China's changing city-level greenhouse gas emissions from municipal solid waste treatment and driving factors

被引:31
作者
Kang, Yating [1 ]
Yang, Qing [1 ,2 ]
Wang, Liang [3 ]
Chen, Yingquan [1 ]
Lin, Guiying [4 ]
Huang, Junling [5 ]
Yang, Haiping [1 ]
Chen, Hanping [1 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Coal Combust, Wuhan 430074, Peoples R China
[2] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[3] Thermal Energy Dept, Sem Saelands Vei 11, N-7034 Trondheim, Norway
[4] Hubei Normal Univ, Coll Urban & Environm Sci, Huangshi 435002, Hubei, Peoples R China
[5] China Three Gorges Corp, Int Clean Energy Res Off, Beijing 100038, Peoples R China
基金
中国国家自然科学基金;
关键词
Chinese prefecture-level cities; Greenhouse gas emissions; Municipal solid waste; Spatial-temporal decomposition; CO2; EMISSIONS; DECOMPOSITION ANALYSIS; CLIMATE-CHANGE; LMDI APPROACH; ENERGY; MANAGEMENT; FORCES; UNCERTAINTY; INDUSTRY; DRIVERS;
D O I
10.1016/j.resconrec.2022.106168
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With cities' intertwined challenges of garbage siege and climate change, it is imperative to explore the greenhouse gas (GHG) emissions from municipal solid waste (MSW) treatment and the determinants of the emissions change. However, related quantitative analysis with high spatial resolution in China has been lacking, which hinder tailored policymaking. To fill the gap, this study develops a long time-series inventory of GHG emissions (including CH4, CO2 and N2O) from MSW for 294 Chinese prefecture-level cities. The temporal and spatial logarithmic mean divisia index (LMDI) model is further used to reveal the drivers behind the emissions change and difference. Results showed that domestic GHG emissions from MSW treatment increased from 39.24 Mt CO(2)e in 2006 to 128.81 Mt CO(2)e in 2019, 63.41%-88.95% of which were CH4 emissions accounting for 8.13%-10.22% of China's total CH4 emissions. First-tier cities and new first-tier cities (6.44%) contributed 35.44% to the national emissions in 2019. Furthermore, the national increased emissions were primarily driven by economic output (66.09%), while the MSW treatment intensity per GDP caused emissions reduction by 5.23%. The spatial decomposition verified that the population size was the dominant driving factor for differences between the national average and city-level emissions. Improvements in MSW treatment structure may be the effective abatement strategy for cities in Northwestern China (e.g., Yinchuan, Xining and Lanzhou). These findings could provide insights into the GHG emission mitigation of cities' MSW sector for a future carbon-neutral society.
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页数:13
相关论文
共 86 条
  • [1] CO2 emissions of Turkish manufacturing industry: A decomposition analysis
    Akbostanci, Elif
    Tunc, Gul Ipek
    Turut-Asik, Serap
    [J]. APPLIED ENERGY, 2011, 88 (06) : 2273 - 2278
  • [3] Multi-country comparisons of energy performance: The index decomposition analysis approach
    Ang, B. W.
    Xu, X. Y.
    Su, Bin
    [J]. ENERGY ECONOMICS, 2015, 47 : 68 - 76
  • [4] The LMDI approach to decomposition analysis: a practical guide
    Ang, BW
    [J]. ENERGY POLICY, 2005, 33 (07) : 867 - 871
  • [5] Ang BW, 1997, ENERGY J, V18, P59
  • [6] A new energy decomposition method: perfect in decomposition and consistent in aggregation
    Ang, BW
    Liu, FL
    [J]. ENERGY, 2001, 26 (06) : 537 - 548
  • [7] [Anonymous], 2012, GLOBAL ENERGY ASSESS, DOI DOI 10.1017/CBO9780511793677.008
  • [8] [Anonymous], 2012, Global anthropogenic non-CO2 greenhouse gas emissions: 1990-2030
  • [9] Tiers and fears: An investigation of the impact of city tiers and uncertainty avoidance on Chinese consumer response to creative advertising
    Bilby, Julie
    Reid, Mike
    Brennan, Linda
    Chen, Jiemiao
    [J]. AUSTRALASIAN MARKETING JOURNAL, 2020, 28 (04): : 332 - 348
  • [10] China city-level greenhouse gas emissions inventory in 2015 and uncertainty analysis
    Cai, Bofeng
    Cui, Can
    Zhang, Da
    Cao, Libin
    Wu, Pengcheng
    Pang, Lingyun
    Zhang, Jihong
    Dai, Chunyan
    [J]. APPLIED ENERGY, 2019, 253