Spatio-temporal evolution and influencing factors of green innovation pattern in the Yellow River Basin based on green patents

doi:10.3969/j.issn.1004-9479.2023.09.20222520

Abstract

Abstract:

Green innovation is both eco-friendly and innovation-driven. It is important to clarify the spatio-temporal pattern and the influencing factors of green innovation for ecological protection and high-quality development in the Yellow River Basin. Based on the panel data of 60 cities at prefecture level and above in the Yellow River Basin from 2005 to 2019, this study analyzed the spatio-temporal evolution characteristics and influencing factors of green innovation in the Yellow River Basin by using kernel density estimation, social network analysis and spatial Durbin model from the perspective of "cluster-network". The results show that: (1) The growth rate of green innovation in the Yellow River Basin slows down, the upper and middle cities of the Yellow River Basin catch up, and the gap between cities narrows gradually, with the characteristics of dynamic agglomeration. (2) The spatial agglomeration pattern of green innovation shows a gradual development trend from east to west. The level of green innovation in Shandong peninsula urban agglomeration and Central Henan urban agglomeration make an overall rise, agglomerating in patches, while development pattern of the other urban agglomerations form a point-like jumping up, which is "core-periphery" characteristics. (3) The density of green innovation cooperation network is low and highly unbalanced, forming a "pyramid" with Shandong Peninsula urban agglomeration, Zhengzhou, Xi' an and Yinchuan as a quadrilateral backbone. (4) Integrating into green innovation cooperation network has a significant and steady promoting effect on the improvement of green innovation level. There is an inverted U-shaped relationship between environmental regulation intensity and green innovation. The improvement of human capital level, economic development level and transportation network development level is conducive to green innovation, while the centrality, environmental regulation, human capital and economic development level show a positive spatial spillover effect.

Key words: the Yellow River Basin, green innovation pattern, cooperative network, spatio-temporal evolution, Spatial Durbin Model

摘要：

绿色创新具有生态环境友好和以创新为发展动力的双重属性，厘清绿色创新时空格局及影响因素对黄河流域生态保护和高质量发展至关重要。基于2005—2019年黄河流域60个地级及以上城市面板数据，运用Kernel密度估计、社会网络分析和空间杜宾模型，从“集聚-网络”视角探析了黄河流域绿色创新格局的时空演进特征及影响因素。结果表明：①黄河流域绿色创新水平增速放缓，上中游城市后发追赶，城市间差距逐渐缩小，存在动态集聚特征。②绿色创新空间集聚格局呈现由东至西渐进发展态势，山东半岛城市群和中原城市群绿色创新水平整体跃迁，形成片状集聚，而其余城市群形成点状跳跃式的“核心-边缘”发展格局。③绿色创新合作网络密度低且高度不均衡，形成以山东半岛城市群、郑州、西安和银川为四边形骨干的“金字塔”形。④融入绿色创新合作网络对绿色创新水平的提升具有显著且稳健地促进作用。环境规制强度与绿色创新呈现出倒“U”形关系，人力资本水平、经济发展水平和交通网络发展水平的提升有利于绿色创新，网络中心度、环境规制、人力资本和经济发展水平表现出正向的空间溢出效应。

关键词: 黄河流域, 绿色创新格局, 合作网络, 时空演进, 空间杜宾模型

Jianhui REN, Linlin LAI, Ze HE, Zhaohui CHONG. Spatio-temporal evolution and influencing factors of green innovation pattern in the Yellow River Basin based on green patents[J]. World Regional Studies, 2023, 32(9): 78-92.

任建辉, 赖琳琳, 何则, 种照辉. 基于绿色专利的黄河流域绿色创新格局的时空演进及影响因素[J]. 世界地理研究, 2023, 32(9): 78-92.

