Suchergebnisse

Chapter1: Introduction -- Chapter2: The Mechanism of High Speed Railway's Influence on Regional Development -- Chapter3: The Characteristics of Technical economy in High Speed Railway -- Chapter4: Time efficiency and Space-time Competitiveness of High Speed Railway -- Chapter5: High Speed Railway and Regional Traffic Structure -- Chapter6: High Speed Railway and Regional Economy -- Chapter7: High Speed Railway and Regional Economic Structure -- Chapter8: The Economic Belt of High Speed Railway -- Railway and Regional Land Development -- Chapter10: High Speed Railway and Regional Service Quality -- Chapter 11: The Development Mode and Policy of High Speed Railway -- Chapter 12: High Speed Railway and National Strategy. .

Abstract: Under the "new normal" economy, the driving force of industrial agglomeration has gradually shifted from factor- and investment-driven to innovation-driven economic growth. This article uses the local and global industrial clustering indices based on the density accumulation function to measure overall industrial agglomeration. The authors then adopt the system GMM (generalised method of moments) model to analyse the technology spillover effect of industrial agglomeration. Findings have identified three pathways of technology spillover: spatial agglomeration, specialisation spillover and technological networks. Enterprises at the centre of industrial agglomerations have stronger technological innovation capabilities. Specialisation agglomeration has a significantly positive impact on technological innovation. Technology cooperation networks have a significantly positive impact on improving technological innovation and thus form an important pathway for technology spillover in industrial agglomerations.

AbstractThe rapid development of the Internet had a profound influence on the spatial distribution of economic output activities. On the basis of theoretical analysis, this study takes China's regions as research samples and construct a spatial econometric model to empirically analyze the impact of Internet development on regional productivity. The analysis reveals that the distribution of regional productivity in China had significant spatial correlation; regional productivity had positive spatial externalities; Internet development had significant promotion effects and positive spatial spillover effects to regional productivity, but the effects are heterogeneous in different subregions. The subregion analysis shows that the optimization effect of the Internet on regional productivity exhibits heterogeneity in different subregions. The threshold effect analysis reveals the increasing marginal effect of Internet development on regional productivity, and economic development and human capital supply are important factors restricting this marginal effect. Altogether, this study provides useful references on the positive effects of the Internet on regional productivity in the spatial dimension, and suggests that the policy makers can optimize the spatial distribution of regional productivity by promoting Internet access in various regions and narrowing the digital divide among regions.

A fictitious economy is the product of a highly developed economy, and is represented by bank capital, stocks, and bonds. Based on the economic reality of China's continuously expanding fictitious economy and rapidly rising housing prices in recent years, a structural vector autoregressive model is constructed and employed to show that, in the short term, the fictitious economy contributes most to the fluctuations in housing prices, followed by the interest rate, while money supply plays a very small contributory role. However, in the long term, the influence of the fictitious economy decreases, and the monetary policy and real economy will be the main factors influencing changes in housing prices. (China/GIGA)

AbstractGovernment support plays a crucial role in enhancing regional innovation capabilities and creating distinctive innovation clusters. Using the innovation competitiveness indicators of the 31 provinces in China, this paper examines the provincial government collaboration and innovation competitiveness values through Super‐SBM data envelopment analysis and analytic hierarchy process. Then, the particle swarm optimization (PSO) algorithm is employed to relate government collaboration to innovation competition and cooperation among provinces in hope of exploring the developmental trends of innovation cluster optimization. Research findings show that only 25.8% of the provincial government support proves effective for regional innovation, and the competitiveness values of the provinces are generally low and need to be improved. By incorporating the role of government support into the innovation competition and cooperation among provinces, there has been a tendency of innovation activities clustering in innovative regions and three major clusters have formed. The trend of clustering will continue to evolve outwards so that the overall innovation level of China would be enhanced.