Uncovering the mystery of financial development in mitigating CO2 emissions in China: A spatial dependence and heterogeneity perspective

Figures & data

Table 1. Identified economic factors of CO₂ emissions.

	Variable	Measure (unit)	References
Dependent variable	CO2 emissions ($lnC{O}_2$)	Per capita carbon emissions (kg per person)	Shan et al. (2020)
Factor of interest	Financial development ($lnfdev$)	Ratio of the sum of bank deposits and bank loans to GDP (%)	Saidi and Mbarek (2017)
Control factors	Population size ($lnpop$)	Permanent residents at the end of each year (10^4 persons)	Lantz and Feng (2006)
	Economic development ($lnpgdp$)	Per capita GDP (10^4 Yuan)	Apergis and Ozturk (2015)
	Technological progress ($lntec$)	Per capita patent counts granted (piece)	Acs, Anselin, and Varga (2002)
	Financial constraint ($lnloanrate$)	Ratio of bank loans to bank deposits (%)	Ji (2013)
	Urbanization ($lnurb$)	Ratio of urban population (%)	Saidi and Mbarek (2017)
	Industrial structure ($lnis$)	Ratio of the added value of the secondary industry to GDP (%)	Apergis and Ozturk (2015)
	Construction intensity ($lncons$)	Ratio of the total floor space area of buildings under construction to the provincial area (10^−6)	Bai et al. (2016)
	Trade openness ($lntrade$)	Ratio of the sum of exports and imports to GDP (%)	Jalil and Feridun (2011)
	Vehicle population ($lnpveh$)	Civilian vehicle ownership per 10^3 persons (unit)	Xu et al. (2016)
	Foreign direct investment ($lnfdi$)	Ratio of foreign direct investment to GDP (%)	Yan and Sheng (2014)

Table 2. Descriptive statistics and preliminary tests.

Factor	Mean	Std.dev	Minimum	Maximum	CD test	LLC test (First difference)
$lnC{O}_2$	8.42	0.64	6.88	10.15	80.85*** (0.00)	−11.24*** (0.00)
$lnfdev$	5.55	0.30	4.91	6.56	73.49*** (0.00)	−12.39*** (0.00)
$lnpop$	8.14	0.76	6.21	9.32	46.04*** (0.00)	−6.69*** (0.00)
$lnpgdp$	8.18	0.78	6.23	9.98	94.83*** (0.00)	−5.96*** (0.00)
$lntec$	0.35	1.43	−2.37	3.90	92.61*** (0.00)	−10.51*** (0.00)
$lnloanrate$	4.33	0.19	3.71	4.90	49.19*** (0.00)	−15.08*** (0.00)
$lnurb$	3.81	0.34	2.82	4.50	70.64*** (0.00)	−3.63*** (0.00)
$lnis$	3.57	0.27	2.46	4.05	38.83*** (0.00)	−8.55*** (0.00)
$lncons$	6.83	1.64	1.91	10.37	91.29*** (0.00)	−8.54*** (0.00)
$lntrade$	2.87	1.00	0.59	5.14	39.66*** (0.00)	−13.86*** (0.00)
$lnpveh$	3.56	1.00	1.52	5.56	94.69 *** (0.00)	−7.68*** (0.00)
$lnfdi$	3.44	0.89	0.94	6.36	40.18*** (0.00)	−18.15*** (0.00)

Std.dev is the standard deviation. CD test and LLC test indicate the cross-sectional dependence test and Levin-Lin-Chu test for stationarity, respectively. ***indicates significance at the 1% level.

Table 3. Estimation results of the pooled panel data regression model.

	$lnC{O}_2$	p-value
Intercept	−2.6388	.1056
$lnfdev$	0.0999	.1617
$lnpop$	−0.1354***	.0000
$lnpgdp$	0.3083	.3193
$lnpgd{p}^2$	0.0148	.4367
$lntec$	−0.3815***	.0000
$lnloanrate$	0.4756***	.0000
$lnurb$	0.3131***	.0000
$lnis$	0.9319***	.0000
$lncons$	0.0198	.1118
$lntrade$	−0.0162	.4423
$lnpveh$	0.4990***	.0000
$lnfdi$	−0.0654***	.0019
Adjusted R^2	0.8393
LM (lag)	60.9870***	.0000
Robust LM (lag)	67.4990***	.0000
LM (error)	3.4506*	.0632
Robust LM (error)	9.9626***	.0016

LM indicates Lagrange multiplier test. *** and * indicate statistical significance at the 1% and 10% level, respectively.

Figure 1. Moran’s I scatter diagrams of CO₂ emissions in the selected years.

Table 4. Values of Moran’s I.

