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A GIS-based green supply chain model for assessing the effects of carbon price uncertainty on plastic recycling

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Listed:
  • Hongtao Ren
  • Wenji Zhou
  • Ying Guo
  • Lizhen Huang
  • Yongping Liu
  • Yadong Yu
  • Liyun Hong
  • Tieju Ma

Abstract

Recycling plastic can abate the environmental pollution as well as CO2 emissions by saving the carbon-intensive feedstock input. The uncertain carbon price places significant effects on the establishment and operation of the whole supply chain. This study develops a green supply chain model combined with geographic information system (GIS) to account for carbon price uncertainty and evaluate its effects on the closed-loop supply chain (CLSC) of plastic recycling. A two-stage stochastic programming model is constructed, in which the stochastic variable, CO2 price is modelled as a geometric Brownian motion process. Six scenarios are designed with respect to price expectation and volatility. A case study is performed with the GIS information of the plastic supply chain in Zhejiang province, China. The results illustrate that triggering the establishment of reverse logistics requires a carbon price threshold significantly beyond the current level. Lower price volatility would facilitate the decision-making of investment into the reverse logistics. Mechanisms to alleviate the market variation shall be introduced. A sound market condition is desired to obtain the optimal balance that encourages the CLSC without creating extra pressure on the firms. The proposed modelling framework can be easily applied to other sectors with similar characteristics.

Suggested Citation

  • Hongtao Ren & Wenji Zhou & Ying Guo & Lizhen Huang & Yongping Liu & Yadong Yu & Liyun Hong & Tieju Ma, 2020. "A GIS-based green supply chain model for assessing the effects of carbon price uncertainty on plastic recycling," International Journal of Production Research, Taylor & Francis Journals, vol. 58(6), pages 1705-1723, March.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:6:p:1705-1723
    DOI: 10.1080/00207543.2019.1693656
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    Citations

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    Cited by:

    1. Changping Zhao & Juanjuan Sun & Yun Zhang, 2022. "A Study of the Drivers of Decarbonization in the Plastics Supply Chain in the Post-COVID-19 Era," Sustainability, MDPI, vol. 14(23), pages 1-20, November.
    2. De Boeck, Kim & Decouttere, Catherine & Jónasson, Jónas Oddur & Vandaele, Nico, 2022. "Vaccine supply chains in resource-limited settings: Mitigating the impact of rainy season disruptions," European Journal of Operational Research, Elsevier, vol. 301(1), pages 300-317.
    3. Ding, Bingqing & Makowski, Marek & Nahorski, Zbigniew & Ren, Hongtao & Ma, Tieju, 2022. "Optimizing the technology pathway of China's liquid fuel production considering uncertain oil prices: A robust programming model," Energy Economics, Elsevier, vol. 115(C).
    4. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    5. Mohamed Kriouich & Hicham Sarir, 2024. "Artificial Intelligence Application in Production Scheduling Problem Systematic Literature Review: Bibliometric Analysis, Research Trend, and Knowledge Taxonomy," SN Operations Research Forum, Springer, vol. 5(2), pages 1-24, June.
    6. Hongtao Ren & Wenji Zhou & Hangzhou Wang & Bo Zhang & Tieju Ma, 2022. "An energy system optimization model accounting for the interrelations of multiple stochastic energy prices," Annals of Operations Research, Springer, vol. 316(1), pages 555-579, September.
    7. Guo, Ying & Zhou, Wenji & Ren, Hongtao & Yu, Yadong & Xu, Lei & Fuss, Maryegli, 2023. "Optimizing the aluminum supply chain network subject to the uncertainty of carbon emissions trading market," Resources Policy, Elsevier, vol. 80(C).
    8. Yang Hu, 2023. "Perspectives in closed-loop supply chains network design considering risk and uncertainty factors," Papers 2306.04819, arXiv.org.
    9. De Lima, Felipe Alexandre & Seuring, Stefan, 2023. "A Delphi study examining risk and uncertainty management in circular supply chains," International Journal of Production Economics, Elsevier, vol. 258(C).
    10. Rohit Agrawal & Vishal A. Wankhede & Anil Kumar & Sunil Luthra & Abhijit Majumdar & Yigit Kazancoglu, 2022. "An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling," Operations Management Research, Springer, vol. 15(3), pages 609-626, December.

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