Analysis of influencing factors of CO₂ emissions based on different coal dependence zones in China

Figures & data

Figure 1. Theoretical framework graphical depiction.

Source: Created by authors, represents the theoretical framework as discussed in Section 2.1.

Table 1. Correlation coefficient of CO₂ emissions from fossil energy.

energy type	Minimum average calorific value (TJ / 10,000t)	Carbon content per unit calorific value (tC/TJ)	Carbon oxidation rate
raw coal	20908	27.280	0.920
coke	28435	29.410	0.930
diesel fuel	42652	20.170	0.980
kerosene	43070	19.600	0.990
gasoline	43070	18.900	0.980
natural gas	38931	15.320	0.990
fuel oil	41816	21.090	0.990

Data source: The carbon content and carbon oxidation rate of the calorific value of each energy unit are from the “Guidelines for Compilation of Provincial Greenhouse Gas Inventories”; the minimum calorific value refers to the “General Principles of Comprehensive Energy Consumption Calculation GBT2589-2020”.

Table 2. Coal dependency zones.

Coal dependence zoning	Coal Dependence (I)	Area
high	I ≥ 0.8	Ningxia, Inner Mongolia, Shanxi, Guizhou, Xinjiang, Shaanxi, Heilongjiang, Jilin, Anhui, Henan
middle	0.7 > I > 0.8	Gansu, Hunan, Chongqing, Shandong, Jiangsu, Zhejiang, Jiangxi, Qinghai, Hubei, Yunnan
Low	I ≤ 0.7	Fujian, Guangxi, Hebei, Liaoning, Sichuan, Guangdong, Tianjin, Hainan, Shanghai, Beijing

Source: Authors own calculations on the data obtained from China Statistical Yearbook, China Energy Statistical Yearbook, and the Statistical Yearbook.

Table 3. Data statistics.

variable symbol	variable name	Number of samples	average value	standard deviation	minimum	maximum value
CO2	CO2 emissions / 10,000t	450	29392.27	20021.08	1646.21	93711.69
P	Population size / 10,000 people	450	4479.22	2689.43	543	11521
A	per capita / 10,000 yuan	450	2.97	1.86	0.45	10.78
T	Technical level /%	450	1.50	1.10	0.20	6.30
U	Urbanization rate /%	450	54.10	13.80	26.90	89.60
SE	Energy consumption structure /%	450	73.70	12.50	5.50	92.50
SI	Industrial structure /%	450	48.50	12	16.1	85

Source: Authors own calculations on the data obtained from China Statistical Yearbook, China Energy Statistical Yearbook, and the Statistical Yearbook.

Table 4. The fixed effect regression results of the influencing factors of CO₂ in each province from 2005 to 2019.

variable	Model 1		Model 2
	FE	RE	FE	RE
lnp	0.667 *** (0.163)	0.753 *** (0.075)	1.029 *** (0.155)	0.779 *** (0.071)
lnA	0.500 *** (0.047)	0.520 *** (0.043)	2.622 *** (0.483)	2.284 *** (0.459)
lnT	0.049 (0.040)	0.024 (0.038)	0.605 *** (0.092)	0.615 *** (0.092)
lnu	0.103 (0.141)	0.06 (0.134)	−0.421 ** (0.163)	−0.407 ** (0.159)
lnSE	0.294 *** (0.041)	0.317 *** (0.040)	0.246 *** (0.037)	0.244 *** (0.037)
lnSI	0.326 *** (0.044)	0.339 *** (0.043)	0.192 *** (0.044)	0.206 *** (0.044)
lnA^2	–	–	−0.101 *** (0.023)	−0.083 *** (0.022)
lnT^2	–	–	−0.123 *** (0.021)	−0.127 *** (0.021)
intercept term	−8.21 *** (1.423)	−8.946 *** (0.732)	19.922 *** (2.681)	16.394 *** (2.201)
Hausman test	Chi2(7)= 11.642 Prob > chi2 = 0.113		Chi2(8)=15.821 Prob > chi2 = 0.045

Notes: (1)*p < 0.1, **p < 0.05, ***p < 0.01; (2): Standard deviations are in parentheses.

Table 5. The fixed effect regression results of the influencing factors of CO₂ in each province from 2005 to 2019.

Variable	Coal Dependence
	Highly Dependent Area	Moderately Dependent Areas	Low Dependency Area
Lnp	0.543 *** (0.091)	0.950 *** (0.074)	0.403 *** (0.101)
lnA	3.459 *** (1.004)	2.724 *** (0.844)	2.976 *** (0.705)
lnT	0.595 ** (0.236)	−0.705 ** (0.352)	0.688 *** (0.100)
lnU	−0.134 (0.281)	−1.090 *** (0.369)	−0.894 *** (0.234)
lnSE	3.122 *** (0.446)	1.301 *** (0.254)	0.360 *** (0.029)
lnSI	0.207 ** (0.084)	0.173 * (0.095)	0.316 *** (0.053)
lnA^2	−0.148 *** (0.049)	−0.098 ** (0.042)	−0.123 *** (0.033)
lnT^2	−0.108 * (0.056)	0.120 (0.076)	−0.099 *** (0.022)
intercept term	7.251 (5.261)	−5.002 (3.468)	1.345 (3.227)

Notes: (1)*p < 0.1, **p < 0.05, ***p < 0.01; (2): Standard deviations are in parentheses.