基于多尺度的中国城市工业污染时空分异及影响因素

doi:10.11821/dlyj020180480

摘要/Abstract

摘要：

工业污染作为影响人类健康、社会进步以及可持续发展的热点问题,已引起全球视野的高度关注。采用变异系数、泰勒指数、EDSA及SDM模型等方法,从大区-城市群-城市尺度探析了中国城市工业污染的时空分异特征及影响因素,旨在为政府制定环境政策及推动生态文明建设提供参考。结果发现：① 2005—2015年中国城市工业污染指数总体呈波动下降趋势,但污染差异逐趋增大;大区、城市群、城市尺度的工业污染指数分别呈现“东高西低、集群化、级差化”特征,且各尺度污染差异显著;工业污染重心偏向东部并呈现南移态势。② 中国城市工业污染类型转移由简单转移过渡为复杂转移;大区保持“东部>中部>东北>西部”的污染格局不变;城市群污染呈现由北向南、由内向外跃迁格局;城市污染呈现由中心向外围缩减、碎片化向集中分布格局,且存在一定的空间依赖性。③ 各尺度工业污染的影响因素存在差异,城市化率与产业结构对3个尺度工业污染均有显著影响,人口密度、能源强度和环境管制强度对大区和城市尺度工业污染影响显著,工业发展水平和外资水平仅对大区尺度工业污染产生显著影响,而科技水平对3个尺度的工业污染均未产生显著影响。最后,提出了降低城市工业污染排放的对策建议。

关键词: 多尺度, 工业污染, 时空分异, 影响因素

Abstract:

Industrial pollution, as one of the major issues closely related to human health, social progress and sustainable development, has drawn a great concern for global vision. This paper uses the methods of coefficient of variation, Theil index, EDSA and SDM model to analyze the spatio-temporal differentiation and the influencing factors of urban industrial pollution (UIP) in China from region-urban agglomeration-urban scales, aiming to provide reference for formulating environmental policies and promoting the development of ecological civilization. The results show that: (1) China's UIP index was declining in fluctuations in 2005-2015, but the overall inequality presented an expanding trend. The industrial pollution index respectively presented the features of "high in the east and low in the west, clustering, and grade-difference" at the regional, urban agglomeration, and urban scales, and the pollution difference of each scale was significant. The gravity center curve of UIP was biased towards the east and showed a southward shift. (2) China's UIP types had transferred from simple shift to complex shift. The pollution pattern of "Eastern>Central>Northeast>West" remained unchanged at regional scale. Urban agglomeration-scale pollution exhibited a pattern of transition from north to south and from inside to outside. Urban-scale pollution presented an evolutionary pattern of shrinking from the center to the periphery and fragmentation to concentration distribution, and it had a certain spatial dependence. (3) There were differences in the influencing factors of industrial pollution at multi-scales. Urbanization rate and industrial structure had significant effects on industrial pollution at the three scales, population density, energy intensity and environmental regulatory intensity, which all had significant impact on industrial pollution at regional and urban scales, the level of industrial development and foreign capital only had significant influence on industrial pollution at regional scale, while the level of science and technology had no significant impact on industrial pollution at the three scales. Finally, we put forward some countermeasures and suggestions to reduce the UIP emissions.

Key words: multi-scales, industrial pollution, spatio-temporal differentiation, influencing factors

李花,赵雪雁,王伟军,薛冰. 基于多尺度的中国城市工业污染时空分异及影响因素[J]. 地理研究, 2019, 38(8): 1993-2007.

LI Hua,ZHAO Xueyan,WANG Weijun,XUE Bing. Spatio-temporal differentiation and influencing factors of urban industrial pollution in China based on multi-scales: 2005-2015[J]. GEOGRAPHICAL RESEARCH, 2019, 38(8): 1993-2007.

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	指标	定义	代码
因变量	Y：工业污染指数	工业废水、SO₂及工业烟粉尘排放量加权得出	Plu
自变量	X₁：城市化水平	年末非农人口/总人口（%）	Urb
	X₂：人口密度	年末总人口/行政国土面积（人/km²）	Pop
	X₃：工业发展水平	工业产值/年末总人口（元/人）	Idp
	X₄：产业结构	工业产值/GDP（%）	Ind
	X₅：外资水平	实际利用外资额/GDP（%）	Ope
	X₆：科技水平	科技支出/财政支出（‰）	Tec
	X₇：能源强度	工业用电量/工业总产值（万kW·h/亿元）	Eng
	X₈：环境管制强度	环保投资额/GDP（%）	Inv

	lnUrb	lnPop	lnIdp	lnInd	lnOpe	lnTec	lnEng	lnInv
东部	0.051	0.08	0.071	0.381^***	0.025	-0.01	0.127^***	-0.093^***
东北	-0.023	0.184	0.333^**	-0.301	0.117^**	0.099	0.039	-0.029
中部	-0.105^**	0.195^**	0.05	0.214	-0.094^**	-0.014	0.033	0.032
西部	0.036	0.203^**	0.027	0.04	0.013	0.001	-0.004	0.044
城市群	-0.556^**	0.151	-0.145	-0.451^**	-0.044	0.016	0.068	-0.037
城市	0.039	0.132 ^***	0.053	0.184 ^***	-0.007	0.001	0.028^**	-0.056^***
	W×lnUrb	W×lnPop	W×lnIdp	W×lnInd	W×lnOpe	W×lnTec	W×lnEng	W×lnInv
东部	-0.444^***	-0.093	-0.031	0.211	0.099^**	0.017	-0.088	-0.057^*
东北	-0.861	0.384	-0.396^**	-0.378	-0.147^**	0.005	0.125	-0.002
中部	0.067	-0.155	0.043	-0.755^***	-0.027	0.09	-0.118^***	0.046
西部	-0.243^***	-0.281^***	-0.117	-0.604^***	0.022	-0.081	0.005	0.006
城市群	-0.501	-0.293	0.129	0.932	0.022	0.063	-0.068	0.087
城市	0.178 ^***	-0.104^*	0.088	-0.272^**	0.001	0.016	0.028	-0.034 ^**