Spatial spillover effect of green innovation on economic development quality in China: Evidence from a panel data of 270 prefecture-level and above cities

Peng, Wen-bin; Yin, Yong; Kuang, Change; Wen, Zezhou; Kuang, Jinsong

doi:10.1016/j.scs.2021.102863

Cited by 143 publications

(56 citation statements)

References 57 publications

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“…The amount of science and technology expenditure also reflects the importance of the attachment of a region to GI, which will significantly improve GI capacity and efficiency. It is expressed by the proportion of local governmental expenditure on technology [52]. 4.…”

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confidence: 99%

Digital Economy Development and Urban Green Innovation CA-Pability: Based on Panel Data of 274 Prefecture-Level Cities in China

et al. 2022

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The digital economy (DE) plays a crucial role in green innovation (GI) and green development as a new economic form. Based on the panel data of 274 Chinese prefecture-level cities from 2011 to 2019, this paper constructs a comprehensive DE index and conducts two-way fixed effect regression to explore its impact on GI capabilities and examines the mediating effect of industrial structure transformation and upgrading. The research results show that: (1) The development of the DE has significantly improved the GI capability of cities. (2) In terms of space, the development of DE and GI ability development in eastern cities outperform that in central and western regions. However, the development of eastern cities is almost saturated and slow, while the development of central and western cities is faster. (3) The DE has a significant role in promoting GI capabilities in the central and western regions. Although the eastern region has a positive impact, it is negligible. In terms of urban scale, the DE of the large, medium, and small cities positively affects GI capabilities to the effect. (4) The transformation and upgrading of the industrial structure mediate the relationship between the DE and GI. Based on the above conclusions, relevant suggestions for improving GI capabilities around the development of the DE and industrial transformation and upgrading must be devised.

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confidence: 99%

Digital Economy Development and Urban Green Innovation CA-Pability: Based on Panel Data of 274 Prefecture-Level Cities in China

et al. 2022

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“…China's infrastructure initially promoted innovation efficiency and then inhibited it [22]. As shown by the results, before 2014, infrastructure had a positive impact on innovation efficiency.…”

Section: Analyses Of the Mechanism Of Innovation Efficiencymentioning

confidence: 78%

“…Goodchild (1992) posits that a region's socio-economic conditions are interrelated with its neighbors, acknowledging the presence of spatial interactions between objects [20]. Additionally, as 3S technology has matured in recent years, spatial measurement methods have gradually become mainstream in regional economics [21,22]. Spatial measurement methods can simultaneously use cross-sectional and sequence data, providing abundant information for regression analyses and explaining spatial effects.…”

Section: Analysis Of Influencing Factors Of Innovation Efficiency 221...mentioning

confidence: 99%

Spatio-Temporal Evolution and Mechanism Analysis of China’s Regional Innovation Efficiency

Xiang

Wei

et al. 2021

Sustainability

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Improving regional innovation efficiency is the key to developing an innovative country. Exploring the spatio-temporal evolution characteristics of regional innovation efficiency is crucial in the formulation of regional policies and the choice of innovation models. This study used the superdata envelopment analysis method with undesirable outputs in evaluating the innovation efficiency of Chinese provinces. To assess the spatial spillover effects of innovation factors, the spatial autocorrelation and spatial Durbin model were adopted to characterize the spatio-temporal evolution, spatial correlation, and mechanisms of innovation efficiency. The highlights of the results are as follows: (1) The time-series changes in innovation efficiency showed a general trend from declining to increasing. (2) There were pronounced regional differences in innovation efficiency. The innovation efficiencies at the provincial level evolved from being decentralized to concentrated. The innovation efficiency was relatively stable in the eastern region and increased significantly in the central and western regions. The east–center–west evolution pattern gradually weakened. (3) The innovative efficiency exhibited spatial dependence, and the spatial agglomeration continued to increase. The extent of hot spots expanded, while cold spots shrunk slightly. (4) The scientific research environment, entrepreneurial environment, labor quality, and market environment were the essential elements that improved innovation efficiency. The impact of the different factors on innovation efficiency at different periods exhibited significant spatial heterogeneity.

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“…The theory of flow space provides a methodological basis for the construction of urban green innovation networks. The accurate description of network relationships serves as a basis for investigating the structural complexity, evolutionary expansion, spatial directivity, and micro-self-organization effects for inter-city green innovation networks [31]. The existing research is usually based on relevant data of a certain kind and it often employs a gravity model or VAR model to simulate and calculate the green innovation relationships between cities.…”

Section: Green Innovation Networkmentioning

confidence: 99%

Analysis on Evolution Characteristics and Dynamic Mechanism of Urban Green Innovation Network: A Case Study of Yangtze River Economic Belt

Ren-jie¹,

Tai

Cheng

et al. 2021

Sustainability

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This study puts forward a logical framework for green innovation network analysis, which includes a spatial dimension, a relational dimension, and a systems dimension. Here, we put forward some basic research ideas concerning the optimization and regulation of green innovation networks in terms of the systems dimension and we investigate the micro-dynamic mechanisms of green innovation network expansion using a spatial econometric model. Our main research results are as follows: The efficiency of green innovation in the Yangtze River Economic Belt has improved significantly, however, the gap between cities has gradually increased, and a problem of efficiency regression has emerged. The green innovation network has changed from the primary stage dominated by Edge Network to the rapid growth stage dominated by Supporting Network, and formed a complex network pattern with diversified hierarchical structure. Node symmetry is helpful in forming more extroverted connections and promoting the expansion of green innovation networks. Node proximity and connection symmetry inhibit the growth and development of networks, and knowledge flow cooperation networks can accelerate the evolution of green innovation networks. Finally, this paper holds that we should combine the actual development needs, emphasize the basic principles of differentiated development, and construct the development pattern of regional collaborative innovation. This can also provide a theoretical reference for enriching our understanding of green innovation networks while narrowing the gap between cities.

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Spatial spillover effect of green innovation on economic development quality in China: Evidence from a panel data of 270 prefecture-level and above cities

Cited by 143 publications

References 57 publications

Digital Economy Development and Urban Green Innovation CA-Pability: Based on Panel Data of 274 Prefecture-Level Cities in China

Digital Economy Development and Urban Green Innovation CA-Pability: Based on Panel Data of 274 Prefecture-Level Cities in China

Spatio-Temporal Evolution and Mechanism Analysis of China’s Regional Innovation Efficiency

Analysis on Evolution Characteristics and Dynamic Mechanism of Urban Green Innovation Network: A Case Study of Yangtze River Economic Belt

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