黄河流域城市生态福利绩效测算及驱动因素研究

doi:10.12118/j.issn.1000-6060.2022.383

Abstract

Abstract:

The improvement of ecological well-being performance (EWP) is an inevitable choice for the construction of urban ecological civilization and is of great significance to the sustainable development. This paper established the evaluation index system of EWP from the perspective of ecological well-being. Based on the panel data of 59 prefecture-level cities in the Yellow River Basin of China from 2006 to 2019, this article used the undesired output SBM model to measure the EWP of 59 prefecture-level cities. The spatial autocorrelation method and a geographically and temporally weighted regression model were then used to analyze spatial distribution characteristics and influencing factors of interurban EWP in the Yellow River Basin. the results showed the following: (1) The overall EWP level of prefecture-level cities in the Yellow River Basin is relatively low, with 19.7% room for improvement. (2) A significant positive spatial autocorrelation is observed in EWP in the Yellow River Basin. The “hot spot” high-high cities are mainly distributed in the upstream areas with low population density, and the “cold spots” low-low cities are mostly cities in the middle and lower reaches of the Yellow River Basin with rapid economic development and relatively concentrated population. (3) Precipitation, educational development level, and industrial structure level are the positive key factors affecting EWP, while population density, economic intensity, and financial development level contribute to negative effects. Among all the influencing factors, precipitation, educational development level, and population density have the largest marginal effect on urban EWP. The research results make up for the deficiency of the “time-space” non-stationarity analysis of EWP impact factors, and can provide reference for the relevant departments to formulate EWP policies in cities.

Key words: ecological well-being performance, SBM model, remote sensing data, GTWR model, Yellow River Basin

DONG Jiefang, ZHANG Kaili, QU Xueshu, RUAN Zheng. Measurement and influencing factors of ecological well-being performance of cities in Yellow River Basin[J].Arid Land Geography, 2023, 46(5): 834-845.

Figures/Tables 12

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References 38

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目标层	准则层	指标变量
投入	资源型投入	能源消费量（标准煤）/t·人^-1
		城市用水量/t·人^-1
		人均建成区面积/km²·(10⁴人)^-1
	非资源型投入	城镇单位从业人员期末数/10⁴人
	非资源型投入	固定资产投资总额/元·人^-1
产出	期望产出	人均GDP/10⁴元
		万人中小学在校人数/人
		万人医院床位数/张
		生境质量年均值
		人均绿地面积/km²·人^-1
	非期望产出	PM_2.5年均浓度
		工业二氧化硫排放量/t·(10⁴人)^-1
		工业粉尘排放量/t·(10⁴人)^-1
		工业废水排放量/t·(10⁴人)^-1

因素分类	影响因素	测度方法
自然因素	降水量（X₁）/mm	年均降水量
	气温（X₂）/℃	年均气温
社会因素	人口密度（X₃）/km²·人^-1	年末户籍人口/行政区域面积
	教育发展水平（X₄）/%	教育事业费支出/GDP
经济因素	经济强度（X₅）/%	固定资产投资/GDP
	对外开放程度（X₆）/%	外商投资工业总产值/GDP
	金融发展水平（X₇）/%	年末金融机构存贷款余额/GDP
	产业结构水平（X₈）/%	第三产业增加值/GDP

城市	2006年	2008年	2010年	2012年	2014年	2016年	2019年	均值	增长率/%
定西	6.682	2.168	3.357	4.457	1.227	1.050	3.733	3.031	-4.38
陇南	6.275	4.835	1.902	1.203	0.811	1.044	2.503	2.208	-6.82
天水	1.607	0.763	1.129	3.340	1.081	2.388	1.166	1.367	-2.44
庆阳	1.089	1.589	2.147	1.284	1.020	1.025	1.181	1.268	0.63
榆林	1.450	1.441	1.052	1.197	1.021	2.009	1.701	1.250	1.23
铜川	1.065	0.376	0.466	0.399	0.475	0.331	0.312	0.451	-9.02
阳泉	0.353	0.360	0.308	0.409	0.430	0.319	0.645	0.382	4.76
大同	0.387	0.399	0.357	0.312	0.312	0.309	0.378	0.348	-0.17
兰州	0.381	0.343	0.317	0.279	0.353	0.364	0.469	0.342	1.62
西宁	0.346	0.348	0.340	0.285	0.276	0.468	0.313	0.321	-0.76
宝鸡	0.545	0.481	0.462	0.628	5.022	0.563	2.612	1.050	12.81
济南	0.449	1.006	0.443	0.581	0.727	1.160	1.723	0.809	10.89
太原	0.351	0.483	0.503	0.351	0.324	0.487	1.250	0.540	10.25
西安	0.299	0.298	0.301	0.412	0.375	1.001	1.019	0.563	9.88
乌海	0.391	0.320	0.390	0.524	1.014	0.811	1.293	0.650	9.64
陇南	6.275	4.835	1.902	1.203	0.811	1.044	2.503	2.208	-6.82
石嘴山	1.122	1.025	1.230	0.375	1.024	1.016	0.388	0.935	-7.84
运城	1.605	0.725	0.638	0.532	0.524	0.519	0.535	0.675	-8.11
固原	3.167	1.018	1.220	0.698	0.657	1.004	1.025	1.128	-8.31
铜川	1.065	0.376	0.466	0.399	0.475	0.331	0.312	0.451	-9.02
流域均值	0.955	0.806	0.728	0.813	0.692	0.762	1.076	0.803	2.40

年份	Moran’s I值	P值	年份	Moran’s I值	P值
2006	0.184	0.001	2013	0.083	0.029
2007	0.191	0.001	2014	0.042	0.160
2008	0.129	0.008	2015	0.167	0.011
2009	0.155	0.018	2016	0.173	0.006
2010	0.134	0.018	2017	0.180	0.001
2011	0.075	0.185	2018	0.176	0.005
2012	0.127	0.020	2019	0.162	0.013

变量	系数	标准差	t值	P值	VIF
截距	0.803^***	0.020	39.76	0.000	-
X₁	0.086^***	0.029	2.96	0.003	2.05
X₂	0.004	0.037	0.10	0.922	3.27
X₃	-0.086^***	0.033	-2.60	0.009	2.70
X₄	0.825^***	0.026	9.58	0.000	1.66
X₅	-0.047^**	0.025	-1.91	0.057	1.49
X₆	0.030	0.024	1.37	0.170	1.18
X₇	-0.134^***	0.022	-4.63	0.007	2.06
X₈	-0.081^***	0.029	2.71	0.000	2.17

Measurement and influencing factors of ecological well-being performance of cities in Yellow River Basin

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模型参数	数值
R²	0.181
校正R²	0.173
联合F统计量（P值）	22.643^***
联合Chi-square统计量（χ²）	107.288^***
Koenker（BP）统计量（P值）	66.839^***
Jarque-Bera检验	270.492^***
AICc	1456.910

模型参数	数值
带宽	0.115
残差平方和	150.474
显著水平（Sigma）	0.026
AICc	1161.780
R²	0.563
校正R²	0.550

自变量	平均值	下四分位数	中位数	上四分位数	最小值	最大值
X₁	0.122	0.012	0.064	0.197	-0.459	0.998
X₃	-0.092	-0.129	-0.018	0.039	-2.906	0.676
X₄	0.102	-0.015	0.093	0.218	-0.583	0.740
X₅	-0.078	-0.124	-0.039	0.011	-1.647	0.359
X₇	-0.043	-0.133	-0.082	0.026	-0.368	0.556
X₈	0.023	-0.066	0.017	0.123	-0.495	0.584

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