中国工业烟粉尘排放时空演化及其影响因素

doi:10.13249/j.cnki.sgs.2020.12.001

Abstract

Abstract:

This essay intended to evaluate the spatial-temporal characteristics and driving factors of industrial soot and dust emissions in the provinces of China from 1999 to 2017. The data were collected and analyzed using different methods such as Gini coefficient, pollutant distribution index, standard deviational ellipse analysis, exploratory spatial data analysis and SDM model. The study concluded that: 1) The trend for industrial soot and dust emissions ratio and area ratio in different types of districts is inconsistent. The emission distribution in high, medium, relatively low and low areas, decreased significantly, however, at a relatively high emissions areas expanded significantly. 2) The Gini coefficient, disequilibrium index and concentration index of industrial soot and dust emissions in China are all fluctuating and decreasing, indicating that their spatial distribution presents a diffusion trend. 3) The standard deviational ellipse flatness of industrial soot and dust emissions revealed a significant decline, indicating that the direction of its spatial distribution was weakened. The average azimuth angle is 32.428°, which indicates that the spatial distribution pattern of industrial soot and dust emissions is in a northeast-southwest direction, which is basically consistent with the trend of industrial distribution in China. In addition, the standard deviational ellipse of industrial soot and dust emissions short axis expansion is much higher than that of long axis expansion. This implies that the evolution of spatial distribution of industrial soot and dust emissions in China is mainly due to the growth of the eastern and Western directions. In the meantime, the coverage area of the standard deviational ellipse is increasing, indicates the expanding of spatial distribution of industrial soot and dust emissions in China. Furthermore, the standard deviational ellipse center of industrial soot and dust emissions in China gradually moved from Henan Province to Shanxi Province. In the meantime, the spatial distribution of industrial soot and dust emissions in China tends to be stable after great changes. 4) The global Moran’s I of industrial soot and dust emissions in China changed from 0.126 to 0.312 at 5% significance test. Thus, there was a spatial correlation in the spatial distribution of industrial soot and dust emissions in China. Furthermore, the number of provinces in hotspots and coldspots of industrial soot and dust emissions in China increased, provinces in transition-spots and sub-hotspots decreased, while the number of provinces in sub-coldspots unchanged. 5) The increase of energy consumption, proportion of secondary industry, population density and economic development level will increase the emission of industrial soot and dust, while the improvement of foreign investment level, governance technology level and strength of environmental regulation will help to reduce the emission of industrial soot and dust.

Key words: emission of industrial soot and dust, spatial spillover, spatial dubin model, provincial unit

CLC Number:

F061.5

Guo Zheng, Yao Shimou, Wu Changyan. Spatial-temporal Pattern of Industrial Soot and Dust Emissions in China and Its Influencing Factors[J].SCIENTIA GEOGRAPHICA SINICA, 2020, 40(12): 1949-1957.

Figures/Tables 9

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References 21

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类型区	1999年		2005年		2011年		2017年
类型区	排放量比例	面积比例	排放量比例	面积比例	排放量比例	面积比例	排放量比例	面积比例
低排放区	3.465	21.721	3.081	21.721	1.576	13.505	3.218	14.362
较低排放区	3.956	22.883	12.636	31.925	12.772	20.897	11.234	17.798
中等排放区	13.076	25.898	31.914	17.426	21.233	15.334	27.521	23.973
较高排放区	42.108	17.558	15.427	16.384	21.257	26.433	36.946	40.310
高排放区	37.396	11.940	36.943	12.544	43.163	23.831	21.081	3.557

年份	中心坐标	长半轴/km	短半轴/km	方位角/(°)	面积/(10⁴ km²)	扁率
1999	112.975°E，33.555°N	1059.360	756.080	31.060	251.614	0.286
2005	112.746°E，33.946°N	1067.651	786.473	25.095	263.778	0.263
2011	112.593°E，34.628°N	1083.912	974.011	26.436	331.654	0.101
2017	113.163°E，35.935°N	1093.657	969.848	47.121	333.206	0.113

	指标	定义	代码
因变量	Y：工业烟尘粉尘排放	工业烟粉尘排放总量/万t	Plu
自变量	X₁：能源消耗	能源消耗量/万t标准煤	Eng
	X₂：产业结构	第二产业产值/GDP/%	Ind
	X₃：外资水平	实际利用外资额/万元	Ope
	X₄：治理技术水平	科技支出/财政支出/%	Tec
	X₅：人口密度	年末总人口/行政区面积/(人/km²)	Pop
	X₆：经济发展水平	GDP/年末总人口/元	Eco
	X₇：环境规制力度	环保投资总额/GDP/‰	Env

变量	系数	T统计量	变量	滞后项系数	T统计量
注：*、、*分别表示通过1%，5%和10%水平下的显著性检验；W×LnX表示各因子的空间溢出效应；未含港澳台数据。
$ {\rm{Ln}}Eng $	0.343***	4.277	W× $ {\rm{Ln}}Eng $	0.241***	2.994
$ {\rm{Ln}}Ind $	0.352	1.093	W× $ {\rm{Ln}}Ind $	0.319	1.006
$ {\rm{Ln}}Ope $	?0.059	?1.065	W× $ {\rm{Ln}}Ope $	0.062	1.105
$ {\rm{Ln}}Tec $	?0.094*	?1.927	W× $ {\rm{Ln}}Tec $	?0.034	?0.678
$ {\rm{Ln}}Pop $	0.385	0.732	W× $ {\rm{Ln}}Pop $	0.199	0.380
$ {\rm{Ln}}Eco $	0.515*	1.740	W× $ {\rm{Ln}}Eco $	?0.936***	?3.120
$ {\rm{Ln}}Env $	?0.017	?0.205	W× $ {\rm{Ln}}Env $	?0.212***	?2.592
R²	0.498	ρ	0.253***	Log-Likelihood	?68.238

Spatial-temporal Pattern of Industrial Soot and Dust Emissions in China and Its Influencing Factors

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