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.

The growing use of electrical and electronic appliances, coupled with shorter product lifespans, has accelerated the rise in waste electrical and electronic equipment (WEEE). Accurate forecasting is essential for addressing environmental challenges, conserving resources, and advancing the circular economy (CE). This research employs a Trigonometry-Based Discrete Grey Model (TBDGM(1,1)) that integrates the Jaya algorithm and Least Squares Estimation (LSE) for parameter estimation. By leveraging Jaya’s parameter-free robustness and LSE’s computational efficiency, the model enhances prediction accuracy for small-sample and nonlinear datasets. WEEE data from Washington State (WA) in the USA and Türkiye are utilized to validate the model, demonstrating cross-context adaptability. To evaluate performance, the model is benchmarked against five state-of-the-art discrete grey models. For the WA dataset, additional benchmarking against methods used in prior e-waste forecasting literature enables a dual-layer comparative analysis, which strengthens the validity and practical relevance of the approach. Across evaluations and multiple performance metrics, TBDGM(1,1) attains satisfactory and competitive prediction performance on the WA and Türkiye datasets relative to comparator models. Using TBDGM(1,1), Türkiye’s e-waste is forecast for 2021–2030, with the 2030 amount projected at approximately 489 kilotones. The findings provide valuable insights for policymakers and researchers, offering a standardized and reliable forecasting tool that supports CE-driven strategies in e-waste management.

The rapid increase in the use of solar photovoltaic (PV) panels emphasizes the crucial role of renewable energy sources in meeting energy needs while reducing the environmental impact of fossil fuels. Consequently, managing the end-of-life (EOL) of these panels has gained importance, as the growing number of EOL PV panels brings significant waste management concerns. Major solar energy markets are projected to generate considerable PV waste by 2050; however, recycling infrastructure remains inadequate in many regions. Given the high value of materials in PV panels, including base metals like aluminum, critical metals such as indium and gallium, and hazardous metals like lead and cadmium, adopting recycling approaches that align with circular economy is increasingly essential. This chapter provides a comprehensive review of EOL solar PV panel management, examining studies on PV waste estimation, exploring recycling processes, material composition and relevant policies, analyzing key challenges and opportunities, and offering recommendations for sustainable practices.

This paper proposes a novel metaphor-free optimization algorithm called Approach-Avoidance Optimization Algorithm (AAOA) to solve real-world optimization problems. AAOA is built on the principle that candidate solutions should move towards better solutions and away from worse ones. The algorithm utilizes an effective collective search strategy by enabling randomized variable-level information exchange among candidate solutions, thereby facilitating a comprehensive and efficient exploration of the search space. Due to its simplicity, ease of implementation, and parameter-free nature, AAOA is particularly suitable for users without deep expertise in optimization. The performance of AAOA was evaluated through extensive experiments on the CEC 2017 benchmark functions across various dimensions, as well as on 30 real-world data clustering problems. Its performance was compared against eight metaphor-free algorithms using different evaluation criteria. Experimental results demonstrated that AAOA achieved competitive or superior performance in most cases, highlighting its potential as a robust and practical tool for high-dimensional and complex optimization tasks. This study contributes to the growing body of research on simple yet effective optimization algorithms that do not rely on metaphorical analogies.