Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data

doi:10.1007/s11442-022-2028-z

Abstract

Abstract:

This essay combines the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data and the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data into a “synthetic DMSP” dataset, from 1992 to 2020, to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang, China. Then, this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector technique. Results reveal that (1) total carbon emissions continued to grow, while the growth rate slowed down in the past five years. (2) Large regional differences exist in total carbon emissions across various regions. Total carbon emissions of these regions in descending order are the northern slope of the Tianshan (Mountains) > the southern slope of the Tianshan > the three prefectures in southern Xinjiang > the northern part of Xinjiang. (3) Economic growth, population size, and energy consumption intensity are the most important factors of spatial differentiation of carbon emissions. The interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’ spatial differentiation. This paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps.

Key words: carbon emissions, nighttime light data, spatio-temporal variations, influencing factors, Xinjiang

ZHANG Li, LEI Jun, WANG Changjian, WANG Fei, GENG Zhifei, ZHOU Xiaoli. Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data[J].Journal of Geographical Sciences, 2022, 32(10): 1886-1910.

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Energy category	Coal	Coke	Crude	Gasoline	Kerosene	Diesel	Fuel oil	Natural gas	Electricity
Standard coal coefficient B_i (tce/t)	0.7143	0.9714	1.4285	1.4714	1.4714	1.4571	1.4286	1.33	0.1229
Carbon emission coefficients K_i (t/tce)	0.7559	0.855	0.5857	0.5538	0.5714	0.5921	0.6185	0.4483	0.272

Influencing factor	Index	q
Influencing factor	Index	1992	2000	2010	2020
Economic growth	Gross Domestic Product (GDP)	0.654***	0.685***	0.746***	0.431*
Economic growth	The secondary industry output value (SV)	0.564***	0.612***	0.672***	0.440***
Industrial structure	The ratio of secondary industry output value to GDP (SP)	0.211**	0.352***	0.215***	0.133**
Population size	Population size (POP)	0.517**	0.420	0.462*	0.398*
Population size	Urban population size (UP)	0.529**	0.571***	0.562***	0.391*
Urbanization level	Urbanization rate (UR)	0.115*	0.313***	0.258***	0.197**
Energy consumption intensity	Energy consumption intensity per unit of GDP (EIG)	0.354***	0.205*	0.268	0.378*

Interacting factors	1992	Interacting factors	2000	Interacting factors	2010	Interacting factors	2020
GDP∩EIG	0.990	SV∩EIG	0.934	GDP∩EIG	0.942	SV∩EIG	0.869
POP∩EIG	0.905	GDP∩EIG	0.918	SV∩EIG	0.857	GDP∩EIG	0.860
SV∩EIG	0.879	POP∩SP	0.782	SV∩UR	0.830	UP∩ EIG	0.789
UP∩ EIG	0.866	UP∩SP	0.777	GDP∩UR	0.816	POP∩EIG	0.777
POP∩GDP	0.798	POP∩SV	0.768	SV∩UP	0.813	UR∩ EIG	0.616
POP∩SP	0.714	UP∩ EIG	0.768	POP∩SV	0.811	UR∩ POP	0.602
GDP∩SP	0.709	SV∩UP	0.746	GDP∩SP	0.794	POP∩SP	0.583
SV∩SP	0.701	SV∩UR	0.733	SV∩SP	0.793	UR∩ SV	0.582
GDP∩POP	0.674	GDP∩SP	0.749	GDP∩POP	0.774	SV∩UP	0.544
GDP∩SV	0.673	GDP∩POP	0.724	UR∩POP	0.757	SV∩POP	0.543