Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China

doi:10.1007/s11442-018-1486-9

Abstract

Abstract:

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation (Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club’ agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.

Key words: carbon emissions, spatial spillover effects, dynamic spatial panel data model, Chinese carbon emission reduction policies, environmental Kuznets curve

Wensong SU, Yanyan Liu, Shaojian WANG, Yabo ZHAO, Yongxian SU, Shijie LI. Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China[J].Journal of Geographical Sciences, 2018, 28(4): 495-513.

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References 36

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	n	P (≤ 50%)	L (between 51% and 100%)	D (between 101% and 150%)	R (≥ 150%)
Between 1992 and 2013
P	3,098	0.957	0.042	0.000	0.000
L	2,002	0.023	0.936	0.040	0.000
D	872	0.006	0.054	0.873	0.068
R	1,252	0.005	0.006	0.034	0.955
1992-2000
P	1,342	0.949	0.049	0.001	0.001
L	657	0.038	0.906	0.055	0.002
D	323	0.015	0.065	0.848	0.071
R	430	0.014	0.014	0.044	0.928
2001-2013
P	1,756	0.964	0.036	0.000	0.000
L	1,345	0.016	0.951	0.033	0.000
D	549	0.000	0.047	0.887	0.066
R	822	0.000	0.001	0.029	0.970

		Between 1992 and 2000				Between 2001 and 2013
		P	L	D	R	P	L	D	R
P	P	0.962	0.038	0.000	0.000	0.972	0.028	0.000	0.000
	L	0.043	0.904	0.053	0.000	0.091	0.904	0.025	0.000
	D	0.057	0.096	0.836	0.011	0.000	0.112	0.864	0.024
	R	0.000	0.000	0.000	1.000	0.000	0.000	0.000	1.000
L	P	0.913	0.087	0.000	0.000	0.946	0.054	0.000	0.000
	L	0.027	0.926	0.047	0.000	0.042	0.942	0.016	0.000
	D	0.017	0.154	0.818	0.011	0.000	0.063	0.916	0.021
	R	0.000	0.000	0.066	0.934	0.000	0.000	0.064	0.936
D	P	0.902	0.098	0.000	0.000	0.868	0.132	0.000	0.000
	L	0.015	0.931	0.054	0.000	0.061	0.928	0.011	0.000
	D	0.000	0.139	0.813	0.038	0.000	0.088	0.884	0.018
	R	0.000	0.000	0.072	0.928	0.000	0.000	0.036	0.964
R	P	1.000	0.000	0.000	0.000	0.784	0.216	0.000	0.000
	L	0.015	0.931	0.054	0.000	0.075	0.914	0.011	0.000
	D	0.000	0.108	0.863	0.029	0.000	0.000	0.908	0.092
	R	0.000	0.000	0.013	0.987	0.000	0.000	0.007	0.993

LM test	W₁ weight matrix		W₃ weight matrix
LM test	χ²	p-value	χ²	p-value
No lag	1675.474	0.000	1684.328	0.000
No lag (robust)	25.856	0.000	26.732	0.002
No error	1671.253	0.000	1669.529	0.000
No error (robust)	18.712	0.248	14.967	0.169

Variable	W₁weight matrix			W₃weight matrix
Variable	PLOS	FE	Sys-GMM	PLOS	FE	Sys-GMM
ln(CE_t?1(θ))	0.864^***	0.266^***	0.703^***	0.864^***	0.268^***	0.702^***
ωln(CE(ρ))	1.455^***	1.622^***	1.082^***	1.396^***	1.624^***	0.179^***
ωln(CE_t?1(γ))	0.688^***	0.247^***	0.726^***	0.687^***	0.249^***	0.724^***
ln(GDPPC)	0.578^*	0.642^***	0.594^***	0.580	0.638^***	0.596^***
ln(GDPPC)²	?0.017^*	0.034^**	0.026^***	0.019	0.035^**	0.025^***
ln(POPD)	0.142	?0.247^**	?0.386^***	0.104	?0.246^**	?0.388^***
ln(TC)	?0.053^***	?0.049^***	?0.064^***	?0.054^***	?0.048^***	?0.064^***
ln(SIND)	0.099^***	0.163^***	0.254^***	0.104^***	0.164^***	0.255^***
ln(ROAD)	?0.067^*	?0.123^***	?0.154^***	?0.066^**	?0.123^**	?0.155^***
ln(FAI)	0.057^***	0.086^***	0.066^***	0.057^***	0.085^***	0.067^***
ln(TO)	?0.023^*	?0.018^***	?0.027^***	0.023^*	?0.019^***	?0.028^***
Sargan P			0.675			0.663
AR(2) P			0.816			0.892