Figures/Tables 8

Fig. 1 Logical framework of mechanism affecting the level of green innovation

Tab. 1 Variable definitions and descriptions

变量类型	变量（符号）	变量解释
被解释变量	绿色创新水平（lnGI）	城市绿色创新申请总量加1后取对数
解释变量	中心度（Degree）	由社会网络分析法计算，包括度数中心度（DC）和介数中心度（BC）
	环境规制（ERS）	采用熵值法计算各类污染物排放量的综合指数来表征环境规制强度，主要包括工业烟（粉）尘排放量、工业SO₂排放量和工业废水排放量
	金融集聚（qfinan）	采用金融行业从业人员数据，通过区位熵反映金融集聚水平
	人力资本（lnhum）	普通本专科及以上人口数占全市常住人口比重的对数
	经济发展水平（lngdp）	采用2005年为基期平减后人均实际GDP的对数值，反映城市经济对绿色创新水平的支撑力度
	交通网络发展水平（lnroad）	包括公路、铁路、民航的客运总量及货运总量和公路网密度，用熵值法对不同指标赋值并加权计算得出
	科教支出（lntec）	用政府教育支出和科学技术支出之和与GDP比值的对数表示

Fig.2 Dynamic evolution characteristics of urban green innovation level in the Yellow River Basin

Fig.3 Urban green innovation level and pattern evolution of green innovation network in the Yellow River Basin

Tab.2 Top 10 cities in degree centrality and between centrality in green innovation cooperation network

排名	2015				2017				2019
排名	城市	DC	城市	BC	城市	DC	城市	BC	城市	DC	城市	BC
1	济南	19.7	西安	10.8	西安	23.9	西安	25.7	西安	36.6	西安	23.9
2	西安	15.5	济南	8.7	济南	21.1	郑州	13.9	青岛	23.9	太原	17.6
3	青岛	12.7	郑州	7.9	青岛	19.7	太原	11.8	太原	22.5	兰州	14.6
4	太原	11.3	青岛	6.9	郑州	19.7	济南	9.8	济南	22.5	郑州	10.9
5	郑州	8.5	太原	6.2	银川	14.1	兰州	9.3	郑州	21.1	青岛	9.4
6	淄博	5.6	兰州	3.6	太原	11.3	银川	7.1	兰州	19.7	济南	6.4
7	榆林	5.6	西宁	2.8	淄博	9.9	青岛	6.2	潍坊	15.5	西宁	4.5
8	兰州	5.6	长治	1.4	兰州	9.9	西宁	5.8	淄博	12.7	鄂尔多斯	3.1
9	银川	5.6	开封	1.4	西宁	9.9	呼和浩特	2.5	鄂尔多斯	11.3	银川	2.7
10	大同	4.2	洛阳	1.4	呼和浩特	8.5	咸阳	2.2	东营	9.9	潍坊	2.4

Tab.3 Global Moran's I index of green innovation in the Yellow River Basin

指数	2005	2006	2007	2008	2009	2010	2011	2012
Moran's I	0.197	0.165	0.204	0.197	0.155	0.106	0.108	0.099
p-value	0.001	0.004	0.000	0.001	0.005	0.030	0.028	0.038
指数	2013	2014	2015	2016	2017	2018	2019
Moran's I	0.059	0.071	0.122	0.126	0.076	0.085	0.075
p-value	0.119	0.085	0.018	0.018	0.083	0.067	0.089

Fig.4 LISA clustering of green innovation in the Yellow River Basin

Tab.4 Regression results of spatial Durbin model for influencing factors of green innovation

变量	主效应项		空间滞后项		直接效应		间接效应		总效应
变量	（1）	（2）	（3）	（4）	（5）	（6）	（7）	（8）	（9）	（10）
DC	0.012^*		0.077^**		0.013^*		0.110^**		0.124^***
	(0.007)		(0.038)		(0.007)		(0.047)		(0.048)
BC		0.019^**		0.159^***		0.021^***		0.215^***		0.236^***
		(0.007)		(0.050)		(0.008)		(0.062)		(0.063)
ERS	0.112^**	0.124^**	0.505	0.623^*	0.115^**	0.127^**	0.735^*	0.861^**	0.849^**	0.988^**
	(0.052)	(0.052)	(0.330)	(0.331)	(0.051)	(0.050)	(0.421)	(0.403)	(0.433)	(0.413)
ERS²	－0.011^**	－0.012^***	－0.074^***	－0.077^***	－0.011^***	－0.012^***	－0.104^***	－0.104^***	－0.116^***	－0.116^***
	(0.004)	(0.004)	(0.025)	(0.025)	(0.004)	(0.004)	(0.031)	(0.030)	(0.032)	(0.031)
qfinan	－0.267^***	－0.223^***	－0.300	0.528	－0.270^***	－0.220^***	－0.420	0.712	－0.690	0.492
	(0.084)	(0.082)	(0.681)	(0.658)	(0.083)	(0.081)	(0.950)	(0.935)	(0.972)	(0.954)
lnhum	0.178^***	0.180^***	0.913^***	0.853^***	0.187^***	0.188^***	1.269^***	1.133^***	1.456^***	1.320^***
	(0.057)	(0.056)	(0.296)	(0.295)	(0.056)	(0.055)	(0.410)	(0.389)	(0.422)	(0.401)
lngdp	0.747^***	0.750^***	0.454^*	0.554^**	0.760^***	0.763^***	0.863^***	0.933^***	1.623^***	1.696^***
	(0.118)	(0.117)	(0.273)	(0.276)	(0.116)	(0.115)	(0.265)	(0.244)	(0.240)	(0.216)
lnroad	0.068^**	0.069^***	－0.046	0.011	0.067^**	0.069^**	－0.043	0.032	0.024	0.102
	(0.026)	(0.026)	(0.101)	(0.099)	(0.027)	(0.027)	(0.134)	(0.124)	(0.137)	(0.128)
lntec	0.001	0.001	0.006	0.020^**	0.001	0.001	0.008	0.026^***	0.009	0.027^***
	(0.004)	(0.004)	(0.009)	(0.008)	(0.004)	(0.004)	(0.013)	(0.009)	(0.012)	(0.009)
ρ	0.257^**	0.226^**
	(0.106)	(0.105)
sigma2_e	0.151^***	0.149^***
sigma2_e	(0.008)	(0.008)
$R 2$	0.609	0.617
N	780	780

