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<p>The transition to electrified drivetrains fundamentally reshapes material demand and future secondary raw material supply. However, existing vehicle composition data are often fragmented or proprietary. This study presents a harmonized, open-access dataset detailing the material and elemental composition of the European passenger car fleet (M1 category) from 1980 to 2050. Using integrated top-down and bottom-up methodologies, we provide high-resolution data for six drivetrain types and 13 vehicle segments, including crossover utility vehicles and battery-electric models. The dataset links products, components, materials, and elements (p-c-m-e), with standardized alloy specifications and quantified uncertainty. Technical validation confirms high internal consistency, with Pearson correlation coefficients (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(r\)</EquationSource><EquationSource Format="MATHML"><math><mi>r</mi></math></EquationSource></InlineEquation>) between independent modeling approaches exceeding 0.96 for total metal, aluminum, and iron/steel content. This dataset supports robust stock-and-flow modeling and recoverability analysis beyond mass-based indicators. By aligning with emerging regulatory tools like Digital Product Passports, it enables evidence-based resource strategies and circular economy planning for critical and strategic raw materials.</p>

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Material Shifts and Trends: A Comprehensive Dataset of European Car Fleet Composition, 1980–2050

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Matthias Rösslein,
  • Manuele Capelli,
  • Max Tippner,
  • Franziska Maisel,
  • Kirsten Remmen

摘要

The transition to electrified drivetrains fundamentally reshapes material demand and future secondary raw material supply. However, existing vehicle composition data are often fragmented or proprietary. This study presents a harmonized, open-access dataset detailing the material and elemental composition of the European passenger car fleet (M1 category) from 1980 to 2050. Using integrated top-down and bottom-up methodologies, we provide high-resolution data for six drivetrain types and 13 vehicle segments, including crossover utility vehicles and battery-electric models. The dataset links products, components, materials, and elements (p-c-m-e), with standardized alloy specifications and quantified uncertainty. Technical validation confirms high internal consistency, with Pearson correlation coefficients (\(r\)r) between independent modeling approaches exceeding 0.96 for total metal, aluminum, and iron/steel content. This dataset supports robust stock-and-flow modeling and recoverability analysis beyond mass-based indicators. By aligning with emerging regulatory tools like Digital Product Passports, it enables evidence-based resource strategies and circular economy planning for critical and strategic raw materials.