Towards low‐carbon sustainable development under Industry 4.0: The influence of industrial intelligence on China's carbon mitigation

Hui Tian, Jiaqi Qin, Chaoyin Cheng, Sohail Ahmad Javeed and Tiansi Chu

Sustainable Development, 2024, vol. 32, issue 1, 455-480

Abstract: Global climate change is becoming an increasingly serious issue. China, the largest carbon emitter, has a long way towards its sustainable development goals (SDGs) and take on its carbon reduction responsibility, especially in the industrial sector. In this vein, industrial intelligence, a key driver in the “Industry 4.0 era”, offers an opportunity in this carbon reduction campaign. This paper seeks to examine the influences of industrial intelligence on carbon emissions, their heterogeneous characteristics and transmission mechanisms, and the moderating role of human capital. Therefore, we properly probed theoretical aspects, and then we used China's provincial data from 2006 to 2019 and a number of tests, namely the fixed effect model, the two‐stage test, and the moderating effect model, to test the relevant assumptions. We first unfolded that industrial intelligence can significantly mitigate carbon emissions. Specifically, the heterogeneous analysis found that carbon reduction efforts are more pronounced in China's central and western regions and resource‐based regions. We further highlight the mechanism identification, which reveals that industrial structure upgrading and technological innovation are the main channels for industrial intelligence to abate CO2 emissions. Importantly, we pinpoint the role of human capital, which positively moderates the relationship between industrial intelligence and carbon emissions and can well amplify the industrial structure effect of industrial intelligence. These results are conducive to accurately assessing the environmental benefits of industrial intelligence and providing policy enlightenment for China to alleviate carbon emissions, and they will also offer a reference for other developing countries to learn from.

Date: 2024
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https://doi.org/10.1002/sd.2664

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Persistent link: https://EconPapers.repec.org/RePEc:wly:sustdv:v:32:y:2024:i:1:p:455-480

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