中国城市环境污染监管水平的空间演化特征与影响因素

doi:10.11821/dlyj020181135

Abstract

Abstract:

Based on the data on China's 2010-2016 prefecture-level city pollution supervision level, The Rank-Size rule is used to analyze the scale distribution of urban environmental pollution supervision level in China. Using spatial autocorrelation, spatial econometric model and other geoscience research methods, we adopt the quantitative analysis to examine the regional differences, spatial and temporal distribution patterns, influencing factors and spatial spillover effects of urban pollution regulation in China. Four conclusions are listed below: (1) From 2010 to 2016, China's pollution supervision level showed an overall upward trend, and its regional agglomeration was obvious. There was a significant spatial autocorrelation in the pollution supervision level. Among them, the Yangtze River Delta and the Pearl River Delta were relatively stable high-value gathering areas of pollution control, and in recent years extended to the eastern Shandong Peninsula urban agglomeration, while the central and western regions were stable low-value gathering areas, indicating that the pollution supervision level had obvious regional aggregation. (2) The level of urban pollution regulation in China presents a sub-type distribution. The cities with high pollution supervision level are mainly distributed in the eastern and central regions. The scale of pollution supervision between high-order cities is gradually narrowing, and the distribution among cities is relatively well distributed. The dispersion trend is more obvious than the concentration trend. (3) Considering the complexity of the influencing factors, the spatial measurement model is used to analyze the influencing factors in the selection of many research methods. The urban population density, economic development level and proportion of secondary industry output value have a significant positive impact on urban pollution supervision level, whereas urban scale has a negative impact on pollution supervision level. (4) Urban economic development, population density, openness, and industrial structure have a significant direct effect on the spillover effect of urban pollution supervision level, while the ratio of the output value of the secondary industry and sulphur dioxide emissions have a significant indirect effect on the pollution supervision level, that is, the promotion of the secondary industry in the region is conducive to that of pollution supervision in neighboring cities

Key words: pollution supervision level, spatial autocorrelation, rank-size rule, spatial spillover effect

Bo YU, Xu YANG, Xiangli WU, Yuanhe CAO, Ying CAI, Xuewei WANG, Cheng ZHAO. Spatial evolution and influencing factors of urban environmental pollution supervision level in China[J].GEOGRAPHICAL RESEARCH, 2019, 38(7): 1777-1790.

Figures/Tables 8

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References 29

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变量	说明	观测值	均值	标准差	最小值	最大值	VIF
year	环境污染监管的对应年份	815	2013	1.999	2010	2016	—
lnSOC	中国城市污染监管的水平（对数形式）	815	3.656	0.428	2.116	4.446	—
lnPOP	城市市辖区的人口密度（对数形式）	815	5.135	0.798	3.016	8.123	3.85
lnURB	城市建成区土地面积（对数形式）	815	5.061	0.797	3.091	7.375	4.54
lnGDP	城市市区年末生产总值（对数形式）	815	17.039	0.883	14.445	19.456	4.40
lnFDI	外商直接投资占比（对数形式）	815	5.677	0.982	0.000	6.673	1.49
lnINT	城市电力强度指数（对数形式）	815	0.967	1.428	-4.241	3.634	1.09
SEC	第二产业产值占比	815	0.508	0.098	0.193	0.900	3.84
TER	第三产业产值占比	815	0.412	0.106	0.100	0.802	4.72
lnWAT	城市年废水排放量（对数形式）	815	8.888	0.940	4.477	11.295	2.17
lnSO2	城市年二氧化硫排放量（对数形式）	815	10.885	0.866	6.188	13.258	1.61
lnSMO	城市年烟尘粉尘排放量（对数形式）	815	10.236	1.058	6.586	15.458	1.54

年份	Moran's I	方差	Z得分	P值
2010	0.5394	0.0135	4.7165	0.0000
2011	0.4332	0.0135	3.7963	0.0001
2012	0.4380	0.0139	3.7909	0.0002
2013	0.6008	0.0125	5.4398	0.0000
2014	0.5066	0.0126	4.5768	0.0000
2015	0.4206	0.0039	6.8075	0.0000
2016	0.4788	0.0040	7.7095	0.0000

