长三角多维城市化对PM2.5浓度的非线性影响及驱动机制

doi:10.11821/dlxb202105017

Abstract

Abstract:

Exploring the nonlinear effects and driving mechanism of multidimensional urbanization on PM_2.5 concentrations is of great significance in the high-quality development of urban agglomerations. This paper uses inverse distance weighted spatial interpolation, spatial autocorrelation and standard deviational ellipse to analyze the spatiotemporal heterogeneity of PM_2.5 concentrations in the Yangtze River Delta. It also applies the systematic dynamic panel regression model to explore the nonlinear effects and driving mechanism of multidimensional urbanization on PM_2.5 concentrations based on the remote sensing images and statistical data of urban panels in the study area from 2000 to 2017. The main conclusions are as follows: (1) From 2000 to 2017, the pollution level caused by PM_2.5 concentrations evolved from low to high. Overall, the PM_2.5 concentrations showed a spatial trend of increasing from southeast to northwest. (2) The PM_2.5 concentrations exhibited significant spatial agglomeration and correlation characteristics. Meanwhile, the centroid of the PM_2.5 concentrations generally shifted from southeast to northwest, and the PM_2.5 concentrations tended to be dispersed in the east-west direction, while in contrast they were gradually polarized in the north-south direction. (3) There were significant differences in the impact of different development stages of the urbanization subsystem on the PM_2.5 concentrations. The economic urbanization exhibits an inverted N relationship with the PM_2.5 concentrations, which indicates that an Environmental Kuznets Curve (EKC) existed between the economic urbanization and PM_2.5 concentrations. When the per capita GDP exceeds RMB 63,709 yuan, economic urbanization will have a restraining effect on the PM_2.5 concentrations. This illustrates the fact that the improvement of urban development quality and transformation of urban development methods are the key to controlling PM_2.5 pollution. However, the relationship among population urbanization, land urbanization and PM_2.5 concentrations is only the left part of the inverted U curve, which indicates that population urbanization and land urbanization are still at a certain distance from the inflection point of air quality improvement. Last but not least, the population size, foreign investment and industrial structure all have a significant positive effect on PM_2.5 concentrations, while environmental regulations have a significant negative effect on PM_2.5 concentrations. It is worth noting that the spatiotemporal heterogeneity of PM_2.5 concentrations is formed under the interactive overlay and cycle accumulation of socioeconomic factors, government regulations, and other factors in this region. Among them, socioeconomic factors play a leading role. This paper provides a new research perspective to explore the effects of multidimensional urbanization on PM_2.5 concentrations, so that we can achieve coordination between environmental protection and urban sustainable development in the Yangtze River Delta.

Key words: multidimensional urbanization, nonlinear effect, PM_2.5 concentrations, driving mechanism, Yangtze River Delta

GUO Xiangyang, MU Xueqing, DING Zhengshan, QIN Dongli. Nonlinear effects and driving mechanism of multidimensional urbanization on PM_2.5 concentrations in the Yangtze River Delta[J].Acta Geographica Sinica, 2021, 76(5): 1274-1293.

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年份	Moran's I	Z统计量	P值	年份	Moran's I	Z统计量	P值
2000	0.773	6.524	< 0.001	2009	0.890	7.441	< 0.001
2001	0.826	6.900	< 0.001	2010	0.905	7.524	< 0.001
2002	0.857	7.154	< 0.001	2011	0.891	7.466	< 0.001
2003	0.874	7.294	< 0.001	2012	0.856	7.226	< 0.001
2004	0.848	7.109	< 0.001	2013	0.884	7.397	< 0.001
2005	0.832	6.988	< 0.001	2014	0.872	7.316	< 0.001
2006	0.891	7.404	< 0.001	2015	0.902	7.528	< 0.001
2007	0.882	7.390	< 0.001	2016	0.892	7.413	< 0.001
2008	0.864	7.272	< 0.001	2017	0.905	7.566	< 0.001

年份	重心坐标	x轴(km)	y轴(km)	旋转角θ(°)	偏移方向	偏移距离(km)
2000	118°53'27''E,31°43'34''N	275.98	167.73	134.30
2001	118°52'50''E,31°46'79''N	276.95	165.59	133.25	西偏北	3.905
2002	118°51'24''E,31°48'14''N	271.75	168.90	132.81	西偏北	1.919
2003	118°49'38''E,31°50'70''N	275.84	169.10	132.59	西偏北	3.348
2004	118°49'34''E,31°44'53''N	270.55	170.00	133.53	东偏南	6.816
2005	118°47'32''E,31°47'18''N	271.79	170.33	133.55	西偏北	3.515
2006	118°49'12''E,31°53'59''N	271.25	169.77	133.75	西偏北	7.329
2007	118°44'12''E,31°52'14''N	274.33	168.69	132.33	西偏南	5.006
2008	118°48'26''E,31°47'34''N	271.70	169.67	132.61	东偏南	6.625
2009	118°49'90''E,31°50'47''N	268.39	170.51	132.10	西偏北	3.812
2010	118°44'56''E,31°55'45''N	274.41	170.92	132.86	西偏北	7.502
2011	118°47'44''E,31°52'50''N	266.38	170.47	132.95	东偏南	4.267
2012	118°47'55''E,31°49'10''N	271.19	169.71	133.12	东偏南	3.782
2013	118°47'17''E,31°52'44''N	270.78	169.78	132.58	西偏北	3.731
2014	118°45'10''E,31°49'50''N	269.08	169.95	133.55	西偏南	3.813
2015	118°46'10''E,31°54'47''N	263.21	171.55	131.61	西偏北	5.607
2016	118°48'22''E,31°57'29''N	266.51	170.29	132.84	东偏北	9.152
2017	118°36'47''E,31°58'40''N	266.33	171.91	132.80	西偏南	11.198

