Reduction of carbon emissions under sustainable supply chain management with uncertain human learning
Customers' growing concern for environmentally friendly goods and services has created a competitive and environmentally responsible business scenario. This global awareness of a green environment has motivated several researchers and companies to work on reducing carbon emissions and sustainab...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-09-01
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| Series: | AIMS Environmental Science |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/environsci.2023032?viewType=HTML |
| _version_ | 1797366760564850688 |
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| author | Richi Singh Dharmendra Yadav S.R. Singh Ashok Kumar Biswajit Sarkar |
| author_facet | Richi Singh Dharmendra Yadav S.R. Singh Ashok Kumar Biswajit Sarkar |
| author_sort | Richi Singh |
| collection | DOAJ |
| description | Customers' growing concern for environmentally friendly goods and services has created a competitive and environmentally responsible business scenario. This global awareness of a green environment has motivated several researchers and companies to work on reducing carbon emissions and sustainable supply chain management. This study explores a sustainable supply chain system in the context of an imperfect flexible production system with a single manufacturer and multiple competitive retailers. It aims to reduce the carbon footprints of the developed system through uncertain human learning. Three carbon regulation policies are designed to control carbon emissions caused by various supply chain activities. Despite the retailers being competitive in nature, the smart production system with a sustainable supply chain and two-level screening reduces carbon emissions effectively with maximum profit. Obtained results explore the significance of uncertain human learning, and the total profit of the system increases to 0.039% and 2.23%, respectively. A comparative study of the model under different carbon regulatory policies shows a successful reduction in carbon emissions (beyond 20%), which meets the motive of this research. |
| first_indexed | 2024-03-08T17:09:18Z |
| format | Article |
| id | doaj.art-e34ee162d400439997ca70f600e7d4cd |
| institution | Directory Open Access Journal |
| issn | 2372-0352 |
| language | English |
| last_indexed | 2024-03-08T17:09:18Z |
| publishDate | 2023-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Environmental Science |
| spelling | doaj.art-e34ee162d400439997ca70f600e7d4cd2024-01-04T02:36:58ZengAIMS PressAIMS Environmental Science2372-03522023-09-0110455959210.3934/environsci.2023032Reduction of carbon emissions under sustainable supply chain management with uncertain human learningRichi Singh0Dharmendra Yadav1S.R. Singh2Ashok Kumar 3Biswajit Sarkar 41. Department of Mathematics, Meerut College, Meerut, Uttar Pradesh 250003, India2. Department of Mathematics, Vardhaman College, Bijnor, Uttar Pradesh 246701, India3. Department of Mathematics, Chaudhary Charan Singh University, Meerut, Uttar Pradesh 250001, India1. Department of Mathematics, Meerut College, Meerut, Uttar Pradesh 250003, India4. Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, South Korea 5. Center for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, 600077, IndiaCustomers' growing concern for environmentally friendly goods and services has created a competitive and environmentally responsible business scenario. This global awareness of a green environment has motivated several researchers and companies to work on reducing carbon emissions and sustainable supply chain management. This study explores a sustainable supply chain system in the context of an imperfect flexible production system with a single manufacturer and multiple competitive retailers. It aims to reduce the carbon footprints of the developed system through uncertain human learning. Three carbon regulation policies are designed to control carbon emissions caused by various supply chain activities. Despite the retailers being competitive in nature, the smart production system with a sustainable supply chain and two-level screening reduces carbon emissions effectively with maximum profit. Obtained results explore the significance of uncertain human learning, and the total profit of the system increases to 0.039% and 2.23%, respectively. A comparative study of the model under different carbon regulatory policies shows a successful reduction in carbon emissions (beyond 20%), which meets the motive of this research.https://www.aimspress.com/article/doi/10.3934/environsci.2023032?viewType=HTMLsupply chain managementsmart productiondeteriorating productsemission reductionuncertain human learning |
| spellingShingle | Richi Singh Dharmendra Yadav S.R. Singh Ashok Kumar Biswajit Sarkar Reduction of carbon emissions under sustainable supply chain management with uncertain human learning AIMS Environmental Science supply chain management smart production deteriorating products emission reduction uncertain human learning |
| title | Reduction of carbon emissions under sustainable supply chain management with uncertain human learning |
| title_full | Reduction of carbon emissions under sustainable supply chain management with uncertain human learning |
| title_fullStr | Reduction of carbon emissions under sustainable supply chain management with uncertain human learning |
| title_full_unstemmed | Reduction of carbon emissions under sustainable supply chain management with uncertain human learning |
| title_short | Reduction of carbon emissions under sustainable supply chain management with uncertain human learning |
| title_sort | reduction of carbon emissions under sustainable supply chain management with uncertain human learning |
| topic | supply chain management smart production deteriorating products emission reduction uncertain human learning |
| url | https://www.aimspress.com/article/doi/10.3934/environsci.2023032?viewType=HTML |
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