The effect of manufacturing intelligence on green innovation performance in China
Haochang Yang,
Lianshui Li and
Yaobin Liu
Technological Forecasting and Social Change, 2022, vol. 178, issue C
Abstract:
Accelerating the intelligent transformation of manufacturing industry is an important strategic choice to realize the green innovation transformation. Based on the perspectives of static efficiency and dynamic productivity, this paper analyzes the effect of manufacturing intelligence on green innovation performance and its internal mechanism from theoretical and empirical levels by using the dynamic spatial lag model (DSAR), mediating effect model and moderating effect model. The results show that: In the whole country, manufacturing intelligence has a significant promotion effect on green innovation performance; The reason why manufacturing intelligence can promote the improvement of green innovation performance is that manufacturing intelligence is conducive to the production of "technology promotion effect" and "cost reduction effect", so as to promote green technology innovation, then effectively increase the desirable outputs and significantly decrease the undesirable outputs; The effect of manufacturing intelligence on green innovation performance has obvious regional heterogeneity: the improvement effect of manufacturing intelligence on green innovation performance in the eastern region is significantly higher than that in the central and western regions. In addition, further analysis shows that green technological progress rather than green technical efficiency is the main driving force for manufacturing intelligence to improve the dynamic green innovation performance.
Keywords: Manufacturing intelligence; Green innovation performance; Dynamic spatial lag model (DSAR); Mediating effect model; Regional heterogeneity; Moderating effect model (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (37)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:178:y:2022:i:c:s0040162522001019
DOI: 10.1016/j.techfore.2022.121569
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