Carbon dioxide (CO₂) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data

Figures & data

Table 1. Statistics on service industry CO₂ emissions, traffic CO₂ emissions, secondary industry CO₂ emissions, total CO₂ emissions, and nighttime lights in Chinese cities.

City	2012					2015
	TNT	SC (10^4 t)	TC (10^4 t)	IC (10^4 t)	TO (10^4 t)	TNT	SC (10^4 t)	TC (10^4 t)	IC (10^4 t)	TO (10^4 t)
Nanjing	128,864	55	397	7586	8679	129,602	26	639	7185	8805
Chongqing	128,683	301	1420	12,368	19,191	158,970	301	1962	13,029	20,387
Shenzhen	128,600	116	569	939	2063	140,986	222	685	2426	4645
Jining	53,337	233	311	7820	9093	54,089	140	289	8800	10,090
Taizhou	53,124	14	201	2861	3727	57,639	12	232	2699	3320
Baoding	52,310	178	325	2946	5552	57,577	155	418	2643	4832

Note: TNT, SC, TC, IC, and TO represent total nighttime lights, service industry CO₂ emissions, traffic CO₂ emissions, secondary industry CO₂ emissions, and total CO₂ emissions, respectively.

Figure 1. The spatial distribution of the study area.

Note: the numbers in brackets represent the population and the number of cities, respectively.

Figure 2. The corrected Suomi NPP-VIIRS data from 2012 and 2015.

Note: Enlarged views on the right are pictures for the Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta. By comparing the blue areas, the luminous intensity in 2015 was found to be higher than that in 2012 in some regions.

Table 2. Correlations between nighttime lights and service industry CO₂ emissions for all selected cities.

	(1)	(2)	(3)	(4)
TNT	0.382***	0.752**	10.245***	9.203***
(TNT)^2			−0.482***	−0.442***
Q				1.405***
M		1.568*	1.588**	2.372***
A		−0.159	−0.135	−0.137
G		−0.133	−0.121	−0.342*
H		0.184	0.102	−0.014
J		0.103	0.117	0.059
City fixed effects	No	Yes	Yes	Yes
Observations	408	408	408	408
Adjusted R^2	0.227	0.834	0.840	0.853

Note: *, **, *** represent significance at the 10%, 5%, and 1% levels, respectively. TNT, Q, M, A, G, H, and J represent total nighttime lights, the proportion of the tertiary industry in total industry, NDVI, the population, built-up urban areas, the proportion of college students in the total population, and electricity consumption, respectively. Due to the data analysis in 2012 and 2015, only city fixed effects were included.

Figure 3. The relationship between nighttime lights and TC, IC.

Noted: TNT, IC TC represent total nighttime lights, secondary industry CO₂ emissions, and traffic CO₂ emissions, respectively. Note: logarithmic transformation can be more intuitive to find the data relationship, and convenient for statistical inference.

Table 3. Correlations between nighttime lights and traffic CO₂ emissions for all selected cities.

	(1)	(2)	(3)	(4)
TNT	0.514***	0.544***	1.832	0.405***
(TNT)^2			−0.065
D				0.328***
M		0.149	0.152	0.285
A		−0.011	−0.008	−0.116
G		0.326***	0.328***	0.247**
H		0.185**	0.174*	0.129
J		0.071	0.073	0.054
City fixed effects	No	Yes	Yes	Yes
Observations	408	408	408	408
Adjusted R^2	0.512	0.900	0.900	0.906

Note: D represents the road network density.

Table 4. Correlations between nighttime lights and secondary industry CO₂ emissions for all selected cities.

	(1)	(2)	(3)	(4)
TNT	0.728***	0.567***	1.418	0.558***
(TNT)^2			−0.043
L				−0.180
M		−0.327	−0.325	−0.251
A		−0.124	−0.122	−0.124
G		−0.323**	−0.322**	−0.346**
H		0.025	0.017	0.018
J		0.462***	0.463***	0.460***
City fixed effects	No	Yes	Yes	Yes
Observations	408	408	408	408
Adjusted R^2	0.417	0.912	0.912	0.913

Note: L represents the proportion of the secondary industry in total industry.

Data availability statement

The monthly Suomi NPP-VIIRS data are openly available in Earth Observation Group at https://payneinstitute.mines.edu/eog/. China’s city CO₂ emissions were collected from China City Greenhouse Gases Emissions Datasets.