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Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index

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Han Sun at China University of Geosciences
Han Sun
Chao Huang
Chao Huang
Chao Huang
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Shan Ni
Shan Ni
Shan Ni
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Abstract and Figures

Analyzing the driving factors of PM2.5 pollution in different industries is of great significance for developing energy conservation and emission reduction policies in China's industries. In this study, the consumption-based PM2.5 emissions of China's industries are estimated by using an input–output model; on this basis, the generalized Divisia index method (GDIM) is used to measure the contributions of driving factors to the changes in PM2.5 emissions from China's six major industries. The results show that China's consumption-based PM2.5 emissions presented a downward trend from 2007 to 2015, the changes in industrial PM2.5 emissions had a much higher impact on China's total PM2.5 emissions changes than other industries and occupied a dominant position. The generalized Divisia index decomposition analysis results show that investment, output and energy consumption scale were the primary contributors to the increase of PM2.5 emissions in six sectors, with investment scale contributing the most. The investment PM2.5 emission intensity, output PM2.5 emission intensity and energy consumption PM2.5 intensity play a major role in suppressing PM2.5 emissions, while investment efficiency and energy intensity have a smaller inhibitory effect. Therefore, the government should guide investments to more high-end, low-emission industries and encourage companies to increase green investments and use renewable energy and clean energy. Avoiding excessive investments and improving investment efficiency in related industries can also effectively alleviate PM2.5 emissions.
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Vol.:(0123456789)
Environment, Development and Sustainability (2022) 24:10209–10231
https://doi.org/10.1007/s10668-021-01862-7
1 3
Driving factors ofconsumption‑based PM2.5 emissions
inChina: anapplication ofthegeneralized Divisia index
HanSun1,2· ChaoHuang1 · ShanNi1
Received: 15 March 2021 / Accepted: 26 September 2021 / Published online: 1 October 2021
© The Author(s), under exclusive licence to Springer Nature B.V. 2021
Abstract
Analyzing the driving factors of PM2.5 pollution in different industries is of great signifi-
cance for developing energy conservation and emission reduction policies in China’s indus-
tries. In this study, the consumption-based PM2.5 emissions of China’s industries are esti-
mated by using an input–output model; on this basis, the generalized Divisia index method
(GDIM) is used to measure the contributions of driving factors to the changes in PM2.5
emissions from China’s six major industries. The results show that China’s consumption-
based PM2.5 emissions presented a downward trend from 2007 to 2015, the changes in
industrial PM2.5 emissions had a much higher impact on China’s total PM2.5 emissions
changes than other industries and occupied a dominant position. The generalized Divisia
index decomposition analysis results show that investment, output and energy consumption
scale were the primary contributors to the increase of PM2.5 emissions in six sectors, with
investment scale contributing the most. The investment PM2.5 emission intensity, output
PM2.5 emission intensity and energy consumption PM2.5 intensity play a major role in sup-
pressing PM2.5 emissions, while investment efficiency and energy intensity have a smaller
inhibitory effect. Therefore, the government should guide investments to more high-end,
low-emission industries and encourage companies to increase green investments and use
renewable energy and clean energy. Avoiding excessive investments and improving invest-
ment efficiency in related industries can also effectively alleviate PM2.5 emissions.
Keywords PM2.5 emission· Generalized Divisia index· Input–output model· Factor
decomposition
* Chao Huang
xxchao027@163.com
Han Sun
sunhan2004@126.com
Shan Ni
nishan@cug.edu.cn
1 School ofEconomics andManagement, China University ofGeosciences (Wuhan), 388 LUMO
Road, Hongshan District, Wuhan430074, Hubei, China
2 Key Laboratory ofStrategic Research intheMinistry ofNatural Resources, Wuhan430074, China
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Thus, to address these drawbacks, the generalized Divisia index method (GDIM) was presented by Vaninsky [37]. Subsequently, the application of this approach is gradually growing [37][38][39][40][41], with only a few cases focusing on PM 2.5 pollution [28,42,43]. ...
... The generalized Divisia index method (GDIM) is an effective decomposition approach that links the changes in pollutant emissions with socio-economic factors through the deformation of Kaya identity [28,42,43]. In contrast to classical econometric models, the GDIM approach mainly decomposes PM 2.5 emissions based on time-series data into different influencing factors without residual terms. ...
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