航空和高铁对中国城市创新能力的影响

doi:10.18306/dlkxjz.2022.12.002

摘要/Abstract

摘要：

交通运输是创新网络中人才流、资本流等知识与技术流动的物理空间承载，其对城市的创新能力影响已成为经济地理学的交叉前沿热点。论文基于2007—2018年中国城市尺度数据，以航空和高铁运输为例，构建交通运输对城市创新能力影响效应的理论框架，采用双向固定效应面板回归模型，实证检验航空和高铁对城市创新能力的多重异质性机制，并探讨了知识传播、资本积累、产业升级在交通运输与创新能力之间的中介效应。研究发现：① 航空和高铁建设均对城市创新能力有显著正向影响，高铁对城市的创新溢出效应约为航空的3倍。② 航空和高铁对不同类型城市的创新溢出效应存在显著异质性。城市等级异质性方面，航空和高铁对中心城市创新能力的正向影响强度高于非中心城市。人口规模异质性方面，航空对大、中城市创新能力提升有显著正向影响，对小城市有抑制作用；高铁运输则对不同人口规模城市的创新能力均有正向影响，呈现大城市>中等城市>小城市的态势。区域异质性方面，航空和高铁对东、中、西、东北地区的创新能力均有不同程度的提升作用，表现出显著的“马太效应”，东部地区优势地位凸显。③ 航空和高铁均可通过促进技术转移、风险资本配置、外商资本配置间接促使城市创新能力提升。此外，航空还能够通过促进产业升级间接促使城市创新能力提升。

关键词: 航空运输, 高铁运输, 城市创新能力, 双向固定效应面板回归, 中介效应

Abstract:

Transportation is the physical carrier of knowledge and technology spatial flows such as talent and capital flows in innovation networks, and its impacts on urban innovation capacity have become a cross-cutting frontier in economic geography. Based on the city-scale data of China from 2007 to 2018 and taking air and high-speed rail transportation as examples, this study constructed a theoretical framework for the effect of transportation on urban innovation capacity, adopted the two-way fixed-effects panel regression model to empirically test the multiple heterogeneity mechanisms of aviation and high-speed rail on urban innovation capacity, and explored the mediation effects of knowledge dissemination, capital accumulation, and industrial upgrading between transportation and innovation capacity. The results are as follows: 1) Both aviation and high-speed rail development have significant positive impacts on urban innovation capacity, and the spillover effect of high-speed rail on urban innovation is about three times that of aviation. 2) The innovation spillover effects of aviation and high-speed rail on different types of cities are significantly different. In terms of urban grade heterogeneity, the positive impact of aviation and high-speed rail on the innovation capacity of central cities is higher than that of other cities. In terms of population size heterogeneity, aviation has a significant positive impact on the innovation capacity of large and medium-sized cities and an inhibitory effect on small cities, while high-speed rail transportation has a positive impact on urban innovation capacity of cities of different population sizes, showing the trend of large cities > medium-sized cities > small cities. In terms of regional heterogeneity, aviation and high-speed rail have improved the innovation capacity of the eastern, central, western, and northeastern cities of China in varying degrees with a significant Matthew effect, and eastern China has a prominent dominant position. 3) Both aviation and high-speed rail can indirectly promote urban innovation capacity by stimulating technology transfer, venture capital allocation, and foreign capital allocation. Aviation can also indirectly promote urban innovation capacity by stimulating industrial upgrading.

Key words: air transport, high-speed rail transport, urban innovation capacity, two-way fixed effects panel regression, mediation effects

罗雪, 毛炜圣, 王帮娟, 刘承良. 航空和高铁对中国城市创新能力的影响[J]. 地理科学进展, 2022, 41(12): 2203-2217.

LUO Xue, MAO Weisheng, WANG Bangjuan, LIU Chengliang. The impacts of aviation and high-speed rail on urban innovation capacity in China[J]. PROGRESS IN GEOGRAPHY, 2022, 41(12): 2203-2217.

