中国省域科技创新模式及其时空演变

doi:10.11821/dlyj201806012

摘要/Abstract

摘要：

中国的经济增长模式正在由要素驱动型向创新驱动型转变,适宜的科技创新模式是促进中国经济社会协调可持续发展的关键。基于熵值法构建综合指标评价体系,运用Theil指数对中国科技创新投入与产出水平的时空演变进行分析,并结合K-means聚类分析法将其分别归为相同的5个等级。根据不同的投入—产出组合,得出投入产出协调型、投入领先型与产出领先型3个科技创新类型及其子类。最后,利用多阶段DEA模型,从科技创新投入的角度,将科技创新模式划分为混合驱动型、创新平台驱动型、人力与资本驱动型和人力驱动型四类;从科技创新产出的角度,划分为经济创新导向型、知识与经济创新导向型和知识创新导向型三类模式。对比发现,中国科技创新模式与科技创新类型的时空演变呈现高度的相关性。在此基础上,为中国未来区域科技创新的发展提出若干建议。

关键词: 科技创新模式, 时空演变, 熵值法, K-means聚类分析, 多阶段DEA模型, 省域尺度

Abstract:

Given the fact that China's economic growth pattern is transforming from the previous factor-driven type into innovation-driven one, the more innovative a country is, the stronger competition advantage it will obtain and maintain around the world. Likewise, appropriate science and technology innovation pattern tends to be the key to promoting China's economic and social development in a balanced and sustainable manner. In this paper, entropy technology is introduced for the purpose of building a comprehensive index evaluation system. Based on weighted values generated by the evaluation system, this paper analyzes the temporal and spatial evolution of provincial science and technology innovation input and output level in 2004, 2009 and 2014 via Theil index. Then, by exercising K-means clustering analysis, 31 provincial weighted values of science and technology innovation input are divided into 5 grades from high to low, so are the weighted values of science and technology innovation output. On this account, there are totally 11 input-output combinations of science and technology innovation in China which can be classified into 3 types, namely, input-output coordination, input leading and output leading. Among them, there are 3 subtypes of input-output coordination which are high-, medium- and low-level input-output coordination in regard of input and output grades, while the latter one can be divided into 2 subtypes which involve high- and low-level output leading in comparison of input and output grades. This paper further studies the temporal and spatial evolution of all the types. The results show that: (1) input-output coordination maintains the dominant position in China on the move; (2) Innovation platform construction imposes the most significant influence on science and technology innovation type transformation in eastern China. By means of multi-stage DEA model, this paper identifies four science and technology innovation patterns in terms of science and technology innovation input, namely mixed driven, innovation platform driven, labor-capital driven and labor driven pattern, and three science and technology innovation patterns in terms of science and technology innovation output, which involve economy, knowledge-economy and knowledge innovation oriented pattern. It is obvious that science and technology innovation types are associated with the patterns in temporal and spatial evolution. What's more, this paper makes some suggestions to regional science and technology innovation development of China in the future.

Key words: science and technology innovation patterns, temporal and spatial evolution, entropy technology, K-means clustering analysis, multi-stage DEA model, provincial scale

李哲, 申玉铭, 曾春水. 中国省域科技创新模式及其时空演变[J]. 地理研究, 2018, 37(6): 1223-1237.

Zhe LI, Yuming SHEN, Chunshui ZENG. Science and technology innovation patterns and their spatial and temporal evolution of provinces in China[J]. GEOGRAPHICAL RESEARCH, 2018, 37(6): 1223-1237.

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一级指标	二级指标	权重	三级指标	权重
科技创新投入	人力投入	0.1042	万人R&D人员全时当量	0.0581
			R&D人员占全社会就业人数比例	0.0410
			企业研发人员占比	0.0051
	资本投入	0.1053	人均R&D经费投入规模	0.0645
			R&D经费投入占GDP比例	0.0370
			企业研发投入占比	0.0038
	创新平台建设	0.2611	万人高等院校数	0.0321
			万人国家重点实验室数	0.1272
			万人国家工程技术研究中心数	0.0784
			万人国家级高新技术产业开发区数	0.0234
科技创新产出	知识创新产出	0.2033	万人科技论文数	0.0833
			科技论文数/科技研发人员数	0.0307
			万人专利申请数	0.0672
			发明专利申请比例	0.0221
	经济创新产出	0.3034	人均新产品销售收入	0.0616
			万人核准注册商标数	0.0336
			人均技术市场成交合同额	0.0184
			高新技术产业主营业务收入比例	0.0992
			高技术产业新产品销售收入占主营业务收入比例	0.0620
			劳动生产率	0.0286
	节能减排绩效	0.0227	综合能耗产出率	0.0082
			亿元GDP工业废气排放量	0.0046
			亿元GDP工业废水排放量	0.0038
			固体废物综合利用率	0.0061

科技创新类型	子类	2004年	2009年	2014年
投入产出协调型	高水平型	北京、上海	北京、上海、江苏、天津、浙江、广东	北京、上海、江苏、天津、浙江、广东、福建、陕西、山东
	中等水平型	江苏、浙江、天津、广东、辽宁	陕西、湖北、山东、辽宁、福建、吉林、重庆	湖北、湖南、辽宁、吉林、安徽、黑龙江
	低水平型	湖北、山西、重庆、四川、海南、甘肃、新疆、江西、河北、湖南、广西、西藏	宁夏、山西、安徽、海南、江西、甘肃、河南、新疆、河北、广西、西藏	甘肃、海南、河北、山西、新疆、广西、西藏
投入领先型	-	吉林、陕西、黑龙江、宁夏、内蒙古、青海	黑龙江、内蒙古、青海	宁夏、青海、内蒙古
产出领先型	高水平型	福建、山东	四川、湖南	重庆、四川、河南、江西
产出领先型	低水平型	安徽、河南、贵州、云南	贵州、云南	贵州、云南