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

Han Sun (), Chao Huang () and Shan Ni ()
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Han Sun: China University of Geosciences (Wuhan)
Chao Huang: China University of Geosciences (Wuhan)
Shan Ni: China University of Geosciences (Wuhan)

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2022, vol. 24, issue 8, No 20, 10209-10231

Abstract: 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.

Keywords: PM2.5 emission; Generalized Divisia index; Input–output model; Factor decomposition (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10668-021-01862-7

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