黄河流域资源型城市生态效率时空演变及驱动因素

doi:10.31497/zrzyxb.20210115

自然资源学报 ›› 2021, Vol. 36 ›› Issue (1): 223-239.doi: 10.31497/zrzyxb.20210115

黄河流域资源型城市生态效率时空演变及驱动因素

阎晓¹^,²(), 涂建军³()

1.山西财经大学资源型经济转型协同创新中心,太原 030006
2.山西财经大学资源型经济转型发展研究院,太原 030006
3.西南大学经济管理学院,重庆 400700

收稿日期:2020-05-18 修回日期:2020-07-17 出版日期:2021-01-28 发布日期:2021-03-28
通讯作者: 涂建军 E-mail:yanxiao85China@163.com;654532972@qq.com
作者简介:阎晓（1985- ）,女,山西大同人,博士,副教授,研究方向为产业生态、生态经济与区域可持续发展。E-mail:yanxiao85China@163.com
基金资助:
国家自然科学基金项目(41701630);国家社会科学基金西部项目(16XSH001);山西省高等学校哲学社会科学研究项目(2017246)

The spatio-temporal evolution and driving factors of eco-efficiency of resource-based cities in the Yellow River Basin

YAN Xiao¹^,²(), TU Jian-jun³()

1. Cooperative Innovation Center for Transition of Resource-based Economics, Shanxi University of Finance & Economics, Taiyuan 030006, China
2. Research Institute of Transition of Resource-based Economics, Shanxi University of Finance & Economics, Taiyuan 030006, China
3. College of Economics and Management, Southwest University, Chongqing 400700, China

Received:2020-05-18 Revised:2020-07-17 Online:2021-01-28 Published:2021-03-28
Contact: TU Jian-jun E-mail:yanxiao85China@163.com;654532972@qq.com

摘要/Abstract

摘要：

生态效率是区域发展质量和人地协调程度的综合体现。采用TOPSIS法测度2003—2017年黄河流域37个资源型城市的生态效率,借助泰尔指数、全局空间自相关和热点分析揭示其时空演变规律,利用面板Tobit回归模型探寻关键驱动因素。结果表明：（1）黄河流域资源型城市生态效率总体上以2007年为分水岭,先平稳、后上升;不同城市生态效率的增速和增幅差异较大,下游城市明显高于中、上游城市,再生型城市明显高于成长、成熟和衰退型城市。（2）城市间生态效率差异大小先略微下降、后持续上升;生态效率空间格局由随机分布向集聚分布演变,低值集聚区从山西、河南交界处向山西中、北部移动,高值集聚区分布具有空间粘性,一直位于下游山东境内。（3）产业转型、科技创新、基础设施完善和区位条件对生态效率改善具有显著正向驱动作用,外向型经济、资源依赖和环境规制抑制生态效率提高,城镇化、工业化和外资利用对生态效率演变的影响不显著;不同类型资源型城市生态效率改善的主要驱动因素存在异质性。

关键词: 黄河流域, 资源型城市, 生态效率, 时空演变, 驱动因素

Abstract:

Eco-efficiency is the comprehensive embodiment of regional development quality, and the synthetic reflection of coordination degree between socio-economic system and environmental system. Taking the Yellow River Basin as the research area, this study explored the spatio-temporal evolution and driving forces of eco-efficiency of resource-based cities. At first, eco-efficiencies of 37 resource-based cities from 2003 to 2017 were evaluated, using the TOPSIS method. Then, the spatio-temporal changing trends were revealed through the Theil index, the Global Spatial Autocorrelation analysis and the Hotspot analysis. At last, the key driving factors of eco-efficiency change were explored by the Panel Tobit Regression model. The results showed that: (1) On the whole, the eco-efficiency of resource-based cities in the Yellow River Basin stabilized first and then increased during 2003 and 2017, with 2007 as the turning point. However, the quantity and rate of eco-efficiency growth varied considerably among resource-based cities. The two indexes of downstream cities were significantly higher than those of middle and upstream cities, and these two indexes of regenerative cities were significantly higher than those of growing cities, grow-up cities and recessionary cities. (2) The eco-efficiency gap between resource-based cities decreased slightly at first and then increased continuously during 2003 and 2017. Meanwhile, the eco-efficiency spatial distribution pattern of resource-based cities evolved from random state to aggregate state. Specifically, the low-value agglomeration areas were distributed at the junction of Shanxi and Henan provinces at first, and then moved upstream to the central and northern parts of Shanxi province. The high-value agglomeration areas, however, remained consistently in Shandong province, which is located in the lower reaches of the Yellow River. (3) In general, industrial transformation, scientific and technological innovation, infrastructure improvement and location conditions had significant positive effects on the improvement of eco-efficiency in the study area. However, export-oriented economy, resource dependence and environmental regulation had significant inhibitory effects, and urbanization, industrialization and foreign capital utilization had no significant impact. It is worth noting that the driving factors of eco-efficiency were heterogeneous across different types of resource-based cities, which means that different resource-based cites should take different measures to improve their eco-efficiencies.

