Driving factors and spatiotemporal effects of environmental stress in urban agglomeration: Evidence from the Beijing-Tianjin-Hebei region of China

doi:10.1007/s11442-021-1834-z

Abstract

Abstract:

Environmental stress is used as an indicator of the overall pressure on regional environmental systems caused by the output of various pollutants as a result of human activities. Based on the pollutant emissions and socioeconomic databases of the counties in Beijing-Tianjin-Hebei region, this paper comprehensively calculates the environmental stress index (ESI) for the urban agglomeration using the entropy weight method (EWM) at the county scale and analyzes the spatiotemporal patterns and the differences among the four types of major functional zones (MFZ) for the period 2012-2016. In addition, the socioeconomic driving forces of environmental stress are quantitatively estimated using the geographically weighted regression (GWR) method based on the STIRPAT model framework. The results show that: (1) The level of environmental stress in the Beijing-Tianjin-Hebei region was significantly alleviated during that time period, with a decrease in ESI of 54.68% by 2016. This decrease was most significant in Beijing, Tangshan, Tianjin, Shijiazhuang, and other central urban areas, as well as the Binhai New Area. The level of environmental stress in counties decreased gradually from the central urban areas to the suburban areas, and the high-level stress counties were eliminated by 2016. (2) The spatial spillover effect of environmental stress increased further at the county scale from 2012 to 2016, and spatial locking and path dependence emerged in the cities of Tangshan and Tianjin. (3) Urbanized zones (development-optimized and development-prioritized zones) were the major areas bearing environmental pollution in the Beijing-Tianjin-Hebei region in that time period. The ESI accounted for 65.98% of the whole region, where there was a need to focus on the prevention and control of environmental pollution. (4) The driving factors of environmental stress at the county scale included population size and the level of economic development. In addition, the technical capacity of environmental waste disposal, the intensity of agricultural production input, the intensity of territorial development, and the level of urbanization also had a certain degree of influence. (5) There was spatial heterogeneity in the effects of the various driving factors on the level of environmental stress. Thus, it was necessary to adopt differentiated environmental governance and reduction countermeasures in respect of emission sources, according to the intensity and spatiotemporal differences in the driving forces in order to improve the accuracy and adaptability of environmental collaborative control in the Beijing-Tianjin-Hebei region.

Key words: environmental stress, spatiotemporal effects, driving factors, urban agglomeration, major functional zones (MFZ), Beijing-Tianjin-Hebei region

ZHOU Kan, YIN Yue, LI Hui, SHEN Yuming. Driving factors and spatiotemporal effects of environmental stress in urban agglomeration: Evidence from the Beijing-Tianjin-Hebei region of China[J].Journal of Geographical Sciences, 2021, 31(1): 91-110.

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Area	2012					2016
Area	COD (t)	NH_3-N (t)	SO₂ (t)	NO_x (t)	ESI	COD (t)	NH_3-N (t)	SO₂ (t)	NO_x (t)	ESI
Beijing	16954.62	1862.13	8531.76	8876.63	0.10	7917.70	506.92	3019.09	3191.49	0.04
Tianjin	32776.27	3630.82	32074.49	40024.72	0.28	14753.91	2235.31	10087.73	13820.09	0.12
Hebei	9933.54	814.72	9871.90	8921.88	0.07	3023.46	451.93	5804.73	4786.54	0.03
Beijing- Tianjin- Hebei region	11473.35	1017.54	10785.39	10332.41	0.08	3906.25	536.92	5800.44	5083.22	0.04

Indicator	2012			2016
Indicator	Moran’s I	z-score	p-value	Moran’s I	z-score	p-value
COD	0.1666	3.6114	0.0003	0.1309	2.9777	0.0029
NH_3-N	0.1592	3.6328	0.0003	0.1933	4.4142	0.0000
SO₂	0.2186	4.8671	0.0000	0.1865	4.1760	0.0000
NO_x	0.1847	4.0896	0.0000	0.2720	5.9211	0.0000
ESI	0.2014	4.3945	0.0000	0.2325	5.0628	0.0000

Variable	2012					2016
Variable	Coefficient	Standard error	T statistic	P value	VIF	Coefficient	Standard error	T statistic	P value	VIF
Intercept	-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
Model diagnosis	Chi-square statistics	Correction R2	AIC	K(BP) test	Jarque- Bera test	Chi-square statistics	Correction R2	AIC	K(BP) test	Jarque- Bera test
Model diagnosis	1277.1215	0.8469	-411.5215	33.50139^**	3816.7202^**	996.9724	0.7847	-376.3448	31.2104^**	3411.6170^**

Variable	2012
Variable	Minimum value	Maximum value	Average value	Upper quartile	Median	Lower quartile	Minimum value	Maximum value	Average value	Upper quartile	Median	Lower quartile
Intercept	-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
Model diagnosis	Bandwidth		Correction R2		AIC		Bandwidth		Correction R2		AIC
Model diagnosis	99877.656		0.948		-527.574		105540.116		0.900		-454.605