The heterogeneous effects of socioeconomic determinants on PM2.5 concentrations using a two-step panel quantile regression
The heterogeneous effects of socioeconomic determinants on PM2.5 concentrations using a two-step panel quantile regression
The haze pollution caused by high PM
2.5 concentrations has adverse health effects worldwide, especially in rapidly developing China. As meteorological conditions are uncontrollable, this study aims to investigate how anthropogenic factors affect the PM
2.5 concentration under high, medium and low emission levels. The distribution of socioeconomic variables is often non-normal, with important information hidden in the tail. By using balanced panel data of 273 Chinese cities from 2010 to 2016, two-step panel quantile regression is adopted to examine the cross-quantile heterogeneity of seven socioeconomic variables: economic growth, industrial structure, urbanization, foreign direct investment (FDI), population density, public transportation and energy consumption. The empirical results show that the relationships of PM
2.5 concentration with economic growth, urbanization, industrialization and FDI are heterogeneous. Compared with other variables, population density exerts the greatest positive effect on PM
2.5 pollution across all quantile cities. Moreover, the impact of GDP per capita on PM
2.5 concentration in the lower 25th quantile cities is stronger than those in the 25th-50th, 50th-75th and upper 75th quantile cities. The effects of FDI in the upper 75th and lower 25th quantile cities are higher than those in the 25th-50th and 50th-75th quantile cities, which supports the “pollution haven” hypothesis. The impact of industrial structure on PM
2.5 concentration in the upper 75th quantile cities is larger than those in the 0-25th, 25th-50th, and 50th-75th quantile cities. The heterogeneous effects of these socioeconomic determinants could assist policymakers in implementing differentiated policies that fit cities with different levels of air pollution.
PM concentrations, Panel quantile regression, Prefecture-level cities, Spatiotemporal variations
Yan, Dan
7ccd8b83-bf40-4ec2-8c8c-b685090b44e8
Ren, Xiaohang
970abdf4-ff20-4244-9952-f9ee910736ee
Kong, Ying
bee9f273-52cd-4a23-aa02-2a750c803132
Ye, Bin
be70d256-0f14-4f76-90c0-e44fc9e8d5ac
Liao, Zangyi
f1e5bcfc-6b21-43de-806f-3e308821c53b
15 August 2020
Yan, Dan
7ccd8b83-bf40-4ec2-8c8c-b685090b44e8
Ren, Xiaohang
970abdf4-ff20-4244-9952-f9ee910736ee
Kong, Ying
bee9f273-52cd-4a23-aa02-2a750c803132
Ye, Bin
be70d256-0f14-4f76-90c0-e44fc9e8d5ac
Liao, Zangyi
f1e5bcfc-6b21-43de-806f-3e308821c53b
Yan, Dan, Ren, Xiaohang, Kong, Ying, Ye, Bin and Liao, Zangyi
(2020)
The heterogeneous effects of socioeconomic determinants on PM2.5 concentrations using a two-step panel quantile regression.
Applied Energy - Elsevier, 272, [115246].
(doi:10.1016/j.apenergy.2020.115246).
Abstract
The haze pollution caused by high PM
2.5 concentrations has adverse health effects worldwide, especially in rapidly developing China. As meteorological conditions are uncontrollable, this study aims to investigate how anthropogenic factors affect the PM
2.5 concentration under high, medium and low emission levels. The distribution of socioeconomic variables is often non-normal, with important information hidden in the tail. By using balanced panel data of 273 Chinese cities from 2010 to 2016, two-step panel quantile regression is adopted to examine the cross-quantile heterogeneity of seven socioeconomic variables: economic growth, industrial structure, urbanization, foreign direct investment (FDI), population density, public transportation and energy consumption. The empirical results show that the relationships of PM
2.5 concentration with economic growth, urbanization, industrialization and FDI are heterogeneous. Compared with other variables, population density exerts the greatest positive effect on PM
2.5 pollution across all quantile cities. Moreover, the impact of GDP per capita on PM
2.5 concentration in the lower 25th quantile cities is stronger than those in the 25th-50th, 50th-75th and upper 75th quantile cities. The effects of FDI in the upper 75th and lower 25th quantile cities are higher than those in the 25th-50th and 50th-75th quantile cities, which supports the “pollution haven” hypothesis. The impact of industrial structure on PM
2.5 concentration in the upper 75th quantile cities is larger than those in the 0-25th, 25th-50th, and 50th-75th quantile cities. The heterogeneous effects of these socioeconomic determinants could assist policymakers in implementing differentiated policies that fit cities with different levels of air pollution.
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More information
Accepted/In Press date: 19 May 2020
e-pub ahead of print date: 29 May 2020
Published date: 15 August 2020
Additional Information:
Funding Information:
This work is supported by Shenzhen Municipal Development and Reform Commission and the Shenzhen Environmental Science and New Energy Technology Engineering Laboratory (No. SDRC [2016]172 ). The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (No. 71803074 ) and the China Postdoctoral Science Foundation (No. 2019M650733 ).
Publisher Copyright:
© 2020 Elsevier Ltd
Keywords:
PM concentrations, Panel quantile regression, Prefecture-level cities, Spatiotemporal variations
Identifiers
Local EPrints ID: 441162
URI: http://eprints.soton.ac.uk/id/eprint/441162
ISSN: 0306-2619
PURE UUID: bc0aedae-e839-4a42-be7e-0d392dd8a004
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Date deposited: 03 Jun 2020 16:31
Last modified: 06 Jun 2024 04:05
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Author:
Dan Yan
Author:
Xiaohang Ren
Author:
Ying Kong
Author:
Bin Ye
Author:
Zangyi Liao
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