Comparative Analysis of Material Footprints in Electricity Generation of Deep Learning-Based Prediction Model and Energy Development Scenarios

Abstract

Escalating global production and consumption are driving rapid growth in energy demand, increasing pressure on finite natural resources. In response, this study proposes a data-driven framework that integrates deep learning-based electricity demand forecasting with economy-wide input-output material footprint analysis to support long-term energy planning and policymaking. The innovative aspect of this framework is its ability to jointly assess future electricity generation and related material requirements within a single analytical structure. A comparative analysis is conducted for Türkiye, Germany, and Spain, evaluating the material footprint of electricity generation across renewable and fossil-based energy sources under business-as-usual (BAU) and alternative energy development scenarios. The forecasting models demonstrate strong predictive performance, achieving Mean Absolute Percentage Error (MAPE) values of 1.39% for Türkiye, 4.39% for Germany, and 3.90% for Spain, significantly outperforming conventional statistical methods. Scenario-based results indicate that sustainability-oriented pathways (ST and GCA) can reduce material requirements by approximately 20-30% compared to the BAU scenario, particularly for metal-intensive inputs such as iron and refined oil. The findings underscore the importance of integrating material footprint considerations into energy transition strategies and provide practical insights for policymakers seeking to balance energy security with resource sustainability. The study highlights the value of integrated analytical approaches in supporting more resilient and resource-efficient energy systems.