What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index
Abstract
Air pollution in China attracts the world’s attention, so it is important to study its driving factors for air pollutants. The combined Production Decomposition Analysis and Logarithmic Mean Divisia Index (PDA–LMDI) model is applied to construct a regional contribution index in this study to explore the regional differences in factors affecting sulfur dioxide (SO2), nitrogen oxides (NOx), and particulate matter with diameter not greater than 2.5 µm (PM2.5) from 2005 to 2015 in China. The regional emission coefficient had a great inhibitory effect, which reduced SO2, NOx, and PM2.5 by 25,364.9, 10,449.3, and 11,295.3 kilotons (kt) from 2005 to 2015, respectively. For this inhibitory effect, the degree to emission reduction was great for North and East China, followed by South and Central China, and small for Southwest. Northwest. and Northeast China. The regional technical efficiency, technology improvement, capital-energy substitution and labor-energy substitution effects each reduced SO2, NOx, and PM2.5 by about 3500, 3100, and 1500 kt from 2005 to 2015, respectively. For the regional technical efficiency and technology improvement effects, the degree to emission reduction was great in East and Central China, and small in South Northwest and Northeast China. For the regional capital- and labor-energy substitution effects, the degreemore »
- Authors:
-
- China Univ. of Mining and Technology, Xuzhou (China). Management School
- Jiangsu Normal Univ., Xuzhou (China). Business School
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Tsinghua Univ., Beijing (China). School of Environment, State Key Joint Lab. of Environmental Simulation and Pollution Control
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1577847
- Report Number(s):
- PNNL-SA-149644
Journal ID: ISSN 2071-1050; SUSTDE
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Sustainability (Basel)
- Additional Journal Information:
- Journal Name: Sustainability (Basel); Journal Volume: 11; Journal Issue: 17; Journal ID: ISSN 2071-1050
- Publisher:
- MDPI
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; air pollutants; driving factor; regional analysis; combined decomposition; production decomposition analysis; logarithmic mean divisa index; emission reduction
Citation Formats
Xu, Shichun, Miao, Yongmei, Li, Yiwen, Zhou, Yifeng, Ma, Xiaoxue, He, Zhengxia, Zhao, Bin, and Wang, Shuxiao. What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index. United States: N. p., 2019.
Web. doi:10.3390/su11174650.
Xu, Shichun, Miao, Yongmei, Li, Yiwen, Zhou, Yifeng, Ma, Xiaoxue, He, Zhengxia, Zhao, Bin, & Wang, Shuxiao. What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index. United States. https://doi.org/10.3390/su11174650
Xu, Shichun, Miao, Yongmei, Li, Yiwen, Zhou, Yifeng, Ma, Xiaoxue, He, Zhengxia, Zhao, Bin, and Wang, Shuxiao. Mon .
"What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index". United States. https://doi.org/10.3390/su11174650. https://www.osti.gov/servlets/purl/1577847.
@article{osti_1577847,
title = {What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index},
author = {Xu, Shichun and Miao, Yongmei and Li, Yiwen and Zhou, Yifeng and Ma, Xiaoxue and He, Zhengxia and Zhao, Bin and Wang, Shuxiao},
abstractNote = {Air pollution in China attracts the world’s attention, so it is important to study its driving factors for air pollutants. The combined Production Decomposition Analysis and Logarithmic Mean Divisia Index (PDA–LMDI) model is applied to construct a regional contribution index in this study to explore the regional differences in factors affecting sulfur dioxide (SO2), nitrogen oxides (NOx), and particulate matter with diameter not greater than 2.5 µm (PM2.5) from 2005 to 2015 in China. The regional emission coefficient had a great inhibitory effect, which reduced SO2, NOx, and PM2.5 by 25,364.9, 10,449.3, and 11,295.3 kilotons (kt) from 2005 to 2015, respectively. For this inhibitory effect, the degree to emission reduction was great for North and East China, followed by South and Central China, and small for Southwest. Northwest. and Northeast China. The regional technical efficiency, technology improvement, capital-energy substitution and labor-energy substitution effects each reduced SO2, NOx, and PM2.5 by about 3500, 3100, and 1500 kt from 2005 to 2015, respectively. For the regional technical efficiency and technology improvement effects, the degree to emission reduction was great in East and Central China, and small in South Northwest and Northeast China. For the regional capital- and labor-energy substitution effects, the degree of emission reduction was great for North East and Central China, and small for Northwest and South China. The regional output proportion effect increased SO2, NOx, and PM2.5 by 1211.2, 320.1, and 277.8 kt from 2005 to 2015, respectively. The national economic growth had a relatively great promoting effect and increased SO2, NOx, and PM2.5 by 26,445.5, 23,827.5, and 11,925.5 kt from 2005 to 2015, respectively. Each region should formulate relevant policies and measures for emission reduction according to local conditions.},
doi = {10.3390/su11174650},
journal = {Sustainability (Basel)},
number = 17,
volume = 11,
place = {United States},
year = {Mon Aug 26 00:00:00 EDT 2019},
month = {Mon Aug 26 00:00:00 EDT 2019}
}
Web of Science
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