Decomposition and decoupling analysis of electricity consumption carbon emissions in China

doi:10.1007/s42524-022-0215-3

Front. Eng

2022, Vol. 9

Issue (3) : 486-498 https://doi.org/10.1007/s42524-022-0215-3

RESEARCH ARTICLE

Decomposition and decoupling analysis of electricity consumption carbon emissions in China

Yuwen ZHENG¹, Yifang ZHENG², Guannan HE¹(

), Jie SONG¹(

)

¹. Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China
². School of Management, Shandong University, Jinan 250100, China

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Abstract

Electricity consumption is one of the major contributors to greenhouse gas emissions. In this study, we build a power consumption carbon emission measurement model based on the operating margin factor. We use the decomposition and decoupling technology of logarithmic mean Divisia index method to quantify six effects (i.e., emission intensity, power generation structure, consumption electricity intensity, economic scale, population structure, and population scale) and comprehensively reflect the degree of dependence of electricity consumption carbon emissions on China’s economic development and population changes. Moreover, we utilize the decoupling model to analyze the decoupling state between carbon emissions and economic growth and identify corresponding energy efficiency policies. The results of this study provide a new perspective to understand carbon emission reduction potentials in the electricity use of China.

Keywords electricity consumption carbon emission measurement LMDI model decoupling model data driven

Corresponding Author(s): Guannan HE,Jie SONG

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 30 June 2022 Online First Date: 11 August 2022 Issue Date: 05 September 2022

Cite this article:

Yuwen ZHENG,Yifang ZHENG,Guannan HE, et al. Decomposition and decoupling analysis of electricity consumption carbon emissions in China[J]. Front. Eng, 2022, 9(3): 486-498.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-022-0215-3
https://academic.hep.com.cn/fem/EN/Y2022/V9/I3/486

Categories	Status	$Δ C O 2$	$Δ G D P$	$T t$	Practical implications
Negative decoupling	Expansionary negative decoupling	$> 0$	$> 0$	$T t > 1.2$	Both GDP and CO₂ emissions are growing; the growth rate of GDP is slower than that of CO₂ emissions
	Weak negative decoupling	$< 0$	$< 0$	$0 ? T t < 0.8$	Both GDP and CO₂ emissions are declining; the decline rate of GDP is faster than that of CO₂ emissions
	Strong negative decoupling	$> 0$	$< 0$	$T t < 0$	CO₂ emissions are growing, but GDP is declining
Decoupling	Recessive decoupling	$< 0$	$< 0$	$T t > 1.2$	Both GDP and CO₂ emissions are declining; the decline rate of GDP is slower than that of CO₂ emissions
	Weak decoupling	$> 0$	$> 0$	$0 ? T t < 0.8$	Both GDP and CO₂ emissions are growing; the growth rate of GDP is faster than that of CO₂ emissions
	Strong decoupling	$< 0$	$> 0$	$T t < 0$	CO₂ emissions are declining, but GDP is growing
Coupling	Expansionary coupling	$> 0$	$> 0$	$0.8 ? T t ? 1.2$	CO₂ emissions and GDP are growing at similar rates
Coupling	Recessive coupling	$< 0$	$< 0$	$0.8 ? T t ? 1.2$	CO₂ emissions and GDP are declining at similar rates

Tab.1 Decoupling/Coupling states in the Tapio decoupling model

Fig.1 2015–2019 overall electricity consumption carbon emission data.

Fig.2 Proportion of China’s thermal power generation in 2015 and 2019, respectively.

Fig.3 Accumulation map of the contribution value of each provincial-level region for 2015–2019.

Fig.4 Contribution rate of each provincial-level region in China from 2015 to 2019.

