“双碳”目标下交通运输业碳排放脱钩效应与峰值预测

陈涛; 李晓阳; 陈斌

doi:10.19818/j.cnki.1671-1637.2024.04.008

“双碳”目标下交通运输业碳排放脱钩效应与峰值预测

doi: 10.19818/j.cnki.1671-1637.2024.04.008

陈涛^1,,
李晓阳¹,
陈斌^{1, 2}

1.
长安大学汽车学院，陕西西安 710064
2.
四川省交通运输发展战略和规划科学研究院，四川成都 610001

基金项目:

国家自然科学基金项目 51978075

四川省交通运输科技项目 2021-ZL-02

详细信息

作者简介:
陈涛(1974-)，男，陕西铜川人，长安大学教授，工学博士，从事道路交通安全研究

中图分类号: U491
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- 被引次数: 0
出版历程
- 收稿日期: 2024-02-20
- 网络出版日期: 2024-09-26
- 刊出日期: 2024-08-28

Decoupling effect and peak prediction of carbon emission in transportation industry under dual-carbon target

CHEN Tao^1
,,
LI Xiao-yang¹,
CHEN Bin^{1, 2}

1.
School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China
2.
Institute of Transportation Development Strategy and Planning of Sichuan Province, Chengdu 610001, Sichuan, China

Funds:

National Natural Science Foundation of China 51978075

Transportation Science and Technology Project of Sichuan Province 2021-ZL-02

More Information

Author Bio:
CHEN Tao(1974-), male, professor, PhD, chentao@chd.edu.cn

摘要

摘要: 为助力交通运输业实现碳达峰、碳中和的战略发展目标，从历史验证和未来预测2个角度分析了中国交通运输业的碳排放变动趋势和影响因素，利用对数平均迪氏分解(LMDI)模型分解了2000至2020年中国交通运输业CO₂排放量变化的影响因素，结合Tapio脱钩模型分析了行业碳排放与经济发展的脱钩状态及脱钩的驱动因素；以影响因素分解结果作为情景分析法指标选择的依据，设定不同情景下的预测指标变动量，利用岭回归构建了STIRPAT预测模型。分析结果表明：研究期内CO₂排放总量呈现逐年增长的趋势，2000至2020年间累计增加了约6.94亿吨，运输强度的降低是碳排放增加的主要抑制因素，累计效应约为-6.26亿吨；人均GDP的增长是碳排放增加的最主要促进因素，累计效应约为12.94亿吨；能源消耗仍然以化石燃料为主，能源结构并未得到显著优化；行业碳排放的脱钩指数处于稳定的下降阶段，脱钩状态有所改善，主要表现为扩张负脱钩、增长连接和弱脱钩3种状态，能源结构的优化是助力脱钩最有潜力的因素；未来中国交通运输业碳排放变化趋势呈现先快速增长，在峰值附近增速减缓，达到峰值后有短期的平台，最后转入下降阶段；基准情景、悲观情景和乐观情景下中国交通运输业CO₂排放量峰值分别出现在2040、2045和2035年，峰值分别约为12.10亿吨、12.63亿吨和11.30亿吨。
- 交通运输 /
- 碳排放 /
- LMDI分解 /
- Tapio脱钩模型 /
- 情景分析 /
- 岭回归
Abstract: To help the transportation industry achieve the strategic development goals of carbon peaking and carbon neutrality, the change trend and influencing factors of carbon emission in China's transportation industry were analyzed from two perspectives of historical verification and future prediction. The logarithmic mean Divisia index (LMDI) model was used to decompose the influencing factors of CO₂ emission change in China's transportation industry from 2000 to 2020. The decoupling state of carbon emission and economic development in the industry and the driving factors of decoupling were analyzed by combining the Tapio decoupling model. The decomposition results of influencing factors were used as the basis for the selection of the indicators in the scenario analysis method, and the variations of prediction indicators under different scenarios were set. A prediction model of stochastic impacts by regression on population, affluence, and technology (STIRPAT) was constructed by using ridge regression. Analysis results show that the total CO₂ emission exhibits an increasing trend year by year during the study period, with a cumulative increase of 694 million tons from 2000 to 2020. The decrease in transportation intensity is the main inhibiting factor for the increase in carbon emission, with a cumulative effect of -626 million tons. The growth of per capita GDP is the most important factor promoting the increase in carbon emission, and the cumulative effect is 1 294 million tons. The energy consumption is still dominated by fossil fuels, and the energy structure is not significantly optimized. The decoupling index of industrial carbon emission is in a stable decline stage, and the decoupling state improves, mainly manifesting in three states, such as the expansion negative decoupling, growth connection, and weak decoupling. The optimization of energy structure is the most potential factor to help the decoupling. In the future, the carbon emission in China's transportation industry will rapidly grow at first, slow down near the peak, reach a plateau for a short period after the peak, and finally decline. In the baseline, pessimistic, and optimistic scenarios, the peak CO₂ emission in China's transportation industry will occur in 2040, 2045, and 2035, respectively, with peaks of about 1.210 billion, 1.263 billion, and 1.130 billion tons, respectively.
- transportation /
- carbon emission /
- LMDI decomposition /
- Tapio decoupling model /
- scenario analysis /
- ridge regression

