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Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method
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 quickl...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843937/ https://www.ncbi.nlm.nih.gov/pubmed/31635054 http://dx.doi.org/10.3390/ijerph16204004 |
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author | Yang, Junliang Shan, Haiyan |
author_facet | Yang, Junliang Shan, Haiyan |
author_sort | Yang, Junliang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6843937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68439372019-11-25 Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method Yang, Junliang Shan, Haiyan Int J Environ Res Public Health Article 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. MDPI 2019-10-19 2019-10 /pmc/articles/PMC6843937/ /pubmed/31635054 http://dx.doi.org/10.3390/ijerph16204004 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Junliang Shan, Haiyan Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method |
title | Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method |
title_full | Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method |
title_fullStr | Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method |
title_full_unstemmed | Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method |
title_short | Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method |
title_sort | identifying driving factors of jiangsu’s regional sulfur dioxide emissions: a generalized divisia index method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843937/ https://www.ncbi.nlm.nih.gov/pubmed/31635054 http://dx.doi.org/10.3390/ijerph16204004 |
work_keys_str_mv | AT yangjunliang identifyingdrivingfactorsofjiangsusregionalsulfurdioxideemissionsageneralizeddivisiaindexmethod AT shanhaiyan identifyingdrivingfactorsofjiangsusregionalsulfurdioxideemissionsageneralizeddivisiaindexmethod |