Tab.4 Regression results of spatial Durbin model for influencing factors of green innovation

变量	主效应项		空间滞后项		直接效应		间接效应		总效应
变量	（1）	（2）	（3）	（4）	（5）	（6）	（7）	（8）	（9）	（10）
DC	0.012^*		0.077^**		0.013^*		0.110^**		0.124^***
	(0.007)		(0.038)		(0.007)		(0.047)		(0.048)
BC		0.019^**		0.159^***		0.021^***		0.215^***		0.236^***
		(0.007)		(0.050)		(0.008)		(0.062)		(0.063)
ERS	0.112^**	0.124^**	0.505	0.623^*	0.115^**	0.127^**	0.735^*	0.861^**	0.849^**	0.988^**
	(0.052)	(0.052)	(0.330)	(0.331)	(0.051)	(0.050)	(0.421)	(0.403)	(0.433)	(0.413)
ERS²	－0.011^**	－0.012^***	－0.074^***	－0.077^***	－0.011^***	－0.012^***	－0.104^***	－0.104^***	－0.116^***	－0.116^***
	(0.004)	(0.004)	(0.025)	(0.025)	(0.004)	(0.004)	(0.031)	(0.030)	(0.032)	(0.031)
qfinan	－0.267^***	－0.223^***	－0.300	0.528	－0.270^***	－0.220^***	－0.420	0.712	－0.690	0.492
	(0.084)	(0.082)	(0.681)	(0.658)	(0.083)	(0.081)	(0.950)	(0.935)	(0.972)	(0.954)
lnhum	0.178^***	0.180^***	0.913^***	0.853^***	0.187^***	0.188^***	1.269^***	1.133^***	1.456^***	1.320^***
	(0.057)	(0.056)	(0.296)	(0.295)	(0.056)	(0.055)	(0.410)	(0.389)	(0.422)	(0.401)
lngdp	0.747^***	0.750^***	0.454^*	0.554^**	0.760^***	0.763^***	0.863^***	0.933^***	1.623^***	1.696^***
	(0.118)	(0.117)	(0.273)	(0.276)	(0.116)	(0.115)	(0.265)	(0.244)	(0.240)	(0.216)
lnroad	0.068^**	0.069^***	－0.046	0.011	0.067^**	0.069^**	－0.043	0.032	0.024	0.102
	(0.026)	(0.026)	(0.101)	(0.099)	(0.027)	(0.027)	(0.134)	(0.124)	(0.137)	(0.128)
lntec	0.001	0.001	0.006	0.020^**	0.001	0.001	0.008	0.026^***	0.009	0.027^***
	(0.004)	(0.004)	(0.009)	(0.008)	(0.004)	(0.004)	(0.013)	(0.009)	(0.012)	(0.009)
ρ	0.257^**	0.226^**
	(0.106)	(0.105)
sigma2_e	0.151^***	0.149^***
sigma2_e	(0.008)	(0.008)
$R 2$	0.609	0.617
N	780	780

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