Measuring Green Innovation Efficiency for China’s High-Tech Manufacturing Industry: A Network DEA Approach

Deng, Qunzhao; Zhou, Shuzhen; Feng, Peng

doi:10.1155/2020/8902416

Cited by 18 publications

(13 citation statements)

References 43 publications

(59 reference statements)

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“…To overcome the deficiencies of previous studies, the present study introduced a social network analysis to analyze the spatial correlation network characteristics of China's GIE. Social network analysis is an interdisciplinary analysis method for network relationships, which has been widely used in the fields of economics and management [6,19,51,52]. The social network analysis method was selected to study China's GIE based mainly on the following reasons [19].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Wang

Zhang

2021

Sustainability

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Wang

Zhang

2021

Sustainability

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“…For TI, the knowledge stock can be calculated using the perpetual inventory method; RD and DT can be calculated using the same method for measuring TI [53]. FDI is expressed by the proportion of foreign capital in the paid-in capital of the industry [54].…”

Section: Datamentioning

confidence: 99%

Effect of International Technology Transfer on the Technical Efficiency of High‐Tech Manufacturing in China: A RAGA‐PP‐SFA Analysis

2021

Self Cite

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The sustainable development of China’s high-tech manufacturing (HTM) sector is restricted by dependence on technology introduction and foreign direct investment (FDI), low input-output efficiency, and environmental pollution. This study aimed to examine the roles of technology introduction and FDI in improving the technical efficiency of Chinese HTM from an environmental perspective. By integrating stochastic frontier analysis (SFA) and projection pursuit (PP) based on the real-coded accelerated genetic algorithm (RAGA), this study constructed a RAGA-PP-SFA model that considers undesirable outputs. This model includes various outputs, including environmental pollution, in the production function to improve estimation accuracy. Moreover, to verify the robustness of the estimation results, the results were provided when environmental pollutants were taken as input factors. The results showed that technology introduction could significantly promote HTM’s technical efficiency, while FDI had no significant positive effect. By comparing the estimated results with those that did not consider environmental pollution, this study not only reveals different roles of technology introduction and FDI in improving HTM’s technical efficiency but also confirms that ignoring environmental pollution will overestimate their roles (especially the role of FDI) in such improvement.

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“…However, the GIE in most provinces is improving, and there are significant regional differences [18][19][20]. In addition, some scholars have evaluated the GIE of Chinese industrial enterprises and high-tech manufacturing from the perspective of the innovation value chain, and discussed the GIE in the R&D phase and the achievement transformation phase [21,22]. Zhong et al.…”

Section: Introductionmentioning

confidence: 99%

The Spatial Temporal Evolution Pattern and Influencing Factors of Green Innovation Efficiency: Based on Provincial Panel Data of Chinese Industrial Enterprises

Wang¹,

Gong-li²,

Wang³

2022

Pol. J. Environ. Stud.

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Based on the panel data of 30 provinces in China from 2009 to 2017, the Super-SBM model with undesirable output is used to measure the green innovation efficiency (GIE) of Chinese industrial enterprises, and the Moran's I is used to analyze the spatial correlation. Then, spatialtemporal distribution characteristics are analyzed. Finally, the spatial panel model is used to examine the influencing factors of GIE. The results show that the GIE of Chinese industrial enterprises is at a low level, but it shows an upward trend in the time dimension. The changing trends of industrial enterprise's GIE in various regions are different. The GIE of industrial enterprises in eastern China is changing in a wave-like manner. The central and western are on an upward trend, which is consistent with the overall. Spatially, the GIE of industrial enterprises decreases from east to west. Most of the areas where the GIE of industrial enterprises is above the mid-high level are located in the southeast coast. The green innovation efficiency of industrial enterprises in various provinces has an obvious positive spatial correlation, but it has weakened in recent years. The level of economic development, environmental regulations, opening to the outside world, and technological innovation environment have a positive impact on the green innovation efficiency of industrial enterprises, while the level of urbanization has a significant negative impact on it. At last, this paper presents recommendations for the development of green innovation efficiency of Chinese industrial enterprises according to the findings.

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Measuring Green Innovation Efficiency for China’s High-Tech Manufacturing Industry: A Network DEA Approach

Cited by 18 publications

References 43 publications

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

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

Effect of International Technology Transfer on the Technical Efficiency of High‐Tech Manufacturing in China: A RAGA‐PP‐SFA Analysis

The Spatial Temporal Evolution Pattern and Influencing Factors of Green Innovation Efficiency: Based on Provincial Panel Data of Chinese Industrial Enterprises

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