图/表 11

图1

表1

图2

表2

表3

表4

表5

表6

表7

中介效应检验

Air					HSR
变量	模型17： tech	模型18： VC	模型19： FDI	模型20：Industry-up	变量	模型21： tech	模型22： VC	模型23： FDI	模型24：Industry-up
$β 1$	0.0708^***	0.0708^***	0.0708^***	0.0708^***	$β 2$	0.2192^***	0.2192^***	0.2192^***	0.2192^***
	(0.0028)	(0.0028)	(0.0028)	(0.0028)		(0.0087)	(0.0087)	(0.0087)	(0.0087)
$α 1$	0.0031^***	0.0022^***	1.5534^**	<0.0001^***	$α 2$	0.0202^***	0.0037^***	1.1175^**	<-0.0001
	(0.0024)	(0.0001)	(0.1311)	(0.0000)		(0.0008)	(0.0004)	(0.4144)	(<0.0001)
$γ 3$	7.2373^***	14.2107^***	0.0050^***	899.4700^***	$γ 3$	7.2373^***	14.2107^***	0.0050^***	899.4700^**
	(0.1608)	(0.3198)	(0.0004)	(284.792)		(0.1608)	(0.3198)	(0.0004)	(284.792)
Sobel检验	0.0222^***	0.0314^***	0.0078^***	0.0020^***	Sobel检验	0.1461^***	0.0520^***	0.0056^**	-0.00012
	(0.0018)	(0.0019)	(0.0009)	(0.0007)		(0.0064)	(0.0055)	(0.0021)	(0.0006)
Goodman-1	0.0222^***	0.0314^***	0.0078^***	0.0020^***	Goodman-1	0.1461^***	0.0520^***	0.0056^**	-0.00012
	(0.0018)	(0.0019)	(0.0009)	(0.0007)		(0.0064)	(0.0055)	(0.0021)	(0.0006)
中介效应大小	0.0222^***	0.0314^***	0.0078^***	0.0020^***	中介效应大小	0.1461^***	0.0520^***	0.0056^**	-0.00012
	(0.0018)	(0.0019)	(0.0009)	(0.0007)		(0.0064)	(0.0055)	(0.0021)	(0.0006)
中介效应占总效应比重	31.39%	44.32%	11.02%	2.82%	中介效应占总效应比重	66.64%	23.71%	2.56%	—

表7

图3

表8

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变量名称	含义	计算方法
被解释变量
innovation	创新能力	发明专利申请数量
核心解释变量
Air	航空通达性	航空班次
HSR	高铁通达性	高铁班次
控制变量
Population-S	人口规模	年末人口总数取对数
Tech-spending	创新投入水平	科学技术支出与教育支出之和占地方财政一般预算支出的比值
Population-Q	人力资本质量	科学研究、技术服务和地质勘查业人员数取对数
中介变量
tech	技术转移	专利转出与转入的总量
VC	风险投资	风险投资事件数
FDI	外商直接投资	外商直接投资额
Industry-up	产业升级	第三产业与第二产业的比值

变量	模型1	模型2
Air	0.0749^***	0.0708^***
	(0.0193)	(0.0192)
HSR	0.2330^***	0.2192^***
	(0.0359)	(0.0329)
控制变量
Population-S		6872.3180^*
		(3558.8330)
Tech-spending		8614.6720^**
		(4267.8250)
Population-Q		1267.1640^**
		(413.3714)
个体固定效应	Yes	Yes
时间固定效应	Yes	Yes
R²	0.5123	0.5337
样本量	3466	3466

变量	模型3：	模型4：	模型5：	模型6：	模型7：
变量	滞后1年	滞后2年	滞后3年	滞后4年	滞后5年
Air	0.0750^***	0.0869^***	0.0930^***	0.1014^***	0.0966^***
	(0.0203)	(0.0241)	(0.0254)	(0.0277)	(0.0262)
HSR	0.2866^***	0.2950^***	0.3784^***	0.4206^***	0.4250^***
	(0.0440)	(0.0436)	(0.0514)	(0.0547)	(0.0567)
控制变量	Yes	Yes	Yes	Yes	Yes
个体固定效应	Yes	Yes	Yes	Yes	Yes
时间固定效应	Yes	Yes	Yes	Yes	Yes
R²	0.5443	0.5465	0.5612	0.5489	0.5280
样本量	3176	2886	2596	2306	2017

变量	模型8：	模型9：
变量	中心城市	非中心城市
Air	0.0629^**	0.0266^**
	(0.0262)	(0.0206)
HSR	0.2946^***	0.1705^***
	(0.0545)	(0.0435)
控制变量	Yes	Yes
个体固定效应	Yes	Yes
时间固定效应	Yes	Yes
R²	0.6183	0.3994
样本量	432	3034

变量	模型10：	模型11：	模型12：
变量	大城市	中等城市	小城市
Air	0.0626^**	0.0237^**	-0.0071^**
	(0.0214)	(0.0209)	(0.0031)
HSR	0.2528^***	0.0959^***	0.0174^**
	(0.0430)	(0.0396)	(0.0083)
控制变量	Yes	Yes	Yes
个体固定效应	Yes	Yes	Yes
时间固定效应	Yes	Yes	Yes
R²	0.5951	0.3984	0.3362
样本量	1248	1164	1054