变量名称	符号	具体指标	单位	均值	标准误差	最小值	最大值
PM_2.5浓度	PM	PM_2.5年均浓度值	μg/m³	46.310	13.170	14.900	74.040
经济城市化	E-URB	人均GDP	元/人	39654	35134	1360	171765
人口城市化	P-URB	非农业人口数量/总人口	%	36.476	18.394	7.760	84.570
土地城市化	L-URB	城市建设用地面积/总面积	%	9.663	10.012	0.330	60.770
人口规模	POP	常住人口数量	万人	502.980	343.180	44.270	2426
外商投资	FDI	外商直接投资额占GDP的比重	%	7.360	7.650	0.110	71.160
产业结构	IND	第二产业产值占GDP的比重	%	50.340	941.520	26.320	76.000
环境规制	ER	环境污染治理投资额占GDP的比重	%	0.810	0.860	0.050	3.910

自变量	经济城市化(E-URB）			人口城市化(P-URB)			土地城市化(L-URB)
	OLS	FE	SGMM	OLS	FE	SGMM	OLS	FE	SGMM
	模型1	模型2	模型7	模型8	模型9	模型14	模型15	模型16	模型21
PM_2	0.805^*** (291.050)	0.585^*** (55.620)	0.690^*** (75.470)	0.857^*** (476.620)	0.646^*** (66.460)	0.753^*** (86.870)	0.818^*** (320.880)	0.717^*** (99.490)	0.742^*** (109.320)
lnURB	-0.102^*** (-9.450)	-0.179^*** (-17.790)	-10.262^*** (-8.840)	0.168^*** (12.730)	0.067^*** (14.710)	0.095^*** (4.670)	0.076^*** (4.520)	0.011^** (2.280)	0.008^*** (5.500)
lnURB²	0.452^*** (22.306)	0.301^*** (32.930)	0.978^*** (12.520)	0.355^*** (10.250)	0.309^*** (10.220)	0.315^*** (6.240)	0.032^*** (2.620)	0.033^*** (2.730)	0.041^*** (5.040)
lnURB³	-0.121^*** (-3.336)	-0.101^*** (-3.264)	-0.031^*** (-9.328)
lnPOP	0.135^*** (7.450)	0.122^*** (7.780)	0.140^*** (6.950)	0.098^*** (5.760)	0.095^*** (6.480)	0.025^*** (4.220)	0.126^*** (6.970)	0.119^*** (7.680)	0.037^*** (6.820)
lnFDI	0.068^*** (5.430)	0.090^*** (8.110)	0.061^*** (7.790)	0.095^*** (8.200)	0.105^*** (10.280)	0.034^*** (10.030)	0.078^*** (6.360)	0.094^*** (8.640)	0.032^*** (10.190)
lnIND	0.206^*** (2.950)	0.231^*** (3.730)	0.229^*** (7.100)	0.441^*** (6.670)	0.412^*** (7.040)	0.142^*** (9.160)	0.234^*** (3.320)	0.256^*** (4.100)	0.132^*** (7.150)
lnER	-0.040^*** (-5.320)	-0.040^*** (-6.170)	-0.044^*** (-7.360)	-0.031^*** (-4.390)	-0.033^*** (-5.330)	-0.008^*** (-3.680)	-0.040^*** (-5.310)	-0.039^*** (-6.080)	-0.013^*** (-4.150)
_cons	3.075^*** (602.120)	-0.427^*** (-4.370)	6.561^*** (3.400)	2.722^*** (6.300)	4.365^*** (10.700)	1.233^*** (12.870)	0.701^*** (57.960)	0.693^*** (49.430)	1.661^*** (13.120)
AR(1)	0.001	0.004	0.000	0.005	0.003	0.000	0.012	0.012	0.000
AR(2)	0.125	0.138	0.202	0.126	0.118	0.198	0.128	0.146	0.233
Sargan test	0.114	0.126	0.232	0.142	0.121	0.236	0.164	0.137	0.246

Nonlinear effects and driving mechanism of multidimensional urbanization on PM_2.5 concentrations in the Yangtze River Delta

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Nonlinear effects and driving mechanism of multidimensional urbanization on PM2.5 concentrations in the Yangtze River Delta

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Nonlinear effects and driving mechanism of multidimensional urbanization on PM_2.5 concentrations in the Yangtze River Delta