Scenario simulations for the peak of provincial household CO2 emissions in China based on the STIRPAT model

被引:67
|
作者
Zhao, Litong [1 ]
Zhao, Tao [1 ]
Yuan, Rong [2 ]
机构
[1] Tianjin Univ, Sch Management & Econ, Tianjin 300072, Peoples R China
[2] Chongqing Univ, Sch Business Management & Econ, Chongqing 400045, Peoples R China
关键词
Household CO2 emissions; Provincial disparities; Peaking time; Scenarios; STIRPAT model; Partial least square regression; CARBON EMISSIONS; ENERGY-CONSUMPTION; IMPACT; ACHIEVE; TARGETS; DECARBONIZATION; DECOMPOSITION; POPULATION; SECTOR; TRENDS;
D O I
10.1016/j.scitotenv.2021.151098
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
As household CO2 emissions (HCEs) are a key source of China's CO2 emissions, exploring the mitigation potential of HCEs is significant to achieve China's 2030 emission target. However, rare literatures analyzed the future evolution of HCEs from the provincial perspective. Here, we employ the STIRPAT model and build three scenarios (i.e., baseline, low and high scenarios) to investigate the trajectories and peak times of HCEs in 30 provinces up to 2040. The results show that 25 provinces can peak HCEs before 2030 in at least one scenario, while 5 provinces cannot achieve the 2030 emission target in any scenarios. Moreover, Guangxi and Hainan will maintain growth up to 2040 in all three scenarios. At the national level, China's household sector can achieve HCEs peak in all three scenarios. Further reduction of emission intensity helps national HCEs reach the peak around 2025 in the high scenario at 1063 MtCO(2). The findings suggest that Guangdong, Jiangsu, Hebei, Henan, Zhejiang and Anhui are key provinces for future HCEs reductions, because they account for more than 40% of national HCEs in 2040 in all three scenarios. Energy efficiency improvement and clean energy applications will be effective for emission reductions. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] Examining the spatiotemporal variations and inequality of China's provincial CO2 emissions
    Wu, Xiaokun
    Hu, Fei
    Han, Jingyi
    Zhang, Yagang
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2020, 27 (14) : 16362 - 16376
  • [42] China's provincial CO2 emissions embodied in international and interprovincial trade
    Guo, Ju'e
    Zhang, Zengkai
    Meng, Lei
    ENERGY POLICY, 2012, 42 : 486 - 497
  • [43] Provincial carbon footprints and interprovincial transfer of embodied CO2 emissions in China
    Sanmang Wu
    Yalin Lei
    Shantong Li
    Natural Hazards, 2017, 85 : 537 - 558
  • [44] Examining the spatiotemporal variations and inequality of China’s provincial CO2 emissions
    Xiaokun Wu
    Fei Hu
    Jingyi Han
    Yagang Zhang
    Environmental Science and Pollution Research, 2020, 27 : 16362 - 16376
  • [45] An LSTM-STRIPAT model analysis of China's 2030 CO2 emissions peak
    Zuo, Zhili
    Guo, Haixiang
    Cheng, Jinhua
    CARBON MANAGEMENT, 2020, 11 (06) : 577 - 592
  • [46] Factor Decomposition Analysis of China's Energy-Related CO2 Emissions Using Extended STIRPAT Model
    Wen, Lei
    Cao, Ye
    Weng, Jianfeng
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2015, 24 (05): : 2261 - 2267
  • [47] Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China
    Wang, Ping
    Wu, Wanshui
    Zhu, Bangzhu
    Wei, Yiming
    APPLIED ENERGY, 2013, 106 : 65 - 71
  • [48] Provincial carbon footprints and interprovincial transfer of embodied CO2 emissions in China
    Wu, Sanmang
    Lei, Yalin
    Li, Shantong
    NATURAL HAZARDS, 2017, 85 (01) : 537 - 558
  • [49] Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis
    Xu, Guangyue
    Schwarz, Peter
    Yang, Hualiu
    ENERGY POLICY, 2019, 128 : 752 - 762
  • [50] Analysis of CO2 emissions peak:China's objective and strategy
    Jiankun He
    Chinese Journal of Population,Resources and Environment, 2014, (03) : 189 - 198