Qinhuangdao are still in the stage of industrialization [54] and have a secondary industry-dominated structure, some cities shut down many small-sized, high-pollution, and high-energy-consumption enterprises in order to meet environmental quality requirements, and reached the peak of pollution ahead of schedule at the cost of low and medium economic growth.…”

Section: Effects Of Socioeconomic Factors On Pm 25 Concentrationsupporting

confidence: 90%

The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas

Wang

Zhang

Zhao

et al. 2020

IJERPH

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The study investigated the spatiotemporal evolution of PM2.5 concentration in the Beijing–Tianjin–Hebei region and surrounding areas during 2015–2017, and then analyzed its socioeconomic determinants. First, an estimation model considering spatiotemporal heterogeneous relationships was developed to accurately estimate the spatial distribution of PM2.5 concentration. Additionally, socioeconomic determinants of PM2.5 concentration were analyzed using a spatial panel Dubin model, which aimed to improve the robustness of the model estimation. The results demonstrated that: (1) The proposed model significantly increased the estimation accuracy of PM2.5 concentration. The mean absolute error and root-mean-square error were 9.21 μg/m3 and 13.10 μg/m3, respectively. (2) PM2.5 concentration in the study area exhibited significant spatiotemporal changes. Although the PM2.5 concentration has declined year by year, it still exceeded national environmental air quality standards. (3) The per capita GDP, urbanization rate and number of industrial enterprises above the designated size were the key factors affecting the spatiotemporal distribution of PM2.5 concentration. This study provided scientific references for comprehensive PM2.5 pollution control in the study area.

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Section: Effects Of Socioeconomic Factors On Pm 25 Concentrationsupporting

confidence: 90%

The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas

Wang

Zhang

Zhao

et al. 2020

IJERPH

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“…This inverted U -shape relationship was then named as EKC by Panayotou (1993) in 1993. EKC analysis has been successfully used in many regions in recent years to forecast pollution emissions ( Sinha and Bhattacharya, 2017 , Dong et al, 2018 , Ding et al, 2019 ). A segmented quadratic equation is needed to fit the EKC for a precise forecasting of the pollution emission peak.…”

Section: Literature Reviewmentioning

confidence: 99%

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

Meng

Zhou

2020

Ecological Indicators

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Highlights Air pollution emission level of the Beijing–Tianjin–Hebei region is fitted. Fitting curve to air pollution emission level reached the peak in 2015. Air pollution emissions in 2025 will decrease to the level of 2007. Industrial waste gas emissions play a decisive role for the pollution level change. Air quality is sensitive to the operation of building material industries.

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“…The EKC states that particulate matter emissions increase proportionately with income until a certain level of economic development is achieved. 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: 87%

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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Environmental Kuznets curve for PM2.5 emissions in Beijing, China: What role can natural gas consumption play?

Cited by 76 publications

References 45 publications

The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas

The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

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

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