Year	Moran’s I	p-value	Year	Moran’s I	p-value
1997	0.4299***	0.0001	2008	0.3615***	.0008
1998	0.3957***	0.0003	2009	0.3191***	.0023
1999	0.3881***	0.0004	2010	0.3352***	.0015
2000	0.4073***	0.0002	2011	0.2523***	.0098
2001	0.4533***	0.0001	2012	0.2853***	.0047
2002	0.4158***	0.0002	2013	0.3092***	.0028
2003	0.4037***	0.0003	2014	0.3152***	.0024
2004	0.4048***	0.0003	2015	0.2716***	.0068
2005	0.4123***	0.0002	2016	0.2703***	.0071
2006	0.3591***	0.0010	2017	0.2102**	.0234
2007	0.3617***	0.0009

***and ** indicate statistical significance at 1% level and 5% level, respectively.

Table 5. Estimation results of the RE-ESF panel model.

	$lnC{O}_2$	p-value
Intercept	−4.0832***	.0001
$lnfdev$	−0.1481**	.0223
$lnpop$	−0.0702	.1348
$lnpgdp$	1.6111***	.0000
$lnpgd{p}^2$	−0.0826***	.0000
$lntec$	−0.1317***	.0000
$lnloanrate$	0.6258***	.0000
$lnurb$	0.0599	.1031
$lnis$	0.4010***	.0000
$lncons$	0.1026***	.0000
$lntrade$	−0.0603***	.0024
$lnpveh$	0.3641***	.0000
$lnfdi$	0.0428**	.0109
Adjusted R^2	0.9675
rlogLik	315.1886
AIC	−594.3772
BIC	−514.3542

rlogLik is the restricted log-likelihood. AIC represents the Akaike information criterion and BIC represents the Bayesian information criterion. *** and ** indicate statistical significance at the 1% and 5% level, respectively.

Table 6. Summary of the spatially varying coefficients of the RE-ESF-SNVC model.

	Minimum	Q1	Median	Mean	Q3	Maximum
Intercept	−4.652	−4.583	−4.556	−4.547	−4.507	−4.438
$lnfdev$	−0.535	−0.440	−0.293	−0.286	−0.125	−0.039
$lnpop$	0.224	0.283	0.321	0.305	0.327	0.384
$lnloanrate$	0.299	0.488	0.703	0.651	0.823	0.890
$lnis$	−0.006	0.126	0.242	0.214	0.317	0.420
$lncons$	−0.011	0.030	0.033	0.031	0.035	0.038
$lnpveh$	0.127	0.238	0.277	0.292	0.351	0.457
Adjusted R^2	0.982
rlogLik	409.671
AIC	−761.342
BIC	−632.417

Q1 indicates the first quartile and Q3 indicates the third quartile. rlogLik is the restricted log-likelihood. AIC represents the Akaike information criterion and BIC represents the Bayesian information criterion.

Figure 2. Median values of the estimated spatially varying coefficients.

Table 7. Median values and locations of the spatially varying coefficients for each province.

Province	$lnfdev$	Location	$lnloanrate$	Province	$lnfdev$	Location	$lnloanrate$
Anhui	−0.277	A	0.689	Jiangxi	−0.123	B	0.516
Beijing	−0.535	A	0.854	Jilin	−0.486	A	0.867
Chongqing	−0.125	B	0.408	Liaoning	−0.531	A	0.890
Fujian	−0.134	B	0.570	Ningxia	−0.347	A	0.705
Gansu	−0.308	B	0.757	Qinghai	−0.267	B	0.769
Guangdong	−0.058	B	0.386	Shaanxi	−0.314	B	0.635
Guangxi	−0.039	B	0.299	Shandong	−0.457	A	0.826
Guizhou	−0.060	B	0.317	Shanghai	−0.298	B	0.776
Hebei	−0.524	A	0.844	Shanxi	−0.440	A	0.756
Heilongjiang	−0.425	A	0.823	Sichuan	−0.157	B	0.488
Henan	−0.321	A	0.661	Tianjin	−0.527	A	0.857
Hubei	−0.185	B	0.512	Xinjiang	−0.288	B	0.820
Hunan	−0.076	B	0.384	Yunnan	−0.119	B	0.412
Inner Mongolia	−0.506	A	0.829	Zhejiang	−0.226	A	0.700
Jiangsu	−0.342	A	0.777	Hainan	−0.096	B	0.389

A and B denote the location of a province/city is above and below the geographically dividing line, respectively.

Figure 3. Results of the geographically dividing line.

Table 8. One-way analysis of variance (ANOVA).

Source	Sum of squares	Degree of freedom	Mean square	F-statistic	p-value	F-critical value
Between	0.5009	1	0.5009	49.4099	.0000	4.1960
Within	0.2839	28	0.0101
Total	0.7848	29

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