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Green supply chain transformation and emission reduction based on machine learning

Artificial intelligence techniques provide more possibilities for supply chain transformations in the face of global warming and environmental degradation. This study examines the Cournot game model of two competing supply chains with various carbon emission technologies as well as the possibility o...

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Detalles Bibliográficos
Autores principales: Wu, Tao, Zuo, Minxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358532/
https://www.ncbi.nlm.nih.gov/pubmed/36972522
http://dx.doi.org/10.1177/00368504231165679
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author Wu, Tao
Zuo, Minxin
author_facet Wu, Tao
Zuo, Minxin
author_sort Wu, Tao
collection PubMed
description Artificial intelligence techniques provide more possibilities for supply chain transformations in the face of global warming and environmental degradation. This study examines the Cournot game model of two competing supply chains with various carbon emission technologies as well as the possibility of upgrading machine learning technology. The investment risk of a supply chain's technology upgrade is either symmetric or asymmetric information. In the case of symmetric information, results show that the machine learning technology upgrade risk does not affect the market equilibrium outcomes of the duopoly model. However, in the case of asymmetric information, technology upgrade risk is vital in determining the quantities and prices of competition equilibrium. To achieve the goal of green supply chain transformation, the government should provide more technology and financial support to traditional supply chains to upgrade their machine learning technology on carbon emissions.
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spelling pubmed-103585322023-08-09 Green supply chain transformation and emission reduction based on machine learning Wu, Tao Zuo, Minxin Sci Prog Applying Artificial Intelligence Techniques to Encourage Economic Growth and Maintain Sustainable Societies Artificial intelligence techniques provide more possibilities for supply chain transformations in the face of global warming and environmental degradation. This study examines the Cournot game model of two competing supply chains with various carbon emission technologies as well as the possibility of upgrading machine learning technology. The investment risk of a supply chain's technology upgrade is either symmetric or asymmetric information. In the case of symmetric information, results show that the machine learning technology upgrade risk does not affect the market equilibrium outcomes of the duopoly model. However, in the case of asymmetric information, technology upgrade risk is vital in determining the quantities and prices of competition equilibrium. To achieve the goal of green supply chain transformation, the government should provide more technology and financial support to traditional supply chains to upgrade their machine learning technology on carbon emissions. SAGE Publications 2023-03-27 /pmc/articles/PMC10358532/ /pubmed/36972522 http://dx.doi.org/10.1177/00368504231165679 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Applying Artificial Intelligence Techniques to Encourage Economic Growth and Maintain Sustainable Societies
Wu, Tao
Zuo, Minxin
Green supply chain transformation and emission reduction based on machine learning
title Green supply chain transformation and emission reduction based on machine learning
title_full Green supply chain transformation and emission reduction based on machine learning
title_fullStr Green supply chain transformation and emission reduction based on machine learning
title_full_unstemmed Green supply chain transformation and emission reduction based on machine learning
title_short Green supply chain transformation and emission reduction based on machine learning
title_sort green supply chain transformation and emission reduction based on machine learning
topic Applying Artificial Intelligence Techniques to Encourage Economic Growth and Maintain Sustainable Societies
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358532/
https://www.ncbi.nlm.nih.gov/pubmed/36972522
http://dx.doi.org/10.1177/00368504231165679
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