甘肃省行业碳排放影响因素及脱钩努力研究

doi:10.12118/j.issn.1000－6060.2022.126

Abstract

Abstract:

Accurate identification of specific focus points of industry carbon reduction is crucial to realize China’s goal of “carbon peak by 2030 and carbon neutral by 2060”. This study used the Logarithmic Mean Divisia Index (LMDI) method to decompose the influencing factors and their effects on the carbon emission of 13 subsectors (from 2010 to 2019) in Gansu Province. The Tapio decoupling model was used to analyze the relationship between carbon emission and economic growth. Accordingly, a decoupling effort model of influencing factors, excluding economic factors, was constructed to analyze the efforts made by other factors to decoupling. The following results are obtained. (1) From 2010 to 2019, carbon emissions for subsectors in Gansu Province increased by 3843.13×10⁴ t, mainly in petroleum, chemical, steel, and power industries. Specifically, the energy consumption structure of Gansu Province was characterized by high carbon emissions. Coal consumption made up 64.89% of the entire fossil energy consumption in 2019. Energy consumption intensity emerged a decreasing trend, whereas energy efficiency kept improving. (2) Economic growth and population scale exhibited an incremental effect caused by the economic growth effect. Energy intensity and structure demonstrated a reduction effect, and the reduction effect of energy intensity was more significant. However, the influence direction of the industrial structure effect fluctuated greatly in different time periods and industries. The industrial structure effect on chemical and construction industries had a relatively significant reduction, whereas that on steel and power industries was increased carbon emissions. (3) The decoupling effect of carbon emissions from the economic growth of 13 subsectors improved. From 2010 to 2013, all industries exhibited a weak decoupling effect, except for mining and light manufacturing that showed negative decoupling and expansion connection. From 2013 to 2016, some industries, such as agriculture, chemical, and steel manufacturing, underwent a strong decoupling effect. From 2016 to 2019, all sectors changed to strong or recessionary decoupling, except for the power sector, which remained weak. (4) The energy intensity effect played the most important role in decoupling. Particularly, the decoupling effect of energy and industrial structures was small but gradually increasing, whereas that of the population scale was not evident. Evidently, reducing energy consumption intensity and improving energy use efficiency are crucial points to accelerate the process of carbon emission reduction and effectively enhance the decoupling level in Gansu Province. On the basis of this finding, the following should be proposed. First, governments and enterprises should actively introduce low-carbon production technologies and high-efficiency energy-saving equipment, encourage innovation, and focus on the development and optimization of energy-saving and environmental protection technologies. Second, governments should comprehensively consider the characteristics of local industrial structures, carbon emission levels, and emission reduction potentials of subsectors and then formulate differentiated quota schemes for industrial carbon emissions for high- and low-energy industries.

Key words: carbon emission, LMDI model, decoupling, industry segmentation, Gansu Province

WU Xi, CHEN Qiangqiang. Influencing factors and decoupling efforts of industry-related carbon emissions in Gansu Province[J].Arid Land Geography, 2023, 46(2): 274-283.

Figures/Tables 8

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 1

Tab. 2

Relative contribution of influencing factors of industry carbon emissions in Gansu Province"

