A top-bottom estimation method for city-level energy-related CO2 emissions

被引:0
|
作者
Jing, Qiao-Nan [1 ]
Hou, Hui-Min [1 ]
Bai, Hong-Tao [1 ]
Xu, He [1 ]
机构
[1] Research Center for Strategic Environmental Assessment, Nankai University, Tianjin,300350, China
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Due to the fast process of urbanization in China recently, rapid growth of urban carbon emissions has been greatly brought about, it's generally recognized that accurate city-level carbon emission data are crucial for formulating scientific and reasonable carbon emission reduction policies. By clarifying the key categories of carbon emission sources, different kinds of carbon emissions can be targeted and precisely controlled. However, recent researches on carbon emissions were mainly concentrated at the national, regional and provincial levels, and due to the opacity and inaccuracy of the required basic data, complete carbon emission inventories for general prefecture cities have not been well compiled for a long period. To solve the problem, on the basis of previous studies, the provincial energy balance table and reasonable distribution indicators are used to estimate carbon emissions in subordinate cities from provincial carbon emissions data in our research, and a set of top-bottom urban energy consumption carbon emission estimation methods was constructed. The comparison with the publicly available city level carbon emission database showed that the estimation gap was all within 10%, which proved the feasibility and accuracy of the method. We also tried to extend the method on the time scale and provide the validation. This paper provided a scientific method and reasonable ideas for acquiring carbon emissions data of Chinese cities that were continuous in both time and space scale, and could also provide reliable data support for allocating carbon emission reduction tasks and emission reduction consultations between cities. © 2019, Editorial Board of China Environmental Science. All right reserved.
引用
收藏
页码:420 / 427
相关论文
共 50 条
  • [1] A top-bottom method for city-scale energy-related CO2 emissions estimation: A case study of 41 Chinese cities
    Jing, Qiaonan
    Bai, Hongtao
    Luo, Wen
    Cai, Bofeng
    Xu, He
    [J]. JOURNAL OF CLEANER PRODUCTION, 2018, 202 : 444 - 455
  • [2] China's city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces
    Wang, Shaojian
    Liu, Xiaoping
    [J]. APPLIED ENERGY, 2017, 200 : 204 - 214
  • [3] Estimating spatiotemporal variations of city-level energy-related CO2 emissions: An improved disaggregating model based on vegetation adjusted nighttime light data
    Liu, Xiaoping
    Ou, Jinpei
    Wang, Shaojian
    Li, Xia
    Yan, Yuchao
    Jiao, Limin
    Liu, Yaolin
    [J]. JOURNAL OF CLEANER PRODUCTION, 2018, 177 : 101 - 114
  • [4] A bottom-up decomposition analysis of energy-related CO2 emissions in Greece
    Diakoulaki, D.
    Mavrotas, G.
    Orkopoulos, D.
    Papayannakis, L.
    [J]. ENERGY, 2006, 31 (14) : 2638 - 2651
  • [5] A Decomposition Analysis of Energy-Related CO2 Emissions: The Top 10 Emitting Countries
    Kone, Aylin Cigdem
    Buke, Tayfun
    [J]. ENERGY SYSTEMS AND MANAGEMENT, 2015, : 65 - 77
  • [6] Energy-related CO2 emissions keep falling
    Johnson, Jeff
    [J]. CHEMICAL & ENGINEERING NEWS, 2016, 94 (13) : 17 - 17
  • [7] Recent trends in energy-related CO2 emissions
    Ellis, J
    Treanton, K
    [J]. ENERGY POLICY, 1998, 26 (03) : 159 - 166
  • [8] Structural patterns of city-level CO2 emissions in Northwest China
    Tian, Jing
    Shan, Yuli
    Zheng, Heran
    Lin, Xiyan
    Liang, Xi
    Guan, Dabo
    [J]. JOURNAL OF CLEANER PRODUCTION, 2019, 223 : 553 - 563
  • [9] Decarbonising energy-related CO2 emissions in the glass industry
    Zier, Michael
    Pflugradt, Noah
    Kotzur, Leander
    Stolten, Detlef
    [J]. Glass International, 2022, 45 (01): : 44 - 46
  • [10] Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China
    Wensong Su
    Yanyan Liu
    Shaojian Wang
    Yabo Zhao
    Yongxian Su
    Shijie Li
    [J]. Journal of Geographical Sciences, 2018, 28 : 495 - 513