The concept of weighing the hidden material burden of products dates back to the early 1990s, when Friedrich Schmidt-Bleek introduced the MIPS method, Material Input Per Service unit, famously popularized as the ecological backpack. The metaphor captures the idea that every object carries a load of extracted natural resources far beyond the materials it visibly contains. Over the decades this idea matured into the material footprint, a member of the footprint family alongside the carbon and ecological footprints. At the national scale, material footprints are estimated through input-output analysis and attribute all globally mobilized resources to final consumers. At the product scale, the equivalent indicator is the Product Material Footprint, or PMF, typically calculated within a Life Cycle Assessment, the ISO 14040-standardized framework that inventories all inputs and outputs of a product system and then translates them into environmental impacts.

Herein lies a technical puzzle. In standard life cycle impact assessment, elementary flows drawn from the inventory are converted into impact scores using characterization factors, precomputed multipliers that link each flow to a normalized effect. For the material footprint, researchers at the Wuppertal Institute and later Mostert and Bringezu developed sets of characterization factors mapped onto ecoinvent’s resource flows, and the most recent update by Mostert and colleagues in 2025 represents the most comprehensive PMF method available. But this mapping approach has a weakness: characterization factors are typically aggregated globally, often as medians, and matching them to inventory flows introduces unknown errors, particularly for metal ores that are regionally underrepresented or absent. The Wuppertal team proposes an alternative they call direct PMF calculation, or D-PMF, which reads the resource extraction flows already documented in ecoinvent’s life cycle inventories and sums them with characterization factors of one, essentially trusting the database’s own bookkeeping rather than an external overlay.

The PMF consists of two impact categories with distinct meanings. Raw Material Input, or RMI, accounts for used extractions from the environment: all raw materials sold by mining, agriculture, forestry and fisheries that enter a product over its complete life cycle. Total Material Requirement, or TMR, goes further and includes unused extraction, the natural material that must be moved and dumped to enable extraction, such as overburden in coal mining or crop residues left in fields. For abiotic resources, the researchers exploited ecoinvent’s convention of recording metal and mineral extraction as the target material plus a separate gangue flow, so that the total extracted mass is captured. For the unused part, they created a new elementary flow called overburden, attached to the waste flows that ecoinvent uses for mining spoils. For biotic resources, where ecoinvent rarely documents biomass as an elementary flow, the team built on an approach by Stefan Pauliuk and used the gross calorific value of biogenic products, dividing the documented energy content by an average energy density of 19.5 megajoules per kilogram to estimate dry biomass. Unused biomass was estimated from residue-to-crop ratios found in the literature, with water content corrections for agricultural products.

The method was implemented as a life cycle impact assessment method in the open-source Python framework Brightway2, and the scripts were released on GitHub. The team then compared their direct calculation against the characterization-factor benchmark across a broad product set: fifteen major industrial metals, ten common minerals, five fossil fuels, ten agricultural products, five wood products, and three everyday exemplars, a laptop, a building and one kilowatt-hour of German electricity. The results reveal a split verdict. For most minerals and energy carriers, the two methods agree within a factor of two. For metals, however, deviations can reach an order of magnitude, and the reasons are instructive. Characterization factors are global medians, while ecoinvent datasets are often regionalized. The database’s tin dataset, for example, reflects an underground mine in Peru, whereas most tin is mined above ground, producing less gangue than the global average assumes. Gold is even more extreme: gangue values in ecoinvent version 3.11 range from roughly 23,000 to over two million kilograms per kilogram of gold, a variance that no single global factor can capture.

The product-level consequences are striking. For the laptop, a metal-intensive product whose footprint is dominated by gold production in integrated circuits and printed wiring boards, the direct method yields an RMI 27 percent lower than the benchmark, 531 kilograms instead of 725, and a TMR 53 percent lower, 823 kilograms instead of 1,775. At the resource level, the benchmark method’s largest contributor is the flow for gold itself, while the direct method’s largest contributors are gangue and overburden, exposing how differently the two approaches allocate the hidden mass of mining. For the building, which draws mainly on granite, gravel and clay, deviations shrink to between 0.5 and 12 percent. Electricity tells the most dramatic TMR story: while RMI values are close, 0.53 versus 0.59 kilograms per kilowatt-hour, the direct method’s TMR is five times higher, 3.5 versus 0.71 kilograms, driven by overburden in lignite and hard coal mining. Notably, the direct values for coal are corroborated by independent German federal data suggesting 9 to 13.5 kilograms of overburden per kilogram of lignite, lending credibility to the database figures.

