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

干旱区地理 ›› 2023, Vol. 46 ›› Issue (5): 834-845.doi: 10.12118/j.issn.1000-6060.2022.383

黄河流域城市生态福利绩效测算及驱动因素研究

董洁芳¹(),张凯莉²(),屈学书¹,阮征³

1.运城学院黄河文化生态研究院/文化旅游系，山西运城 044000
2.西北大学城市与环境学院，陕西西安 710127
3.陕西省地质科技中心，陕西西安 710065

收稿日期:2022-08-03 修回日期:2022-09-11 出版日期:2023-05-25 发布日期:2023-06-05
通讯作者: 张凯莉（1993-），女，博士，主要从事生态规划和生态系统评估等方面的研究. E-mail: 202010238@stumail.nwu.edu.cn
作者简介:董洁芳（1984-），女，博士，副教授，主要从事区域经济与生态旅游开发研究. E-mail: dongjiefang-2005@163.com
基金资助:
国家社会科学基金项目(18BJY120);山西省社科联重点课题(SSKLZDKT2022147);山西省黄河文化生态研究院项目(HH202101);运城学院旅游管理重点学科(XK-2021031)

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

DONG Jiefang¹(),ZHANG Kaili²(),QU Xueshu¹,RUAN Zheng³

1. Yellow River Cultural and Ecological Research Institute, Department of Cultural and Tourism, Yuncheng University, Yuncheng 044000, Shanxi, China
2. College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, Shaanxi, China
3. Shaanxi Geological Science and Technology Center, Xi’an 710065, Shaanxi, China

Received:2022-08-03 Revised:2022-09-11 Online:2023-05-25 Published:2023-06-05

摘要/Abstract

摘要：

生态福利绩效（EWP）的提升是生态文明建设的必然选择，对区域可持续发展具有重要意义。从生态福利视角出发，构建指标体系，基于面板数据，采用非期望产出超效率SBM模型对2006—2019年黄河流域59个地级城市EWP进行测算，运用空间探索方法和时空地理加权回归（GTWR）模型对流域EWP的空间分布特征及驱动因素进行解析。结果表明：（1）黄河流域城市EWP值普遍较低，平均存在19.7%的提升空间。（2）黄河流域城市EWP存在显著正向空间自相关，“热点”高-高型城市主要分布在人口密度较低的上游地区；“冷点”低-低型多为黄河中下游经济发展较快、人口相对集中的城市。（3）降水量、教育发展水平和产业结构水平对城市EWP的提升具有显著促进作用；人口密度、经济强度及金融发展水平对城市EWP的改善具有明显抑制作用。其中，降水量、教育发展水平和人口密度对城市EWP的边际效应较大。研究结果弥补了EWP影响因子“时-空”非平稳性分析的不足，可为有关部门制定城市EWP政策提供参考依据。

关键词: 生态福利绩效, SBM模型, 遥感数据, GTWR模型, 黄河流域

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

董洁芳, 张凯莉, 屈学书, 阮征. 黄河流域城市生态福利绩效测算及驱动因素研究[J]. 干旱区地理, 2023, 46(5): 834-845.

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.

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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

黄河流域城市生态福利绩效测算及驱动因素研究

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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