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

被引:10
|
作者
Yang, Junliang [1 ]
Shan, Haiyan [1 ,2 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Management Sci & Engn, Nanjing 210044, Jiangsu, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Weather Serv Sci Res Ctr, Nanjing 210044, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
industrial sulfur dioxide emissions; factor decomposition; Jiangsu province; generalized Divisia index method; STRUCTURAL DECOMPOSITION ANALYSIS; INDUSTRIAL SO2 EMISSIONS; TIANJIN-HEBEI REGION; ECONOMIC-GROWTH; CARBON-DIOXIDE; CO2; EMISSIONS; ENERGY-CONSUMPTION; CHINA; POLLUTION; GREEN;
D O I
10.3390/ijerph16204004
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:20
相关论文
共 38 条
  • [31] Regional mapping of climate variability index and identifying socio-economic factors influencing farmer’s perception in Bangladesh
    Sifat E. Rabbi
    Reza Shant
    Sourav Karmakar
    Azhar Habib
    Jürgen P. Kropp
    Environment, Development and Sustainability, 2021, 23 : 11050 - 11066
  • [32] Regional mapping of climate variability index and identifying socio-economic factors influencing farmer's perception in Bangladesh
    Rabbi, Sifat E.
    Shant, Reza
    Karmakar, Sourav
    Habib, Azhar
    Kropp, Juergen P.
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2021, 23 (07) : 11050 - 11066
  • [33] Analysing driving factors of India's transportation sector CO2 emissions: Based on LMDI decomposition method
    Jain, Siddharth
    Rankavat, Shalini
    HELIYON, 2023, 9 (09)
  • [34] Identifying Edaphic Factors and Normalized Difference Vegetation Index Metrics Driving Wildlife Mortality From Anthrax in Kenya's Wildlife Areas
    Obanda, Vincent
    Otieno, Viola A.
    Kingori, Edward M.
    Ndeereh, David
    Lwande, Olivia W.
    Chiyo, Patrick I.
    FRONTIERS IN ECOLOGY AND EVOLUTION, 2021, 9
  • [35] Decomposition of Driving Factors Affecting China's Carbon Dioxide Emissions Based on Comparative Analysis between Modified Kaya Identity and IPAT Equation
    Zhao Ao
    Wu Chunyou
    CONTEMPORARY INNOVATION AND DEVELOPMENT IN STATISTICAL SCIENCE, 2012, : 521 - 526
  • [36] Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method
    Huo, Tengfei
    Cao, Ruijiao
    Xia, Nini
    Hu, Xuan
    Cai, Weiguang
    Liu, Bingsheng
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 320
  • [37] Driving factors and reduction paths dynamic simulation optimization of carbon dioxide emissions in China's construction industry under the perspective of dual carbon targets
    Xian, Yujie
    Wang, Huihui
    Zhang, Zeyu
    Yang, Yunsong
    Zhong, Yuhao
    ENVIRONMENTAL IMPACT ASSESSMENT REVIEW, 2025, 112
  • [38] Regional Differences in Fossil Energy-Related Carbon Emissions in China's Eight Economic Regions: Based on the Theil Index and PLS-VIP Method
    Liu, Xianzhao
    Yang, Xu
    Guo, Ruoxin
    SUSTAINABILITY, 2020, 12 (07)