Influencing factors and contribution analysis of CO2 emissions originating from final energy consumption in Sichuan Province, China

doi:10.1016/j.regsus.2022.11.006

Abstract

Abstract:

Within the context of CO₂ emission peaking and carbon neutrality, the study of CO₂ emissions at the provincial level is few. Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO₂ emissions. Therefore, using logarithmic mean Divisia index (LMDI) model to analysis the influence degree of different influencing factors on CO₂ emissions from final energy consumption in Sichuan Province, so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors. Based on the data of final energy consumption in Sichuan Province from 2010 to 2019, we calculated CO₂ emission by the indirect emission calculation method. The influencing factors of CO₂ emissions originating from final energy consumption in Sichuan Province were decomposed into population size, economic development, industrial structure, energy consumption intensity, and energy consumption structure by the Kaya-logarithmic mean Divisia index (LMDI) decomposition model. At the same time, grey correlation analysis was used to identify the correlation between CO₂ emissions originating from final energy consumption and the influencing factors in Sichuan Province. The results showed that population size, economic development and energy consumption structure have positive contributions to CO₂ emissions from final energy consumption in Sichuan Province, and economic development has a significant contribution to CO₂ emissions from final energy consumption, with a contribution rate of 519.11%. The industrial structure and energy consumption intensity have negative contributions to CO₂ emissions in Sichuan Province, and both of them have significant contributions, among which the contribution rate of energy consumption structure was 325.96%. From the perspective of industrial structure, secondary industry makes significant contributions and will maintain a restraining effect; from the perspective of energy consumption structure, industry sector has a significant contribution. The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.

Key words: CO₂ emissions, Final energy consumption, Logarithmic mean Divisia index (LMDI) model, Industrial structure, Grey relation analysis, Sichuan Province

LIU Wei, JIA Zhijie, DU Meng, DONG Zhanfeng, PAN Jieyu, LI Qinrui, PAN Linyan, Chris UMOLE. Influencing factors and contribution analysis of CO₂ emissions originating from final energy consumption in Sichuan Province, China[J]. Regional Sustainability, 2022, 3(4): 356-372.

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Fuel type	B	S	A	R
Raw coal	20,934 kJ/kg	0.7143 kgce/kg	26.37 t C/TJ	94.00%
Coal washed and dressed	26,377 kJ/kg	0.9000 kgce/kg	25.41 t C/TJ	93.00%
Coke	28,470 kJ/kg	0.9714 kgce/kg	29.50 t C/TJ	93.00%
Crude oil	41,868 kJ/kg	1.4286 kgce/kg	20.10 t C/TJ	98.00%
Gasoline	43,124 kJ/kg	1.4714 kgce/kg	18.90 t C/TJ	98.00%
Kerosene	43,124 kJ/kg	1.4714 kgce/kg	19.60 t C/TJ	98.00%
Diesel oil	42,705 kJ/kg	1.4571 kgce/kg	20.20 t C/TJ	98.00%
Fuel oil	41,868 kJ/kg	1.4286 kgce/kg	21.10 t C/TJ	98.00%
Liquefied petroleum gas (LPG)	50,242 kJ/kg	1.7143 kgce/kg	17.20 t C/TJ	98.00%
Natural gas	32,238 kJ/m³	1.3300 kgce/m³	15.30 t C/TJ	99.00%

Variable	Definition	Unit	Variable	Definition	Unit
i	Industrial structure. i=1, agriculture, forestry, animal husbandry and fishery sector; i=2, industry sector; i=3, construction sector; i=4, transportation, storage and post sector; i=5, wholesale, retail trade, hotels and catering service sector; i=6, other service sectors.	-	ΔC	CO₂ emission variation (ΔC=ΔC^t-ΔC⁰, where 0 indicates the base year and t denotes the target year)	10⁴ t
P	Resident population	10⁴	ΔC_P	Contribution of the population size to CO₂ emission variation	10⁴ t
G	Gross regional product (GDP)	100 million CNY	ΔC_A	Contribution of the economic development to CO₂ emission variation ($A=\frac{G}{P}$)	10⁴ t
G_i	Output value of sector i	100 million CNY	ΔC_S	Contribution of the industrial structure to CO₂ emission variation ($S=\frac{G}{P}$)	10⁴ t
E_i	Energy consumption of sector i	10⁴ t of standard coal	ΔC_I	Contribution of the energy consumption intensity to CO₂ emission variation ($I=\frac{E}{G}$)	10⁴ t
F_ij	Consumption of energy source j in industrial structure i, and energy sources include raw coal, coal washed and dressed, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, liquefied natural gas, and natural gas	10⁴ t or 100 million m³	ΔC_ES	Contribution of the energy consumption structure to CO₂ emission variation ($ES=\frac{F}{E}$)	10⁴ t
C_ij	CO₂ emissions stemming from the consumption of energy source j in industrial structure i	10⁴ t	ΔC_EF	Contribution of the CO₂ emission factor to CO₂ emission variation ($EF=\frac{C}{F}$). The proposed CO₂ emission factor is a constant, so EFt ij= EF0 ij, resulting in $\ln (\frac{EF_{ij}^{t}}{EF_{ij}^{0}})=0$ and further ΔC_EF=0.	10⁴ t

