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

ESG Modeling and Prediction Uncertainty of Electronic Waste

“…In their study, Ozsut Bogar and Gungor [94] employed the Distribution Delay (DD) method to calculate the waste mobile phone (WMP) quantity in Turkey between 2001 and 2020. Furthermore, the authors applied seven different time series methods (namely, simple exponential smoothing, Holt's, logistics, Gompertz, logarithmic, Bass, and ARIMA models) and calculated the MAPE and RMSE values for each method.…”

“…For the given data, also provided in Table 1, Holt's method was determined to be the best method to forecast the WMP quantities for the years from 2021 to 2035. This study builds on Ozsut Bogar and Gungor's [94] study and proposes an optimized univariate nonlinear grey Bernoulli model with fractional order accumulation. Nonlinear grey Bernoulli models are essentially the integration of the Bernoulli distribution to traditional grey models and are generally applied to handle series with saturated regions such as the s-curve or sigmoid [60,61,95,96].…”

“…Hence, the nonlinear grey Bernoulli model with fractional order accumulation is applied to obtain higher accuracy in the e-waste prediction model. Similarly, to Ozsut Bogar and Gungor's [94] study, the data from 2001 to 2020 are employed for modeling using Equation (1) to Equation (32). The corresponding RMSEs (via Equation (33)) are then computed.…”

“…The PSO configuration and the range for associated variables, i.e., background value coefficient p, exponential coefficient m, and fractional order value r, are provided in Table 2. Similarly, to Ozsut Bogar and Gungor's [94] study, the data from 2001 to 2020 are employed for modeling using Equation (1) to Equation (32). The corresponding RMSEs (via Equation (33)) are then computed.…”

“…In their study, Ozsut Bogar and Gungor [94] utilized various forecasting methods and compared the obtained RMSE values. Our proposed model outperforms its counterparts discussed in Ozsut Bogar and Gungor's [94] study. They provided forecasted WMP quantities for 2001-2035 using Holt's method.…”