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

地理学报 ›› 2021, Vol. 76 ›› Issue (5): 1274-1293.doi: 10.11821/dlxb202105017

长三角多维城市化对PM_2.5浓度的非线性影响及驱动机制

郭向阳¹^,²(), 穆学青³, 丁正山¹^,²(), 秦东丽¹^,²

1.南京师范大学地理科学学院,南京 210023
2.江苏省地理信息资源开发与利用协同创新中心,南京 210023
3.云南师范大学地理学部,昆明 650500

收稿日期:2020-01-08 修回日期:2020-12-18 出版日期:2021-05-25 发布日期:2021-07-25
通讯作者: 丁正山(1967-), 男, 江苏南京人, 博士, 教授, 博导, 主要从事区域发展研究。E-mail: dingzhengshan@263.net
作者简介:郭向阳(1990-), 男, 河南开封人, 博士生, 主要从事城市可持续发展与旅游地理研究。E-mail: guoxiangyang01@163.com
基金资助:
国家自然科学基金项目(41961021);国家自然科学基金项目(41671147);国家自然科学基金项目(41901205);江苏省研究生科研创新计划项目(KYCX20_1174)

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

GUO Xiangyang¹^,²(), MU Xueqing³, DING Zhengshan¹^,²(), QIN Dongli¹^,²

1. School of Geographical Science, Nanjing Normal University, Nanjing 210023, China
2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3. Department of Geography, Yunnan Normal University, Kunming 650500, China

Received:2020-01-08 Revised:2020-12-18 Published:2021-05-25 Online:2021-07-25
Supported by:
National Natural Science Foundation of China(41961021);National Natural Science Foundation of China(41671147);National Natural Science Foundation of China(41901205);Graduate Student Scientific Research Innovation Projects in Jiangsu Province(KYCX20_1174)

摘要/Abstract

摘要：

探索多维城市化对PM_2.5浓度的非线性影响及驱动机制,是城市群高质量发展的重要课题。以2000—2017年长江三角洲地区城市面板遥感影像和统计数据为样本,采用反距离权重空间插值、空间自相关和标准差椭圆等方法探查其PM_2.5浓度的时空演变规律,并运用系统动态面板回归模型研判多维城市化对PM_2.5浓度的非线性影响及驱动机制。结果表明：① 2000—2017年,长三角PM_2.5浓度由低污染等级向高污染等级演替;PM_2.5浓度整体呈现由东南向西北方向递增的空间趋势。② PM_2.5浓度呈现显著空间集聚与关联特征;PM_2.5浓度重心总体由东南向西北方向偏移,在东西方向上趋向分散,在南北方向上逐渐极化。③ 长三角城市化子系统不同发展阶段对PM_2.5浓度的非线性影响存在显著差异。经济城市化与PM_2.5浓度呈倒“N”型曲线关系,二者存在环境库兹涅茨曲线（EKC）关系,当人均GDP大于63709元时,经济城市化对PM_2.5浓度将产生抑制效应,表明城市综合质量提升和发展方式转变是PM_2.5治理的关键;而人口城市化、土地城市化与PM_2.5浓度的关系仅是倒“U”型曲线的左侧部分,二者与空气质量改善的拐点尚有一定距离。人口规模、外商直接投资、工业产业结构均对PM_2.5浓度具有显著的正向效应,而环境规制对PM_2.5浓度具有显著的抑制效应。长三角PM_2.5浓度的时空异质性特征是在经济社会因素和政府调控等诸多因素交互叠加、循环累积作用下形成,其中,经济社会因素扮演着主角。本文为探索多维城市化对PM_2.5浓度的非线性影响提供了新视角,以期为实现长三角环境保护与城市可持续发展的协调提供重要参考。

关键词: 多维城市化, 非线性影响, PM_2.5浓度, 驱动机制, 长江三角洲地区

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

郭向阳, 穆学青, 丁正山, 秦东丽. 长三角多维城市化对PM_2.5浓度的非线性影响及驱动机制[J]. 地理学报, 2021, 76(5): 1274-1293.

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

长三角多维城市化对PM_2.5浓度的非线性影响及驱动机制

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

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长三角多维城市化对PM2.5浓度的非线性影响及驱动机制

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

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摘要/Abstract

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图/表 10

参考文献 55

相关文章 13

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长三角多维城市化对PM_2.5浓度的非线性影响及驱动机制

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