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

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  • Junliang Yang

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Haiyan Shan

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
    Weather Service Science Research Center, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

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.

Suggested Citation

  • Junliang Yang & Haiyan Shan, 2019. "Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method," IJERPH, MDPI, vol. 16(20), pages 1-20, October.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:20:p:4004-:d:278289
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    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Vaninsky, Alexander, 2014. "Factorial decomposition of CO2 emissions: A generalized Divisia index approach," Energy Economics, Elsevier, vol. 45(C), pages 389-400.
    3. Shao, Shuai & Liu, Jianghua & Geng, Yong & Miao, Zhuang & Yang, Yingchun, 2016. "Uncovering driving factors of carbon emissions from China’s mining sector," Applied Energy, Elsevier, vol. 166(C), pages 220-238.
    4. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei, 2018. "How does information and communication technology affect China's energy intensity? A three-tier structural decomposition analysis," Energy, Elsevier, vol. 151(C), pages 748-759.
    5. Magacho, Guilherme R. & McCombie, John S.L. & Guilhoto, Joaquim J.M., 2018. "Impacts of trade liberalization on countries’ sectoral structure of production and trade: A structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 70-77.
    6. Bin Su & B. W. Ang, 2012. "Structural Decomposition Analysis Applied To Energy And Emissions: Aggregation Issues," Economic Systems Research, Taylor & Francis Journals, vol. 24(3), pages 299-317, March.
    7. Wang, H. & Zhou, P., 2018. "Assessing Global CO2 Emission Inequality From Consumption Perspective: An Index Decomposition Analysis," Ecological Economics, Elsevier, vol. 154(C), pages 257-271.
    8. Hu, Bin & Li, Zhengtao & Zhang, Lin, 2019. "Long-run dynamics of sulphur dioxide emissions, economic growth and energy efficiency in China," MPRA Paper 94588, University Library of Munich, Germany.
    9. Liang, Wei & Gan, Ting & Zhang, Wei, 2019. "Dynamic evolution of characteristics and decomposition of factors influencing industrial carbon dioxide emissions in China: 1991–2015," Structural Change and Economic Dynamics, Elsevier, vol. 49(C), pages 93-106.
    10. Wang, Yuan & Han, Rong & Kubota, Jumpei, 2016. "Is there an Environmental Kuznets Curve for SO2 emissions? A semi-parametric panel data analysis for China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1182-1188.
    11. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    12. Wang, H. & Ang, B.W., 2018. "Assessing the role of international trade in global CO2 emissions: An index decomposition analysis approach," Applied Energy, Elsevier, vol. 218(C), pages 146-158.
    13. Liu, Qiaoling & Wang, Qi, 2015. "Reexamine SO2 emissions embodied in China's exports using multiregional input–output analysis," Ecological Economics, Elsevier, vol. 113(C), pages 39-50.
    14. Chen, Shuo & Li, Yiran & Yao, Qin, 2018. "The health costs of the industrial leap forward in China: Evidence from the sulfur dioxide emissions of coal-fired power stations," China Economic Review, Elsevier, vol. 49(C), pages 68-83.
    15. Oliveira, Gilson Adamczuk & Tan, Kim Hua & Guedes, Bruno Turmina, 2018. "Lean and green approach: An evaluation tool for new product development focused on small and medium enterprises," International Journal of Production Economics, Elsevier, vol. 205(C), pages 62-73.
    16. Peter Rafaj & Markus Amann & José Siri & Henning Wuester, 2014. "Changes in European greenhouse gas and air pollutant emissions 1960–2010: decomposition of determining factors," Climatic Change, Springer, vol. 124(3), pages 477-504, June.
    17. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    18. Ling, Zaili & Huang, Tao & Li, Jixiang & Zhou, Sheng & Lian, Lulu & Wang, Jinxiang & Zhao, Yuan & Mao, Xiaoxuan & Gao, Hong & Ma, Jianmin, 2019. "Sulfur dioxide pollution and energy justice in Northwestern China embodied in West-East Energy Transmission of China," Applied Energy, Elsevier, vol. 238(C), pages 547-560.
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