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.
Highlights
China’s economic development and technological innovation capabilities are at the world advanced level, but while achieving rapid economic development in the past, the development model of high pollution, high energy consumption, and low efficiency has exerted a huge impact on the ecological environment [7]
The remainder of this paper is structured as follows: Section 2 presents the literature review; Section 3 explains the research methods; Section 4 analyzes the spatial heterogeneity of green innovation efficiency (GIE); Section 5 analyzes the spatial correlation network of GIE; Section 6 concludes with the main findings and policy implications
The Augmented Dickey–Fuller (ADF) test on the GIE data revealed that the original data were not stable, but the data became stable after the first-order difference
Summary
Since the reform and opening up, China’s economy has maintained rapid growth [1], and China’s GDP as a proportion of the world’s total economy has continued to rise, ranking second in the world [2]. Based on social network analysis, this paper analyzes the structural characteristics of the spatial correlation network of China’s green innovation efficiency in order to better understand the status and role of China’s provinces in the spatial correlation network of green innovation development. From the perspectives of relational data and network analysis, this paper makes a clearer judgment of the correlation between green innovation efficiency in different provinces and reveals the overall characteristics of the correlation network structure of green innovation efficiency in China This aims to reduce the difference in green innovation efficiency between regions and promote the sustainable and coordinated development of green innovation between regions. The remainder of this paper is structured as follows: Section 2 presents the literature review; Section 3 explains the research methods; Section 4 analyzes the spatial heterogeneity of GIE; Section 5 analyzes the spatial correlation network of GIE; Section 6 concludes with the main findings and policy implications
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