The deeper finding concerns data coverage. Analyzing ecoinvent versions 3.4 through 3.11, the researchers found that the share of mining and metallurgical processes containing the gangue flows they should contain has climbed to 99 percent, a highly beneficial trend that makes direct RMI calculation genuinely viable. Overburden coverage, by contrast, has stagnated or even declined, sitting at roughly 50 percent of relevant processes. This gap makes direct TMR quantification impossible without supplementing the database with external data, which undermines the very consistency and transparency advantages that motivated the direct approach. The same limitation applies to biotic TMR, since ecoinvent does not monitor unused biomass at all, and the residue-to-crop ratios used to estimate it carry high uncertainty. Even the biotic RMI workaround has quirks: using an average energy density overestimates extraction for high-energy products such as cork or oil seeds, while the tomato case showed the energy-based estimate of 0.05 kilograms of dry biomass per kilogram integrating well with the fruit’s known 92 percent water content.

Beyond the numbers, the study articulates an epistemological tension within industrial ecology. Material flow accounting treats absolute physical mass extraction from nature as an environmental pressure in its own right, whereas utility-focused impact assessment methods frame resource issues around depletion risks, supply constraints or downstream damages. The direct PMF method operationalizes the former philosophy within a bottom-up, process-based framework, aligning conceptually with input-side indicators like cumulative energy demand. It cannot eliminate allocation challenges for multi-metal ores, where economic allocation based on volatile commodity prices remains necessary, but by adopting ecoinvent’s own allocation rules it minimizes the mixing of data sources and applies uniformly across all ecoinvent system models, including Cut-off, Consequential and APOS. It also preserves geographic specificity that global factors erase, although the authors caution that a single highly specific dataset, like the Peruvian tin mine, can mislead when used to model generic global production.

The practical implications reach into corporate procurement, product design and sustainability reporting, especially as the European Union’s Ecodesign for Sustainable Products Regulation introduces mandatory digital product passports that could improve supply chain data availability. The authors argue that a coordinated harmonization initiative among public authorities, data providers and scientific organizations is needed to make TMR directly calculable, noting that most detailed mining datasets are currently commercial and expensive. Their conclusion is candid: direct calculation from ecoinvent is a valid option for Raw Material Input today, but not yet for Total Material Requirement. The quantification of the product material footprint, they observe, is ultimately not a methodological problem but one of data collection, and database providers are urged to document resource extraction with the same rigor now applied to primary energy content and carbon dioxide emissions. Future studies, they suggest, would benefit from reporting both methods side by side, particularly where metal ores and overburden dominate the results.

Subject of Research: Direct calculation of product material footprints from life cycle inventory data in the ecoinvent database

Article Title: Re-evaluating ecoinvent’s suitability for direct product material footprint calculation

Article References: Buschbeck, C., Bickel, M., Teubler, J., & Liedtke, C. (2026). Re-evaluating ecoinvent’s suitability for direct product material footprint calculation. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00183-0

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00183-0

Keywords: material footprint, ecoinvent, life cycle assessment, raw material input, total material requirement, resource extraction, industrial ecology, mining, overburden, sustainable development goals, life cycle inventory, Wuppertal Institute

The study, published in the Journal of Industrial Ecology by Hessel Roodenburg, Ernst Worrell and Javanshir Fouladvand, addresses a striking gap in the scientific literature. While the humanitarian and economic costs of war have been extensively documented — armed conflicts caused more than 100 million deaths in the twentieth century, and the Kiel Institute for the World Economy estimates that a war-affected country’s economic output falls by roughly 30 percent within five years — the underlying material basis of warfare has remained empirically unquantified. Previous environmental studies documented consequences such as habitat destruction, deforestation and heavy-metal contamination, but never traced the causal flows of materials that generate these effects.

The framework borrows its structure from military science itself. The researchers organized warfare using the doctrinal warfighting functions defined by NATO and US Army doctrine, focusing on the four physically resource-intensive functions: Movement and Manoeuvre, Fires, Protection, and Sustainment. Command and Control, Intelligence and Information were excluded because they lack direct material throughput. Material inputs were then categorized using the NATO and US Army Classes of Supply taxonomy, covering fuels, fortification materials, ammunition, repair parts and medical supplies, all organized across air, ground, naval and defensive domains.

At the heart of the model lies a deceptively simple multiplicative equation: the mass of each supply class consumed equals the number of deployed platforms of each type, multiplied by a standardized monthly intensity per platform, multiplied by a logistical factor accounting for fuel self-consumption in distribution. Intensity values were derived using a quartile-based approach, with the 25th, 50th and 75th percentiles of compiled datasets representing low, medium and high operational scenarios. Where data were scarce, a multiplier approach scaled baseline consumption by factors of 0.5, 1 or 2. Platform losses were estimated at 6.5 percent of deployed platforms per month, based on observed conflict data.

