Investigating the Spatial Heterogeneity and Correlation Network of Green Innovation Efficiency in China

Abstract

With environmental problems becoming increasingly serious worldwide, scholars’ research views on innovation have begun to pay more attention to the technological value from an ecological perspective, instead of simply analyzing the importance of technological innovation from the perspective of economic value. Currently, improving green innovation efficiency (GIE) has been considered as a critical path to realizing economic transformation and green development. Based on the global Super-Epsilon-based measure (EBM) model, Moran index, vector autoregression (VAR) model, and block model, this study investigated the temporal and spatial characteristics of GIE in 30 provinces in China from 2009 to 2017, and analyzed the spatial heterogeneity and spatial correlation network characteristics. The results showed that in spatial terms, China’s GIE presented an extremely unbalanced development model. In provinces with a higher GIE, there was an overall improvement of GIE, but there was a lower impact in provinces with a lower GIE. The efficiency of China’s green innovation could be divided into four blocks. The first block was the main overflow, the second block was the broker, the third block was the bilateral spillover, and the fourth block was the net benefit. The four blocks had their own functions, and a very significant correlation was observed among them.

Evaluation of Green Development Efficiency of the Major Cities in Gansu Province, China

“…As the economy of China develops, environmental challenges facing the country are on the rise, depicting the idea of substituting economic growth for a green [1] and sustainable environment [78]. A green economy utilizes less water [13] and minimizes pollution through the discharge of wastewater and other forms of pollutants [29]. The discharge of excessive industrial wastewater is prominent in Jiayuguan and Pingliang cities.…”

“…Based on panel data from 28 provinces in China and using research and development expenditure input as a key indicator, [28] found differences in green development efficiency (GDE) of the provinces with Beijing (1.273) and Shanxi (0.219) ranking the highest and lowest, respectively. Similarly, [29] reported that in spatial terms, China's green innovation efficiency is unbalanced across the 30 provinces considered in their study from 2009-2017. Green innovation efficiency is higher in the eastern region than the national level, while the other regions are central > western > northeastern.…”

A social network analysis in dynamic evaluate critical industries based on input-output data of China

“…(1) Network density: Network density is used to evaluate the extent of the inter-industry correlation in the network [ 38 ]. The greater the number of association relationships in the network, the greater the network density [ 39 ]. The value of density ranges from 0 to 1, and the closer it is to 1, the greater the network density is.…”

Coupling coordination degree and influencing factors of green science and technology innovation efficiency and digital economy level: Evidence from provincial panel data in China

“…For instance, using the method of the Dagum Gini coefficient and its subgroup decomposition, kernel density estimation, and the spatial Markov chain, Yao et al (2022) discussed the convergence characteristics and dynamic evolution rule of industrial green technology innovation efficiency in 110 cities of the Yangtze River Economic Belt from 2006 to 2020 (Yao et al, 2022). Utilizing the combination of the global Super-Epsilon-based measure (EBM) model, vector autoregression model, block model, and Moran index, Wang and Zhang (2021) conducted an empirical study on the spatial-temporal features of the green innovation efficiency of China's 30 provinces and analyzed its spatial heterogeneity and spatial correlation network characteristics (Wang and Zhang, 2021). Moreover, some scholars have conducted studies on the spatial differences in green innovation efficiency using different scales.…”