中国城市群空气质量时空演化及收敛趋势

doi:10.13249/j.cnki.sgs.2022.11.009

Abstract

Abstract:

Based on the real-time monitoring data of China’s urban air quality from 2015 to 2020, the paper quantitatively analyzed the air quality differences in urban agglomerations and their sources and convergence mechanisms, using methods such as Dagum Gini coefficient, coefficient of variation and convergence model. The results show that: 1) Since 2015, the air quality of China’s 20 urban agglomerations has not shown an ideal trend of improvement year by year, but the air quality of most urban agglomerations tends to improve in the process of fluctuation, and O₃ has begun to replace PM_2.5 as the most important primary pollutant in part of urban agglomerations. 2) The spatial differentiation characteristics of air quality in China’s urban agglomerations are obvious, but the spatial differences have been reduced during the study period, and the differences between urban agglomerations are much larger than those within urban agglomeration. 3) The air quality of China’s urban agglomerations not only has a trend of σ convergence, but also a trend of absolute and conditional β convergence. Whether or not other heterogeneous influencing factors other than the initial air quality are considered, the urban air quality within each urban agglomeration is moving towards steady-state level.

Key words: air quality, convergence trend, spatial Durbin model, urban agglomeration

CLC Number:

Wan Qing, Luo Xiang, Pan Fangjie, Jin Gui. Spatial-temporal Evolution and Convergence Trend of Air Quality in China’s Urban Agglomerations[J].SCIENTIA GEOGRAPHICA SINICA, 2022, 42(11): 1943-1953.

Figures/Tables 7

Table 1

Fig. 1

Table 2

Fig. 2

Table 3

Table 4

Table 5

References 37

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年份	总体	群内	贡献率/%	群间	贡献率/%	超变密度	贡献率/%
2015	0.130	0.005	3.8	0.101	77.2	0.025	19.0
2016	0.128	0.004	3.2	0.105	81.9	0.019	14.8
2017	0.121	0.004	3.1	0.099	82.2	0.018	14.7
2018	0.123	0.004	3.3	0.103	83.7	0.016	13.0
2019	0.122	0.004	3.3	0.101	83.0	0.017	13.7
2020	0.118	0.004	3.1	0.099	84.2	0.015	12.7

城市群	2015年	2016年	2017年	2018年	2019年	2020年
总体	0.235	0.229	0.216	0.218	0.216	0.209
京津冀城市群	0.207	0.186	0.184	0.144	0.163	0.141
长三角城市群	0.101	0.113	0.106	0.140	0.124	0.097
珠三角城市群	0.112	0.101	0.120	0.104	0.106	0.113
长江中游城市群	0.180	0.119	0.093	0.127	0.139	0.117
成渝城市群	0.119	0.097	0.104	0.087	0.084	0.093
辽中南城市群	0.098	0.087	0.109	0.108	0.086	0.093
山东半岛城市群	0.205	0.192	0.164	0.166	0.132	0.144
海峡西岸城市群	0.129	0.100	0.085	0.073	0.053	0.052
哈长城市群	0.169	0.124	0.132	0.101	0.104	0.114
中原城市群	0.079	0.055	0.054	0.030	0.039	0.046
江淮城市群	0.119	0.059	0.077	0.092	0.078	0.098
关中城市群	0.084	0.203	0.226	0.201	0.199	0.178
北部湾城市群	0.097	0.072	0.059	0.063	0.040	0.052
天山北坡城市群	0.219	0.216	0.183	0.184	0.171	0.172
晋中城市群	0.035	0.060	0.080	0.088	0.086	0.065
呼包鄂榆城市群	0.083	0.092	0.070	0.072	0.069	0.105
滇中城市群	0.147	0.134	0.084	0.059	0.058	0.068
黔中城市群	0.090	0.115	0.056	0.066	0.064	0.072
兰西城市群	0.055	0.115	0.079	0.081	0.084	0.074
宁夏沿黄城市群	0.058	0.043	0.042	0.022	0.041	0.048

	绝对β收敛		条件β收敛
	传统收敛模型	空间杜宾模型	传统收敛模型	空间杜宾模型
注：括号中的值为估计系数的t统计量或z统计量，^***表示估计系数在1%统计水平上显著，空白为无此项。
β	−0.837^*** （−29.70）	−0.802^*** （−31.97）	−0.855^*** （−30.31）	−0.825^*** （−32.74）
ρ		0.593^*** （24.54）		0.573^*** （21.38）
γ		0.444^*** （12.25）		0.437^*** （11.03）
控制变量	否	否	是	是
空间固定效应	是	是	是	是
时间固定效应	是	是	是	是
R²	0.63	0.20	0.67	0.50
样本数	975	975	945	945

Spatial-temporal Evolution and Convergence Trend of Air Quality in China’s Urban Agglomerations

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城市群名称	2015年			2020年
城市群名称	PM_2.5	O₃	PM₁₀	PM_2.5	O₃	PM₁₀
京津冀城市群	139	86	94	77	136	70
长三角城市群	152	114	42	51	142	22
珠三角城市群	81	86	23	21	113	17
长江中游城市群	204	38	80	102	87	32
成渝城市群	199	39	66	99	99	31
辽中南城市群	149	106	62	104	105	72
山东半岛城市群	171	91	94	93	150	82
海峡西岸城市群	43	24	79	25	121	29
哈长城市群	148	45	69	104	52	35
中原城市群	223	33	87	124	143	72
江淮城市群	235	0	82	89	144	31
关中城市群	97	64	177	91	106	93
北部湾城市群	106	33	57	50	40	53
天山北坡城市群	85	20	213	95	127	94
晋中城市群	159	35	115	96	153	83
呼包鄂榆城市群	66	124	137	55	136	91
滇中城市群	29	32	85	12	104	7
黔中城市群	84	27	64	36	51	7
兰西城市群	72	54	215	66	110	133
宁夏沿黄城市群	82	62	191	68	119	142

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