HTML全文

图 1 各影响因素的CO₂排放贡献

Figure 1. Contributions of CO₂ emissions of each influencing factor

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图 2 碳排放与经济变化的脱钩与耦合结果

Figure 2. Decoupling and coupling results of carbon emission and economic change

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图 3 2010至2020年交通运输业CO₂排放真实值与模型值对比

Figure 3. Comparison between actual and model values of CO₂ emissions in transportation industry from 2010 to 2020

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图 4 2021至2050年交通运输业CO₂排放量预测变化

Figure 4. Predicted changes in CO₂ emissions in transportation industry from 2021 to 2050

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表 1 各能源碳排放量相关参数

Table 1. Carbon emission related parameters of each energy source

能源种类	平均低位发热值/(kJ·kg^-1)	含碳量/(t·10^-12J)	碳氧化率
原煤	20 908	26.37	0.94
焦炭	28 435	29.42	0.93
原油	41 816	20.08	0.98
汽油	43 070	18.90	0.98
煤油	43 070	19.60	0.98
柴油	42 652	20.20	0.98
燃料油	41 816	21.10	0.98
液化石油气	50 179	17.20	0.98
天然气	38 931	15.32	0.99

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表 2 各能源的标准煤折算系数

Table 2. Standard coal conversion coefficients of each energy source

能源种类	标准煤折算系数	能源种类	标准煤折算系数
原煤	0.714 3	柴油	1.457 1
焦炭	0.971 4	燃料油	1.428 6
原油	1.428 6	液化石油气	1.714 3
汽油	1.471 4	天然气	12.143 0
煤油	1.471 4	电力	1.229 0

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表 3 各运输方式的客货运周转量转换系数

Table 3. Passenger and freight turnover conversion coefficients of each transportation mode

