IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i24p11074-d1545945.html
   My bibliography  Save this article

Carbon Tax Refund System for Recycling in Reverse Supply Chain Network to Minimize GHG Emissions and Costs

Author

Listed:
  • Haruto Takeshita

    (Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan)

  • Yuki Kinoshita

    (Department of Informatics, Faculty of Engineering, Kindai University, 1 Takaya Umenobe, Higashi-Hiroshima, Hiroshima 739-2116, Japan)

  • Tetsuo Yamada

    (Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan)

Abstract

Material recycling is vital for achieving carbon neutrality because using recycled materials helps avoid greenhouse gas (GHG) emissions that result from using virgin material. Carbon tax has been introduced in many countries to reduce GHG emissions. As recycling can prevent additional GHG emissions, the carbon tax should be refunded based on the GHG volume saved by recycling. The incentive of carbon tax refund can help promote recycling as an environment-friendly and economical activity. To retrieve material values from end-of-life (EOL) products, a reverse supply chain network should be designed based on the status and value of EOL products. This study introduces carbon tax refund into the reverse supply chain network for maximizing saved GHG emissions and minimizing cost. The bi-objective model is formulated using ϵ constraint method and integer programming. Numerical experiments were conducted based on the recycling of a vacuum cleaner and a laptop. The monetary rate at which the carbon tax refund became economically attractive differed according to product type. Thus, variable carbon tax refund rates would be needed, based on product type, to incentivize recycling.

Suggested Citation

  • Haruto Takeshita & Yuki Kinoshita & Tetsuo Yamada, 2024. "Carbon Tax Refund System for Recycling in Reverse Supply Chain Network to Minimize GHG Emissions and Costs," Sustainability, MDPI, vol. 16(24), pages 1-17, December.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11074-:d:1545945
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/24/11074/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/24/11074/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Haddadsisakht, Ali & Ryan, Sarah M., 2018. "Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax," International Journal of Production Economics, Elsevier, vol. 195(C), pages 118-131.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wanke, Peter Fernandes & Chiappetta Jabbour, Charbel José & Moreira Antunes, Jorge Junio & Lopes de Sousa Jabbour, Ana Beatriz & Roubaud, David & Sobreiro, Vinicius Amorim & Santibanez Gonzalez‬, Erne, 2021. "An original information entropy-based quantitative evaluation model for low-carbon operations in an emerging market," International Journal of Production Economics, Elsevier, vol. 234(C).
    2. Yang, Yuxiang & Goodarzi, Shadi & Jabbarzadeh, Armin & Fahimnia, Behnam, 2022. "In-house production and outsourcing under different emissions reduction regulations: An equilibrium decision model for global supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    3. Belhadi, Amine & Venkatesh, Mani & Kamble, Sachin & Abedin, Mohammad Zoynul, 2024. "Data-driven digital transformation for supply chain carbon neutrality: Insights from cross-sector supply chain," International Journal of Production Economics, Elsevier, vol. 270(C).
    4. Pazoki, Mostafa & Samarghandi, Hamed, 2020. "Take-back regulation: Remanufacturing or Eco-design?," International Journal of Production Economics, Elsevier, vol. 227(C).
    5. Yanfen Mu & Feng Niu, 2022. "To Be or Not to Be? Strategic Analysis of Carbon Tax Guiding Manufacturers to Choose Low-Carbon Technology," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    6. Fu, Shuaishuai & Chen, Weida & Ding, Junfei & Zhang, Guoqing, 2023. "Is carbon asset pledge financing contributing to the operation of emission-dependent engineering machinery remanufacturing with emission abatement?," International Journal of Production Economics, Elsevier, vol. 266(C).
    7. Ying Ji & Jianhui Du & Xiaoqing Wu & Zhong Wu & Deqiang Qu & Dan Yang, 2021. "Robust optimization approach to two-echelon agricultural cold chain logistics considering carbon emission and stochastic demand," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13731-13754, September.
    8. Tong Shu & Qian Liu & Shou Chen & Shouyang Wang & Kin Keung Lai, 2018. "Pricing Decisions of CSR Closed-Loop Supply Chains with Carbon Emission Constraints," Sustainability, MDPI, vol. 10(12), pages 1-25, November.
    9. Tao, Yi & Wu, Jianhuang & Lai, Xiaofan & Wang, Fan, 2020. "Network planning and operation of sustainable closed-loop supply chains in emerging markets: Retail market configurations and carbon policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    10. Qiang Guo & Li He & Yi He, 2022. "Omnichannel service operations with order‐online‐and‐dine‐in‐store strategy," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2311-2325, September.
    11. Mingqiang Yin & Min Huang & Xiaohu Qian & Dazhi Wang & Xingwei Wang & Loo Hay Lee, 2023. "Fourth-party logistics network design with service time constraint under stochastic demand," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1203-1227, March.
    12. Zhitao Xu & Shaligram Pokharel & Adel Elomri, 2023. "An eco-friendly closed-loop supply chain facing demand and carbon price uncertainty," Annals of Operations Research, Springer, vol. 320(2), pages 1041-1067, January.
    13. Biswajit Debnath & Rihab El-Hassani & Amit K Chattopadhyay & T Krishna Kumar & Sadhan K Ghosh & Rahul Baidya, 2022. "Time Evolution of a Supply Chain Network: Kinetic Modeling," Papers 2209.01138, arXiv.org.
    14. Yu, Min & Cruz, Jose M. & Li, Dong & Masoumi, Amir H., 2022. "A multiperiod competitive supply chain framework with environmental policies and investments in sustainable operations," European Journal of Operational Research, Elsevier, vol. 300(1), pages 112-123.
    15. Gajanan B. Panchal & Hassan Mirzahosseinian & Sunil Tiwari & Ajay Kumar & Sachin Kumar Mangla, 2023. "Supply chain network redesign problem for major beverage organization in ASEAN region," Annals of Operations Research, Springer, vol. 324(1), pages 1067-1098, May.
    16. Zhitao Xu & Adel Elomri & Shaligram Pokharel & Fatih Mutlu, 2019. "The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models," Sustainability, MDPI, vol. 11(11), pages 1-20, May.
    17. Shraddha Mishra & Surya Prakash Singh, 2022. "A stochastic disaster-resilient and sustainable reverse logistics model in big data environment," Annals of Operations Research, Springer, vol. 319(1), pages 853-884, December.
    18. Mahsa Taherifar & Negin Hasani & Mahsa Zokaee & Amir Aghsami & Fariborz Jolai, 2024. "A scenario-based sustainable dual-channel closed-loop supply chain design with pickup and delivery considering social conditions in a natural disaster under uncertainty: a real-life case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(8), pages 19443-19490, August.
    19. Malladi, Krishna Teja & Sowlati, Taraneh, 2020. "Bi-objective optimization of biomass supply chains considering carbon pricing policies," Applied Energy, Elsevier, vol. 264(C).
    20. Alzaman, Chaher & Zhang, Zhi-Hai & Diabat, Ali, 2018. "Supply chain network design with direct and indirect production costs: Hybrid gradient and local search based heuristics," International Journal of Production Economics, Elsevier, vol. 203(C), pages 203-215.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11074-:d:1545945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.