区域协同创新对创新绩效的影响机制研究

doi:10.13249/j.cnki.sgs.2020.02.001

地理科学 ›› 2020, Vol. 40 ›› Issue (2): 165-172.doi: 10.13249/j.cnki.sgs.2020.02.001

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区域协同创新对创新绩效的影响机制研究

范斐^1,², 连欢², 王雪利²(), 王嵩²

^1. 武汉大学区域与城乡发展研究院, 湖北武汉 430072
^2. 武汉大学中国中部发展研究院, 湖北武汉 430072

收稿日期:2019-02-01 修回日期:2019-05-24 出版日期:2020-02-10 发布日期:2020-04-09
通讯作者: 王雪利 E-mail:2316855262@qq.com
作者简介:范斐（1984-）,男,河南南阳人,副教授,硕导,主要从事城市与区域创新研究。E-mail: ffan@whu.edu.cn
基金资助:
国家自然科学基金项目(41501141);广东省软科学项目(2019A101002120);湖北省软科学项目(2019ADC130);深圳市哲学社会科学规划项目(SZ2019C003);成都市软科学项目资助(2019-RK00-00006-ZF)

Threshold Effect of Regional Collaborative Innovation on Innovation Performance

Fan Fei^1,², Lian Huan², Wang Xueli²(), Wang Song²

^1. Institute of Regional and Urban-Rural Development, Wuhan University, Wuhan 430072, Hubei, China
^2. Institute of Development of Central China, Wuhan University, Wuhan 430072, Hubei, China

Received:2019-02-01 Revised:2019-05-24 Online:2020-02-10 Published:2020-04-09
Contact: Wang Xueli E-mail:2316855262@qq.com
Supported by:
National Natural Science Foundation of China(41501141);Guangdong Soft Science Project(2019A101002120);Hubei Soft Science Project(2019ADC130);Shenzhen Planning Fund Project of Philosophy and Social Science(SZ2019C003);Chengdu Soft Science Project(2019-RK00-00006-ZF)

摘要/Abstract

摘要：

区域协同创新有利于促进区域间创新要素流动,优化科技资源的合理配置,提升区域创新绩效。采用改进的DEA模型,在测度中国62个城市2003~2016年创新绩效的基础上,运用门槛回归模型分析在不同经济发展水平条件下区域协同创新对创新绩效的影响机制。结果表明：① 研究期内62个城市的创新绩效整体上在波动中稳步提升,区域创新绩效存在较大的空间非均衡性。② 区域协同创新对于区域创新绩效的提升具有促进作用,区域创新绩效与区域协同创新核心解释变量之间存在着非线性关系,专利合作数对区域创新绩效的影响呈现正向双门槛特征,科技论文合作数对区域创新绩效的影响呈现正向单门槛特征,两者对区域创新绩效的影响都随着经济发展水平的提升呈现出不同程度的下降趋势。③ 科技人员流动量在跨过经济发展水平第一门槛10.088而低于第二门槛10.255时,对创新绩效的促进作用最明显;科技资金流动量在经济发展水平第一门槛9.427以下时对创新绩效的影响最显著。

关键词: 区域协同创新, 创新绩效, 门槛效应

Abstract:

Innovation is the main driving force for regional coordinated development, sustainable development and high-quality development. Collaborative innovation, as an important form of integration of innovation factors, is conducive to increasing the mobility of factors within the region, and reasonable allocation of innovative elements, which will improve regional innovation performance. Based on the improved DEA model to measure/evaluate the innovation performance of 62 major cities in China (not including the urban data of Hong Kong, Macao and Taiwan due to data limitation) in 2003-2016, this article takes the impact(s) of regional innovation cooperation and inter-regional innovation resource flow on regional innovation performance as the starting point and uses the threshold regression model to comprehensively analyze the impact mechanism of collaborative innovation on regional innovation performance under different economic development levels. The results show that: 1) Through the analysis of the innovation performance of each city, it is found that the overall innovation performance of 62 major cities in the study period presented an increasing trend with fluctuations. The average innovation performance value increased from 0.624 in 2003 to 0.684 in 2016, and regional innovation performance had a large spatial variation. From the perspective of the four major sectors in the country, the average level of innovation performance of major cities in the eastern and northeastern regions was higher than the national average. The average innovation performance of major cities in the central and western regions was lower than the national average at the end of the study. 2) Collaborative innovation had a certain promotion effect on the improvement of regional innovation performance. There was a nonlinear relationship between the four core explanatory variables and innovation performance in this article. The impact of patent cooperation on regional innovation performance was a positive double threshold, when the level of economic development was below the first threshold of 10.441. The number of patent cooperation had the greatest impact on regional innovation performance, and its elasticity coefficient was 0.039. The number of scientific paper cooperation had a positive single threshold for regional innovation performance. The impact on regional innovation performance has shown a downward trend with varying degrees of economic development. 3) The flow of scientific and technological personnel and the flow of scientific and technological capital were different under different economic development levels, and the degree and direction of innovation performance were different. The flow of scientific and technological personnel had the most obvious effect on the innovation performance when it crosses the first threshold of economic development level of 10.088 and below the second threshold of 10.255, while the impact of scientific and technological capital flow on innovation performance was most significant when the economic development level was below the first threshold of 9.427. To improve China’s innovation performance level, in addition to focusing on optimizing the allocation of innovation resources within the city, we should also consider collaborative innovation for the region in terms of innovation factor flow and regional innovation cooperation in the process of formulating urban innovation performance policies and technology resource management, of influencing the impact of innovation performance, and formulating different collaborative innovation to promote regional innovation performance strategies under different economic development levels.