型区集聚	2010年	2013年	2016年
HH（高高集聚）	广州、惠州、佛山、中山、深圳、珠海南通、扬州、南京、镇江、上海、嘉兴、宁波、绍兴、台州、福州、舟山、温州	福州、温州、台州、宁波、绍兴、杭州、嘉兴、上海、南通、苏州、无锡、南京、常州、扬州、湖州、盐城、连云港、威海、烟台、青岛、潍坊、淄博、济南、泰安、济宁	烟台、青岛、淄博、济南、泰安、日照、连云港、盐城、南通、上海、苏州、常州、嘉兴、湖州、杭州、绍兴、宁波、温州、台州、广州、佛山、中山、深圳、珠海、东莞
HL（高低集聚）			沈阳
LH（低高集聚）	韶关、湖州	汕头
LL（低低集聚）	赤峰	齐齐哈尔、大庆、抚顺、本溪、太原、临汾、玉溪、昆明、曲靖、攀枝花	牡丹江、金昌、兰州、太原、延安、临汾、张家界

年份	120个地级市			前15个地级市
年份	q	\|q\|	R²	q	\|q\|	R²
2010	-0.4200	0.4200	0.8108	-0.1300	0.1300	0.9612
2011	-0.4453	0.4453	0.7840	-0.1016	0.1016	0.8742
2012	-0.3704	0.3704	0.7741	-0.0810	0.0810	0.9080
2013	-0.3784	0.3784	0.7646	-0.1787	0.1787	0.9409
2014	-0.2859	0.2859	0.7513	-0.0463	0.0463	0.8606
2015	-0.2428	0.2428	0.7799	-0.0613	0.0613	0.9477
2016	-0.2439	0.2439	0.7952	-0.0432	0.0432	0.9521

VARIABLES	GLS1	GLS2	GLS3	SLM	SEM	SDM
Constant	2.712***	-0.142*	0.847*	0.406**	0.613***	0.044***
lnWAT	0.208***		0.051***	0.053**	0.053*	0.052*
lnSO2	-0.101***		-0.074***	0.018	0.020	0.036*
lnSMO	0.020		-0.009	0.013	0.012	0.014
lnPOP		-0.002	0.006	0.081*	0.083*	0.095**
lnURB		-0.097***	-0.094***	-0.084**	-0.084**	-0.074*
lnGDP		0.293***	0.263***	-0.110	-0.105	0.168***
lnFDI		-0.005	-0.010	0.025*	0.026*	0.026*
lnSEC		0.330***	0.430***	0.157	0.149	0.168*
lnTER		0.499***	0.548***	-0.117	-0.118	-0.014
lnINT		-0.024**	-0.022**	-0.041	-0.001	-0.006
W × lnSOC						0.585***
W × lnWAT						-0.397*
W × lnSO2						-0.282**
W × lnSMO						0.204*
W × lnPOP						-0.094
W × lnURB						0.510*
W × lnGDP						-1.216*
W × lnFDI						-0.003
W × lnSEC						5.248**
W × lnTER						2.826**
W × lnINT						0.062
λ&ρ				0.888***	0.862***	0.862***
R-sq /Wald	190.570	411.970	461.750	0.505	0.538	0.696
adj. R-sq				0.501	0.529	0.673
Log-likelihood	-346.049	-266.397	-250.568	92.673	94.313	104.197
Observations	763	763	763	763	763	763

Spatial evolution and influencing factors of urban environmental pollution supervision level in China

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	总效应		直接效应		间接效应
	系数	P值	系数	P值	系数	P值
lnWAT	0.0362	0.0312	1.0330	0.0051	-0.5070	0.1045
lnSO2	-0.5697	0.0500	0.0312	0.1321	-0.6009	0.0392
lnSMO	0.5012	0.1032	0.0196	0.2283	0.4906	0.1120
lnPOP	0.0770	0.0416	0.0925	0.0264	-0.0147	0.1831
lnURB	1.0190	0.0564	-0.0607	0.0198	1.0790	0.4764
lnGDP	-2.8750	0.0649	0.2101	0.0005	-2.6640	0.0259
lnFDI	0.0116	0.0417	0.0243	0.0471	-0.0128	0.2477
lnINT	0.1320	0.1035	-0.0494	0.1098	0.1370	0.2133
lnSEC	12.3900	0.0064	0.6360	0.0301	4.0880	0.0220
lnTER	6.1410	0.2209	0.0719	0.3972	6.0690	0.1003

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