Green supply chain transformation and emission reduction based on machine learning

Wu, Tao; Zuo, Minxin

doi:10.1177/00368504231165679

Cited by 5 publications

(3 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the following paper, Wu and Zuo 12 consider whether AI techniques provide more possibilities for supply chain transformation to overcome the adverse effects of global warming and environmental degradation. A Cournot game model with an upgrading ML technology is employed to evaluate the use of green technologies of two competing supply chains.…”

Section: Contributions Of Collectionsmentioning

confidence: 99%

Special collections for applying artificial intelligence techniques to encourage economic growth and maintain sustainable societies

Nanping,

Lee,

Chen

et al. 2024

Science Progress

View full text Add to dashboard Cite

Efficient resource use requires substantial evaluation and re-evaluation of production processes. Since not all production details can be properly and timely monitored and adjusted, improvement of resource allocation has long been a critical issue in commodity production, technique selection, and sustainable development. The progress of artificial intelligence (AI) offers a possibility. For example, with deep learning techniques and extensive data analysis, most previously unincorporated or unknown information associated with a firm's activities can be appropriately reflected and calculated. Once the firms take the report, a more efficient and economically friendly production strategy could be made. The central theme of this special collection is to invite studies on how the design and application of AI benefit not only the fields of computer science and information engineering but also the interdisciplinary fields, including renewable energy development, environmental protection, and economic analysis. Fourteen papers are published in this special collection.

show abstract

Section: Contributions Of Collectionsmentioning

confidence: 99%

Special collections for applying artificial intelligence techniques to encourage economic growth and maintain sustainable societies

Nanping,

Lee,

Chen

et al. 2024

Science Progress

View full text Add to dashboard Cite

show abstract

“…By comprehensively understanding and implementing these factors and dimensions, businesses can mitigate environmental impact and gain a competitive edge in the market. Wu and Zuo [ 29 ] investigated the Cournaud game model involving two competitive supply chains with different carbon emission technologies and the potential for improving machine learning technology. The findings revealed that the market equilibrium generated by the duopoly model remained unaffected by the threat of machine learning technology upgrades.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Liu,

Guan,

Wang

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…The use of AI technology can improve energy efficiency and reduce resource waste, thereby realizing low carbon development [15]. In addition, AI technologies provide more opportunities for traditional supply chains to transition to green supply chains by reducing carbon emissions [16]. However, scholars have opposing views on this topic.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation

Chen,

Jin

2023

Processes

View full text Add to dashboard Cite

Carbon emissions have gained worldwide attention in the industrial era. As a key carbon-emitting industry, achieving net-zero carbon emissions in the manufacturing sector is vital to mitigating the negative effects of climate change and achieving sustainable development. The rise of intelligent technologies has driven industrial structural transformations that may help achieve carbon reduction. Artificial intelligence (AI) technology is an important part of digitalization, providing new technological tools and directions for the low carbon development of enterprises. This study selects Chinese A-share listed companies in the manufacturing industry from 2012 to 2021 as the research objects and uses a fixed-effects regression model to study the relationship between AI and carbon emissions. This study clarifies the significance of enterprise AI technology applications in realizing carbon emissions reduction and explores the regulatory mechanism from the perspective of the innovation effect. The results show that the application of enterprise AI technology positively impacts carbon emissions reduction. Simultaneously, green technological innovation, green management innovation, and green product innovation play moderating roles; in other words, enterprise green innovation strengthens the effect of AI on carbon emissions reduction. This study clarifies the necessity of intelligent manufacturing and enriches theories related to AI technology and carbon emissions.

show abstract

Green supply chain transformation and emission reduction based on machine learning

Cited by 5 publications

References 54 publications

Special collections for applying artificial intelligence techniques to encourage economic growth and maintain sustainable societies

Special collections for applying artificial intelligence techniques to encourage economic growth and maintain sustainable societies

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation

Contact Info

Product

Resources

About