Current status, future prediction and offset potential of fossil fuel CO2 emissions in China

被引:24
作者
Cao, Jiaxi [1 ]
Zhang, Jian [1 ]
Chen, Ye [2 ]
Fan, Rong [1 ]
Xu, Lei [1 ]
Wu, Entao [1 ]
Xue, Yuan [3 ]
Yang, Junliu [4 ]
Chen, Yiming [5 ]
Yang, Bo [5 ]
Wu, Shuhong [1 ,6 ]
机构
[1] Beijing Forestry Univ, Sch Ecol & Nat Conservat, Beijing 100083, Peoples R China
[2] Beijing Forestry Univ, Sch Grassland Sci, Beijing 100083, Peoples R China
[3] Xian Shiyou Univ, Sch Econ & Management, Xian 710065, Peoples R China
[4] Beijing Forestry Univ, Sch Soil & Water Conservat, Beijing 100083, Peoples R China
[5] China Forestry Grp Corp, Beijing 100016, Peoples R China
[6] Beijing Forestry Univ, Sch Ecol & Nat Conservat, Beijing, Peoples R China
关键词
Carbon emission; Carbon sink; BP neural network; Prediction; CCER; CARBON EMISSION; ENERGY-CONSUMPTION; AUTOCORRELATION COEFFICIENT; GENETIC ALGORITHM; FOREST BIOMASS; CITY-LEVEL; INEQUALITY; URBANIZATION; OPTIMIZATION; NETWORKS;
D O I
10.1016/j.jclepro.2023.139207
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Reducing carbon emissions and increasing carbon sinks are key strategies to effectively remove greenhouse gases from the atmosphere. Assessing the current carbon emission status and predicting future carbon emission scenarios could help formulate effective regional carbon emission reduction targets. However, it is necessary to enhance the carbon sink capacity of terrestrial ecosystems and improve forest management methods to promote greenhouse gas absorption. In this study, the spatiotemporal characteristics and dynamic evolution of fossil fuel CO2 (FFCO2) emissions in China from 2000 to 2019 were analyzed using the standard deviation ellipse, kernel density of emissions, and Theil index. A backpropagation (BP) neural network optimized with a genetic algorithm (GA) was used to predict FFCO2 emission during 2020-2030. The biomass increment methodology was used to predict the potential carbon sinks generated by Chinese Certified Emission Reduction (CCER) carbon-sink forestry projects during 2020-2030. The results showed that China's FFCO2 emissions exhibited a gradual increasing trend during 2000-2019, with an average annual growth rate of 6.29%. China's FFCO2 emissions show a greater distribution on the southeastern coast than in the northwestern interior. The GA-BP prediction shows that China's FFCO2 emissions will continue to fluctuate and increase between 2020 and 2030. The potential of carbon sinks will be 0.69 x 108 Mg C generated by the CCER carbon-sink forestry projects during 2020-2030, which could offset 0.56% of FFCO2 emissions. In the future, imbalances in regional development should be considered when formulating carbon-reduction strategies. Moreover, using carbon-sink of forestry projects of CCER to balance economic development including poverty eradication and environmental conservation should be considered. Specifically, establishing a methodology of projects for the management of natural forests' carbon-sink will be an important future strategy.
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页数:17
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共 78 条
  • [1] Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation
    Afyouni, Soroosh
    Smith, Stephen M.
    Nichols, Thomas E.
    [J]. NEUROIMAGE, 2019, 199 : 609 - 625
  • [2] Monthly, global emissions of carbon dioxide from fossil fuel consumption
    Andres, R. J.
    Gregg, J. S.
    Losey, L.
    Marland, G.
    Boden, T. A.
    [J]. TELLUS SERIES B-CHEMICAL AND PHYSICAL METEOROLOGY, 2011, 63 (03): : 309 - 327
  • [3] Autocorrelation coefficient for the graph bipartitioning problem
    Angel, E
    Zissimopoulos, V
    [J]. THEORETICAL COMPUTER SCIENCE, 1998, 191 (1-2) : 229 - 243
  • [4] [Anonymous], 2003, Institute for Global Environmental Strategies, Kanagawa
  • [5] Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran
    Assareh, E.
    Behrang, M. A.
    Assari, M. R.
    Ghanbarzadeh, A.
    [J]. ENERGY, 2010, 35 (12) : 5223 - 5229
  • [6] Potential for low-cost carbon dioxide removal through tropical reforestation
    Busch, Jonah
    Engelmann, Jens
    Cook-Patton, Susan C.
    Griscom, Bronson W.
    Kroeger, Timm
    Possingham, Hugh
    Shyamsundar, Priya
    [J]. NATURE CLIMATE CHANGE, 2019, 9 (06) : 463 - +
  • [7] The effect of grazing management on plant species richness on the Qinghai-Tibetan Plateau
    Cao, J.
    Holden, N. M.
    Lue, X-T.
    Du, G.
    [J]. GRASS AND FORAGE SCIENCE, 2011, 66 (03) : 333 - 336
  • [8] Application of back-propagation networks in debris flow prediction
    Chang, Tung-Chueng
    Chao, Ru-Jen
    [J]. ENGINEERING GEOLOGY, 2006, 85 (3-4) : 270 - 280
  • [9] Optimal two-step prediction in regression
    Chetelat, Didier
    Lederer, Johannes
    Salmon, Joseph
    [J]. ELECTRONIC JOURNAL OF STATISTICS, 2017, 11 (01): : 2519 - 2546