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Forecasting Waste Mobile Phone (WMP) Quantity and Evaluating the Potential Contribution to the Circular Economy: A Case Study of Turkey

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  • Zeynep Ozsut Bogar

    (Department of Industrial Engineering, Faculty of Engineering, Pamukkale University, 20160 Denizli, Turkey)

  • Askiner Gungor

    (Department of Industrial Engineering, Faculty of Engineering, Pamukkale University, 20160 Denizli, Turkey)

Abstract

Information and communication technology (ICT)-based products have a significant effect on increasing levels of waste electrical and electronic equipment (WEEE) or electronic waste (e-waste) due to their shorter lifespan as a result of rapid technological changes. Mobile phones are the most popular ICT products, and their market share is increasing gradually. Therefore, effective management of waste mobile phones (WMP) is sought as their recovery brings enormous economic and regulatory benefits. Forecasting the quantities of WMP and their recoverable material content generates valuable data for the related stakeholders in the circular economy (CE) in the design and management of their supply chain networks. This paper presents an approach to determining the WMP quantity for Turkey considering the system from sales to end-of-life (EOL) stages and the years between 2001 and 2035. The proposed model includes two main parts: estimation and forecasting. Firstly, the generated WMP quantity is estimated based on dynamic lifespan and sales using the Distribution Delay (DD) Method considering the years from 2001 to 2020. To select the most suitable model for future projection, seven different time series methods (e.g., Simple Exponential Smoothing, Holt’s, Logistics, Gompertz, Logarithmic, Bass, and ARIMA models) are considered to estimate the generated WMP. For the given data, the Holt’s Method is determined to be the best method to forecast the WMP quantities for the years from 2021 to 2035. In addition, waste materials amount and revenue potentials are estimated for the years from 2001 to 2035. The WMP for Turkey is expected to be approximately 11.5 million units and has a 52 million US$ revenue potential in 2035. The present study contributes to the literature, as it is the first holistic forecasting study on the quantification of WMPs in Turkey. Moreover, since WMPs include remarkable recovery potential in terms of CE, the data and findings of this study may help policymakers, governments, producers, consumers, and all stakeholders to establish effective e-waste management approaches.

Suggested Citation

  • Zeynep Ozsut Bogar & Askiner Gungor, 2023. "Forecasting Waste Mobile Phone (WMP) Quantity and Evaluating the Potential Contribution to the Circular Economy: A Case Study of Turkey," Sustainability, MDPI, vol. 15(4), pages 1-38, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3104-:d:1062118
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    1. Idiano D’Adamo & Paolo Rosa & Sergio Terzi, 2016. "Challenges in Waste Electrical and Electronic Equipment Management: A Profitability Assessment in Three European Countries," Sustainability, MDPI, vol. 8(7), pages 1-19, July.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Cucchiella, Federica & D’Adamo, Idiano & Lenny Koh, S.C. & Rosa, Paolo, 2015. "Recycling of WEEEs: An economic assessment of present and future e-waste streams," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 263-272.
    4. Walter R. Stahel, 2016. "The circular economy," Nature, Nature, vol. 531(7595), pages 435-438, March.
    5. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    6. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    7. Ikhlayel, Mahdi, 2016. "Differences of methods to estimate generation of waste electrical and electronic equipment for developing countries: Jordan as a case study," Resources, Conservation & Recycling, Elsevier, vol. 108(C), pages 134-139.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. Oguchi, Masahiro & Kameya, Takashi & Yagi, Suguru & Urano, Kohei, 2008. "Product flow analysis of various consumer durables in Japan," Resources, Conservation & Recycling, Elsevier, vol. 52(3), pages 463-480.
    10. Esther Thiébaud (†Müller) & Lorenz M. Hilty & Mathias Schluep & Rolf Widmer & Martin Faulstich, 2018. "Service Lifetime, Storage Time, and Disposal Pathways of Electronic Equipment: A Swiss Case Study," Journal of Industrial Ecology, Yale University, vol. 22(1), pages 196-208, February.
    11. Park, Jeong-a & Hong, Seok-jin & Kim, Ik & Lee, Ji-yong & Hur, Tak, 2011. "Dynamic material flow analysis of steel resources in Korea," Resources, Conservation & Recycling, Elsevier, vol. 55(4), pages 456-462.
    12. Yan, Lingyu & Wang, Anjian & Chen, Qishen & Li, Jianwu, 2013. "Dynamic material flow analysis of zinc resources in China," Resources, Conservation & Recycling, Elsevier, vol. 75(C), pages 23-31.
    13. Yvonne Ryan‐Fogarty & Damian Coughlan & Colin Fitzpatrick, 2021. "Quantifying WEEE arising in scrap metal collections: Method development and application in Ireland," Journal of Industrial Ecology, Yale University, vol. 25(4), pages 1021-1033, August.
    14. Sohal, Amrik & De Vass, Tharaka, 2022. "Australian SME's experience in transitioning to circular economy," Journal of Business Research, Elsevier, vol. 142(C), pages 594-604.
    15. Merve Sahan & Mehmet Ali Kucuker & Burak Demirel & Kerstin Kuchta & Andrew Hursthouse, 2019. "Determination of Metal Content of Waste Mobile Phones and Estimation of Their Recovery Potential in Turkey," IJERPH, MDPI, vol. 16(5), pages 1-14, March.
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    Cited by:

    1. Gazi Murat Duman & Elif Kongar, 2023. "ESG Modeling and Prediction Uncertainty of Electronic Waste," Sustainability, MDPI, vol. 15(14), pages 1-20, July.

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