Evaluating Carbon Footprint Calculation Methodologies in Product Manufacturing

Wednesday, October 22, 2025: 3:30 PM
Mrs. Dima Yassine Sibai , University of Michigan Dearborn, Dearborn, MI
Mr. Ali Kassab , University of Michigan - Dearborn, Dearborn, MI
Dr. Pravansu Mohanty , University of Michigan-Dearborn, Dearborn, MI
Prof. Christopher Pannier , University of Michigan, 2240 Engineering complex, MI
Prof. Georges Ayoub, PhD , University of Michigan - Dearborn, Dearborn, MI
Human activities, particularly industrial processes and product manufacturing, are major sources of greenhouse gas (GHG) emissions, contributing to climate change and environmental degradation. The carbon footprint concept, which quantifies GHG emissions throughout a product's life cycle, has become essential in understanding and mitigating environmental impacts. Accurately calculating these emissions is especially critical during the design phase, where designers can identify emission hotspots and implement sustainable practices.

This work critically reviews key methodologies used in carbon footprint calculations within manufacturing, including Life Cycle Assessment, Input-Output Analysis, and carbon footprint standards. While these approaches provide valuable insights, they exhibit significant limitations, such as data inconsistencies, allocation challenges, geographical variability, and differing system boundary definitions. These discrepancies can lead to variations in carbon footprint assessments, complicating comparisons across designs and manufacturing processes.

To address these challenges, this work proposes a universal methodology for computing carbon footprint that is integrated into the early design phase. This integration would not only enhance transparency and consistency in carbon footprint calculations but also improve the comparability of results across design decisions and manufacturing processes. By embedding this methodology early in the design process, designers can make informed, data-driven decisions to minimize environmental impact while optimizing product performance.