年份	效应	农业	采掘业	轻工制造业	纺织制造业	石油制造业	化工制造业	钢铁制造业	机电制造业	电力行业	建筑业	运输业	贸易餐饮业	其他服务业
2010—2013	ΔC_P	1.6^*	0.7^*	1.5^*	0.8^*	1.6^*	1.1^*	1.5^*	0.7^*	1.6^*	1.8^*	2.0^*	1.4^*	1.6^*
	ΔC_GP	59.5^***	25.9^**	57.4^***	30.3^**	60.3^***	41.3^**	57.3^**	26.3^**	58.3^***	67.9^***	74.8^***	53.8^***	59.4^***
	ΔC_SI	-28.5^**	16.1^*	37.5^**	34.6^**	-24.9^*	26.6^**	5.9^*	-3.7^*	10.4^*	-15.6^*	-0.3^*	-34.9^**	10.9^*
	ΔC_EI	-9.8^*	52.6^***	-3.3^*	-34.1^**	-11.6^*	-30.5^**	-34.7^**	-67.7^***	-29.8^**	-14.7^*	-21.2^*	-5.5^*	-25.9^**
	ΔC_ES	0.6^*	4.8^*	0.3^*	0.2^*	1.6^*	-0.5^*	0.6^*	-1.6^*	0.0^*	（0.0）^*	-1.7^*	-4.3^*	-2.3^*
	ΔC	0.30	6.62	1.24	0.02	7.14	5.15	6.15	-0.27	14.02	0.25	1.61	0.06	0.21
2013—2016	ΔC_P	2.3^*	3.6^*	1.3^*	1.0^*	1.6^*	1.2^*	2.3^*	1.0^*	1.6^*	1.9^*	2.7^*	1.4^*	0.9^*
	ΔC_GP	44.6^**	70.0^***	25.7^**	19.1^*	31.6^**	23.8^*	45.1^**	18.7^*	30.9^**	37.3^**	54.0^***	27.8^**	17.9^*
	ΔC_SI	-12.8^*	-4.6^*	33.5^**	-3.0^*	-60.0^***	20.7^*	-0.4^*	-5.4^*	1.5^*	-7.8^*	-26.2^**	-28.7^**	9.3^*
	ΔC_EI	-36.2^**	-18.1^*	-38.9^**	-73.5^***	6.3^*	-53.5^***	-49.1^**	-74.8^***	-65.9^***	-44.2^**	13.9^*	34.6^**	68.3^***
	ΔC_ES	-4.2^*	3.8^*	0.7^*	-3.4^*	0.5^*	0.8^*	-3.1^*	-0.2^*	0.0^*	-8.7^*	-3.2^*	-7.5^*	-3.5^*
	ΔC	-0.07	1.66	0.61	-0.04	-6.54	-1.19	-0.91	-0.17	-13.56	-0.16	1.35	0.22	1.83
2016—2019	ΔC_P	2.9^*	2.0^*	0.7^*	0.8^*	1.8^*	3.4^*	3.5^*	0.4^*	2.0^*	3.4^*	3.3^*	2.6^*	2.6^*
	ΔC_GP	26.4^**	19.0^*	6.3^*	7.6^*	16.9^*	31.5^**	32.3^**	3.7^*	19.0^*	31.2^**	30.4^**	24.1^*	24.1^*
	ΔC_SI	1.0^*	-57.1^***	-21.6^*	-62.0^***	38.5^**	-52.0^***	2.1^*	-6.4^*	51.1^***	-31.7^**	6.5^*	18.2^*	14.5^*
	ΔC_EI	-66.1^***	-21.4^*	-70.2^***	-19.3^*	-41.6^**	-9.8^*	-60.4^***	-42.5^**	-27.9^**	-32.0^**	-56.0^***	-48.9^**	-54.3^***
	ΔC_ES	-3.6^*	0.5^*	-1.2^*	-10.2^*	1.2^*	-3.3^*	1.7^*	-47.0^**	0.0^*	-1.8^*	-3.8^*	-6.2^*	-4.4^*
	ΔC	-0.43	-3.61	-3.03	-0.03	6.45	-2.21	-3.08	-0.15	20.23	-0.16	-0.74	-0.06	-0.26

Tab. 2

Tab. 3

Tab. 4

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行业	2010年	2011年	2012年	2013年	2014年	2015年	2016年	2017年	2018年	2019年
农业	2.20	2.28	2.33	2.50	2.14	2.17	2.42	2.44	2.22	1.99
采掘业	2.74	5.17	7.01	9.36	10.39	10.33	11.02	11.66	9.54	7.41
轻工制造业	1.80	1.74	1.84	3.04	4.31	1.89	3.64	2.63	1.62	0.61
纺织制造业	0.05	0.07	0.09	0.08	0.05	0.04	0.04	0.02	0.02	0.01
石油制造业	45.64	52.21	49.54	52.78	49.43	48.91	46.24	49.22	50.96	52.69
化工制造业	14.88	15.71	19.04	20.03	20.00	20.36	18.84	18.05	17.34	16.63
钢铁制造业	32.50	36.47	38.12	38.66	39.28	43.01	37.74	38.64	36.65	34.67
机电制造业	0.62	0.64	0.48	0.35	0.24	0.19	0.18	0.07	0.05	0.03
电力行业	55.73	68.83	68.69	69.75	67.16	58.74	56.19	55.44	65.93	76.42
建筑业	1.20	1.20	1.31	1.45	1.10	1.14	1.28	1.25	1.19	1.13
运输业	6.17	6.57	7.39	7.78	10.17	9.42	9.13	9.24	8.82	8.39
贸易餐饮业	0.90	0.83	0.84	0.96	0.79	0.92	1.18	1.17	1.14	1.11
其他服务业	0.78	0.80	0.82	0.99	2.03	2.44	2.81	2.79	2.68	2.56

