Measurement and Comparison of Urban Haze Governance Level and Efficiency based on the DPSIR Model: A Case Study of 31 Cities in North China

doi:10.5814/j.issn.1674-764x.2020.06.002

Abstract

Abstract:

In the decisive stage of developing of a moderately prosperous society in all aspects, hazy weather has become a major obstacle to the further advancement of China. Therefore, improving the level and efficiency of haze governance has become essential. Based on the DPSIR model, this paper builds a haze governance level and efficiency index system using the entropy method and the super-efficiency data envelope-analysis (DEA) model to analyze the data for 31 cities in North China from 2007 to 2016. From the aspects of spatial differences and influence factors influencing the comparative analysis, the results are as follows. (1) During the investigation period, the level and efficiency of city haze governance in North China showed a trend of fluctuation and decline, with obvious stages in their characteristics. Haze governance efficiency is much higher than its level, and its mean value reaches the DEA level which indicates that it is effective. (2) A significant regional gradient difference occurs between these two aspects. The haze governance level presents a convex distribution pattern of “east low-middle high-west low”, while the haze governance efficiency presents a concave distribution pattern of “east high-middle low-west high”. (3) The regression results show that economic growth has a negative effect on both haze governance level and efficiency. By contrast, the industrial structure has a positive effect on haze governance level and efficiency, but the significance of its effect on these two is different. On this basis, policy suggestions are proposed for improving the level and efficiency of haze governance in various cities in North China.

Key words: haze governance level, haze governance efficiency, entropy method, super-efficiency DEA model

XIAO Qinlin, TIAN Chao, WANG Yanjun, LI Xiuqing, XIAO Liming. Measurement and Comparison of Urban Haze Governance Level and Efficiency based on the DPSIR Model: A Case Study of 31 Cities in North China[J]. Journal of Resources and Ecology, 2020, 11(6): 549-561.

Figures/Tables 8

References 32

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Target layer	Rule layer	Index layer	Unit	Direction
Haze governance level	Driving force	Municipal public infrastructure investment	×10⁴ yuan	positive
		Urban personnel in the management of water conservancy, environment, and public facilities	×10⁴ person	positive
		Energy consumption per unit of GDP	tons of standard coal (×10⁴ yuan)^-1	negative
	Pressure	Effluent discharge	t	negative
		Sulfur dioxide emission	t	negative
		Dust discharge	t	negative
	State	Proportion of secondary industry	%	negative
	State	Mean of PM_2.5	μg m^-3	negative
	Impact	Domestic tourism revenue	×10⁴ yuan	positive
		Comprehensive utilization rate of solid waste	%	positive
		Green coverage in built-up areas	%	positive
	Response	Spending on science and technology as a share of GDP	%	positive
		Spending on education as a share of GDP	%	positive
		Number of patent applications granted in different regions	number	positive

Target layer	Rule layer	Index layer	Unit	Direction
Haze governance level	Driving force	Municipal public infrastructure investment	×10⁴ yuan	positive
		Urban personnel in the management of water conservancy, environment, and public facilities	×10⁴ person	positive
		Energy consumption per unit of GDP	tons of standard coal (×10⁴ yuan)^-1	negative
	Pressure	Effluent discharge	t	negative
		Sulfur dioxide emission	t	negative
		Dust discharge	t	negative
	State	Proportion of secondary industry	%	negative
	State	Mean of PM_2.5	μg m^-3	negative
	Impact	Domestic tourism revenue	×10⁴ yuan	positive
		Comprehensive utilization rate of solid waste	%	positive
		Green coverage in built-up areas	%	positive
	Response	Spending on science and technology as a share of GDP	%	positive
		Spending on education as a share of GDP	%	positive
		Number of patent applications granted in different regions	number	positive

Index type	Primary index	Secondary indicators	Unit	Direction
Input indicators	Capital investment	Municipal public infrastructure investment	×10⁴ yuan	positive
		Spending on science and technology as a share of GDP	%	positive
		Spending on education as a share of GDP	%	positive
	Labor input	Urban personnel in the management of water conservancy, the environment, and public facilities	×10⁴ person	positive
	Technology input	Number of patent applications granted in different regions	number	positive
	Resources input	Energy consumption per unit of GDP	tons of standard coal (×10⁴ yuan)^-1	negative
Output indicators	Desirable output	Domestic tourism revenue	×10⁴ yuan	positive
		Comprehensive utilization rate of solid waste	%	positive
		Green coverage in built-up areas	%	positive
	Undesirable output	Industrial wastewater discharge	t	negative
		Industrial sulfur dioxide emissions	t	negative
		Industrial dust emission	t	negative
		PM_2.5	μg m^-3	negative

Index type	Primary index	Secondary indicators	Unit	Direction
Input indicators	Capital investment	Municipal public infrastructure investment	×10⁴ yuan	positive
		Spending on science and technology as a share of GDP	%	positive
		Spending on education as a share of GDP	%	positive
	Labor input	Urban personnel in the management of water conservancy, the environment, and public facilities	×10⁴ person	positive
	Technology input	Number of patent applications granted in different regions	number	positive
	Resources input	Energy consumption per unit of GDP	tons of standard coal (×10⁴ yuan)^-1	negative
Output indicators	Desirable output	Domestic tourism revenue	×10⁴ yuan	positive
		Comprehensive utilization rate of solid waste	%	positive
		Green coverage in built-up areas	%	positive
	Undesirable output	Industrial wastewater discharge	t	negative
		Industrial sulfur dioxide emissions	t	negative
		Industrial dust emission	t	negative
		PM_2.5	μg m^-3	negative

