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Corporate Social Responsibility Practices in the U.S.: Using Reverse Supply Chain Network Design and Optimization Considering Carbon Cost

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  • Bandar Alkhayyal

    (Department of Engineering Management, College of Engineering, Prince Sultan University, Riyadh 12435, Saudi Arabia)

Abstract

A research model using the market price for greenhouse gas (GHG) emissions illustrates how the policies, and economic and environment implications of the carbon price can be formulated using a deterministic equilibrium model. However, with increasing carbon costs, the optimal reverse supply chain (RSC) system is being required to adapt and has undergone many distinct shifts in character as it seeks out new configurations through which costs may be effectively managed and minimized. The model was studied comprehensively in terms of quantitative performance using orthogonal arrays. The results were compared to top-down estimates produced through economic input-output life cycle assessment (EIO-LCA) models, providing a basis to contrast remanufacturing GHG emission quantities with those realized through original equipment manufacturing operations. Introducing a carbon cost of $40/t CO2e increased modeled remanufacturing costs by 2.7%, but also increased original equipment costs by 2.3%. The research presented in this study puts forward the theoretical modeling of optimal RSC systems and provides an empirical case study concerning remanufactured appliances, an area of current industrial literature in which there is a dearth of study.

Suggested Citation

  • Bandar Alkhayyal, 2019. "Corporate Social Responsibility Practices in the U.S.: Using Reverse Supply Chain Network Design and Optimization Considering Carbon Cost," Sustainability, MDPI, vol. 11(7), pages 1-22, April.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2097-:d:221020
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    Cited by:

    1. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    2. V. Sathiya & M. Chinnadurai & S. Ramabalan & Andrea Appolloni, 2021. "Mobile robots and evolutionary optimization algorithms for green supply chain management in a used-car resale company," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9110-9138, June.
    3. Shouxu Song & Yongting Tian & Dan Zhou, 2021. "Reverse Logistics Network Design and Simulation for Automatic Teller Machines Based on Carbon Emission and Economic Benefits: A Study of the Anhui Province ATMs Industry," Sustainability, MDPI, vol. 13(20), pages 1-24, October.
    4. Ruozhen Qiu & Shunpeng Shi & Yue Sun, 2019. "A p -Robust Green Supply Chain Network Design Model under Uncertain Carbon Price and Demand," Sustainability, MDPI, vol. 11(21), pages 1-22, October.

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