Key words: regional collaborative innovation, innovation performance, threshold effect

中图分类号:

K902

范斐, 连欢, 王雪利, 王嵩. 区域协同创新对创新绩效的影响机制研究[J]. 地理科学, 2020, 40(2): 165-172.

Fan Fei, Lian Huan, Wang Xueli, Wang Song. Threshold Effect of Regional Collaborative Innovation on Innovation Performance[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(2): 165-172.

图/表 5

图1

表1

表2

表3

表4

模型参数估计结果"

变量	模型（1）	模型（2）	模型（3）	模型（4）
变量	专利合作数（x₁）	科技论文合作数（x₂）	科技人员流动量（x₃）	科技资金流动量（x₄）
$X (T it ≤ δ 1)$	0.039^***	0.138^***	-2.140^***	5.078^***
$X (T it ≤ δ 1)$	（-4.41）	（13.57）	(-2.83)	（-2.71）
$X (δ 1 < T it ≤ δ 2)$	0.020^***	0.111^***	12.010^**	0.486^***
$X (δ 1 < T it ≤ δ 2)$	（-2.61）	（13.84）	（-2.27）	（-3.5）
$X (T it > δ 2)$	0.013		0.020	-3.23E-03
$X (T it > δ 2)$	（-1.61）		（-0.5）	（-0.35）
就业人数	-0.009^***	-0.007^***	2.39E-03	-1.79E-03
就业人数	(-2.91)	（-2.93）	（-0.73）	（-0.58）
固定资产投资率	-0.187^***	-0.053^**	-0.129^***	-0.201^***
固定资产投资率	(-6.27)	（-1.99）	（-4.18）	（-6.65）
直接利用外商投资总额	0.019^** （-2.18）	0.005 （0.61）	7.64E-03 （-0.89）	0.026^*** （-3.12）
互联网宽带接入户数	0.029 （-1.64）	0.050^*** （3.43）	0.016 （-0.92）	0.045^*** （-2.59）
社会消费品零售总额	0.080^*** （-3.61）	0.058^*** （3.66）	0.064^*** （-3.34）	0.076^*** （-3.99）
$C$	-1.265^***	-1.761^***	-0.677^***	-1.408^***
$C$	（-4.85）	（-12.40）	（-3.78）	（-7.88)

表4

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阶段划分	2003年	2016年
高级阶段（0.75<Y_it≤1.00）	北京、上海、南京、深圳、东莞、合肥、芜湖、武汉、长沙、西安、广州、佛山、济南、成都、扬州、杭州、青岛、兰州、郑州、大连、重庆、天津	北京、南京、无锡、苏州、镇江、温州、济南、青岛、深圳、鞍山、哈尔滨、大连、湖州、成都、宁波、上海、泉州、长春、绍兴、汕头、佛山、天津、杭州、东莞、西安、重庆、兰州
中级阶段（0.50<Y_it≤0.75）	太原、苏州、长春、徐州、沈阳、厦门、南昌、西宁、哈尔滨、常州、乌鲁木齐、淮安、中山、呼和浩特、福州、南宁、盐城	南昌、泰州、广州、郑州、南通、柳州、武汉、嘉兴、南宁、厦门、合肥、徐州、福州、沈阳、台州、扬州、常州、长沙、安庆、中山
低级阶段（0.25<Y_it≤0.50）	石家庄、镇江、宁波、汕头、昆明、温州、南通、银川、蚌埠、鞍山、无锡、泉州、潍坊、湖州、柳州、绍兴、珠海、金华	芜湖、潍坊、昆明、石家庄、淮安、盐城、金华、惠州、太原、蚌埠、珠海
初级阶段（0<Y_it≤0.25）	台州、安庆、泰州、惠州、嘉兴	呼和浩特、西宁、银川、乌鲁木齐

创新指标	专利合作数	科技论文合作数	科技人员流动量	科技资金流动量
单一门槛检验	10.387*	15.848**	7.293	5.561
双门槛检验	26.780***	6.939	10.774**	14.476*
三门槛检验	0.000**	0.000*	0.000**	0.000**

门槛变量	门槛估计值1	95%置信区间	门槛估计值2	95%置信区间
专利合作数	10.441	[10.215,10.462]	11.216	[9.072,12.130]
科技论文合作数	9.299	[9.233,10.894]
科技人员流动量	10.088	[9.072,11.145]	10.255	[9.072,12.130]
科技资金流动量	9.427	[9.072,12.130]	10.250	[9.923,10.449]

区域协同创新对创新绩效的影响机制研究

Threshold Effect of Regional Collaborative Innovation on Innovation Performance

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