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Open AccessArticle

Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method

by Junliang Yang 1 and Haiyan Shan 1,2,*
1
School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
Weather Service Science Research Center, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(20), 4004; https://doi.org/10.3390/ijerph16204004
Received: 3 September 2019 / Revised: 14 October 2019 / Accepted: 17 October 2019 / Published: 19 October 2019
The Chinese government has made some good achievements in reducing sulfur dioxide emissions through end-of-pipe treatment. However, in order to implement the stricter target of sulfur dioxide emission reduction during the 13th “Five-Year Plan” period, it is necessary to find a new solution as quickly as possible. Thus, it is of great practical significance to identify driving factors of regional sulfur dioxide emissions to formulate more reasonable emission reduction policies. In this paper, a distinctive decomposition approach, the generalized Divisia index method (GDIM), is employed to investigate the driving forces of regional industrial sulfur dioxide emissions in Jiangsu province and its three regions during 2004–2016. The contribution rates of each factor to emission changes are also assessed. The decomposition results demonstrate that: (i) the factors promoting the increase of industrial sulfur dioxide emissions are the economic scale effect, industrialization effect, and energy consumption effect, while technology effect, energy mix effect, sulfur efficiency effect, energy intensity effect, and industrial structure effect play a mitigating role in the emissions; (ii) energy consumption effect, energy mix effect, technology effect, sulfur efficiency effect, and industrial structure effect show special contributions in some cases; (iii) industrial structure effect and energy intensity effect need to be further optimized. View Full-Text
Keywords: industrial sulfur dioxide emissions; factor decomposition; Jiangsu province; generalized Divisia index method industrial sulfur dioxide emissions; factor decomposition; Jiangsu province; generalized Divisia index method
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Yang, J.; Shan, H. Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method. Int. J. Environ. Res. Public Health 2019, 16, 4004.

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