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
  • [21] Correction to: Characteristics, decoupling effect, and driving factors of regional tourism’s carbon emissions in China
    Guobao Xiong
    Junhong Deng
    Baogen Ding
    Environmental Science and Pollution Research, 2022, 29 : 47094 - 47094
  • [22] Driving forces of carbon dioxide emissions in China's cities: An empirical analysis based on the geodetector method
    Xu, Li
    Du, Hongru
    Zhang, Xiaolei
    JOURNAL OF CLEANER PRODUCTION, 2021, 287
  • [23] Carbon Emissions in the Yellow River Basin: Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors Based on a Logarithmic Mean Divisia Index (LMDI) Decomposition Method
    Liu, Ke
    Xie, Xinyue
    Zhao, Mingxue
    Zhou, Qian
    SUSTAINABILITY, 2022, 14 (15)
  • [24] Assessing driving factors of regional water use in production sectors using a structural decomposition method: a case study in Jiangsu Province, China
    Zhang, Lingling
    Wang, Zongzhi
    Li, Xiaohui
    Cai, Ximing
    WATER POLICY, 2016, 18 (02) : 262 - 275
  • [25] Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method
    Zha, Donglan
    Yang, Guanglei
    Wang, Qunwei
    ENERGY ECONOMICS, 2019, 84
  • [26] Identification of the driving factors' influences on regional energy-related carbon emissions in China based on geographical detector method
    Zhang, Xinlin
    Zhao, Yuan
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2018, 25 (10) : 9626 - 9635
  • [27] Identification of the driving factors’ influences on regional energy-related carbon emissions in China based on geographical detector method
    Xinlin Zhang
    Yuan Zhao
    Environmental Science and Pollution Research, 2018, 25 : 9626 - 9635
  • [28] Toxicity Impact Assessment of Nitrogen Oxide and Sulfur Dioxide Emissions in China's Textile Industry With Chemical Footprint Method
    Guo, Yiqi
    Zhu, Lisha
    Ye, Xiangyu
    Wang, Xiaopeng
    Xu, Yu
    Qian, Gao
    Wang, Laili
    AATCC JOURNAL OF RESEARCH, 2023, 10 (04) : 232 - 240
  • [29] What Factors Drive Air Pollutants in China? An Analysis from the Perspective of Regional Difference Using a Combined Method of Production Decomposition Analysis and Logarithmic Mean Divisia Index
    Xu, Shichun
    Miao, Yongmei
    Li, Yiwen
    Zhou, Yifeng
    Ma, Xiaoxue
    He, Zhengxia
    Zhao, Bin
    Wang, Shuxiao
    SUSTAINABILITY, 2019, 11 (17)
  • [30] Regional Differences of the Driving Factors and Decoupling Effect of Carbon Emissions Evidence from China's Pollution-Intensive Industry
    Wang, Lafang
    Liu, Xia
    Tan, Meimei
    INTERNATIONAL REVIEW FOR SPATIAL PLANNING AND SUSTAINABLE DEVELOPMENT, 2016, 4 (04): : 4 - 26