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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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Abstract

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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Availability of data and materials

The energy consumption data, China’s input–output tables, the industry value-added and fixed asset investment data during the current study are available from the National Bureau of Statistics, the China Energy Statistical Yearbook and the China Statistical Yearbook. China’s PM2.5 emission data used to support the findings of this study can be found at https://www.meicmodel.org. We promised that the data are feasible and accurate.

Code availability

The R language is used in this study, and the codes used in this paper are supported by important literature.

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Acknowledgements

This work is supported by the Project of National Social Science Foundation of China (NSSFC): Study on the Spatial Effects and Governance Strategies of the Impact of Urban Haze Pollution on Public Health (No. 17BJY063).

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Authors and Affiliations

  1. School of Economics and Management, China University of Geosciences (Wuhan), 388 LUMO Road, Hongshan District, Wuhan, 430074, Hubei, China

    Han Sun, Chao Huang & Shan Ni

  2. Key Laboratory of Strategic Research in the Ministry of Natural Resources, Wuhan, 430074, China

    Han Sun

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  1. Han Sun
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  2. Chao Huang
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Han Sun contributed to conceptualization, methodology and supervision. Chao Huang contributed to writing—original draft, methodology, software, writing–review and editing. Shan Ni contributed to conceptualization and methodology.

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Correspondence to Chao Huang.

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Sun, H., Huang, C. & Ni, S. Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index. Environ Dev Sustain 24, 10209–10231 (2022). https://doi.org/10.1007/s10668-021-01862-7

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