Urbanization, economic growth, and carbon dioxide emissions in China: A panel cointegration and causality analysis

doi:10.1007/s11442-016-1259-2

Abstract

Abstract:

Elucidating the complex mechanism between urbanization, economic growth, carbon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997-2010, this study empirically examines the relationships among urbanization, economic growth and carbon dioxide (CO₂) emissions at the national and regional levels using panel cointegration and vector error correction model and Granger causality tests. Results showed that urbanization, economic growth and CO₂ emissions are integrated of order one. Urbanization contributes to economic growth, both of which increase CO₂ emissions in China and its eastern, central and western regions. The impact of urbanization on CO₂ emissions in the western region was larger than that in the eastern and central regions. But economic growth had a larger impact on CO₂ emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization, economic growth and CO₂ emissions, indicating that in the long run, urbanization does have a causal effect on economic growth in China, both of which have causal effect on CO₂ emissions. At the regional level, we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run, we observed a unidirectional causation running from land urbanization to CO₂ emissions and from economic growth to CO₂ emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped relationship between CO₂ emissions and economic growth in China, not supporting the environmental Kuznets curve (EKC) hypothesis. Our empirical findings have an important reference value for policy-makers in formulating effective energy saving and emission reduction strategies for China.

Key words: urbanization, economic growth, CO₂ emissions, panel cointegration, Granger causality

Yansui LIU, Bin YAN, Yang *ZHOU. Urbanization, economic growth, and carbon dioxide emissions in China: A panel cointegration and causality analysis[J].Journal of Geographical Sciences, 2016, 26(2): 131-152.

Figures/Tables 13

Table 1

Figure 1

Figure 2

Table 2

Table 3

Table 4

Panel cointegration coefficients by FMOLS and DOLS for China and its eastern, central and western regions"

Whole China
Variable	Dependent variable: pGDP			Variable	Dependent variable: BA
Variable	DOLS (1,1)	DOLS (2,2)	FMOLS	Variable	DOLS (1,1)	DOLS (2,2)	FMOLS
BA	0.90^*** (-27.29)	0.81^*** (-21.66)	0.94^*** (-27.58)	pGDP	0.66*** (-9.64)	0.22 (-0.47)	0.93*** (-43.33)
Variable	Dependent variable: CO₂			Variable	Dependent variable: CO₂
Variable	DOLS (1,1)	DOLS (2,2)	FMOLS	Variable	DOLS (1,1)	DOLS (2,2)	FMOLS
pGDP	0.83^*** (-16.22)	0.60^*** (-2.12)	0.94^*** (-61.07)	BA	1.19*** (-36.78)	1.16*** (-29.56)	1.15*** (-34.88)
Obs	341	279	403	Obs	341	279	403
Eastern region
Variable	Dependent variable: pGDP			Variable	Dependent variable: BA
Variable	DOLS (1,1)	DOLS (2,2)	FMOLS	Variable	DOLS (1,1)	DOLS (2,2)	FMOLS
BA	1.02^*** (-50.73)	0.99^*** (-6.63)	1.03^*** (-38.21)	pGDP	0.66^*** (-9.64)	0.43 (-0.51)	1.06^*** (-26.07)
Variable	Dependent variable: CO₂			Variable	Dependent variable: CO₂
Variable	DOLS (1,1)	DOLS (2,2)	FMOLS	Variable	DOLS (1,1)	DOLS (2,2)	FMOLS
pGDP	0.90^*** (-20.11)	0.57 (-0.77)	0.96^*** (-42.37)	BA	0.98^*** (-75.43)	0.99^*** (-10.35)	0.95^*** (-45.29)
Obs	121	99	143	Obs	121	99	143
Central region
Variable	Dependent variable: pGDP			Variable	Dependent variable: BA
Variable	DOLS (1,1)	DOLS (2,2)	FMOLS	Variable	DOLS (1,1)	DOLS (2,2)	FMOLS
BA	1.17^*** (-26.27	1.91^*** (-52.62)	1.21^*** (-26.97)	pGDP	0.90^*** (-8.34)	0.30 (-1.4)	0.83^*** (-25.16)
Variable	Dependent variable: CO₂			Variable	Dependent variable: CO₂
Variable	DOLS (1,1)	DOLS (2,2)	FMOLS	Variable	DOLS (1,1)	DOLS (2,2)	FMOLS
pGDP	0.64^*** (-10.6)	0.40^*** (-4.28)	0.84^*** (-28.26)	BA	0.99^*** (-38.35)	1.03^*** (-28.1)	0.98^*** (-24.42)
Obs	88	72	104	Obs	88	72	104
Western region
Variable	Dependent variable: pGDP			Variable	Dependent variable: BA
Variable	DOLS (1,1)	DOLS (2,2)	FMOLS	Variable	DOLS (1,1)	DOLS (2,2)	FMOLS
BA	1.24^*** (-32.66)	1.35^*** (-49.14)	1.29^*** (-28.75)	pGDP	0.53^*** (-5.90)	0.25 (-1.92)	0.79^*** (-24.24)
Variable	Dependent variable: CO₂			Variable	Dependent variable: CO₂
Variable	DOLS (1,1)	DOLS (2,2)	FMOLS	Variable	DOLS (1,1)	DOLS (2,2)	FMOLS
pGDP	0.64^*** (-7.24)	0.63^*** (-1.36)	0.97^*** (-29.77)	BA	1.27^*** (-43.92)	1.29^*** (-41.09)	1.24^*** (-27.96)
Obs	88	72	104	Obs	88	72	104