Region	Haze governance level						Haze governance efficiency						Indifference between rank of level versus efficiency
Region	2007	2010	2013	2016	Mean	Ranking for level	2007	2010	2013	2016	Mean	Ranking for efficiency	Indifference between rank of level versus efficiency
Taiyuan	0.736	0.696	0.871	0.855	0.777	1	1.042	1.066	1.212	1.279	1.165	16	↑
Shijiazhuang	0.652	0.604	0.524	0.650	0.629	2	1.003	1.036	1.040	1.029	1.011	24	↑
Hohhot	0.458	0.456	0.364	0.483	0.471	3	1.334	1.427	1.398	1.117	1.401	4	↑
Tangshan	0.485	0.510	0.339	0.342	0.448	4	1.023	1.033	1.022	1.013	0.945	26	↑
Baotou	0.434	0.407	0.342	0.392	0.419	5	1.235	1.328	1.183	1.242	1.214	13	↑
Handan	0.422	0.433	0.353	0.362	0.409	6	1.009	1.031	1.040	0.764	0.963	25	↑
Baoding	0.433	0.413	0.366	0.414	0.401	7	1.109	1.089	1.353	1.668	1.275	9	↑
Qinhuangdao	0.488	0.393	0.305	0.406	0.382	8	1.605	1.240	1.208	1.044	1.278	8	no change
Changzhi	0.354	0.378	0.336	0.361	0.365	9	1.006	0.733	0.691	1.006	0.810	29	↑
Datong	0.368	0.390	0.319	0.334	0.362	10	1.001	1.053	1.049	1.144	1.028	22	↑
Ordos	0.328	0.379	0.276	0.248	0.359	11	1.468	1.672	1.345	1.364	1.437	3	↓
Zhangjiakou	0.380	0.324	0.284	0.355	0.343	12	1.004	1.038	1.096	1.152	1.053	21	↑
Jinzhong	0.334	0.352	0.318	0.342	0.340	13	1.072	1.200	1.374	1.370	1.194	14	↑
Xinzhou	0.350	0.376	0.366	0.303	0.339	14	1.729	1.297	1.125	0.738	1.157	18	↑
Langfang	0.464	0.373	0.258	0.358	0.336	15	1.280	1.049	1.069	1.091	1.088	20	↑
Hulun Buir	0.312	0.321	0.282	0.289	0.327	16	2.251	2.394	1.955	1.211	1.912	1	↓
Chifeng	0.389	0.331	0.266	0.323	0.321	17	1.361	1.138	1.135	1.206	1.170	15	↓
Linfen	0.330	0.352	0.290	0.299	0.319	18	1.039	1.053	0.721	0.701	0.839	28	↑
Chengde	0.362	0.330	0.264	0.319	0.316	19	1.012	1.094	1.212	1.104	1.159	17	↓
Lvliang	0.285	0.320	0.314	0.313	0.313	20	1.398	1.058	0.725	1.001	1.138	19	↓
Jincheng	0.329	0.315	0.287	0.261	0.309	21	1.080	1.007	1.018	1.049	1.026	23	↑
Ulanqab	0.290	0.277	0.251	0.248	0.282	22	1.835	1.224	1.473	1.748	1.580	2		↓
Xingtai	0.309	0.305	0.211	0.296	0.275	23	1.084	1.048	1.003	0.562	0.865	27		↑
Wuhai	0.290	0.305	0.257	0.306	0.273	24	1.591	1.238	1.319	1.318	1.283	7		↓
Cangzhou	0.333	0.281	0.207	0.271	0.269	25	1.389	1.577	1.078	1.029	1.249	11		↓
Yuncheng	0.274	0.261	0.258	0.242	0.264	26	0.494	1.010	0.664	1.001	0.799	31		↑
Shuozhou	0.251	0.270	0.258	0.196	0.256	27	1.390	1.248	1.328	1.236	1.345	5		↓
Yangquan	0.285	0.302	0.227	0.210	0.255	28	1.106	1.132	1.251	1.232	1.263	10		↓
Bayan Nur	0.257	0.353	0.232	0.225	0.255	29	1.008	1.473	1.139	1.117	1.216	12		↓
Tongliao	0.281	0.303	0.229	0.219	0.252	30	0.662	1.182	1.081	0.840	0.808	30		no change
Hengshui	0.298	0.256	0.177	0.237	0.222	31	1.268	1.137	1.459	1.745	1.323	6		↓
Hebei	0.421	0.384	0.299	0.365	0.366	Ⅰ	1.162	1.125	1.144	1.109	1.110	Ⅱ		↑
Shanxi	0.354	0.365	0.349	0.338	0.354	Ⅱ	1.123	1.078	1.014	1.069	1.069	Ⅲ		↑
Inner Mongolia	0.338	0.348	0.278	0.304	0.329	Ⅲ	1.416	1.453	1.337	1.240	1.336	Ⅰ		↓
North China	0.373	0.367	0.311	0.337	0.351	?	1.222	1.203	1.154	1.133	1.161	?		?