Comparative analysis of material footprints in electricity generation of deep learning-based prediction model and energy development scenarios

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dc.authorid0000-0002-2016-8674
dc.authorid0000-0001-5980-9152
dc.authorid0000-0003-2665-737X
dc.authorid0000-0001-6334-444X
dc.authorid0000-0002-5545-0717
dc.contributor.authorAlgorabi, Ömer
dc.contributor.authorNamlı, Ersin
dc.contributor.authorÜnlü, Ramazan
dc.contributor.authorAktoğ, Mehmet Arif
dc.contributor.authorKocakaya, Mustafa Nabi
dc.contributor.authorÇelik, Yasin
dc.date.accessioned2026-10-15T12:44:21Z
dc.date.available2026-10-15T12:44:21Z
dc.date.issued2026
dc.departmentAÇÜ, Sanat ve Tasarım Fakültesi, Mimarlık Bölümü
dc.description.abstractEscalating 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.
dc.identifier.doi10.1177/0958305X261429190
dc.identifier.issn0958305X
dc.identifier.scopus2-s2.0-105034189577
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/11494/6729
dc.indekslendigikaynakScopus
dc.institutionauthorÇelik, Yasin
dc.institutionauthorid0000-0002-5545-0717
dc.language.isoen
dc.publisherSAGE Publications Inc.
dc.relation.ispartofEnergy and Environment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectDeep learning forecasting
dc.subjectElectricity generation
dc.subjectEnergy development plans
dc.subjectMaterial footprint
dc.subjectSustainability
dc.titleComparative analysis of material footprints in electricity generation of deep learning-based prediction model and energy development scenarios
dc.typeArticle

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