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
  • [11] Factors driving the change of household CO2 emissions through 2040 in China: based on decomposition and scenario analyses
    Litong Zhao
    Tao Zhao
    Rong Yuan
    Environmental Science and Pollution Research, 2020, 27 : 36865 - 36877
  • [12] Simulations of CO2 emissions peak and abatement potential in China's building operations
    Sun, Yefei
    Song, Chengyu
    JOURNAL OF BUILDING ENGINEERING, 2024, 86
  • [13] Spatiotemporal Characteristics, Determinants and Scenario Analysis of CO2 Emissions in China Using Provincial Panel Data
    Wang, Shaojian
    Fang, Chuanglin
    Li, Guangdong
    PLOS ONE, 2015, 10 (09):
  • [14] Industrial structure, technological progress and CO2 emissions in China: Analysis based on the STIRPAT framework
    Li, Wenwen
    Wang, Wenping
    Wang, Yu
    Qin, Yingbo
    NATURAL HAZARDS, 2017, 88 (03) : 1545 - 1564
  • [15] The Scenario forecasting analysis of CO2 emissions of China
    Wang, Jianjun
    Li, Li
    SUSTAINABLE DEVELOPMENT OF INDUSTRY AND ECONOMY, PTS 1 AND 2, 2014, 869-870 : 836 - +
  • [16] Industrial structure, technological progress and CO2 emissions in China: Analysis based on the STIRPAT framework
    Wenwen Li
    Wenping Wang
    Yu Wang
    Yingbo Qin
    Natural Hazards, 2017, 88 : 1545 - 1564
  • [17] Analyzing impact factors of CO2 emissions using the STIRPAT model
    Fan, Y
    Liu, LC
    Wu, G
    Wei, YM
    ENVIRONMENTAL IMPACT ASSESSMENT REVIEW, 2006, 26 (04) : 377 - 395
  • [18] Peak energy consumption and CO2 emissions in China
    Yuan, Jiahai
    Xu, Yan
    Hu, Zheng
    Zhao, Changhong
    Xiong, Minpeng
    Guo, Jingsheng
    ENERGY POLICY, 2014, 68 : 508 - 523
  • [19] Using the STIRPAT model to explore the factors driving regional CO2 emissions: a case of Tianjin, China
    Bo Li
    Xuejing Liu
    Zhenhong Li
    Natural Hazards, 2015, 76 : 1667 - 1685
  • [20] Using the STIRPAT model to explore the factors driving regional CO2 emissions: a case of Tianjin, China
    Li, Bo
    Liu, Xuejing
    Li, Zhenhong
    NATURAL HAZARDS, 2015, 76 (03) : 1667 - 1685