Spatio-temporal evolution and influencing factors of urban green development efficiency in China

doi:10.1007/s11442-020-1752-5

Abstract

Abstract:

To resolve conflicts between development and the preservation of the natural environment, enable economic transformation, and achieve the global sustainable development goals (SDGs), green development (GD) is gradually becoming a major strategy in the construction of an ecological civilization and the ideal of building a “beautiful China”, alongside the transformation and reconstruction of the global economy. Based on a combination of the concept and implications of GD, we first used the Slacks Based Model with undesirable outputs (SBM-Undesirable), the Theil index, and the spatial Markov chain to measure the spatial patterns, regional differences, and spatio-temporal evolution of urban green development efficiency (UGDE) in China from 2005 to 2015. Second, by coupling natural and human factors, the mechanism influencing UGDE was quantitatively investigated under the framework of the human-environment interaction. The results showed that: (1) from 2005 to 2015, the UGDE increased from 0.475 to 0.523, i.e., an overall increase of 10%. In terms of temporal variation, there was a staged increase, with its evolution having the characteristics of a “W-shaped” pattern. (2) The regional differences in UGDE followed a pattern of eastern > central > western. For different types of urban agglomeration, the UGDE had inverted pyramid cluster growth characteristics that followed a pattern of “national level > regional level > local level”, forming a stable hierarchical scale structure of “super cities > mega cities > big cities > medium cities > small cities”. (3) UGDE in China has developed with significant spatial agglomeration characteristics. High-efficiency type cities have positive spillover effects, while low-efficiency cities have negative effects. Different types of urban evolution processes have a path dependence, and a spatial club convergence phenomenon exists, in which areas with high UGDE are concentrated and drive low UGDE elsewhere. (4) Under the framework of regional human-environment interaction, the degree of human and social influence on UGDE is greater than that of the natural background. The economic strength, industrial structure, openness, and climate conditions of China have positively promoted UGDE.

Key words: green development, efficiency, green city, green economy, spatial scale, sustainable development goals (SDGs)

ZHOU Liang, ZHOU Chenghu, CHE Lei, WANG Bao. Spatio-temporal evolution and influencing factors of urban green development efficiency in China[J].Journal of Geographical Sciences, 2020, 30(5): 724-742.

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Type	First level indicators	Second level indicators	Third level indicators
Input	Capital	Fixed capital stock	Total social fixed capital investment
	Labor	Number of unit employers	Number of unit employees at the end of the year
	Technology	Number of patent authorizations	Number of patent applications granted by region
	Resources	Water, land, and energy consumption	Total water supply, urban built-up area, total electricity consumption
	Resources	Water, land, and energy consumption	Artificial and natural gas supply, liquefied gas supply
Output	Desirable output	Economic benefits	GDP (constant price in 2005)
		Social benefits	Average wage of urban employees, total retail sales of consumer goods
		Environmental benefits	Area of urban green space, percentage cover of green space, utilization rate of industrial solid waste
		Environmental benefits	centralized treatment rate of sewage treatment plants, treatment rate of harmless domestic garbage
	Undesirable output	Environmental pollution	Amount of industrial wastewater, amount of industrial SO₂ emitted, amount of industrial dust emitted

Type		2005-2008				2008-2009				2009-2015
Type		1	2	3	4	1	2	3	4	1	2	3	4
1	1	0.847	0.097	0.028	0.028	0.762	0.119	0.071	0.048	0.844	0.131	0.010	0.015
	2	0.292	0.477	0.200	0.031	0.190	0.238	0.476	0.095	0.202	0.551	0.191	0.056
	3	0.167	0.250	0.417	0.167	0.167	0.417	0.250	0.167	0.000	0.275	0.625	0.100
	4	0.111	0.111	0.222	0.556	0.222	0.000	0.000	0.778	0.034	0.052	0.190	0.724
2	1	0.674	0.304	0.022	0.000	0.357	0.500	0.071	0.071	0.805	0.161	0.011	0.023
	2	0.250	0.515	0.176	0.059	0.154	0.346	0.385	0.115	0.167	0.592	0.225	0.017
	3	0.118	0.196	0.549	0.137	0.045	0.091	0.500	0.364	0.016	0.203	0.626	0.154
	4	0.000	0.043	0.087	0.870	0.000	0.000	0.222	0.778	0.010	0.051	0.153	0.786
3	1	0.740	0.260	0.000	0.000	0.455	0.364	0.091	0.091	0.692	0.282	0.013	0.013
	2	0.280	0.480	0.200	0.040	0.176	0.294	0.471	0.059	0.130	0.652	0.174	0.043
	3	0.082	0.219	0.548	0.151	0.077	0.231	0.308	0.385	0.017	0.182	0.645	0.157
	4	0.019	0.074	0.148	0.759	0.000	0.100	0.200	0.700	0.009	0.026	0.183	0.783
4	1	0.833	0.133	0.033	0.000	0.600	0.400	0.000	0.000	0.838	0.162	0.000	0.000
	2	0.207	0.517	0.241	0.034	0.056	0.389	0.556	0.000	0.120	0.652	0.163	0.065
	3	0.049	0.246	0.557	0.148	0.000	0.111	0.500	0.389	0.010	0.286	0.480	0.224
	4	0.000	0.010	0.237	0.753	0.000	0.042	0.042	0.917	0.000	0.031	0.221	0.748

	Variable	Model (1)	Z value	Model (2)	Z value	Model (3)	Z value
Human and social factors	rgdp	0.025^***	3.68			0.030^***	4.20
	rgdp²	0.013^***	3.68			0.015^***	4.20
	is	0.087^*	1.55			0.089^*	1.58
	fdi	0.007^**	2.07			0.005^*	1.50
	te	-0.439^***	-6.64			-0.453^***	-6.84
Natural background factors	tem			0.005^**	2.32	0.007^***	3.21
	pre			-0.004	-0.67	-0.001	-0.40
	eq			0.0001	0.28	0.0004	0.38
	ndvi			0.119^*	1.67	-0.027	1.01
	C	0.212^***	-3.71	0.723^***	-3.62	0.099^***	1.01
Correlation test	sigma_u	0.181		0.187		0.178
	sigma_e	0.139		0.141		0.139
	rho	0.628		0.638		0.621
	likelihood ratio test	1305.72		1262.74		1312.67