Forecasting End-of-Life Vehicle Generation in the EU-27: A Hybrid LSTM-Based Forecasting and Grey Systems Theory-Based Backcasting Approach

Abstract

End-of-life vehicle (ELV) forecasting constitutes a crucial aspect of sustainable waste management and resource allocation strategies. While the existing literature predominantly employs time-series forecasting and machine learning methodologies, a dearth of studies leveraging deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, is evident. Moreover, the focus on localized contexts within national or municipal boundaries overlooks the imperative of addressing ELV generation dynamics at an international scale, particularly within entities such as the EU-27. Furthermore, the absence of methodologies to reconcile missing historical data presents a significant limitation in forecasting accuracy. In response to these critical gaps, this study proposes a pioneering framework that integrates grey systems theory (GST)-based backcasting with LSTM-based deep learning methodologies for forecasting ELV generation within the EU until 2040. By introducing this innovative approach, this study not only extends the methodological repertoire within the field but also enhances the applicability of findings to supranational regulatory frameworks. Moreover, the incorporation of backcasting techniques addresses data limitations, ensuring more robust and accurate forecasting outcomes. The results indicate an anticipated decline in the recovery and recycling of ELVs, underscoring the urgent need for intervention by policymakers and stakeholders in the waste management sector. Through these contributions, this study enriches our understanding of ELV generation dynamics and facilitates informed decision-making processes in environmental sustainability and resource management domains.