Research On Spatial-temporal Coupling Of China’s Provincial Innovation Input And Output

Since Schumpeter proposed the "innovation theory" in 1912, the function of technological innovation to economic growth has attracted more and more attention. Research and development (R&D) lies as the core of scientific and technological innovation activities. R&D investment scale and intensity have become the important indicators to measure the scientific and technological innovation input and innovation capability of a country or a region. Overseas studies have done a comprehensive research on the relationship between R&D investment and enterprise or industry performance. Following western theories and methods, domestic scholars also started to explore China’s R&D investment. But the existing studies mainly employed cross-sectional data and econometric method to examine the linear relationship between R&D investment and its performance, with scant attention paid to R&D and its spatial effects. As regional disparities of R&D investment are phenomenal in China, it is pressing to deploy the spatial econometrics model to uncover the spatial distributions of R&D and its local and regional impacts.Past studies have shown that there are huge regional differences in the R&D investment in China, the degree of which is even larger than that of regional economy. The imbalanced distribution of R&D investment across the country will further enlarge the gap of regional technological innovation capability, influencing the regional economy to a greater extent. In order to address the negative impacts, we need to analyse the input distribution and the spatial-temporal evolution of innovative activities from the perspective of geographical space. Only after the coupling relationship between China’s innovation investment and innovation output is unpacked could the corresponding measures be proposed.For the purpose, this thesis analyses the regional disparities and spatial-temporal evolution of China’s R&D investment from 2001 to 2011. The research employs R&D intensity as the major measuring index, and uses other methods such as traditional Statistic analysis, Markov chains and ESDA. The result shows that the absolute disparity of China’s provincial R&D investment has widened while the comparative disparity has reduced; areas with high R&D investment are concentrated in the eastern coastal area. As to provincial display of R&D investment, it has shown a significant feature of spatial autocorrelation since 2005 and the spillover effect of the eastern area has enhanced, while some groups in the Middle and West fall into a vicious circle. Regarding the impact factor of R&D funding intensity, different areas have different impact factors. For the whole country, the most significant factors in a descending order are economic development level, government science and technology investment intensity and human capital accumulation. For the east, the most significant factors are economic development level and the composition of international trade; for the middle are economic development level and opening degree; for the west are economic development level and human capital accumulation.The intensity of R&D investment can reflect the relative level of investment. But in order to reveal the spatial-temporal coupling features of innovation input and output, we also need to analyze the regional disparities and spatial-temporal evolution of them from the absolute number. The results show that, the comparative disparity of funding input scale has been narrowed, while that of personnel input scale and output scale have widened. The degree of disparities of the above three factors are innovation output, fund input and personnel input in a descending order. According to the distribution and the evolution of the gravity center of innovation input and output, we can see that the spatial distribution of innovation development in China is not equilibrium, with the eastern coastal and southeast coastal areas being the high density area of the innovation development and their attraction to innovation fund and innovators still being strengthened. Their contribution to innovation output is greater than that of innovation investment. The innovation disparity between the eastern and western area manifests a tendency of expansion. There is a strong correlation between the capital flow and the personnel flow. But there is decoupling relation between the innovation input and output. In the aspect of coupling relationship, the coupling of capital input and personnel input is better, the coupling of innovation input and innovation output is poor.In order to explore the coupling relationship among innovation input, direct output and indirect output, a comprehensive index system is built from innovation input, innovation output and innovation performance and the coupling degrees and coordinating degrees are calculated to assess the effect level and synergistic function among innovation subsystems. At the national level, coupling degree of innovation subsystems has risen from low coupling stage to antagonistic stage whereas the coupling coordination degree has risen from low coupling coordination to high coupling coordination. At the regional level, the overall coupling degree among innovation subsystems is quite low within the province, the gap of which being even greater if compared to the positive resonance coupling. Most provinces stay at a low coupling stage, with few entering to the antagonistic stage, but the number of which is increasing. Most provinces belong to the type of low coupling coordination, with very few to the type of moderate coupling coordination and with a concentration in the eastern area except for Beijing which belongs to the type of high coupling coordination. In light of the growth situation, the growth points of coupling coordination degree have formed in the East Middle West regions. Combined with coupling degree and coordinating degree, Chinese territory could be divided into five categories.As to the conversion efficiency of the innovation input to the innovation output, pure technical efficiency is the main factor that affects the efficiency of our country’s comprehensive technical efficiency. The gap between the eastern and central area is mainly reflected in the pure technical efficiency, while that between the central and western area is mainly reflected in the scale efficiency. Beijing is located on the efficiency frontier, most provinces are not that effective in terms of comprehensive technical efficiency. The pure technical efficiency of Zhejiang, Guangdong, Hainan, Chongqing, Gansu and Qinghai is higher than the scale efficiency, other provinces being the opposite. The number of provinces at the stage of increasing returns to scale is enlarging while that at the stage of decreasing returns to scale are declining, showing that the regional innovation of China still has a certain scale effect at present. Technical efficiency has a significant positive effects on the coupling of regional innovation system. In addition, support to the innovation by the government, industrial structure, population quality and regional opening degree also have a significant impacts.Finally, based on the regional disparities of innovation investment, coupling results of innovation input and output and influencing factors of innovation development, this thesis puts forward an optimum spatial allocation model of regional innovation. The findings have the following policy implications. It is suggested to promote a coordinated development of regional innovation system, such as expanding innovation investment scale, improving the input-output performance of innovation, strengthening the interaction of regional innovation, strengthening the cultivation and introduction of talent and so on.