Liu, Zheng, Hu, Bin, Zhao, Yuanjun, Lang, Lingling, Guo, Hangxin, Florence, Kelly and Zhang, Shuai ORCID: https://orcid.org/0000-0002-9796-058X (2020) Research on intelligent decision of low carbon supply chain based on carbon tax constraints in human-driven edge computing. IEEE Access, 8. pp. 48264-48273. ISSN 2169-3536 (Online) (doi:10.1109/ACCESS.2020.2978911)

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Abstract

Edge computing moves data and storage to one end of edge nodes. The advantages of direct data collection and intelligent analysis are gradually being considered as disruptive technologies to promote social progress. Many fields and industries are exploring the use of edge technologies. To achieve the goal of improving efficiency and optimizing business models, the supply chain is one of the areas where edge computing technology can be prioritized. Therefore, the organization and coordination of the supply chain must take into account both energy saving and emission reduction and intelligent decision-making effects. This paper establishes a basic decision-making model for the supply chain under the carbon tax constraint and compares and analyzes the optimal decision making problem of the supply chain between the centralized and decentralized decisions of producers and retailers under the carbon tax constraint. Then, the supply chain optimization under the three conditions of considering the repurchase contract, the subsidy policy and the joint strategy of both the repurchase and the subsidy under the constraint of carbon tax are discussed. Research shows that carbon tax can play a role in reducing carbon emissions, but for some industries with smaller profit margins, relying solely on carbon tax policy may lead to reduced benefits and make business development difficult. Therefore, considering the combined strategy of repurchase and subsidy at the same time, the dual goals of emission reduction and economic benefits can be achieved.

Item Type:	Article
Uncontrolled Keywords:	edge computing, carbon tax constraint, low carbon supply chain, intelligent decision
Subjects:	H Social Sciences > H Social Sciences (General)
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of Systems Management & Strategy Faculty of Business > Networks and Urban Systems Centre (NUSC) Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group
Last Modified:	21 Oct 2020 15:21
URI:	http://gala.gre.ac.uk/id/eprint/27334

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