The framework was tested against two contrasting case studies: the Russia-Ukraine war, a prolonged high-intensity conflict with a 950-kilometer active frontline and more than 1.1 million frontline personnel, and Operation Desert Storm of 1991, a short but high-tempo 43-day campaign dominated by air and naval operations. To enable direct comparison, the researchers constructed a normalized two-month scenario in which all parameters remained case-specific but the time horizon was standardized.

The results are striking. Within a two-month window, total carbon dioxide emissions reach approximately 6 megatonnes for the Russia-Ukraine war — comparable to the monthly emissions of Denmark — and 28 megatonnes for Desert Storm, equivalent to the monthly emissions of the entire Dutch industrial sector. In mass terms, the model indicates that 8 to 10 megatonnes of fuels and ammunition are mobilized over two months, comparable to the global monthly cement trade or Sweden’s annual steel output. Fuel accounts for 70 to 80 percent of the total mass flow, with ammunition dominating the remainder and fortification materials adding hundreds of kilotonnes.

The case studies reveal fundamentally different material signatures. Over the full 43-month modeled period of the Russia-Ukraine war, fuel consumption is estimated at 13.1 billion liters for Ukraine and 32.9 billion liters for Russia, generating 36 and 90 megatonnes of carbon dioxide respectively. Residual ammunition mass totals 5.85 megatonnes for Ukraine and 10.77 for Russia, with unguided missiles alone accounting for more than 6.21 megatonnes on the Russian side. The model also estimates roughly 14.5 million unexploded rounds across the war, corresponding to 39 megatonnes of TNT-equivalent on the Russian side and 17 megatonnes for Ukraine — a legacy of contamination that will persist for decades.

Desert Storm tells a different story. Despite lasting only 43 days, the Coalition consumed an estimated 6,652 megaliters of fuel, with fuel transport and logistical activities accounting for roughly three-quarters of the total. The campaign produced 2.27 million unexploded rounds and destroyed thousands of heavy platforms, generating 24 kilotonnes of non-recoverable debris for the Coalition and 12 for Iraq. The comparison between the two conflicts illustrates a key insight: high-tempo air campaigns amplify jet-fuel emissions, while sustained ground warfare accumulates fortification debris and mine-related outputs. In the Russia-Ukraine case, sandbags and concrete dominate fortification consumption, together representing about 90 percent of the total mass.

Perhaps the most consequential finding concerns logistics. In the Russia-Ukraine case, approximately 72 percent of fuel-related emissions arise not from combat platforms but from fuel transport and logistical fuel — the energy cost of moving energy itself. Platform fuel and ammunition contribute smaller shares. This decomposition demonstrates that logistics, not combat, is the primary driver of warfare’s emissions profile, a structural dominance that had previously been suggested but never quantified within a standardized framework. Capturing only the active warfare component, the study estimates that 126 megatonnes of carbon dioxide were emitted in the first three years of the Russia-Ukraine conflict — roughly half of broader estimates that include infrastructure destruction, fires and displacement.

The authors are careful to frame the work as a foundation rather than a definitive account. The system boundaries exclude upstream manufacturing, peacetime military activity and post-war reconstruction, meaning the true footprint of any war would be significantly higher over a full life cycle. Data limitations forced reliance on proxies, such as a spare-parts ratio based on a single US Army maintenance guide, and no formal uncertainty analysis was conducted, so results should be read as order-of-magnitude estimates. Nevertheless, the framework provides something previously absent: a reproducible, transparent method for converting diffuse military resource use into traceable parameters. The researchers emphasize that the goal is not to make war more environmentally friendly, but to raise awareness of its material costs — offering policymakers, humanitarian organizations and researchers evidence-based data to assess warfare’s place within broader climate and resource governance, and to inform post-conflict recovery planning.

Subject of Research: Quantifying the material and carbon footprint of modern warfare using material flow analysis

Article Title: Material footprint of warfare: a systematic framework and empirical analysis

Article References: Roodenburg, H., Worrell, E., & Fouladvand, J. (2026). Material footprint of warfare: a systematic framework and empirical analysis. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00130-z

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00130-z

Keywords: material footprint, warfare, material flow analysis, industrial ecology, Russia-Ukraine war, Operation Desert Storm, military logistics, carbon emissions, fuels, ammunition, environmental impact, sustainability