| 广东省科技创新资源配置时空变异测度 |
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| 引用本文： | 张祥宇.广东省科技创新资源配置时空变异测度[J].科技管理研究,2022(12):59-64. |
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| 作者姓名： | 张祥宇 |
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| 作者单位： | 广东省技术经济研究发展中心 |
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| 基金项目： | 广东省软科学研究项目“粤港澳大湾区高校科技创新资源统计与调查研究”(2019A101002026) |
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| 摘 要： | 为摸清“十二五”“十三五”期间广东科技创新资源在时间和空间上分配的变异特征和规律,采用DEAMalmquist模型、空间可视化方法,从静态和动态两个视角测度及剖析2011—2020年广东科技创新资源配置在时间和空间上的变异情况。结果发现：以2014年实施创新体制机制改革为关键转折点,广东科技创新资源配置效率呈现先降后升的态势,随着科技创新资源的加大投入和利用效率提升,全省已发展为珠三角领衔、粤东西部协同发展的“一核两翼”创新发展格局;动态来看,广东科技创新资源配置效率不断提升,年平均涨幅为8.1%,技术进步是主要影响因素,科技创新资源配置效率综合提高型地市有13个,其中云浮和汕头表现突出,技术进步提高型包括广州、深圳、清远和茂名,综合下降型包括汕头、江门、湛江和揭阳。由此提出广东进一步完善科技投入机制和科学统筹区域创新资源配置的建议。
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| 关 键 词： | 科技创新资源 资源配置 时空变异 DEA-Malmquist 广东省 |
| 收稿时间： | 2022/3/10 0:00:00 |
| 修稿时间： | 2022/6/23 0:00:00 |
Measuring the Spatial and Temporal Divergence of Regional Science and Technology Innovation Resource in Guangdong |
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| Abstract: | In order to grasp the characteristics and patterns of changes in the allocation of Guangdong''s innovation resources in time and space during the 12th and 13th Five-Year Plan period, this paper applied the DEA-Malmquist model and spatial visualization methods to investigate the temporal and spatial changes in the allocation of Guangdong''s science and technology innovation resources from 2011-2020 in both static and dynamic perspectives. The results show that with the implementation of innovation system reform in 2014 as the key turning point, the allocation efficiency of science and technology innovation resources in Guangdong Province showed a trend of first decline and then rise. With the increased investment and efficiency of innovation resources, the province has developed into a "one core and two wings" innovation development pattern, with the Pearl River Delta Region leading the way and the east and west of Guangdong developing synchronously. From a dynamic point of view, during the 12th "Five-Year Plan" and 13th "Five-Year Plan" period, the allocation efficiency of scientific and technological innovation resources in Guangdong province has been continuously improved, with an average annual increase of 8.1%, which is mainly driven by technological improvements. There are 13 cities that are comprehensive and efficient in allocating science and technology innovation resources, among which Yunfu and Shantou are outstanding. Guangzhou, Shenzhen, Qingyuan and Maoming are technology advancement improvement type. Shantou, Jiangmen, Zhanjiang and Jieyang are combined declining type. Based on the above analysis, the article puts forward suggestions for further improving the science and technology investment mechanism and scientifically coordinating the allocation of regional innovation resources in Guangdong Province. |
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| Keywords: | innovation resource allocation resource allocation spatiotemporal variation DEA-Malmquist Guangdong province |
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