The effect of manufacturing intelligence on green innovation performance in China

Yang, Haochang; Li, Lianshui; Liu, Yaobin

doi:10.1016/j.techfore.2022.121569

Cited by 122 publications

(53 citation statements)

References 37 publications

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“…From the perspective of innovation input-output analysis, it can be seen that there is still a big difference between China's innovation performance and the manufacturing powers. Therefore, China's manufacturing industry still has a long way to go in terms of improving its innovation performance [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

Yang

Zhu

2022

Sustainability

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Green innovation, which combines “innovation-driven” and “green development,” is one of the most powerful ways to overcome resource and environmental constraints and enhance manufacturing industry sustainability. Based on the innovation value chain perspective, the green innovation process of manufacturing industry is decomposed into two stages: green scientific and technological R&D and achievement transformation. Then, using the three-stage DEA and Malmquist index model to measure the green innovation performance of China’s manufacturing industry, and compare its regional heterogeneity from the dual perspectives of static efficiency and dynamic productivity. In addition, this paper further discusses the improvement path of green innovation performance of China’s manufacturing industry. The findings are as follows: (1)The green innovation efficiency of manufacturing industry in China is at a comparatively low degree and has great potential for improvement. Moreover, it shows apparent regional heterogeneity: The green innovation efficiency in the eastern region is higher than that in the western region, and both are higher than that in the center region, confirming the phenomenon of “central collapse”. (2)The green innovation productivity of China’s manufacturing industry shows a “W-type” dynamic evolution tendency, with green technological progress as the key driving factor, while the green technical efficiency does not clearly exhibit a “catch-up effect”. Additionally, it shows significant regional heterogeneity: green innovation productivity in the western region is higher than that in the central and eastern regions, indicating a potential “backwardness advantage”. (3) The eastern region of China is located in combination IV, which indicates that it has a high rate of green innovation efficiency but a low rate of green innovation productivity; the central region is located in combination III, which indicates that it has a low rate of both green innovation efficiency and productivity; and the western region is located in combination II, which indicates that it has a low rate of green innovation efficiency but a high rate of green innovation productivity. Last but not least, this paper puts forward three kinds of paths for the improvement of the green innovation performance of China’s manufacturing industry: unilateral breakthrough, step-by-step and stimulating jumping type.

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Section: Introductionmentioning

confidence: 99%

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

Yang

Zhu

2022

Sustainability

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“…Industrial structure. The secondary industry, which emits a large amount of CO 2 and pollutants, is the main demander of green innovation [65]. The ratio of the added value of the secondary industry to GDP is selected as an indicator of industrial structure.…”

Section: ) Control Variablesmentioning

confidence: 99%

“…This initially suggests a spatial correlation of GETI in China, but it needs to be further examined. According to Wang et al [70] and Yang et al [65], the global Moran's I index is selected to uncover the spatial autocorrelation of GETI, and the results are displayed in Table 5. It is clear that there is a positive spatial autocorrelation of GETI between regions, and it is significant at least at the 5% level.…”

Section: Spatial Correlation Testmentioning

confidence: 99%

The Impact of Environmental Regulation on Green Energy Technology Innovation—Evidence from China

2022

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Green energy technology innovation (GETI) is a crucial path to achieve sustainable development. However, few studies have examined the determinants of GETI, especially from the perspective of environmental regulation. To fill this gap, this study investigates the impact of environmental regulation on GETI using the panel data of 30 provinces in Mainland China from 2001 to 2018. We first measure GETI by the latest IPC codes and patent data, and then adopt a dynamic spatial Durbin model (DSDM) to examine the relationship between environmental regulation and GETI. The main conclusions are summarized as follows: (1) There is a significant inverted U-shaped relationship between environmental regulation and GETI; (2) heterogeneity analysis shows that the inverted U-shaped correlation not only exists between environmental regulations and different types of GETI, but also exists between environmental regulations and GETI in different regions. Moreover, the results also show that the spatial spillover effect and path-dependent effect exist in all cases. The findings can provide reference for policymakers to formulate more precise environmental policies. That is, environmental policies in a province should be formulated based on its position on the inverted U-shaped curve. More specifically, when it is on the left side of the inflection point, it is reasonable to strengthen environmental policies, and when it is on the right side of the inflection point, appropriate relaxation of environmental policies should be considered.

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“…Yang et al analyzed the link between producing intelligence and green innovation performance and its internal mechanism using a dynamic space model. He found that manufacturing digitization and intelligence have a positive relationship with green innovation performance [13]. Sun et al found that Internet technology penetration contains a positive abstraction spillover impact through a study on the issue impact of the country's regional green innovation capabilities [14].…”

Section: Introductionmentioning

confidence: 99%

“…Mechanism test results of digitalization on green innovation performance. locales[41]; the impact of digitalization in fringe urban on green innovation is more critical than in focal urban areas. It shows that with regard to the energetic advancement of the digital economy, it is more necessary for cities in relatively disadvantaged areas to carry out in-depth cooperation and innovation and actively integrate into the innovation network.…”

mentioning

confidence: 99%

The Impact of Digitization on Green Innovation Performance: Evidence Based on Panel Data of 228 Prefectural‐Level Cities in China

Jia

Zhao

et al. 2022

Complexity

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At present, in the context of the era of energy and digital reform enabling the “dual carbon” goal, in order to improve the performance of urban green innovation, digitalization has become more and more important. Based on panel data, taking 228 prefecture-level cities in China from 2003 to 2019 as an example, this study expounds three dimensions of promoting green innovation performance in the internal mechanism of digital development: direct transmission mechanism, indirect transmission mechanism, and regional heterogeneity transmission mechanism. This article uses mechanism analysis and adopts a dynamic spatial panel model and systematic GMM method to empirically test the growth effect of green innovation performance endowed by digitization. This article found that green innovation performance showed regional heterogeneity and urban heterogeneity after adding carbon dioxide emissions from energy consumption, and digital development significantly improved the level of innovation performance. Further research found that digitalization has a positive impact on technological innovation, and technological innovation also has a positive impact on green innovation performance. And technological innovation plays a mediating role between digitalization and green innovation performance. Different intensities of environmental regulation play a positive moderating role in the impact of digitalization on green innovation performance.

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The effect of manufacturing intelligence on green innovation performance in China

Cited by 122 publications

References 37 publications

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

The Impact of Environmental Regulation on Green Energy Technology Innovation—Evidence from China

The Impact of Digitization on Green Innovation Performance: Evidence Based on Panel Data of 228 Prefectural‐Level Cities in China

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