Province/City	Time period	Contribution value (million tons)						Contribution rate (%)
Province/City	Time period	Value added	$Δ F$	$Δ E G$	$Δ G P$	$Δ P P$	$Δ P$	$Δ F$	$Δ E G$	$Δ G P$	$Δ P P$	$Δ P$
Heilongjiang	15–16	0.83	?1.83	0.97	1.98	?0.79	0.50	?219	117	237	?95	60
	16–17	0.64	?2.36	0.17	3.05	?0.68	0.46	?367	27	476	?106	71
	17–18	2.02	?2.12	1.67	2.84	?0.70	0.33	?104	82	140	?34	16
	18–19	?0.14	?2.08	18.12	?15.66	?0.81	0.29	1485	?12925	11173	576	?210
Jilin	15–16	?1.96	?3.33	?1.50	3.30	?0.77	0.34	171	77	?169	39	?17
	16–17	1.71	?1.29	2.34	1.00	?0.65	0.31	?76	138	59	?38	18
	17–18	3.04	?0.91	3.43	0.81	?0.52	0.23	?30	113	27	?17	8
	18–19	2.37	?0.08	18.31	?15.55	?0.52	0.21	?4	771	?655	?22	9
Liaoning	15–16	?5.34	?9.87	48.59	?43.90	?1.18	1.02	185	?911	823	22	?19
	16–17	3.08	?5.04	?0.67	9.14	?1.27	0.92	?164	?22	297	?41	30
	17–18	4.58	?8.68	?0.54	14.21	?1.08	0.67	?189	?12	310	?23	15
	18–19	3.34	?4.26	10.52	?2.63	?0.89	0.60	?127	315	?79	?27	18
Guizhou	15–16	7.45	4.31	?3.28	6.02	0.07	0.33	58	?44	81	1	4
	16–17	7.17	0.25	?1.93	8.40	0.11	0.34	4	?27	117	1	5
	17–18	4.13	?0.55	?1.48	5.78	0.12	0.26	?13	?36	140	3	6
	18–19	5.19	2.34	?6.32	8.70	0.22	0.25	45	?122	167	4	5
Sichuan	15–16	?4.20	?5.29	?0.80	1.75	0.02	0.12	126	19	?42	?1	?3
	16–17	0.10	?0.81	?1.26	2.08	?0.01	0.10	?824	?1289	2121	?9	102
	17–18	5.65	3.30	0.30	1.95	0.02	0.08	58	5	34	0	1
	18–19	3.88	2.05	?1.76	3.48	0.02	0.09	53	?46	90	0	2
Yunnan	15–16	?1.85	?1.62	?1.18	0.88	0.00	0.07	88	64	?48	0	?4
	16–17	?0.63	?1.52	?0.16	0.99	0.01	0.05	244	26	?159	?2	?9
	17–18	2.03	1.07	0.00	0.90	0.02	0.04	53	0	44	1	2
	18–19	1.32	0.35	?2.35	3.25	0.03	0.04	26	?179	247	2	3
Chongqing	15–16	0.59	?2.15	?3.29	5.52	0.22	0.29	?363	?556	933	37	49
	16–17	4.85	0.93	?0.84	4.30	0.18	0.28	19	?17	89	4	6
	17–18	10.80	3.83	4.13	2.31	0.30	0.23	36	38	21	3	2
	18–19	2.20	?0.23	?7.45	9.41	0.25	0.22	?10	?338	427	11	10
Tibet	15–16	/	/	/	/	/	/	/	/	/	/	/
	16–17	0.01	0.00	0.00	0.00	0.00	0.00	49	12	34	4	2
	17–18	0.06	0.05	0.00	0.01	0.00	0.00	82	5	11	2	0
	18–19	?0.01	?0.02	0.00	0.01	0.00	0.00	207	20	?109	?15	?3

Tab.2 2015–2019 LMDI decomposition results of some provincial-level regions in China

Fig.5 Decoupling elasticity between carbon emissions and economic development by province and region (Notes: 1) BJ: Beijing, XZ: Tibet, GX: Guangxi, HE: Hebei, HN: Hunan, JX: Jiangxi, SD: Shandong, SN: Shaanxi, AH: Anhui, FJ: Fujian, HL: Heilongjiang, JL: Jilin, LN: Liaoning, IM: Inner Mongolia, TJ: Tianjin; 2) The formula

T t = Δ C O 2 / Δ C O 2 C O 20 C O 20 Δ G D P / Δ G D P G D P 0 G D P 0

is used to calculate the decoupling status of carbon emissions and economic development in each region).

Fig.6 Decoupling elasticity of carbon emissions and economic growth and its decomposition for each province and region in China (Notes: BJ: Beijing, XZ: Tibet, GX: Guangxi, HN: Hunan, SD: Shandong, SN: Shaanxi, GZ: Guizhou, HB: Hubei, CQ: Chongqing, AH: Anhui, FJ: Fujian, HL: Heilongjiang, JL: Jilin, LN: Liaoning, IM: Inner Mongolia, TJ: Tianjin, HI: Hainan, NX: Ningxia, HA: Henan, GS: Gansu).

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