Carbon peak path of the Chinese iron and steel industry based on the LMDI−STIRPAT model

被引：0

作者：

Pan C.-C. ^{[1
,2
]}

Wang B.-W. ^{[1
,2
]}

Hou X.-W. ^{[1
,2
]}

Gu Y.-Q. ^{[1
,2
]}

Xing Y. ^{[1
]}

Liu Y.-S. ^{[1
]}

Wen W. ^{[1
]}

Fang J. ^{[1
]}

机构：

[1] School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing

[2] Smart Energy Research Center, University of Science and Technology Beijing, Beijing

来源：

Gongcheng Kexue Xuebao/Chinese Journal of Engineering | 2023年 / 45卷 / 06期

关键词：

C-D production function; carbon emissions; LMDI method; scenario analysis; STIRPAT model;

D O I：

10.13374/j.issn2095-9389.2022.04.25.002

中图分类号：

学科分类号：

摘要：

Low-carbon development of the iron and steel industry is critical to China’s goal of carbon neutrality and emission peaking. The carbon emissions of China’s iron and steel industry are calculated using the emission factor method in this paper, and the influencing factors of emission growth are investigated using the two-stage logarithmic mean divisia index (LMDI). The results show that carbon emissions from the steel industry continue to rise, reaching a stage peak of 1.848 billion tons in 2014 before declining. Carbon emissions fall by 52.4% during this period, energy intensity decreases by 52.9% per ton of steel; the decline in energy intensity will be much smaller in the future. The scale effect is the most important factor in the growth of carbon emission, accounting for 178.17% of the total, whereas energy intensity is the most important restraining factor, accounting for 76.02% of the total. However, the impact of energy structure and emission factors remains unclear. This is due to the small change in the energy mix and emission factors. The scale effect, which is a major contributor to rising carbon emissions, is broken down once more. Capital stock and total factor productivity drive carbon emission growth, whereas labor factors reflect the transition of the industrial population to low-carbon industries. The STIRPAT model predicts future carbon emissions from the iron and steel industry. The results of the scenario analysis show that carbon emissions will peak in 2025 under the baseline scenario, with carbon emissions totaling 1.904 billion tons. The peak time for carbon emissions in the low carbon scenario is 2021, and the peak is lower, with carbon emissions of 1.867 billion tons. Carbon emissions have already peaked in 2020 in the strong low-carbon scenario and will further decline to 1.439 billion tons in 2030, which is equivalent to 2010 carbon emissions. However, the rapid development scenario will not be able to reach a peak in carbon dioxide emissions before 2030. The forecast results show that both social and economic factors, as well as steel production factors, can have a significant impact on the overall industry’s carbon emission, implying that both the supply and demand sides must contribute to emission reductions. Controlling new capacity, transforming process structure, reducing fossil energy consumption, and promoting the use of hydrogen energy in the smelting process will be critical in the future for the industry’s low-carbon development. © 2023 Science Press. All rights reserved.

引用

页码：1034 / 1044

页数：10

共 40 条

[1] World steel statistics 2021 [EB/OL]
[2] Du Z L, Lin B Q., Analysis of carbon emissions reduction of China’s metallurgical industry, J Clean Prod, 176, (2018)
[3] Ren L, Zhou S, Peng T D, Et al., A review of CO<sub>2</sub> emissions reduction technologies and low-carbon development in the iron and steel industry focusing on China, Renew Sustain Energy Rev, 143, (2021)
[4] Zhu Q, Peng X Z, Lu Z M, Et al., Factors decomposition and empirical analysis of variations in energy carbon emission in China, Resour Sci, 31, 12, (2009)
[5] Ling Y T, Xia S M, Cao M Q, Et al., Carbon emissions in China’s thermal electricity and heating industry: An input-output structural decomposition analysis, J Clean Prod, 329, (2021)
[6] Xu W H, Xie Y L, Xia D H, Et al., A multi-sectoral decomposition and decoupling analysis of carbon emissions in Guangdong province, China, J Environ Manag, 298, (2021)
[7] Ang B W., The LMDI approach to decomposition analysis: A practical guide, Energy Policy, 33, 7, (2005)
[8] Lu Z N, Yang Y, Wang J., Effect of carbon structrue change on carbon productivitysystem carbon productivity —An empirical research based on laspeyres decomposition index, Sci Technol Manag Res, 35, 10, (2015)
[9] Wang Q, Zhao M M, Li R R., Decoupling sectoral economic output from carbon emissions on city level: A comparative study of Beijing and Shanghai, China, J Clean Prod, 209, (2019)
[10] Ang B W, Liu N., Negative-value problems of the logarithmic mean Divisia index decomposition approach, Energy Policy, 35, 1, (2007)

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