京津冀地区县域环境胁迫时空格局及驱动因素

doi:10.11821/dlxb202009009

Abstract

Abstract:

Environmental stress is used to indicate the integrated pressure on regional environmental system caused by various pollutant outputs during human life and production activities. Based on the pollutant emission and socio-economic database of the counties in Beijing-Tianjin-Hebei (BTH) region, this paper comprehensively calculates the environmental stress index (ESI) by entropy weight method at the county scale, and analyzes the spatio-temporal pattern and the differences among four types of Major Functional Zones (MFZ) in this region from 2012 to 2016. In addition, the socio-economic driving forces of environmental stress is quantitatively estimated by means of geographical weighted regression (GWR) method based on the STIRPAT model framework. The results show that: (1) The situation of environmental stress in the BTH region had been significantly alleviated, with an ESI decline of 54.68% since 2012. The decline was most significant in central urban areas of Beijing, Tangshan, Tianjin and Shijiazhuang, and Binhai New District. The degree of environmental stress in counties decreased gradually from the central urban areas to the suburban areas, and the high-level stress counties were eliminated in 2016. (2) The spatial spillover effect of environmental stress had been further enhanced on the county scale since 2012, and the spatial locking and path dependence emerged in cities of Tangshan and Tianjin. (3) Urbanized zones (development-optimized and development-prioritized zones) are the major bearing areas of environmental pollutants in the BTH region, with ESI accounting for 65.98% of the whole region, which should be focused on prevention and control of environmental pollution. (4) The controlling factors of environmental stress in counties include population size and economic development level. In addition, technical capacity of environmental disposal, agricultural production input intensity, territorial development intensity and urbanization had a certain degree of influence. (5) There was spatial heterogeneity in the driving effects of various driving factors on the environmental stress. Therefore, it is necessary to adopt differentiated environmental governance and reduction countermeasures from emission sources, according to the intensity and spatial difference of driving forces, so as to improve the accuracy and adaptability of environmental collaborative control in the BTH region.

Key words: environmental stress, spatio-temporal pattern, Major Functional Zones (MFZ), driving factors, Beijing-Tianjin-Hebei region

ZHOU Kan, LI Hui, SHEN Yuming. Spatiotemporal patterns and driving factors of environmental stress in Beijing-Tianjin-Hebei region: A county-level analysis[J].Acta Geographica Sinica, 2020, 75(9): 1934-1947.

Figures/Tables 9

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地区	2012年					2016年
地区	化学需氧量(t)	氨氮(t)	二氧化硫(t)	氮氧化物(t)	环境胁迫指数	化学需氧量(t)	氨氮(t)	二氧化硫(t)	氮氧化物(t)	环境胁迫指数
北京市	16954.62	1862.13	8531.76	8876.63	0.10	7917.70	506.92	3019.09	3191.49	0.04
天津市	32776.27	3630.82	32074.49	40024.72	0.28	14753.91	2235.31	10087.73	13820.09	0.12
河北省	9933.54	814.72	9871.90	8921.88	0.07	3023.46	451.93	5804.73	4786.54	0.03
京津冀地区	11473.35	1017.54	10785.39	10332.41	0.08	3906.25	536.92	5800.44	5083.22	0.04

项目	2012年			2016年
项目	Moran's I	z值	p值	Moran's I	z值	p值
化学需氧量排放量	0.1666	3.6114	0.0003	0.1309	2.9777	0.0029
氨氮排放量	0.1592	3.6328	0.0003	0.1933	4.4142	0.0000
二氧化硫排放量	0.2186	4.8671	0.0000	0.1865	4.1760	0.0000
氮氧化物排放量	0.1847	4.0896	0.0000	0.2720	5.9211	0.0000
环境胁迫指数	0.2014	4.3945	0.0000	0.2325	5.0628	0.0000

变量	2012年					2016年
变量	系数	标准差	T统计量	p值	VIF	系数	标准差	T统计量	p值	VIF
截距	-0.1521^**	0.0200	-7.5971	0.0000	—	-0.1939^**	0.0232	-8.3752	0.0000	—
TP	0.8183^**	0.0691	11.8378	0.0000	1.9735	0.6341^**	0.0764	8.2948	0.0000	2.0188
PGDP	0.7390^**	0.0684	10.8029	0.0000	1.4166	0.6220^**	0.0764	8.1423	0.0000	1.3983
IS	0.0413	0.0277	1.4900	0.1062	1.1709	0.0763^*	0.0324	2.3520	0.0034	1.2015
TDI	-0.1650^**	0.0498	-3.3139	0.0044	2.4017	-0.1347^**	0.0543	-2.4799	0.0131	2.3414
ETT	0.3260^**	0.0351	9.2818	0.0000	1.0524	0.3028^**	0.0319	9.4878	0.0000	1.0906
UR	0.2135^**	0.0346	6.1780	0.0001	2.0578	0.2630^**	0.0378	6.9495	0.0001	2.0217
IAP	0.2802^**	0.0326	8.5851	0.0002	1.0872	0.2515^**	0.0349	7.1992	0.0007	1.1159
模型诊断	卡方统计量	校正R2	AIC	Koenker (BP)检验	Jarque- Bera检验	卡方统计量	校正R2	AIC	Koenker (BP)检验	Jarque- Bera检验
模型诊断	1277.1215	0.8469	-411.5215	33.50139^**	3816.7202^**	996.9724	0.7847	-376.3448	31.2104^**	3411.6170^**

变量	2012年						2016年
变量	最小值	最大值	平均值	上四分位数	中位数	下四分位数	最小值	最大值	平均值	上四分位数	中位数	下四分位数
截距	-0.407	-0.035	-0.137	-0.170	-0.094	-0.073	-0.399	-0.084	-0.169	-0.197	-0.146	-0.122
TP	0.658	2.896	1.307	0.798	1.083	1.655	0.420	2.985	1.055	0.571	0.867	1.306
PGDP	-1.277	2.071	0.973	0.704	0.749	1.540	0.020	3.405	0.868	0.554	0.607	0.936
IS	-0.052	0.244	0.032	-0.020	0.007	0.060	-0.040	0.345	0.071	0.026	0.047	0.094
TDI	-1.110	0.052	-0.145	-0.144	-0.095	-0.058	-0.377	0.088	-0.074	-0.107	-0.062	-0.022
ETT	0.150	0.621	0.327	0.210	0.293	0.429	0.171	0.486	0.275	0.227	0.252	0.302
UR	-0.004	0.459	0.136	0.076	0.097	0.165	0.004	0.352	0.157	0.110	0.144	0.197
IAP	-0.165	0.918	0.202	0.014	0.161	0.395	-0.119	0.544	0.182	0.028	0.161	0.301
模型诊断	带宽		校正R2		AICc		带宽		校正R2		AICc
模型诊断	99877.656		0.948		-527.574		105540.116		0.900		-454.605

Spatiotemporal patterns and driving factors of environmental stress in Beijing-Tianjin-Hebei region: A county-level analysis

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