Research on the influencing factors of carbon emissions from petrochemical industry in Jilin Province based on the STIRPAT model

doi:10.3969/j.issn.2097-0706.2024.08.002

Abstract

Abstract:

In view that petrochemical industry is a typical industry of high carbon emissions， studying the variation law and influencing factors of its carbon emissions can facilitate the implement of China's "dual carbon" strategy. Based on the data from the nine carbon emission sources which include raw coal， clean coal， coke， gasoline， diesel and crude oil in five sub industries of petrochemical industry in Jilin Province from 2002 to 2021， the temporal evolution characteristics and composition of the carbon emissions are studied. Six indicators affecting the carbon emissions from petrochemical industry in Jilin Province， including economic value added， are selected， and their impacts and mechanisms are deeply studied based on the expanded STIRPAT model and ridge regression parameter estimation. According to empirical analysis results， three suggestions are proposed to promote the low-carbon development and carbon reduction of petrochemical industry in Jilin Province： improving energy efficiency and energy consumption structure； selecting valuable investment projects and low-carbon projects； adopting circular economy and conducting environmental impact assessments.

Key words: "dual carbon" strategy, petrochemical industry, energy mix, carbon emissions, STIRPAT model, energy efficiency, composition of energy consumption, cyclic economy

CLC Number:

TK01

LI Feifei, XU Huiwei, CUI Jindong. Research on the influencing factors of carbon emissions from petrochemical industry in Jilin Province based on the STIRPAT model[J]. Integrated Intelligent Energy, 2024, 46(8): 12-19.

Figures/Tables 8

Fig.1

Table 1

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Fig.2

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年份	C/万t	A_V/亿元	I_E	R	F/亿元	I_CI/（万元·t^-1）	L/(万元·人^-1)
2002	1 135.98	28.80	69.11	0.02	10.60	0.01	2.98
2003	1 362.78	6.59	306.02	0.02	15.86	0.01	3.15
2004	1 487.29	75.01	27.41	0.02	46.15	0.03	3.23
2005	1 273.19	84.69	24.74	0.03	60.34	0.05	3.54
2006	1 327.98	84.58	25.66	0.03	75.65	0.06	3.81
2007	1 295.69	350.68	6.13	0.03	100.65	0.08	5.16
2008	1 251.97	219.71	9.35	0.03	127.84	0.10	5.86
2009	1 246.10	230.24	8.95	0.03	148.94	0.12	6.51
2010	1 356.73	324.58	6.96	0.03	170.42	0.13	7.72
2011	1 585.90	346.38	7.54	0.03	214.00	0.13	8.00
2012	1 436.63	273.67	8.72	0.04	253.34	0.18	9.41
2013	1 349.47	203.08	11.23	0.05	303.21	0.22	10.68
2014	1 417.13	212.27	11.20	0.04	330.10	0.23	12.08
2015	1 245.27	25.69	82.52	0.04	328.87	0.26	12.82
2016	2 161.83	57.51	71.82	0.02	320.98	0.15	13.79
2017	2 227.61	87.03	49.34	0.02	316.70	0.14	14.96
2018	1 785.93	51.92	71.23	0.02	337.96	0.19	16.02
2019	1 129.54	60.65	41.73	0.03	355.11	0.31	17.24
2020	1 132.05	153.38	12.85	0.04	341.01	0.30	19.03
2021	2 075.58	338.30	12.34	0.02	388.35	0.19	21.19

变量	lnC	lnA_V	lnI_E	lnR	lnF	lnI_CI	lnL
lnC	1.000
lnA_V	0.081	1.000
lnI_E	0.148	-0.969	1.000
lnR	-0.652	0.336	-0.488	1.000
lnF	0.371	0.495	-0.358	0.222	1.000
lnI_CI	0.184	0.507	-0.410	0.371	0.981	1.000
lnL	0.405	0.273	-0.114	0.075	0.910	0.879	1.000

项目	未标准化系数		标准化系数	t值	显著性	共线性统计
项目	B	标准错误	β	t值	显著性	容差	VIF值
常量	6.673×10^-5	0		0.814	0.430
lnA_V	4.469×10^-5	0	0.000	1.484	0.162	0.002	407.525
lnI_E	4.026×10^-5	0	0.000	1.331	0.206	0.002	421.436
lnR	-7.052×10^-6	0	0.000	-0.521	0.611	0.163	6.135
lnF	1.000	0	5.074	40 644.538	0.000	0.004	269.864
lnI_CI	-1.000	0	-4.793	-39 760.413	0.000	0.004	251.707
lnL	1.391×10^-6	0	0.000	0.159	0.876	0.077	13.059

项目	非标准化系数		标准化系数	t	p	VIF值
项目	B	标准错误	β	t	p	VIF值
常数	3.175	0.467	-	6.798	0.000^*
lnA_V	0.151	0.035	0.767	4.319	0.001^*	2.802
lnI_E	0.135	0.035	0.696	3.844	0.002^*	2.915
lnR	-0.342	0.095	-0.475	-3.618	0.003^*	1.534
lnF	0.251	0.048	1.273	5.225	0.000^*	5.273
lnI_CI	-0.208	0.052	-0.996	-3.986	0.002^*	5.542
lnL	0.009	0.077	0.027	0.112	0.913	5.231
R²	0.854
调整R²	0.786
F，p	F(6,13)=12.634, p=0.000

项目	平方和	自由度	均方	F值	p 值
回归	0.703	6	0.117	12.634	0.000
残差	0.121	13	0.009
总计	0.824	19