Proceedings of the 2025 6th International Conference on Big Data and Social Sciences (ICBDSS 2025)

Population Density and PM2.5 Pollution: Evidence from China

Authors
Kaijun Nong1, *, Pengfei Liu1, Lei Zhu1
1School of Economics and Management, Beihang University, 37 Xueyuan Road, Beijing, 100191, China
*Corresponding author. Email: nkj314383788@163.com
Corresponding Author
Kaijun Nong
Available Online 26 February 2026.
DOI
10.2991/978-94-6239-598-5_24How to use a DOI?
Keywords
Air pollution; Population density; Environmental protection
Abstract

Air pollution, especially fine particulate matter (PM2.5), is a major global environmental and public health concern. In China’s rapid urbanization, high population concentration intensifies pollution sources and worsens air quality. This study uses high-resolution remote sensing data and national census data from 2000, 2010, and 2020, with county-level units as the basis for analyzing the relationship between population density and PM2.5 pollution.

Baseline OLS regression shows a significant elasticity of 0.087 after controlling for province-by-year fixed effects. To address potential endogeneity, slope and seismic risk index are introduced as exogenous instruments. IV results show that, after controlling for province-by-year fixed effects, the pollution elasticity of population density increases to 0.223.

Further heterogeneity analysis reveals that the impact is more pronounced in economically underdeveloped regions, suggesting weaker pollution control capacity and stronger marginal effects of population agglomeration. These findings highlight the importance of considering regional economic disparities when designing air pollution control policies to achieve more equitable and effective environmental outcomes.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 6th International Conference on Big Data and Social Sciences (ICBDSS 2025)
Series
Advances in Computer Science Research
Publication Date
26 February 2026
ISBN
978-94-6239-598-5
ISSN
2352-538X
DOI
10.2991/978-94-6239-598-5_24How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Kaijun Nong
AU  - Pengfei Liu
AU  - Lei Zhu
PY  - 2026
DA  - 2026/02/26
TI  - Population Density and PM2.5 Pollution: Evidence from China
BT  - Proceedings of the 2025 6th  International Conference on Big Data and Social Sciences (ICBDSS 2025)
PB  - Atlantis Press
SP  - 237
EP  - 247
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-598-5_24
DO  - 10.2991/978-94-6239-598-5_24
ID  - Nong2026
ER  -