Table 4

Table 5

Table 6

Figure 3

Table 7

Table 8

Table 9

Table 10

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Indicators	Unit	Abbreviation	Meaning of indicators	Sources
Built-up area	km²	BA	Land urbanization	CCSY
Urban population	persons	UP	Demographic urbanization	CSY
GDP per capita	yuan RMB	pGDP	Economic growth	CSY
CO₂ emissions	million tons	CO₂	Pollutant emissions	Guan et al., 2012

	Levels
	Variable	LLC	IPS	Fisher-ADF	Fisher-PP
Intercept	BA	-2.75^***	4.04	18.88	26.12
	UP	-4.68^***	1.19	61.71	112.52^***
	pGDP	5.863	12.1	9.23	4.24
	CO₂	1.65	7.18	8.4	6.45
Intercept and trend	BA	-1.50^*	-1.47^*	47.28	52.87
	UP	-9.11^***	-0.27	67.02	27.63
	pGDP	-10.31^***	-1.68^**	91.47^***	68.99
	CO₂	-6.39^***	-2.32^**	89.47^**	74.63
	First differences
	Variable	LLC	IPS	Fisher-ADF	Fisher-PP
Intercept	BA	-6.39^***	-4.65^***	115.01^***	224.74^***
	UP	-11.32^***	-6.40^***	149.70^***	220.52^***
	pGDP	-4.51^***	-0.29	55.29^**	52.95^***
	CO₂	-6.12^***	-4.11^***	106.59^***	195.71^***
Intercept and trend	BA	-5.54^***	-1.65^***	75.35^***	202.62^***
	UP	-18.90^***	-9.45^***	203.53^***	309.78^***
	pGDP	-2.16^***	0.42	59.99^**	105.29^***
	CO₂	-2.59^***	0.10^**	53.40^**	158.00^***

Statistics	Panel A	Panel B	Panel C	Panel D	Panel E
Panel v	1.51^*	1.23	3.22^***	-0.03	3.70^***
Panel rho	-1.75^**	0.10	-3.25^***	0.71	-1.93^**
Panel PP	-2.40^***	-1.17	-7.18^***	0.24	-5.31^***
Panel ADF	-2.98^***	-1.29^*	-8.12^***	-1.19	-8.10^***
Group rho	1.48	2.77	-0.35	2.67	0.13
Group PP	-0.36^*	0.97	-5.74^***	0.96	-5.63^***
Group ADF	-2.04^**	-1.06^*	-7.61^***	-1.26	-8.80^***

Test summary	Chi-Sq. statistic
Chi-Sq. statistic	Quadratic	Cubic
Chi-Sq. statistic	40.304	41.210
Prob.	0.000	0.000
Accept model	Fixed effects	Fixed effects

Dependent variable [ln (CO₂)]	Quadratic model			Cubic model
Dependent variable [ln (CO₂)]	Coefficient	t-statistic	Prob.	Coefficient	t-statistic	Prob.
ln(pGDP)	0.674	3.054	0.000	-6.336	-2.402	0.017
ln(pGDP)²	-0.003	-0.184	0.854	0.997	2.657	0.008
ln(pGDP)³	-			-0.047	-2.666	0.008
ln(BA)	0.293	8.239	0.000	0.286	8.089	0.000
Constant	-1.717	-2.247	0.025	14.589	2.368	0.018
Turning point	-			(127.41, 10201.29)
F-statistic	597.542		0.000	589.035		0.000
Adjusted R²	0.980			0.980
Wald test	H₀: the quadratic model; H₁:the cubic curve
Wald statistic	7.110^***