基于LMDI?STIRPAT模型的中國鋼鐵行業碳達峰路徑研究

潘崇超; 王博文; 侯孝旺; 古月清; 邢奕; 劉育松; 溫維; 方娟

doi:10.13374/j.issn2095-9389.2022.04.25.002

基于LMDI?STIRPAT模型的中國鋼鐵行業碳達峰路徑研究

doi: 10.13374/j.issn2095-9389.2022.04.25.002

潘崇超^{1, 2, ,},
王博文^{1, 2,},
侯孝旺^{1, 2},
古月清^{1, 2},
邢奕¹,
劉育松¹,
溫維¹,
方娟¹

1.
北京科技大學能源與環境工程學院，北京 100083
2.
北京科技大學智慧能源研究中心，北京 100083

基金項目: 中國博士后科學基金特別資助項目（2021TQ0029）

詳細信息

通訊作者:
E-mail: panchch@ustb.edu.cn

中圖分類號: F416.31
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- 文章訪問數: 543
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- 被引次數: 0
出版歷程
- 收稿日期: 2022-04-25
- 網絡出版日期: 2022-07-27
- 刊出日期: 2023-05-31

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

PAN Chong-chao^{1, 2
, ,},
WANG Bo-wen^{1, 2
,},
HOU Xiao-wang^{1, 2},
GU Yue-qing^{1, 2},
XING Yi¹,
LIU Yu-song¹,
WEN Wei¹,
FANG Juan¹

1.
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Smart Energy Research Center, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: panchch@ustb.edu.cn

摘要

摘要: 基于排放因子法核算中國鋼鐵行業2000—2019年碳排放，運用兩階段對數平均迪式分解法（LMDI）和STIRPAT模型分析碳排放增長的影響因素和2030年碳排放。結果表明，碳排放持續增長，2014年達到階段峰值18.48億噸。規模因素是碳排放增加的主要原因，能源強度是最大的抑制因素。情景分析表明，基準情景下將在2025年達峰，碳排放量為19.04億噸；低碳情景下碳達峰時間為2021年，碳排放量為18.67億噸；強低碳情景已于2020年達到碳排放峰值，碳排放量為18.52億噸；快速發展情景則無法在2030年前實現碳達峰。
- 碳排放 /
- LMDI指數 /
- C-D生產函數 /
- 情景分析 /
- STIRPAT模型
Abstract: 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.
- carbon emissions /
- LMDI method /
- C-D production function /
- scenario analysis /
- STIRPAT model

HTML全文

圖 1 2000—2019年中國鋼鐵行業粗鋼產量與CO₂排放量

Figure 1. Crude steel production and CO₂ emission of China's steel industry from 2000 to 2019

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圖 2 主要國家電爐鋼比例和中國鋼鐵碳排放組成。（a）世界主要國家電爐鋼比例; （b）中國鋼鐵行業碳排放組成

Figure 2. Proportion of EAF steel in major countries and carbon emission of steel in China: (a) composition of carbon emissions from the steel industry; (b) proportion of EAF steel in major countries of the world

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圖 3 工業增加值和能源強度

Figure 3. Industrial added value and energy intensity

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圖 4 五年規劃期間的碳排放分解

Figure 4. Carbon emission decomposition during the five-year plan period

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圖 5 分種類能源消費比例

Figure 5. Proportion of energy consumption by category

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圖 6 規模效應二次分解累計變化

Figure 6. Cumulative change of quadratic decomposition of scale effect

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圖 7 鋼鐵行業勞動力人數和資本投入

Figure 7. Labor force and capital input in the steel industry

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圖 8 碳排放實際值與擬合值對比

Figure 8. Comparison between the actual and fitted values of carbon emission

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圖 9 不同情境下鋼鐵行業碳排放預測趨勢圖

Figure 9. Forecast trend of carbon emissions of steel industry under different scenarios

