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<p>Scope 3 emissions, particularly those arising from transportation activities, pose a substantial challenge for manufacturing organisations seeking to reduce their carbon footprint. This study presents the development of a carbon dioxide emissions estimation tool designed to enhance the accuracy and practical application of emissions measurement for inbound transport within the supply chain. The tool integrates key variables, namely vehicle type, distance travelled, and load carried and employs Monte Carlo simulation to account for data uncertainty. Using empirical data from a manufacturing firm, the study evaluates the emissions impacts of various transportation scenarios. Results indicate that distance travelled and vehicle load are the primary determinants of emissions magnitude. While larger vehicles offer improved efficiency when fully utilised, emissions per unit of goods transported depend on load optimisation and vehicle selection. The analysis further reveals the considerable variability introduced by multi-stop routing and uneven load distribution, underscoring the complexity of real-world logistics. These insights underscore the value of scenario-based modelling for emissions estimation and provide a foundation for developing more effective decarbonisation strategies in transport logistics.</p>

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Assessing scope 3 transportation emissions: A calculation tool for sustainable manufacturing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月3日

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  • Konstantinos Salonitis,
  • John Patsavellas,
  • Cristina Garcia Llubia

摘要

Scope 3 emissions, particularly those arising from transportation activities, pose a substantial challenge for manufacturing organisations seeking to reduce their carbon footprint. This study presents the development of a carbon dioxide emissions estimation tool designed to enhance the accuracy and practical application of emissions measurement for inbound transport within the supply chain. The tool integrates key variables, namely vehicle type, distance travelled, and load carried and employs Monte Carlo simulation to account for data uncertainty. Using empirical data from a manufacturing firm, the study evaluates the emissions impacts of various transportation scenarios. Results indicate that distance travelled and vehicle load are the primary determinants of emissions magnitude. While larger vehicles offer improved efficiency when fully utilised, emissions per unit of goods transported depend on load optimisation and vehicle selection. The analysis further reveals the considerable variability introduced by multi-stop routing and uneven load distribution, underscoring the complexity of real-world logistics. These insights underscore the value of scenario-based modelling for emissions estimation and provide a foundation for developing more effective decarbonisation strategies in transport logistics.