Key words: Yellow River Basin, resource-based cities, eco-efficiency, spatio-temporal evolution, driving factors

阎晓, 涂建军. 黄河流域资源型城市生态效率时空演变及驱动因素[J]. 自然资源学报, 2021, 36(1): 223-239.

YAN Xiao, TU Jian-jun. The spatio-temporal evolution and driving factors of eco-efficiency of resource-based cities in the Yellow River Basin[J]. JOURNAL OF NATURAL RESOURCES, 2021, 36(1): 223-239.

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准则层	指标层	具体说明	指标类型
自然资源消耗	水资源消耗	城市用水总量	极小型指标
	能源消耗	城市用电总量	极小型指标
	土地消耗	城市建设用地面积	极小型指标
经济要素投入	劳动力投入	单位从业人员数	极小型指标
经济要素投入	资本投入	固定资产存量	极小型指标
经济价值创造	GDP	以2003年为不变价	极大型指标
生态环境破坏	水环境污染	工业废水排放量	极小型指标
生态环境破坏	大气污染	工业SO₂排放量	极小型指标

指标名称	符号	具体指标	指标说明
城镇化水平	urban	城镇人口占比	城镇常住人口/常住人口总量
工业化水平	indus	工业增加值占比	工业增加值/GDP
产业转型程度	third	第三产业增加值占比	第三产业增加值/GDP
外资吸引度	fdi	外商及港澳台商投资企业产值占比	外商及港澳台商投资企业产值/工业总产值
经济外向度	trade	对外贸易系数	进出口总额/GDP
科技投入强度	tech	科技支出强度	科学技术支出/财政支出
科技产出水平	innov	人均专利授权量	专利授权总量/常住人口总量
资源依赖度	rely	资源型产业从业人员占比	采掘业、电力煤气及水生产供应业从业人员数/年末单位从业人员数
基础设施保障度	traff	人均城市道路面积	—
环境规制强度	envir	污染治理综合指数	工业SO₂去除率、工业烟尘去除率、一般工业固体废物综合利用率和生活垃圾无害化处理率4项指标熵权法均值
区位条件	locat	在黄河流域中的位置	上游附值为1,中游附值为2,下游附值为3

变量	（1）流域整体	（2）成长型城市	（3）成熟型城市	（4）衰退型城市	（5）再生型城市
ln(urban)	0.088	0.070	0.311^***	0.596^***	0.778^***
	(1.291)	(0.667)	(3.126)	(7.359)	(3.104)
ln(indus)	-0.070	-0.067	0.154^**	0.128	-0.443^**
	(-1.167)	(-0.590)	(2.194)	(1.557)	(-2.286)
ln(third)	0.112^**	-0.242^***	0.287^***	0.130^**	0.135
	(2.182)	(-2.943)	(4.819)	(2.134)	(0.384)
ln(fdi)	-0.008	-0.027^**	0.001	-0.016^**	-0.299^***
	(-0.919)	(-2.040)	(0.045)	(-2.251)	(-4.253)
ln(trade)	-0.032^***	-0.020	0.014	-0.011	-0.073
	(-2.981)	(-1.134)	(0.924)	(-1.245)	(-0.985)
ln(tech)	0.064^***	-0.025	0.037^***	-0.006	0.169^***
	(5.861)	(-1.107)	(2.828)	(-0.527)	(5.727)
ln(innov)	0.036^***	0.003	0.021	-0.003	0.045
	(2.947)	(0.147)	(1.267)	(-0.231)	(1.113)
ln(rely)	-0.109^***	-0.039	-0.136^***	-0.069^***	-0.327^***
	(-4.855)	(-1.124)	(-4.180)	(-2.799)	(-2.954)
ln(regul)	-0.145^***	0.084	-0.169^***	-0.073^**	-0.054
	(-4.243)	(1.396)	(-4.472)	(-2.328)	(-0.269)
ln(traff)	0.100^***	0.182^***	0.081^***	0.011	0.119^**
	(4.397)	(3.973)	(3.254)	(0.350)	(2.233)
ln(locat)	0.207^***	-0.138	0.411^**	0.375^**	0.593^***
	(3.003)	(-1.256)	(2.354)	(2.437)	(4.019)
cons	-0.939^**	-0.895	-3.276^***	-4.124^***	-2.143
	(-2.121)	(-1.320)	(-5.320)	(-7.524)	(-1.155)
N	555	105	300	90	60
Wald chi2	366.70	64.00	268.86	295.94	340.12
Prob>chi2	0.000	0.000	0.000	0.000	0.000
Log likelihood	274.949	82.512	213.438	131.872	32.400

黄河流域资源型城市生态效率时空演变及驱动因素

The spatio-temporal evolution and driving factors of eco-efficiency of resource-based cities in the Yellow River Basin

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