An Automated Tool for Freight Carbon Footprint Estimation: Insights from an Automotive Case Study

Reducing carbon dioxide emissions in freight transportation is considered a key objective in contemporary sustainable supply chain management. While several tools and standards have been developed to estimate transport-related emissions, most rely on static assumptions, generic emission factors and are limited to single-scenario evaluation. Therefore, their operational applicability remains restricted especially in dynamic and complex environments where fast responsiveness is essential. Moreover, these tools are often disconnected from real-world constraints and rarely incorporate expert’s input. To address this gap, this study introduces a hybrid decision-support CO2 assessment framework combining theoretical models with field-based inputs. The proposed approach combines structured interviews conducted with 300 supply chain consultants and is operationalized through a dynamic digital tool that enables users to simulate multiple scenarios simultaneously. The tool accounts for critical variables including transport mode, routing distance, vehicle configuration, and shipment characteristics, thereby enabling a contextualized and flexible analysis of carbon emissions. A validation case study was conducted to confirm the applicability of the tool to industrial settings. Computational results show significant variation in emissions across different routing strategies and modal configurations, highlighting the tool’s capacity to support environmentally informed decisions. This research offers both a replicable methodology and a practical contribution: a user-centered, multi-scenario tool that improves the accuracy, adaptability, and strategic value of CO2 emission calculations in freight transport planning.