Does artificial intelligence improve enterprise carbon emission performance? Evidence from an intelligent transformation policy in China

Jianlong Wang, Yong Liu, Weilong Wang and Haitao Wu ()

Technology in Society, 2024, vol. 79, issue C

Abstract: In the pursuit of climate change mitigation and carbon neutrality, climate policy uncertainty (CPU) poses a threat to enterprises' green, low-carbon, and sustainable development. The intelligent transformation of enterprises is a crucial strategy for addressing climate risks and enhancing energy efficiency. However, there is a lack of research on how intelligent transformation impacts low-carbon development from the perspective of micro-enterprises. Based on this gap, we analyze data from Shanghai and Shenzhen A-share listed manufacturing enterprises from 2010 to 2022 to empirically test the impact of intelligent manufacturing (IM) on enterprise carbon emission performance (ECEP) using a difference-in-differences model. We also explore the moderating effect of IM on the relationship between CPU and ECEP. Our findings indicate that IM significantly enhances ECEP. IM boosts the ECEP of enterprises in the eastern region, state-owned enterprises, and capital- and technology-intensive sectors. Green technological innovation, human capital, and organizational resilience are key mechanisms through which IM enhances ECEP. Further analysis reveals that CPU significantly inhibits ECEP, whereas IM positively moderates the impact of CPU. In the context of external environmental uncertainty, this study offers crucial insights into how intelligent technology can strengthen the real economy and facilitate the low-carbon transformation of manufacturing enterprises.

Keywords: Intelligent manufacturing; Climate policy uncertainty; Enterprise carbon emission performance; Difference-in-differences model; Impact mechanism; Moderating effect (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:79:y:2024:i:c:s0160791x24002999

DOI: 10.1016/j.techsoc.2024.102751

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