The impact of artificial intelligence on green transformation of manufacturing enterprises: evidence from China
Zhengang Zhang (),
Peilun Li (),
Liangxiong Huang () and
Yichen Kang ()
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Zhengang Zhang: South China University of Technology
Peilun Li: South China University of Technology
Liangxiong Huang: South China University of Technology
Yichen Kang: South China University of Technology
Economic Change and Restructuring, 2024, vol. 57, issue 4, No 10, 36 pages
Abstract:
Abstract Artificial intelligence (AI) is emerging as a new driving force for green transformation of manufacturing enterprises. Drawing from panel data of manufacturing enterprises listed on China’s A-share market spanning from 2013 to 2019, this study finds that AI can promote green transformation of manufacturing enterprises. This finding maintains its robustness after re-measuring the core independent variable and the dependent variable, addressing endogeneity issues, excluding specific research sample, adding urban-level control variables, adding more fixed effects. The promotion effect emanates from the intermediary roles of enhanced managerial efficiency, reduced financial pressures, and the reinforcement of capabilities for green innovation. Furthermore, this study conducts heterogeneity analysis across three dimensions: corporate ownership, industry affiliation, and geographical location. The analysis discerns a heightened promotion effect when the manufacturing enterprises operate as non-state-owned enterprises, operate within labor-intensive and technology-intensive industries, or are geographically located in the eastern region. The findings offer insights to expedite the application of AI within manufacturing and propel the synergistic convergence of intelligence and sustainability in the sector.
Keywords: Artificial intelligence; Green transformation; Manufacturing enterprises; Managerial efficiency; Financial pressures; Green innovation (search for similar items in EconPapers)
JEL-codes: C12 D21 M11 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10644-024-09730-w
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