运输方式	公路	水路	铁路	民航
转换系数	0.100	0.330	1.000	0.072

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表 4 交通运输业各影响因素的贡献

Table 4. Contributions of each influencing factor in transportation industry 万吨

年份	ΔC_{t, ES}	ΔC_{t, ET}	ΔC_{t, TG}	ΔC_{t, GP}	ΔC_{t, P}	ΔC_t
2001	140.71	-1 171.27	-770.29	2 505.24	186.48	890.87
2002	-393.01	217.78	-959.00	2 434.74	180.63	1 481.13
2003	1 022.94	2 245.13	-2 180.38	3 587.92	186.86	4 862.48
2004	-699.98	-2 736.58	3 015.55	5 720.76	213.02	5 512.78
2005	-1 179.05	-305.76	-354.67	5 773.41	242.39	4 176.33
2006	75.10	-990.52	-2 600.20	6 897.47	237.99	3 619.84
2007	458.64	-2 298.22	-3 811.56	9 845.92	251.22	4 446.00
2008	-277.07	-2 238.93	-4 383.29	8 457.52	265.06	1 823.28
2009	364.87	-3 581.49	459.87	4 489.03	263.69	1 995.98
2010	437.61	-2 189.66	-1 229.12	9 531.72	280.50	6 831.04
2011	901.26	-1 893.94	-3 523.62	10 610.32	399.89	6 493.91
2012	-791.16	681.21	-1 088.58	6 519.53	530.39	5 851.40
2013	215.79	7 398.49	-9 791.75	6 947.18	453.75	5 223.45
2014	-367.53	-3 032.30	-435.23	6 084.93	542.78	2 792.64
2015	-541.87	5 684.21	-6 851.96	5 322.47	415.76	4 028.61
2016	868.48	-1 595.12	-3 062.91	6 486.55	574.65	3 271.65
2017	1 180.41	-579.63	-4 830.83	9 535.53	516.32	5 821.81
2018	1 640.37	-676.08	-6 117.83	9 379.74	369.40	4 595.61
2019	1 210.02	1 689.20	-9 249.86	6 753.28	333.04	735.68
2020	1 026.10	-3 939.74	-4 827.19	2 515.65	142.03	-5 083.17
累计	5 292.63	-9 313.22	-62 592.85	129 398.91	6 585.84	69 371.31

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表 5 脱钩状态划分

Table 5. Decoupling states classification

脱钩分类	脱钩状态	ΔC_t	ΔG_t	ε_t	特征
脱钩	强脱钩	＜0	＞0	＜0	经济增长，碳排放量减少，最理想状态
	弱脱钩	＞0	＞0	[0.0, 0.8)	经济增速大于碳排放量增速
	衰退脱钩	＜0	＜0	>1.2	经济衰退速度小于碳排放量减少速度
连接	增长连接	＞0	＞0	[0.8, 1.2]	经济增速与碳排放量增加速度相当
连接	衰退连接	＜0	＜0	[0.8, 1.2]	经济衰退与碳排放量减少速度相当
负脱钩	扩张负脱钩	＞0	＞0	>1.2	经济增速小于碳排放量增速
	弱负脱钩	＜0	＜0	[0.0, 0.8)	经济衰退速度大于碳排放量减少速度
	强负脱钩	＞0	＜0	＜0	经济衰退，碳排放量增加，最不理想状态

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表 6 交通运输业各影响因素对脱钩指数的贡献

Table 6. Contributions of each influencing factor in transportation industry to decoupling index

年份	ε₁	ε₂	ε₃	ε₄	ε₅	ε_t
2001	0.051	-0.421	-0.277	0.900	0.067	0.320
2002	-0.147	0.082	-0.359	0.912	0.068	0.555
2003	0.276	0.605	-0.588	0.967	0.050	1.310
2004	-0.117	-0.458	0.505	0.958	0.036	0.923
2005	-0.191	-0.050	-0.058	0.937	0.039	0.678
2006	0.010	-0.133	-0.350	0.929	0.032	0.487
2007	0.042	-0.212	-0.352	0.909	0.023	0.410
2008	-0.030	-0.239	-0.469	0.905	0.028	0.195
2009	0.075	-0.734	0.094	0.920	0.054	0.409
2010	0.043	-0.217	-0.122	0.946	0.028	0.678
2011	0.079	-0.166	-0.309	0.930	0.035	0.569
2012	-0.111	0.096	-0.153	0.917	0.075	0.823
2013	0.029	0.985	-1.304	0.925	0.060	0.696
2014	-0.054	-0.446	-0.064	0.896	0.080	0.411
2015	-0.093	0.980	-1.182	0.918	0.072	0.695
2016	0.120	-0.221	-0.424	0.899	0.080	0.453
2017	0.115	-0.056	-0.469	0.927	0.050	0.566
2018	0.164	-0.068	-0.611	0.937	0.037	0.459
2019	0.165	0.231	-1.263	0.922	0.045	0.100
2020	0.371	-1.425	-1.746	0.910	0.051	-1.838

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表 7 2021至2050年情景参数平均变化率预测值

Table 7. Predicted average change rates of scenario parameters from 2021 to 2050 %

