The impact of economic agglomeration on water pollutant emissions from the perspective of spatial spillover effects

doi:10.1007/s11442-019-1702-2

Abstract

Abstract:

Whether economic agglomeration can promote improvement in environmental quality is of great importance not only to China’s pollution prevention and control plans but also to its future sustainable development. Based on the COD (Chemical Oxygen Demand) and NH₃-N (Ammonia Nitrogen) emissions Database of 339 Cities at the city level in China, this study explores the impact of economic agglomeration on water pollutant emissions, including the differences in magnitude of the impact in relation to city size using an econometric model. The study also examines the spillover effect of economic agglomeration, by conducting univariate and bivariate spatial autocorrelation analysis. The results show that economic agglomeration can effectively reduce water pollutant emissions, and a 1% increase in economic agglomeration could lead to a decrease in COD emissions by 0.117% and NH₃-N emissions by 0.102%. Compared with large and megacities, economic agglomeration has a more prominent effect on the emission reduction of water pollution in small- and medium-sized cities. From the perspective of spatial spillover, the interaction between economic agglomeration and water pollutant emissions shows four basic patterns: high agglomeration-high emissions, high agglomeration-low emissions, low agglomeration-high emissions, and low agglomeration-low emissions. The results suggest that the high agglomeration-high emissions regions are mainly distributed in the Beijing-Tianjin-Hebei region, Shandong Peninsula, and the Harbin-Changchun urban agglomeration; thus, local governments should consider the spatial spillover effect of economic agglomeration in formulating appropriate water pollutant mitigation policies.

Key words: economic agglomeration, water pollutant emissions, emission reduction effect, city size, bivariate spatial autocorrelation, spatial spillover effect

ZHOU Kan, LIU Hanchu, WANG Qiang. The impact of economic agglomeration on water pollutant emissions from the perspective of spatial spillover effects[J].Journal of Geographical Sciences, 2019, 29(12): 2015-2030.

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Variables	Unit	Mean	Std. dev.	Median	Min	Max
COD	Ton	10.240	0.850	10.32	6.220	12.54
NH₃-N	Ton	8.190	0.910	8.320	4.170	10.690
EA	10000 yuan / km²	10.56	0.560	10.60	8.510	12.23
WIC	Ton / 100 million yuan	3.480	0.740	3.520	1.450	5.330
WIN	Ton / 100 million yuan	2.330	0.610	2.410	0.147	4.730
PGDP	Yuan	10.450	0.600	10.420	8.860	12.120
POP	10000 person	4.860	1.000	4.950	0.780	7.630
IS	Percentage	3.880	0.250	3.930	2.840	4.410
URB	Percentage	3.790	0.370	3.790	2.540	4.610

	Whole sample		Coastal sample		Inland sample
	lnCOD	lnNH₃-N	lnCOD	lnNH₃-N	lnCOD	lnNH₃-N
LnEA	-0.117**	-0.102***	0.190	-0.019	-0.128**	-0.123***
	(-1.28)	(-2.31)	(1.73)	(-0.25)	(-1.72)	(-2.22)
LnWIC	0.019***		0.038***		0.018***
	(23.45)		(12.93)		(23.16)
LnWIN		0.146***		0.239***		0.145***
		(24.05)		(8.47)		(26.48)
LnPGDP	0.369***	-0.028	-0.461***	-0.347***	0.545***	-0.155
	(7.28)	(-0.27)	(-4.17)	(-4.39)	(9.03)	(-0.74)
LnPOP	0.924***	0.958***	0.908***	0.944***	0.963***	0.982***
	(42.88)	(62.16)	(21.99)	(25.85)	(41.69)	(63.26)
LnIS	0.339***	0.178***	0.109	0.0955	0.213**	0.073
	(5.54)	(4.03)	(0.93)	(0.89)	(2.95)	(1.50)
LnURB	-0.190	0.337***	0.0556	0.194**	-0.063	0.156*
	(-1.64)	(6.68)	(0.29)	(3.39)	(-2.47)	(3.42)
Constant	2.406***	0.185	-0.287	-0.929	1.860***	-0.633
	(5.47)	(0.57)	(-0.32)	(-0.93)	(3.72)	(-1.83)
F value	247.91	609.36	52.24	119.26	172.11	391.67
P value	0.000	0.000	0.000	0.000	0.000	0.000
Adj R²	0.787	0.901	0.719	0.855	0.785	0.894

	Large and megacities		Medium-sized cities		Small-sized cities
	lnCOD	lnNH₃-N	lnCOD	lnNH₃-N	lnCOD	lnNH₃-N
LnEA	-0.089*	0.0286	-0.142**	-0.107	-0.119**	-0.085
	(-2.04)	(0.81)	(-3.06)	(-1.02)	(-2.68)	(-0.64)
LnWIC	0.031***		0.018***		0.015***
	(19.38)		(14.52)		(14.56)
LnWIN		0.205***		0.157***		0.123***
		(17.22)		(15.95)		(15.61)
LnPGDP	0.575***	0.152	0.844***	0.381	0.850***	0.065
	(8.43)	(0.80)	(8.00)	(1.39)	(7.58)	(0.29)
LnPOP	0.880***	0.905***	1.098***	0.961***	1.032***	1.048***
	(27.82)	(35.67)	(8.44)	(10.69)	(19.07)	(26.86)
LnIS	0.204*	0.146*	0.018	-0.101	-0.0579	-0.032
	(2.42)	(2.14)	(0.13)	(-0.98)	(-0.55)	(-0.39)
LnURB	-0.682***	0.542***	0.620	0.267	0.749*	0.771*
	(-7.98)	(7.41)	(2.58)	(1.43)	(2.21)	(2.64)
Constant	-0.182	-1.143*	0.895	-1.568	-0.216	-1.110
	(-0.30)	(-2.24)	(0.82)	(-1.96)	(-0.23)	(-1.60)
F value	687.06	458.74	214.75	44.01	499.32	142.06
P value	0.000	0.000	0.000	0.000	0.000	0.000
Adj R²	0.851	0.827	0.846	0.779	0.885	0.852

Pollutant emissions		Univariate analysis	Bivariate analysis
Pollutant emissions		Univariate analysis	With economic agglomeration	With economic level
COD	Moran’s I	0.2575	0.2025	0.1469
COD	P value	0.0010	0.0100	0.0010
NH₃-N	Moran’s I	0.2542	0.2737	0.1188
NH₃-N	P value	0.0010	0.0100	0.0030

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