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表 1 分種類能源參數

Table 1. Subcategory energy parameters

Species	Average low calorific value/ GJ?t^?1，10⁴ GJ?m^?3）	Carbon content per unit calorific value/（t?TJ^?1）	Carbon oxidation rate/ %
Coal	23.3	26.3	93
Coke	28.4	29.5	93
Gasoline	43.1	18.9	98
Diesel	42.7	20.2	98
Fuel oil	41.8	21.1	98
Natural gas	389.3	15.3	99
Coke oven gas	173.5	12.1	99

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表 2 研究涉及數據的詳細來源

Table 2. Detailed sources of data involved in the study

Data	Provenance
Industrial added value	《China Statistical Yearbook》, 《China Statistical Bulletin》
The number of employees	《China Statistical Yearbook》, 《China Labor Statistics Yearbook》
Crude steel production, Ratio of electric furnace steel	《World Steel Statistics Yearbook》
Proportion of thermal power generation	《China Electric Power Statistical Yearbook》
Population	《China Statistical Yearbook》
Comparable investment	《China Statistical Yearbook》, 《Yearbook of Chinese Iron and Steel Industry》
Urbanization rate	《China Statistical Yearbook》
Share of secondary industry	《China Statistical Yearbook》

下載: 導出CSV

表 3 碳排放增長因素分解

Table 3. Decomposition of carbon emission growth factors

Year	Emission coefficient effect/(10⁴ t)	Industrial scale effect/(10⁴ t)	Energy intensity effect/(10⁴ t)	Energy structure effect/(10⁴ t)	Total/(10⁴ t)
2000—2001	?58.42	2201.82	?1970.18	305.16	478.48
2001—2002	41.97	8562.83	3154.38	218.46	5668.62
2002—2003	?65.86	15837.24	?3255.26	191.38	12707.55
2003—2004	?12.34	12440.75	?936.38	840.08	12332.10
2004—2005	?125.74	22850.70	2983.00	232.35	19973.66
2005—2006	161.98	12720.22	?2053.55	731.49	15667.21
2006—2007	69.97	23586.12	?9355.82	419.28	14719.55
2007—2008	?1248.74	10186.69	?8091.64	?596.63	249.68
2008—2009	571.20	12697.00	?2753.26	?130.43	10384.51
2009—2010	73.32	15786.72	?8499.41	849.67	8210.30
2010—2011	113.39	14850.44	?646.42	168.98	14486.39
2011—2012	?1218.77	15024.78	?6157.91	?1429.32	6218.78
2012—2013	341.08	16655.21	?5193.14	?373.93	11427.99
2013—2014	?717.87	10973.91	?4916.59	?767.02	4656.95
2014—2015	?669.85	9490.75	?16239.19	?1141.70	?8560.00
2015—2016	?360.07	?2965.84	?3471.52	363.60	?6443.38
2016—2017	?89.05	507.10	?729.14	?584.27	?895.35
2017—2018	?229.94	11481.22	?10215.85	616.97	1652.41
2018—2019	?440.03	14962.96	?10694.75	1116.73	4944.91
2000—2019	?3863.75	227850.61	?97210.31	1072.84	127880.38

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表 4 不同情境的基本參數設置

Table 4. Basic parameter settings in different situations

Scenario	Year	Population growth rate/%	Added value per capita /%	Energy intensity /%	Crude steel production /%	Urbanization rate /%	The rate of change of the secondary industry/%
Baseline scenario	2021—2025	0.3	3.4	?4.6	1.5	0.68	?0.5
Baseline scenario	2026—2030	0.2	2	?3	?0.5	0.68	?0.5
Low carbon scenario	2021—2025	0.3	3.4	?5	?1	0.68	?0.5
Low carbon scenario	2026—2030	0.2	2	?4	?1.4	0.68	?0.5
Strong low-carbon scenario	2021—2025	0.15	1.8	?5	?1	0.45	?0.65
Strong low-carbon scenario	2026—2030	0.1	0.8	?4	?2	0.45	?0.65
Rapid development scenario	2021—2025	0.45	4.6	?3	1	0.9	?0.35
Rapid development scenario	2026—2030	0.3	2.6	?2	0.5	0.9	?0.35

下載: 導出CSV

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