高铁影响下的中国区域创新时空演化与效应分解

doi:10.3969/j.issn.1004-9479.2021.02.2019534

摘要/Abstract

摘要：

探究高速铁路对区域创新的影响机制将深远影响我国区域经济发展。运用ESDA、SDM和求偏微分等方法，测算了由高铁开通带来的时空压缩对区域创新产出产生的直接效应和间接效应，并通过分地区回归对异质性进行了深入分析。结果表明：我国创新产出具有显著的空间相关性，且集聚程度不断增强，“核心-外围”区域基本形成，创新活动具有一定的锁定特征；其中，R&D人员投入、FDI、政府财政科技支出和居民消费支出是影响区域创新产出的重要因素；高铁开通对创新的影响存在空间异质性，R&D人员全时当量对东部地区的影响最大，东北和中部地区则同时依赖于R&D人员和R&D资本投入，西部地区并未受到极化影响，反而因时空压缩产生了正向溢出效应。

关键词: 区域创新, 时间距离, 空间面板杜宾模型, 效应分解, 高铁

Abstract:

On the basis of explaining the impact mechanism of high-speed railway on regional innovation, this paper uses ESDA, SDM and partial differentiation are used to measure the direct and indirect effects of time-space compression caused by the opening of high-speed rail on regional innovation output, and the heterogeneity is analyzed by regional regression. The results show that innovation output in China has a significant spatial correlation, and the degree of agglomeration is increasing. The "core periphery" region is basically formed, and innovation activities have certain locking characteristics. Among them, R&D personnel input, FDI, government financial expenditure on science and technology, and residents' consumption are important factors affecting regional innovation output. There are spatial differences in the impact of the opening of high-speed rail on innovation Qualitatively, the full-time equivalent of R&D personnel has the greatest impact on the eastern region, while the northeast and central regions rely on R&D personnel and R&D capital investment at the same time. The western region is not affected by polarization, but has a positive spillover effect due to space-time compression.

Key words: regional innovation, temporal distance, spatial panel Durbin model, effect decomposition, high-speed railway

刘国燕, 李涛. 高铁影响下的中国区域创新时空演化与效应分解[J]. 世界地理研究, 2021, 30(2): 355-366.

Guoyan LIU, Tao LI. Impact of high-speed railway development on regional innovation from the perspective of spatial effect[J]. World Regional Studies, 2021, 30(2): 355-366.

图/表 7

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年份	Moran's I	年份	Moran's I	年份	Moran's I	年份	Moran's I
2002	0.3025^***	2006	0.3504^***	2010	0.3835^***	2014	0.3889^***
2003	0.3219^***	2007	0.3355^***	2011	0.3841^***	2015	0.4068^***
2004	0.3622^***	2008	0.3376^***	2012	0.4083^***	2016	0.3978^***
2005	0.3413^***	2009	0.3499^***	2013	0.4020^***	2017	0.4164^***

年份	Moran's I	年份	Moran's I	年份	Moran's I	年份	Moran's I
2002	0.3025^***	2006	0.3504^***	2010	0.3835^***	2014	0.3889^***
2003	0.3219^***	2007	0.3355^***	2011	0.3841^***	2015	0.4068^***
2004	0.3622^***	2008	0.3376^***	2012	0.4083^***	2016	0.3978^***
2005	0.3413^***	2009	0.3499^***	2013	0.4020^***	2017	0.4164^***

象限	2010年	2017年
象限1：HH	山东、河南、江苏、安徽、湖北、浙江、湖南、福建、广东、辽宁、天津、陕西、河北、上海、北京	山东、河南、江苏、安徽、湖北、浙江、江西、湖南、福建、广东、辽宁、天津、陕西、重庆、河北、上海、北京
象限2：LH	黑龙江、山西、江西、宁夏、贵州、海南、广西、内蒙古、吉林	宁夏、山西、云南、贵州、广西、海南、吉林、内蒙古
象限3：LL	新疆、云南、西藏、甘肃、青海	黑龙江、西藏、新疆、青海、甘肃
象限4：HL	重庆、四川	四川

象限	2010年	2017年
象限1：HH	山东、河南、江苏、安徽、湖北、浙江、湖南、福建、广东、辽宁、天津、陕西、河北、上海、北京	山东、河南、江苏、安徽、湖北、浙江、江西、湖南、福建、广东、辽宁、天津、陕西、重庆、河北、上海、北京
象限2：LH	黑龙江、山西、江西、宁夏、贵州、海南、广西、内蒙古、吉林	宁夏、山西、云南、贵州、广西、海南、吉林、内蒙古
象限3：LL	新疆、云南、西藏、甘肃、青海	黑龙江、西藏、新疆、青海、甘肃
象限4：HL	重庆、四川	四川

变量	系数		t值
C	-4.086^***		-10.876
R&DP	0.373^***		6.813
R&DE	0.020		0.354
PGDP	-0.313^***		-2.936
FDI	0.003^***		7.382
GOV	-0.001^**		-2.284
CON	0.395^***		2.874
FIN	0.885^***		16.702
R²	0.957	Durbin-Watson	1.675
sigma^2	0.144	loglikols	-218.944
LM test no spatial Lag	14.149^***	LM test no spatial error	9.894^***
robust LM test no spatial lag	9.238^***	robust LM test no spatial error	4.982^**