情景设定	变量	2021至2025	2026至2030	2031至2035	2036至2040	2041至2045	2046至2050
悲观情景	C_P	0.34	0.25	-0.12	-0.22	-0.32	-0.45
	C_G	4.75	4.50	4.40	4.37	4.35	4.33
	C_U	1.23	1.20	0.98	0.68	0.48	0.38
	C_J	3.65	3.85	4.15	5.15	5.65	6.25
	C_S	3.00	2.50	1.28	0.95	0.75	0.70
	C_R	-0.98	-0.96	-0.94	-0.93	-0.91	-0.90
基准情景	C_P	0.31	0.22	-0.15	-0.25	-0.35	-0.48
	C_G	4.60	4.35	4.25	4.22	4.20	4.18
	C_U	1.15	1.12	0.90	0.60	0.40	0.30
	C_J	4.00	4.20	4.50	5.50	6.00	6.60
	C_S	2.75	2.25	1.03	0.70	0.50	0.45
	C_R	-1.03	-1.01	-0.99	-0.98	-0.96	-0.95
乐观情景	C_P	0.23	-0.13	-0.22	0.31	0.39	0.54
	C_G	4.45	4.20	4.10	4.07	4.05	4.03
	C_U	1.10	1.07	0.85	0.55	0.35	0.25
	C_J	4.45	4.65	4.95	5.95	6.45	7.05
	C_S	2.40	1.90	0.68	0.35	0.15	0.10
	C_R	-1.09	-1.07	-1.05	-1.04	-1.02	-1.01

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表 8 岭回归分析结果

Table 8. Results of ridge regression analysis

偏倚参数为0.15	非标准化系数		标准化系数	t统计量	显著性水平	拟合优度	F统计量
偏倚参数为0.15	回归系数	标准误差	标准化系数	t统计量	显著性水平	拟合优度	F统计量
常数	-4.661	0.651		-7.157	0.002^***	0.99	63.158 (0.001^***)
ln(P)	1.426	0.166	0.162	8.578	0.001^***
ln(G)	0.090	0.010	0.167	8.665	0.001^***
ln(U)	0.256	0.040	0.130	6.338	0.003^***
ln(J)	0.031	0.037	0.039	0.840	0.448
ln(S)	0.443	0.079	0.312	5.638	0.005^***
ln(R)	0.531	0.159	0.192	3.330	0.029^***

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表 9 三种情景下交通运输业CO₂排放量预测值

Table 9. Predicted CO₂ emissions in transportation industry under three scenarios 亿吨

年份	悲观情景	基准情景	乐观情景	年份	悲观情景	基准情景	乐观情景
2021	10.093 0	10.080 5	10.063 4	2036	12.429 5	12.045 6	11.276 8
2022	10.308 4	10.275 1	10.226 0	2037	12.469 7	12.060 1	11.253 8
2023	10.528 5	10.473 5	10.391 2	2038	12.510 0	12.074 6	11.239 8
2024	10.753 3	10.675 7	10.592 0	2039	12.550 5	12.089 1	11.221 3
2025	10.982 8	10.881 9	10.729 8	2040	12.591 0	12.103 7	11.202 9
2026	11.177 4	11.052 5	10.823 3	2041	12.599 2	12.087 0	11.157 9
2027	11.375 5	11.225 8	10.917 7	2042	12.607 4	12.070 3	11.113 2
2028	11.577 0	11.401 9	11.012 8	2043	12.615 5	12.053 6	11.068 6
2029	11.782 1	11.580 7	11.018 8	2044	12.623 7	12.037 0	11.024 2
2030	11.990 9	11.762 3	11.205 7	2045	12.631 9	12.020 4	10.980 0
2031	12.069 6	11.815 5	11.223 5	2046	12.613 2	11.978 3	10.911 2
2032	12.148 8	11.869 1	11.241 5	2047	12.594 6	11.936 3	10.842 7
2033	12.228 5	11.922 8	11.259 4	2048	12.576 0	11.894 5	10.774 7
2034	12.308 7	11.976 9	11.277 4	2049	12.557 5	11.852 9	10.707 2
2035	12.389 5	12.031 1	11.295 4	2050	12.538 9	11.811 4	10.640 0

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