Carbon Footprints in Manufacturing Are Broken – Digital Product Models Could Fix Them

Every product that rolls off a factory line carries an invisible cargo: the greenhouse gas emissions released while extracting its raw materials, shaping its components, assembling its parts, and shipping it around the world. Quantifying that cargo – the carbon footprint – has become one of the most consequential exercises in modern industry, underpinning everything from corporate net-zero pledges to the European Union’s Carbon Border Adjustment Mechanism. Yet according to a comprehensive review published in Cleaner Engineering and Technology, the methods used to calculate these footprints remain so fragmented and inconsistent that two analysts assessing essentially the same product can arrive at dramatically different answers. The review, led by Dima Yassine Sibai and colleagues, argues that the fix lies not in yet another calculation standard, but in a digital engineering revolution that embeds carbon data directly inside the 3D models engineers already use.

The carbon footprint concept emerged in the early 2000s as a practical way to link products, processes, and organizations to their greenhouse gas emissions, expressed in carbon dioxide equivalents. It was quickly institutionalized through two landmark frameworks. The Greenhouse Gas Protocol, published in 2004 by the World Resources Institute and the World Business Council for Sustainable Development, introduced the now-ubiquitous classification of emissions into Scope 1, Scope 2, and Scope 3 – direct emissions, purchased energy, and value-chain emissions respectively. Meanwhile, the ISO 14040 and 14044 standards formalized Life Cycle Assessment, or LCA, the rigorous methodology that tallies environmental burdens from raw material extraction through manufacturing, use, and disposal. Together these frameworks gave companies a common language for identifying emission hotspots, benchmarking performance, and setting reduction targets, and they now feed into national climate commitments, ESG reporting, and consumer carbon labels.

But the review identifies a persistent structural problem: the landscape is methodologically fragmented. Analysts must choose among process-based LCA, which offers detailed product-level accounting but suffers from truncation errors that omit indirect upstream emissions; input-output analysis, which captures economy-wide supply chains but relies on coarse sector averages; and hybrid models that combine both but demand intensive data and modeling effort. Newer variants push further – dynamic LCA incorporates time-dependent emission factors that track decarbonizing electricity grids, while spatially resolved methods capture regional differences in energy mixes. Each approach embodies a trade-off between specificity, completeness, data availability, and computational effort, and no single method dominates across all manufacturing contexts.

The consequences of this fragmentation are quantifiable. Differences in system boundaries, allocation rules for co-products, functional units, and emission-factor databases such as Ecoinvent, GaBi, and GREET can produce substantially different results for similar products. Sensitivity studies compiled in the review illustrate the scale of the problem: variability in the energy mix has driven differences of up to 50 percent in food-product footprints, while choices of allocation method alone shifted industrial results by as much as 40 percent. Uncertainty treatment compounds the issue. Simple one-at-a-time sensitivity analysis, widely used because it is cheap and intuitive, cannot capture interactions between variables. Monte Carlo simulation propagates uncertainty across thousands of scenarios but depends heavily on the quality of input distributions. Global sensitivity methods such as Sobol indices and the Morris method reveal which parameters and interactions actually drive variability, yet their computational demands limit routine industrial use. Critically, the review notes, uncertainty is usually assessed only after a footprint model has been built, rather than being woven into data selection and boundary definition from the start.

Scope 3 emissions present perhaps the thorniest challenge. For many organizations, value-chain emissions dominate the total footprint, yet they are the hardest to quantify because supply chains are complex, geographically dispersed, and data-poor. The review highlights a particularly troubling blind spot in battery manufacturing: conventional factory-level assessments frequently account for on-site cell assembly and pack integration while excluding the embodied carbon of cathode and anode active materials sourced from other countries – precisely the components that dominate a battery’s total emissions. The result is a systematic underestimation of the true carbon burden, obscured further by static, historical data that fail to reflect evolving energy systems and technological change.

