Impacts of Emission Reduction Technological Changes on China's City-Level PM2.5 Concentration Based on Sustainable Development

被引:0
|
作者
Chen, Jiandong [1 ]
Wei, Yu [1 ]
Gao, Ming [1 ]
Huang, Shuo [1 ]
机构
[1] SouthWestern Univ Finance & Econ, Sch Publ Adm, Chengdu 611130, Peoples R China
基金
中国国家自然科学基金;
关键词
CARBON-DIOXIDE EMISSIONS; CO2; EMISSIONS; DECOMPOSITION; POLLUTION; CONSUMPTION;
D O I
10.1155/2021/4358661
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
As an important field for human activities, cities play a critical role in PM2.5 reductions. Among the determinants for PM2.5 concentration, technological progress is considered to exhibit significant inhibitory effects. Although most extant research has focused on energy technologies or total factor productivity, due to limitations in data and methods, few scholars have focused on emission reduction technological changes at a city-level scale. Therefore, based on the combination of k-means clustering and the log-mean Divisia index method, this study estimates and explores the impact of PM2.5 emission reduction technology (PME) on the temporal changes and spatial differences of 262 Chinese cities' PM2.5 concentration during 2003-2017. The findings show the following: (1) although the results based on econometric methods indicate that emission reduction technological changes decreased China's city-level PM2.5 concentration, there were turning points in the yearly impacts, indicating that the improvements to emission reduction efficiency were not stable; (2) compared with PME, energy intensity played a more stable role in PM2.5 emissions reductions, implying that the improvement of energy efficiency was still very important in controlling PM2.5 concentrations; (3) based on the classified groups after clustering, most cities' PME contributed to negative differences, but the PME of a small number of cities was very weak to largely lower the average level of their group; and (4) distributions of the spatial decomposition of the three classified groups were stable in the period of 2003-2017, implying that the catch-up and transcendence effects of PME within the group were limited. Thus, policymakers should focus on the impact of different policies on PME differences between cities.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis
    Chuanglin Fang
    Zhenbo Wang
    Guang Xu
    Journal of Geographical Sciences, 2016, 26 : 1519 - 1532
  • [2] Spatial-temporal characteristics of PM2.5 in China: A city-level perspective analysis
    Fang Chuanglin
    Wang Zhenbo
    Xu Guang
    JOURNAL OF GEOGRAPHICAL SCIENCES, 2016, 26 (11) : 1519 - 1532
  • [3] Decoding the effect of demographic factors on environmental health based on city-level PM2.5 pollution in China
    Cao, Shuhui
    Wu, Dan
    Liu, Li
    Li, Suli
    Zhang, Shiqiu
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 349
  • [4] Heterogeneity and typology of the city-level synergy between CO2 emission, PM2.5, and ozone pollution in China
    Guan, Yang
    Xiao, Yang
    Rong, Bing
    Kang, Lei
    Zhang, Nannan
    Chu, Chengjun
    JOURNAL OF CLEANER PRODUCTION, 2023, 405
  • [5] The Potential of Green Development and PM2.5 Emission Reduction for China's Cement Industry
    Tian, Li
    ATMOSPHERE, 2023, 14 (03)
  • [6] Fine particulate matter (PM2.5) in China at a city level
    Zhang, Yan-Lin
    Cao, Fang
    SCIENTIFIC REPORTS, 2015, 5
  • [7] Fine particulate matter (PM2.5) in China at a city level
    Yan-Lin Zhang
    Fang Cao
    Scientific Reports, 5
  • [8] Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China
    Song, Zhihao
    Chen, Bin
    Huang, Jianping
    ENVIRONMENTAL POLLUTION, 2022, 297
  • [9] The Influence of Technological Innovation on PM2.5 Concentration in the Yangtze River Delta, China
    Zhang, Xinlin
    Yang, Zhen
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2023, 32 (04): : 3915 - 3925
  • [10] Spatial self-aggregation effects and national division of city-level PM2.5 concentrations in China based on spatio-temporal clustering
    Chen, Ziyue
    Chen, Danlu
    Xie, Xiaoming
    Cai, Jun
    Zhuang, Yan
    Cheng, Nianliang
    He, Bin
    Gao, Bingbo
    JOURNAL OF CLEANER PRODUCTION, 2019, 207 : 875 - 881