Regional differences in impacts of economic growth and urbanization on air pollutants in China based on provincial panel estimation

Xu, Shichun; Miao, Yuqing; Gao, Chang; Long, Ruyin; Chen, Hong; Zhao, Bin; Wang, Yafei

doi:10.1016/j.jclepro.2018.10.114

Cited by 105 publications

(51 citation statements)

References 71 publications

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“…The regional relative income effect reduced APEs in the northern, eastern, southern, and northeastern regions, but promoted APEs in other regions. The promoting or curbing effect of regional relative income on APEs can be explained by the environmental Kuznets curve theory [24,41,77]. At relative a low-income level, the economic structure changed from agriculture to energy-intensive industries, and air pollution was becoming serious accompanied by increases in income; the economic structure turned to low-pollution services and knowledge-intensive industries after a certain income level (inflection point), and this then reduced APEs.…”

Section: The Regional Energy Intensity Effect Curbed Apesmentioning

confidence: 99%

“…Recently, more attention has been focused on analyses of factors influencing APEs. Some studies established econometric models to discuss the relationships between APEs and macroeconomic variables [20][21][22][23] and explore the effects of these macroeconomic variables on APEs [24][25][26][27]. Some scholars analyzed sources of APEs from the sectoral perspective and found that the power and transport sectors were the main sources [28][29][30], followed by the agriculture and household sectors [31,32].…”

mentioning

confidence: 99%

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Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Yang

Miao

et al. 2019

Sustainability

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The regional emission reduction determines the national emission reduction for one country, and the differences in regional economic characteristics may result in regional differences in air pollutant emissions (APEs). In this regard, this study constructs a regional contribution index of different factors through an extended LMDI (Logarithmic Mean Divisia Index) method and investigates regional differences in factors driving the changes of China's major APEs (SO 2 , NO x , and PM 2.5 ). It reveals that the regional emission efficiency effect was a key inhibitory factor on APEs, which was more obvious in the eastern and northern regions. The regional energy intensity had greater inhibitory effects on SO 2 and NO x than on PM 2.5 , and these inhibitory effects were more obvious in the eastern region. The regional population structure promoted APEs for northern, southern, northeastern, and eastern regions, and inhibited APEs for central, southwestern, and northwestern regions. The regional relative income had a slight effect, which curbed APEs for northern, eastern, southern, and northern regions. The national economic growth effect was the key factor in promoting APEs, which was obvious in eastern and northern regions, followed by southern, central, and southwestern regions. Policy implications are put forward based on empirical results.Sustainability 2019, 11, 7113 2 of 22 reduction target for the 13th Five-Year Plan (FYP) (2016-2020) that by 2020 the energy consumption per 10,000 RMB GDP would be cut by 15% in comparison to 2015, and SO 2 and NO x would be within 15.8 and 15.74 million tons, respectively, which are both 15% lower than the level of 2015. There are regional differences in APEs for China because of its serious imbalances in economic development. As for the air pollutant reduction, the national reduction target should be reasonably allocated to each province and municipality as a regional target, to smoothly realize the national emission reduction target. Therefore, it is urgent and important to study the regional differences in factors that affect APEs in China, which is conductive to offer references for designing policies according to regional differences and effectively reduce air pollutant emissions (APEs).Many previous studies touched on the issues related to air pollutant emission trends and characteristics [6][7][8][9], and the harm to the economy and human health [10][11][12][13][14][15][16][17][18][19]. Recently, more attention has been focused on analyses of factors influencing APEs. Some studies established econometric models to discuss the relationships between APEs and macroeconomic variables [20][21][22][23] and explore the effects of these macroeconomic variables on APEs [24][25][26][27]. Some scholars analyzed sources of APEs from the sectoral perspective and found that the power and transport sectors were the main sources [28][29][30], followed by the agriculture and household sectors [31,32]. The decomposition method has been widely used to decompose factors influencing energy u...

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Section: The Regional Energy Intensity Effect Curbed Apesmentioning

confidence: 99%

mentioning

confidence: 99%

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Yang

Miao

et al. 2019

Sustainability

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“…Population growth, industrialization, climate change, and the ever-growing fact of urbanization are further accelerating the production of emissions and the severe impacts on air quality [4][5][6]. Air pollution is known as a profound contributor to human mortality and as a potential danger to the environment and ecological systems [2,7,8]. Thus, intelligent monitoring of air pollutants is of the utmost importance to maintain acceptable levels of air quality for well-being [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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“…The air pollution is known as a profound contributor to human mortality and potential danger to the environment and ecological systems. [2,7,8]. Thus, intelligent monitoring of the air pollutants is of utmost importance to maintain acceptable air quality for well-being [9][10][11][12].…”

mentioning

confidence: 99%

“…Previous studies, e.g. [7][8][9][10][11], report that a substantial amount of mercury outflows to the earth comes from coal-fired power plants. In , roughly 1960 tone/year mercury outflowed to the air from various sections worldwide [23].…”

mentioning

confidence: 99%

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations' boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively.Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

show abstract

Regional differences in impacts of economic growth and urbanization on air pollutants in China based on provincial panel estimation

Cited by 105 publications

References 71 publications

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

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