Carbon footprint assessment in manufacturing: Methodologies, limitations, and a future MBD-enabled digital framework

Manufacturing remains one of the largest contributors to global greenhouse gas (GHG) emissions, making accurate and comparable carbon-footprint (CFP) assessments essential for achieving net-zero and circular-economy goals. Over the past two decades, process-based Life Cycle Assessment (LCA), input-output analysis (IOA), hybrid approaches, and international standards such as ISO 14067:2018 and PAS 2050:2011 have shaped the field. Although each approach provides valuable insights, differences in data quality, system-boundary definitions, and allocation rules often produce inconsistent results, limiting their usefulness for policy and industrial decision-making. This structured scoping review synthesizes the current state of CFP methodologies in manufacturing, highlighting their strengths, limitations, and emerging developments, including dynamic and spatially resolved models. Relevant literature was identified through structured searches of Scopus, Web of Science Core Collection, Engineering Village/Compendex, and IEEE Xplore for publications issued between January 2000 and December 2025. Overall, the reviewed methodologies involve different trade-offs among product-level detail, supply chain coverage, data requirements, and temporal or spatial representativeness, and no single approach is suitable for all manufacturing applications. However, existing reviews have not sufficiently explained how recurring methodological limitations in manufacturing CFP assessment, particularly inconsistent system boundaries, allocation choices, data quality, uncertainty treatment, and fragmented product information, can be translated into specific digital information requirements for design-integrated assessment. Unlike previous reviews that examine CFP methodologies and digital-engineering technologies separately, this review connects the methodological requirements of manufacturing CFP assessment with the information structures provided by Model-Based Definition (MBD). On this basis, the review develops a future-oriented framework in which machine-readable product data, external environmental databases, and traceable assessment parameters could support partially automated and design-integrated CFP evaluation. Practical implementation nevertheless remains constrained by semantic misalignment between CAD/MBD and LCA data structures, reliance on proprietary environmental databases, limited interoperability among commercial tools, and data quality and version control requirements. AI-assisted EF estimation may help address data gaps but requires transparent validation and expert oversight. MBD is therefore positioned not as a replacement for established LCA methods, but as an enabling information structure whose implementation and industrial validation remain areas for future research.