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How Does Intelligent Manufacturing Affect the ESG Performance of Manufacturing Firms? Evidence from China
It is no longer possible for China’s economy to grow by relying on the rapid expansion of manufacturing. On the one hand, China’s previous rough manufacturing development pattern seriously harmed the environment. On the other hand, China’s manufacturing productivity and international competitiveness have decreased as a result of the disappearance of demographic dividends and growing labor costs. China’s manufacturing firms must simultaneously increase productivity while lowering environmental pollution. This study, which takes intelligent manufacturing pilot demonstration projects as a quasi-natural experiment, investigates the impact of intelligent manufacturing (IM) on environmental, social and governance (ESG) performance using data from 2149 listed manufacturing firms in China from 2009 to 2021. The results indicate that ESG performance of the listed firms could be improved using IM. The heterogeneity test reveals that IM in non-state-owned firms helps to improve ESG performance at the 1% significance level, while the effect is not significant in state-owned firms. Moreover, the effect in eastern China is significant at the 1% level and at the 5% level in western China, but not significant in central and northeastern China. The two channels through which IM improves corporate ESG performance are promoting innovation investment and improving the quality of the information environment. This study also verifies that both internal and external supervision could strengthen the positive impact of IM on corporate ESG performance, which provides empirical evidence for strengthening the supervision of manufacturing firms. The conclusions of the study reveal the internal force of manufacturing firms to improve ESG performance and also provide theoretical support for their implementation of IM projects.
How Does Intelligent Manufacturing Affect the ESG Performance of Manufacturing Firms? Evidence from China
It is no longer possible for China’s economy to grow by relying on the rapid expansion of manufacturing. On the one hand, China’s previous rough manufacturing development pattern seriously harmed the environment. On the other hand, China’s manufacturing productivity and international competitiveness have decreased as a result of the disappearance of demographic dividends and growing labor costs. China’s manufacturing firms must simultaneously increase productivity while lowering environmental pollution. This study, which takes intelligent manufacturing pilot demonstration projects as a quasi-natural experiment, investigates the impact of intelligent manufacturing (IM) on environmental, social and governance (ESG) performance using data from 2149 listed manufacturing firms in China from 2009 to 2021. The results indicate that ESG performance of the listed firms could be improved using IM. The heterogeneity test reveals that IM in non-state-owned firms helps to improve ESG performance at the 1% significance level, while the effect is not significant in state-owned firms. Moreover, the effect in eastern China is significant at the 1% level and at the 5% level in western China, but not significant in central and northeastern China. The two channels through which IM improves corporate ESG performance are promoting innovation investment and improving the quality of the information environment. This study also verifies that both internal and external supervision could strengthen the positive impact of IM on corporate ESG performance, which provides empirical evidence for strengthening the supervision of manufacturing firms. The conclusions of the study reveal the internal force of manufacturing firms to improve ESG performance and also provide theoretical support for their implementation of IM projects.
How Does Intelligent Manufacturing Affect the ESG Performance of Manufacturing Firms? Evidence from China
Lipeng Sun (author) / Nur Ashikin Mohd Saat (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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