Influencing factor	Unit	Influencing factor	Unit
Resident population (X₁)	10⁴	Coal washed and dressed consumption (X₁₁)	10⁴ t
GDP (X₂)	100 million CNY	Coke consumption (X₁₂)	10⁴ t
Output value of agriculture, forestry, animal husbandry and fishery sector (X₃)	100 million CNY	Crude oil consumption (X₁₃)	10⁴ t
Output value of the industry sector (X₄)	100 million CNY	Gasoline consumption (X₁₄)	10⁴ t
Output value of construction sector (X₅)	100 million CNY	Kerosene consumption (X₁₅)	10⁴ t
Output value of transportation, storage and post sector (X₆)	100 million CNY	Diesel oil consumption (X₁₆)	10⁴ t
Output value of wholesale, retail trade, hotels and catering service sector (X₇)	100 million CNY	Fuel oil consumption (X₁₇)	10⁴ t
Output value of other service sectors (X₈)	100 million CNY	LPG consumption (X₁₈)	10⁴ t
Energy consumption of standard coal (X₉)	10⁴ t of standard coal	Natural gas consumption (X₁₉)	100 million m³
Raw coal consumption (X₁₀)	10⁴ t	Electricity consumption (X₂₀)	100 million kWh

Grey relation	Rank	Correlation degree	Grey relation	Rank	Correlation degree
?(X₀, X₁)	2	0.9940	?(X₀, X₁₁)	18	0.9365
?(X₀, X₂)	13	0.9683	?(X₀, X₁₂)	4	0.9924
?(X₀, X₃)	11	0.9807	?(X₀, X₁₃)	12	0.9720
?(X₀, X₄)	9	0.9826	?(X₀, X₁₄)	3	0.9938
?(X₀, X₅)	16	0.9597	?(X₀, X₁₅)	7	0.9871
?(X₀, X₆)	14	0.9656	?(X₀, X₁₆)	8	0.9858
?(X₀, X₇)	15	0.9610	?(X₀, X₁₇)	20	0.6533
?(X₀, X₈)	17	0.9455	?(X₀, X₁₈)	19	0.7847
?(X₀, X₉)	1	0.9990	?(X₀, X₉)	6	0.9885
?(X₀, X₁₀)	10	0.9809	?(X₀, X₂₀)	5	0.9891

Year	Contribution amount of economic development to CO₂emissions (×10⁴ t)	Contribution degree of economic development to CO₂emissions (%)
2011	4706.05	224.42
2012	3252.04	110.30
2013	2708.56	-494.20
2014	2196.70	-1093.42
2015	1082.13	-97.44
2016	2005.78	-97.03
2017	3149.78	273.68
2018	3044.98	216.09
2019	2109.94	212.25

Influencing factors and contribution analysis of CO₂ emissions originating from final energy consumption in Sichuan Province, China

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Year	Cumulative CO₂ emission variation (×10⁴ t)
Year	AFAHF	I	C	TSP	WRTHCS	O
2011	-11.36	-302.76	9.02	-230.45	-34.59	50.33
2012	-16.68	-447.40	12.71	-89.13	0.50	42.64
2013	-38.58	-252.64	16.69	-107.10	5.34	57.04
2014	-3.76	-1012.36	12.12	468.52	41.18	39.13
2015	-5.61	-937.32	-9.67	78.44	93.90	56.51
2016	-15.16	-1508.89	-5.49	211.62	72.72	112.98
2017	-26.76	-1298.51	13.81	-181.40	82.68	105.69
2018	-44.03	-833.64	24.06	83.74	-19.52	96.57
2019	0.00	-91.07	-2.63	-118.07	12.00	9.89
Total	-161.93	-6684.59	70.60	116.18	254.21	570.80

Year	Cumulative CO₂ emission variation (×10⁴ t)
Year	AFAHF	I	C	TSP	WRTHCS	O
2011	-94.15	-2424.57	-51.40	-419.48	102.82	-43.53
2012	15.56	651.01	-112.86	-21.51	-117.41	-152.93
2013	-2.92	-2648.93	-202.90	-569.47	-174.60	1151.15
2014	-45.07	-1303.81	-44.32	40.67	121.18	-1226.49
2015	-2.88	-1133.23	24.89	-251.82	-169.91	-20.78
2016	-81.93	-3450.44	127.36	413.86	-123.03	-231.85
2017	-69.16	-249.64	-42.24	-54.17	-126.43	-198.18
2018	-157.48	353.76	4.10	-531.97	-330.80	-80.28
2019	-2.97	-954.34	-43.00	10.38	-94.60	-190.26
Total	-441.01	-11,160.20	-340.37	-1383.50	-912.79	-993.15