The impact of high-tech industrial agglomeration on China’s green innovation efficiency: A spatial econometric analysis

Resolving the conflict between economic growth and ecological sustainability is crucial when transitioning from traditional economic development towards a sustainable green model. In this context, the super-NSBM model was applied to measure the efficiency of green innovation technology research and development. Additionally, it was applied to measure the efficiency of the green innovation achievement transformation among 30 Chinese provinces. This evaluation was conducted for the period from 2011 to 2021, and it utilized a two-stage innovation value chain perspective. The entropy method was used to comprehensively calculate the digital economy development index, and the impact of digital economy development on the two-stage green innovation effect was empirically explored using SDM. The findings revealed the following: Firstly, both two-stage green innovation efficiency and digital economy development exhibited significant spatial characteristics. Secondly, digital economy development had a positive effect on two-stage green innovation efficiency not only in the local region but also in neighboring regions. This conclusion remained valid throughout a series of robustness tests. Thirdly, in terms of regional heterogeneity, the eastern region and non-resource-based regions had greater digital economy development dividends than the central, western, and resource-based regions; therefore, the effect on two-stage green innovation efficiency was more significant. Lastly, both intellectual property protection and data factor endowment exerted positive moderating effects on the influence of the digital economy on two-stage green innovation efficiency. The conclusions of this research provide a theoretical backing for and empirical proof of the mechanism of digital economy development and its impact on two-stage green innovation efficiency. Policy implications are suggested based on these findings, such as reinforcing digitalization, establishing targeted policies, and fostering a favorable external environment conducive to technological innovation.

Section: Discussionmentioning

confidence: 91%

Digital Economy Development and Green Innovation Efficiency from a Two-Stage Innovation Value Chain Perspective

Fan,

2024

“…The relationship between urban spatial structures and GTI has garnered significant attention in academic circles. Some scholars argue that a monocentric structure is more favorable for the development of GTI [61], while others propose an inverted U-shaped relationship between the compactness of spatial structure and GTI [62,63]. This study expanded from a single city to a large sample of Chinese cities to study the impact of AEs on GTI, thus forming a beneficial extension and supplement to the impact of urban spatial structures on GTI.…”

Section: The Impact Of Aes On Gtimentioning

confidence: 99%

Agglomeration Externalities vs. Network Externalities: Impact on Green Technology Innovation in 283 Chinese Cities

Dong,

Liu

2024

The prominence of agglomeration externalities (AEs) and network externalities (NEs) in urban sustainable development has intensified in recent times, with advances in transportation infrastructure and information technology acting as key accelerators. Despite the scholarly attention they receive, the specific spillover effects that these externalities exert on green technology innovation (GTI) remain under-explored. In an effort to bridge this knowledge gap, the present study employs a spatial Durbin model to scrutinize, spanning a decade from 2011 to 2021, the impact and spatial spillover of AEs and NEs on GTI across 283 Chinese cities of prefecture level and above. The findings reveal the following: (1) AEs exert a U-shaped influence on GTI, initially inhibiting it, before ultimately fostering its growth. (2) NEs are found to consistently promote GTI. (3) The spatial spillover effects of AEs on GTI are significantly positive, while those from NEs are not statistically significant. (4) The influences of AEs and NEs on GTI exhibit marked regional variations. This study extends the research scope on the factors influencing GTI by examining the role of AEs and NEs, thereby aiming to offer valuable insights for enhancing the level of GTI.

“…On the one hand, most of the studies indicate that high-tech industrial agglomeration has a positive impact on regional innovation. For example, Liu and Wu found that high-tech industries effectively promote green innovation under different agglomeration patterns [13]. Similarly, Zhang et al pointed out from the perspective of development trends and regional heterogeneity that industrial agglomeration could improve innovation performance through means of knowledge spillover, reducing innovation costs for enterprises, and optimizing reward and punishment incentive systems [14].…”

Section: Introductionmentioning

confidence: 99%

The Relationship between High-Tech Industrial Agglomeration and Regional Innovation: A Meta-Analysis Investigation in China

Ge,

Li,

Sun

et al. 2023

High-tech industrial agglomeration enhances the technological level and value of regional industries. It is considered to be a new and effective way to drive China’s regional innovation and development at present. Numerous studies indicate that high-tech industrial agglomeration contributes positively to regional innovation, but the current academic discussion on this issue has not yet reached a unified opinion. In various research contexts, it has also been pointed out that there may be a negative correlation or non-linear relationship between the two. This contradictory relationship makes it difficult to generalize the current research findings to realistic application scenarios. Therefore, to clarify the relationship between the two scientifically, this study employs meta-analysis, reanalyzing 833 effect values derived from 69 independent research samples based on Chinese data. The findings reveal a moderately positive correlation (r = 0.204) between the agglomeration of high-tech industries and regional innovation in China. In particular, high-tech industrial agglomeration significantly contributes to regional innovation under the paths of diversified agglomeration and competitive agglomeration. We further found that sampling region, measurement approach, measurement perspective, research methodology, and year of publication all exhibit significant moderating effects on the relationship between the two variables. Based on meta-analysis, this study not only scientifically responds to the controversy of the relationship between high-tech industrial agglomeration and regional innovation but also further reveals the inner conduction mechanism between the two. It is of great significance in exploring future studies in related fields.