中国科技创新发展格局与类型划分——基于投入规模和创新效率的分析
Development pattern of scientific and technological innovation and typical zone in China based on the analysis of scale and efficiency
刘汉初 1樊杰 1周侃2
作者信息
- 1. 中国科学院地理科学与资源研究所,区域可持续发展分析与模拟重点实验室,北京 100101;中国科学院大学,北京 100049
- 2. 中国科学院地理科学与资源研究所,区域可持续发展分析与模拟重点实验室,北京 100101
- 折叠
摘要
科技创新是转型期中国区域发展格局重塑的新兴驱动力,对区域协调发展至关重要.地区科技创新能力的提高不仅需要加大科技资源投入规模,更应该注重提升科技综合效率.本文立 足投入产出视角,构建科技创新效率综合测度指标体系,利用阿尔蒙法分布滞后模型引入科技创新的滞后效应,并采用可变规模报酬VRS模型,评估中国30个省域单元的科技创新有效的累计投入规模和创 新效率,揭示创新效率的变化趋势及空间分异规律,根据投入规模和创新效率两个维度划分出科技创新发展类型区.结果表明:①中国科技创新投入规模呈现出"沿海—内陆"的极大差距,区域间科技 创新投入差距已经大于经济发展水平的差距.中国科技创新效率总体水平偏低,但呈不断上升的趋势.②科技创新效率的空间分异明显,高效率单元集中在北京、天津、上海、浙江、广东等发达省份, 科技创新效率在区域层面上总体呈现由东向西梯度递减规律.③科技创新效率与区域经济发展水平的空间耦合程度较高,创新效率依次从发达地区、较发达地区、一般发展区到欠发达地区逐渐降低.④ 中国省域单元可划分为科技创新引领区、科技创新突破区、科技创新提升区、科技创新赶超区4种科技创新发展的类型区,并根据当前各类型区科技创新的优势及问题,提出了提高科技创新发展水平的政 策建议.
Abstract
The increasing influence of scientific and technological (S & T) innovation on the regional development spatial structure in China promotes the Chinese economy into a new era of endogenous growth driven by innovation. A large capital investment, from central to local, has been provided to enhance the capacity for innovation. Such an investment is important for the regional development within the transitional period. However, the S & T innovation efficiency is recognized as a more crucial factor affecting sustainable development in the long run. From the input-output perspective, this study develops a comprehensive framework for measuring the S & T innovation efficiency of 30 provinces in China. A methodology combining the distributed lag models of Almon and the variable returns to scale model is set up to assess the effective cumulative total investment scale of technological innovation and S & T innovation efficiency in China at the provincial level. Based on this assessment, the spatial variation and evolution of the S & T innovation efficiency are examined. Further, the type of region is identified by synthetically considering its investment scale and the S & T innovation efficiency, followed by several policy suggestions. The results are obtained as follows: (1) a large innovational investment gap, which is larger than the economic gap among regions, exists between coastal and inland areas; (2) the total S & T innovation efficiency of China is comparatively low, but the rising trend is continuous; (3) the S & T innovation efficiency shows an obvious spatial heterogeneity as high-efficiency provinces are mainly concentrated in wealthier coastal areas such as cities of Beijing, Tianjin and Shanghai, as well as provinces of Zhejiang and Guangdong; the S & T innovation efficiency experiences a gradual decline from east to west within China; (4) a high spatial coupling between the S & T innovation efficiency and the level of economic development is observed as the S & T innovation efficiency decreases with the decline in the economic level; and (5) the 30 provinces can be classified into four types in terms of their investment scale and S & T innovation efficiency: the regions that are leaders, those that need to make a breakthrough, need to be promoted, and need to develop fast. Thus, policy suggestions could be put forward according to the strength and weakness of each type of region.
关键词
科技创新/创新效率/发展格局/滞后效应/DEA模型/中国Key words
scientific and technological innovation/innovation efficiency/development pattern/hysteresis effect/DEA model/China引用本文复制引用
基金项目
国家自然科学基金重点项目(40830741)
中国科学院科技战略咨询研究院重大咨询项目(Y02015005)
出版年
2018
NETL
NSTL
维普
万方数据