行业	2010—2013年		2013—2016年		2016—2019年
行业	ε	脱钩状态	ε	脱钩状态	ε	脱钩状态
农业	0.70	弱脱钩	-0.17	强脱钩	-1.09	强脱钩
采掘业	3.43	扩张负脱钩	0.77	弱脱钩	1.48	衰退脱钩
轻工制造业	0.95	扩张连接	0.31	弱脱钩	3.15	衰退脱钩
纺织制造业	0.40	弱脱钩	-2.32	强脱钩	1.24	衰退脱钩
石油制造业	0.71	弱脱钩	0.76	弱负脱钩	0.25	弱脱钩
化工制造业	0.48	弱脱钩	-0.12	强脱钩	1.71	衰退脱钩
钢铁制造业	0.43	弱脱钩	-0.10	强脱钩	-0.49	强脱钩
机电制造业	-1.31	强脱钩	-2.88	强脱钩	10.52	衰退脱钩
电力行业	0.53	弱脱钩	-0.75	强脱钩	0.55	弱脱钩
建筑业	0.70	弱脱钩	-0.59	强脱钩	-9.98	强脱钩
运输业	0.66	弱脱钩	1.37	扩张负脱钩	-0.42	强脱钩
贸易餐饮业	0.50	弱脱钩	59.43	扩张负脱钩	-0.19	强脱钩
其他服务业	0.56	弱脱钩	4.72	扩张负脱钩	-0.36	强脱钩

效应	2010—2013年				2013—2016年				2016—2019年
效应	D_P	D_SI	D_EI	D_ES	D_P	D_SI	D_EI	D_ES	D_P	D_SI	D_EI	D_ES
农业	-0.03	0.48^*	0.16^*	-0.01	-0.05	0.29^*	0.81^*	0.09^*	-0.11	-0.04	2.50^**	0.13^*
采掘业	-0.03	-0.62	-2.03	-0.18	-0.05	0.07^*	0.26^*	-0.05	-0.11	3.01^*	1.13^**	-0.03
轻工制造业	-0.03	-0.65	0.06^*	-0.01	-0.05	-1.31	1.51^**	-0.03	-0.11	3.44^*	11.15^**	0.19^*
纺织制造业	-0.03	-1.14	1.12^**	-0.01	-0.05	0.16^*	3.84^**	0.18^*	-0.11	8.17^*	2.54^**	1.35^**
石油制造业	-0.03	0.41^*	0.19^*	-0.03	-0.05	1.90^**	-0.20	-0.02	-0.11	-2.28	2.47^**	-0.07
化工制造业	-0.03	-0.64	0.74^*	0.01^*	-0.05	-0.87	2.25^**	-0.03	-0.11	1.65^*	0.31^*	0.10
钢铁制造业	-0.03	-0.10	0.60^*	-0.01	-0.05	0.01^*	1.09^**	0.07^*	-0.11	-0.06	1.87^**	-0.05
机电制造业	-0.03	0.14^*	2.58^**	0.06^*	-0.05	0.29^*	3.99^**	0.01^*	-0.11	1.75^*	11.58^**	12.81^**
电力行业	-0.03	-0.18	0.51^*	0.00	-0.05	-0.05	2.13^**	0.00	-0.11	-2.69	1.47^**	0.00
建筑业	-0.03	0.23^*	0.22^*	0.00^*	-0.05	0.21^*	1.19^**	0.23^*	-0.11	1.02^*	1.03^**	0.06^*
运输业	-0.03	0.00^*	0.28^*	0.02^*	-0.05	0.49^*	-0.26	0.06^*	-0.11	-0.21	1.84^**	0.12^*
贸易餐饮业	-0.03	0.65^*	0.10^*	0.08^*	-0.05	1.03^*	-1.25	0.27^*	-0.11	-0.75	2.03^**	0.26^*
其他服务业	-0.03	-0.18	0.44^*	0.04^*	-0.05	-0.52	-3.82	0.20^*	-0.11	-0.60	2.25^**	0.18^*

Influencing factors and decoupling efforts of industry-related carbon emissions in Gansu Province

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