The authors’ proposed remedy comes from an unexpected direction: the world of digital engineering. Model-Based Definition, or MBD, transforms the 3D CAD model into the authoritative digital representation of a product, embedding geometry, tolerances, materials, and manufacturing process metadata directly within the model as machine-readable information. Unlike traditional 2D drawings, an MBD model can be automatically interpreted by downstream applications – and, crucially, by LCA tools. When a designer changes a material, a tolerance, or a manufacturing feature, the associated metadata can instantly update the corresponding life cycle inventory entries and recalculate the footprint. In this vision, the product model becomes a digital carrier of sustainability data, bridging the long-standing disconnect between engineering design and environmental analysis.

The review situates MBD within a broader model-based ecosystem. Model-Based Systems Engineering formalizes system requirements and architecture, allowing environmental constraints such as emission targets and recyclability to be treated as design requirements subject to early trade-off analysis. The Model-Based Enterprise extends product data across the organization through standards like STEP AP242 and the Quality Information Framework, enabling a continuous digital thread from design intent to factory execution. Commercial tools such as SolidWorks Sustainability and Autodesk Insight already perform on-the-fly carbon estimates from model metadata, and academic workflows have predicted emissions for welded assemblies and machined parts with minimal manual intervention. In construction, Building Information Modeling systems similarly derive embodied-carbon inventories automatically, demonstrating the transferability of the approach.

To illustrate the potential, the authors sketch a hierarchical, bottom-up carbon-accounting framework for battery production. Each component – cathode, anode, electrolyte, separator, casing – is defined through MBD with embedded material and process metadata linked to life cycle inventory databases. Emissions roll up through assembly stages, then to the factory, the industrial zone, the city, and the national sector, preserving traceability at every level. In a worked example, the MBD-driven approach yields a cradle-to-grave footprint of roughly 88 kilograms of CO2-equivalent per kilowatt-hour of cell capacity for an NMC battery line – about 80 kilograms from materials plus 18 kilograms from process electricity – corresponding to approximately 17,600 tonnes of CO2-equivalent per year for a facility producing 200 megawatt-hours of cells. Conventional top-down estimates for comparable facilities, built from aggregated electricity, fuel, and waste statistics, typically report substantially lower values because they truncate upstream material production. The authors stress the MBD figure is not an upper bound but a physically grounded reconstruction of the true carbon burden.

The authors are careful to position the framework as a future research direction rather than a mature industrial system. MBD cannot resolve methodological choices such as system boundaries, allocation rules, or emission-factor selection – those remain matters of expert judgment. Significant obstacles remain, including semantic misalignment between CAD and LCA ontologies, boundary inconsistencies between design models and life cycle scopes, and the need for versioned, traceable emission-factor metadata. Fully automated inventory generation, AI-driven emission prediction, and continuously updated digital carbon twins all require further validation. The review calls for minimum machine-readable data requirements, standardized mapping mechanisms between product attributes and inventory flows, improved environmental-data traceability, and industrial case studies comparing MBD-enabled and conventional workflows.

If the vision matures, the implications extend well beyond compliance dashboards. A manufacturing world in which every design decision – a cathode chemistry swap, a more efficient drying oven, an optimized formation protocol – propagates instantly through a traceable digital hierarchy would transform carbon accounting from retrospective reporting into proactive, model-driven management. Designers could visualize trade-offs among performance, cost, and carbon intensity during the earliest phases of product development, when decisions carry the greatest leverage. Regulators would gain auditable, consistent data pipelines across supply chains. And the persistent, credibility-eroding discrepancies that have plagued carbon footprinting for two decades could finally give way to numbers that engineers, policymakers, and consumers can all trust – because they were built from the same digital truth as the product itself.

Subject of Research: Methodological limitations of carbon footprint assessment in manufacturing and a proposed model-based digital framework for design-integrated carbon accounting

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

Article References: Yassine Sibai, D., Kassab, A., Pannier, C., Ayoub, G. Y., & Mohanty, P. (2026). Carbon footprint assessment in manufacturing: Methodologies, limitations, and a future MBD-enabled digital framework. Cleaner Engineering and Technology, 34, Article 101325. https://doi.org/10.1016/j.clet.2026.101325

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101325

Keywords: carbon footprint, manufacturing, life cycle assessment, GHG Protocol, Model-Based Definition, digital thread, Scope 3 emissions, uncertainty analysis, sensitivity analysis, battery manufacturing, sustainable design, digital twins