黄河流域绿色创新效率空间格局演化及其影响因素

doi:10.31497/zrzyxb.20220306

摘要/Abstract

摘要：

绿色创新作为培育经济增长新动能,是促进黄河流域生态保护和高质量发展的重要动力。采用网络DEA模型测度2003—2018年黄河流域79市（州、盟）绿色创新效率,进而运用核密度估计、重心—标准差椭圆分析效率的空间演化规律,采用空间计量模型探究其影响因素。结果表明：（1）黄河流域绿色创新效率水平较低,但表现为波动上升的时间趋势;（2）绿色创新效率具有显著的空间非均衡性,呈现出“下游>上游>中游”的空间分异格局,效率重心整体向东南方向移动,表现出南北方向相对稳定、东西方向集聚发展的趋势;（3）黄河流域绿色创新效率存在正向的空间溢出效应,人均收入、产业结构、师资力量、外资利用和环境规制是影响绿色创新效率的重要因素。

关键词: 绿色创新效率, 空间格局演化, 网络DEA模型, 空间计量分析, 黄河流域

Abstract:

As a new driving force of economic growth, green innovation is an important engine for promoting ecological protection and high-quality development in the Yellow River Basin. This paper uses the network DEA model containing unexpected output to measure the green innovation efficiency of 79 prefecture-level cities (prefectures and leagues) from 2003-2018. Furthermore, it adopts the kernel density estimation and center of gravity-center model and standard deviation ellipse model to examine the spatio-temporal evolution pattern of green innovation efficiency, and explore the influencing factors. The results show that: (1) The green innovation efficiency level in the Yellow River Basin is relatively low, but the efficiency of green innovation, the efficiency of science and technology research and development and the transformation efficiency of innovation achievements show a significant upward trend as the elapse of time. (2) There are significant spatial imbalance of green innovation efficiency in the study area. The efficiency of green innovation in the lower reaches of the basin is higher than that of the upper reaches, and the efficiency of green innovation of the upper reaches is higher than that of the middle reaches. The efficiency center of green innovation is gradually moving towards the southeast, and the efficiency of the middle and lower reaches of the basin is significantly improved. The standard deviation ellipse distribution is dominated by northwest-southeast direction, showing a relatively stable north-south direction and a trend of east-west development. The coverage area of the ellipse declines, and the differences of green innovation among regions are narrowing. (3) The efficiency of green innovation in the basin has a positive spatial spillover effect. Increasing per capita income, increasing teacher input, and strengthening environmental regulations improve the efficiency of green innovation in the entire river basin and the upper, middle and lower reaches. The positive effects of industrial structure and the actual utilization of foreign capital on the green innovation efficiency of the whole basin are not consistent with those of each sub-basin. We should take efficient measures according to the actual development of various regions, such as giving full play to the green innovation driving role of producer services, and opening wider to the outside world.

Key words: green innovation efficiency, spatial pattern evolution, network DEA model, spatial measurement analysis, Yellow River Basin

许玉洁, 刘曙光. 黄河流域绿色创新效率空间格局演化及其影响因素[J]. 自然资源学报, 2022, 37(3): 627-644.

XU Yu-jie, LIU Shu-guang. Spatial pattern evolution and influencing factors of green innovation efficiency in the Yellow River Basin[J]. JOURNAL OF NATURAL RESOURCES, 2022, 37(3): 627-644.

图/表 10

表1

图1

图2

图3

图4

表2

图5

表3

黄河流域绿色创新效率影响因素

影响层面	影响因素	变量简称	衡量指标	单位
经济发展水平	人均收入水平	$PGDP$	人均GDP	元
	产业结构	$INDU$	第二产业产值/GDP	%
	产业结构	$SERV$	第三产业产值/GDP	%
科技运行环境	金融发展水平	$FINA$	金融机构存贷款余额	元
	对外开放水平	$ROAD$	人均城市道路面积	m²
	对外开放水平	$WZ$	实际利用外资	元
政府制度导向	师资力量	$TEAC$	普通高等学院专任教师数	人
政府制度导向	环境规制强度	$ENVI$	(工业固体废物综合利用率+污水集中处理率)/2	%

表3

表4

2003—2018年黄河流域绿色创新效率Moran's I检验结果

年份	$I$	$Z$	年份	$I$	$Z$
2003	0.048^***	4.037	2011	0.026^***	2.560
2004	0.027^***	2.604	2012	0.017^**	1.967
2005	0.010^*	1.478	2013	0.043^***	3.665
2006	0.054^***	4.399	2014	0.068^***	5.337
2007	0.024^***	2.439	2015	0.042^***	3.599
2008	0.026^***	2.572	2016	0.013^**	1.670
2009	0.026^***	2.527	2017	0.010^*	1.499
2010	0.025^***	2.457	2018	0.034^***	3.050

表4

表5

绿色创新效率影响因素空间面板回归结果

解释变量	全流域	上游	中游	下游
$PGDP$	0.361^*** (0.049)	0.262^*** (0.054)	0.371^*** (0.012)	0.503^*** (0.039)
$INDU$	-0.006^*** (0.001)	-0.010^*** (0.002)	-0.004^** (0.002)	0.009^** (0.005)
$SERV$	0.004^** (0.002)	0.007^*** (0.002)	0.001^** (0.001)	-0.003^* (0.002)
$FINA$	-0.005 (0.001)	-0.015 (0.010)	-0.018 (0.015)	0.029 (0.037)
$ROAD$	-0.001 (0.001)	0.003 (0.002)	-0.013 (0.014)	0.004 (0.003)
$WZ$	0.018^** (0.009)	0.013^* (0.007)	0.014^** (0.007)	-0.016^*** (0.004)
$TECH$	0.026^** (0.013)	0.137^** (0.057)	0.075^*** (0.020)	0.019^** (0.009)
$ENVI$	0.002^*** (0.000)	0.004^*** (0.001)	0.003^* (0.002)	0.001^** (0.001)
$ρ$	0.452^*** (0.083)	-0.242^** (0.115)	0.368^*** (0.107)	0.417^*** (0.095)
$Sigma_2 e$	0.038^*** (0.002)	0.052^*** (0.004)	0.032^*** (0.002)	0.040^*** (0.003)
$R 2$	0.31	0.28	0.25	0.34

表5

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阶段	类别	具体指标	指标名称	单位
第一阶段	投入指标	资金投入	科学技术支出	万元
		资金投入	教育支出	万元
		劳动投入	科技服务人员	人
		劳动投入	普通高等学校在校学生	人
		资/能源投入	全社会用电量	万kW・h
		资/能源投入	供水总量	万t
	中间指标	创新产出（投入）	发明专利	分
			实用新型	分
			外观专利	分
第二阶段	产出指标	经济产出	地区生产总值	万元
		环境产出	绿地面积	hm²
			工业废水排放量	万t
			工业二氧化硫排放量	t
			工业烟（粉）尘排放量	t

年份	X轴标准差/km	Y轴标准差/km	方向角/(°)	重心坐标X	重心坐标Y
2003	9.29	2.89	94.95	110.64°E	36.62°N
2008	7.63	2.98	96.26	111.33°E	36.38°N
2013	8.07	2.97	93.36	111.24°E	36.42°N
2018	8.56	2.85	95.60	111.18°E	36.39°N