Decomposition analysis of PM2.5 emissions based on LMDI and Tapio decoupling model: study of Hunan and Guangdong

被引:20
|
作者
Lai, Wenwei [1 ,2 ,3 ]
Hu, Qinglong [4 ]
Zhou, Qian [5 ]
机构
[1] Nanchang Inst Sci & Technol, Nanchang 330108, Jiangxi, Peoples R China
[2] Jiangxi Univ Finance & Econ, Nanchang 330077, Jiangxi, Peoples R China
[3] Jiangxi Acad Civil Mil Integrat, Nanchang 330029, Jiangxi, Peoples R China
[4] Jinan Univ, Sch Econ, Guangzhou 510000, Guangdong, Peoples R China
[5] Zhongnan Univ Econ & Law, Econ Sch, Wuhan 430073, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
PM2.5; LMDI model; Tapio decoupling; Energy consumption;
D O I
10.1007/s11356-021-13819-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper takes energy consumption PM2.5 emission as research object, and quantitatively analyzes the PM2.5 emission level in Hunan and Guangdong provinces from 2012 to 2017. We build a PM2.5 emission decomposition model divided by five sectors, including industry, transportation, construction, resident, and other, and use attribution method and Tapio decoupling index to analyze the relationship between economic development and PM2.5 emission level. The results show that (1) the difference in PM2.5 emissions between the two provinces appeared in 2015; (2) the contribution rate of total PM2.5 emissions is 83.1%, and coal consumption is the determine factor of PM2.5 emissions; industry is the main source of sector contribution with rate of 70.91%; (3) Guangdong's pollution control capacity is much higher than that of Hunan, while Hunan's PM2.5 marginal emission-reduction potential is much higher than that of Guangdong; (4) economic growth is the first increasing emission reason of PM2.5 emission changes, while the intensity of industrial energy consumption is the first reduction emission reason; (5) there is a big difference between the economic development of the two provinces and the decoupling of PM2.5 pollution.
引用
收藏
页码:43443 / 43458
页数:16
相关论文
共 50 条
  • [41] Decoupling PM2.5 emissions and economic growth in China over 1998-2016: A regional investment perspective
    Zhang, Xi
    Geng, Yong
    Shao, Shuai
    Song, Xiaoqian
    Fan, Meiting
    Yang, Lili
    Song, Jiekun
    SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 714
  • [42] A Deep Learning PM2.5 Hybrid Prediction Model Based on Clustering-Secondary Decomposition Strategy
    Zeng, Tao
    Liu, Ruru
    Liu, Yahui
    Shi, Jinli
    Luo, Tao
    Xi, Yunyun
    Zhao, Shuo
    Chen, Chunpeng
    Pan, Guangrui
    Zhou, Yuming
    Xu, Liping
    ELECTRONICS, 2024, 13 (21)
  • [43] Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model
    Zijing Yuan
    Shangce Gao
    Yirui Wang
    Jiayi Li
    Chunzhi Hou
    Lijun Guo
    Neural Computing and Applications, 2023, 35 : 15397 - 15413
  • [44] Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model
    Yuan, Zijing
    Gao, Shangce
    Wang, Yirui
    Li, Jiayi
    Hou, Chunzhi
    Guo, Lijun
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (21): : 15397 - 15413
  • [45] Analysis of PM2.5 Synergistic Governance Path from a Socio-Economic Perspective: A Case Study of Guangdong Province
    Fan, Kunkun
    Li, Daichao
    Li, Cong
    Jin, Xinlei
    Ding, Fei
    Zeng, Zhan
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2023, 12 (08)
  • [46] Uncertainty quantification of PM2.5 concentrations using a hybrid model based on characteristic decomposition and fuzzy granulation
    Zhang, Linyue
    Wang, Jianzhou
    Li, Zhiwu
    Zeng, Bo
    Huang, Xiaojia
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 324
  • [47] The Research of PM2.5 Concentrations Model Based on Regression Calculation Model
    Li, Junmin
    Wang, Luping
    2016 INTERNATIONAL CONFERENCE ON MATERIALS SCIENCE, RESOURCE AND ENVIRONMENTAL ENGINEERING, 2017, 1794
  • [48] A graph-based LSTM model for PM2.5 forecasting
    Gao, Xi
    Li, Weide
    ATMOSPHERIC POLLUTION RESEARCH, 2021, 12 (09)
  • [49] PSO Hammerstein Model Based PM2.5 Concentration Forecasting
    Lin, Lin
    Lin, Weixing
    Yu, Haizhen
    Shi, Xuhua
    2018 13TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2018, : 918 - 923
  • [50] PM2.5 forecasting for an urban area based on deep learning and decomposition method
    Zaini, Nur'atiah
    Ean, Lee Woen
    Ahmed, Ali Najah
    Malek, Marlinda Abdul
    Chow, Ming Fai
    SCIENTIFIC REPORTS, 2022, 12 (01)