The problem the researchers address is known as multifunctionality. Many industrial processes are inherently multitaskers. A waste incineration plant does not simply dispose of trash; it may generate electricity and heat, and its leftover bottom ash may contain recoverable metals and minerals. In life cycle assessment, any process with more than one functional flow—whether a product output or a waste input that is being treated—must be handled carefully, because the environmental burdens and benefits of the process need to be distributed among its multiple functions. Conventional LCA has developed a toolkit for this over decades, including allocation methods that partition impacts by mass or economic value, and substitution approaches that credit a coproduct for displacing an alternative product elsewhere in the economy.

What makes pLCA fundamentally harder is time. In a conventional assessment, the analyst can observe the process as it exists today and classify each flow with reasonable confidence. In a prospective assessment, the process may not yet exist at industrial scale, and the economic and regulatory environment around it may shift dramatically before it does. The authors highlight a striking example: waste heat that is considered a nonfunctional byproduct at laboratory scale may become a valuable, marketable product once the process is upscaled and integrated into district heating networks. A flow that is a waste today can become a product tomorrow, and that single reclassification can change whether a process counts as multifunctional at all—and therefore how its impacts are calculated.

Earlier work had already established foundations for dealing with these questions. Guinée and colleagues proposed a four-step approach for identifying and handling multifunctional processes in LCA, moving from distinguishing product and waste flows, to identifying functional flows, to spotting multifunctional processes, to handling them with a chosen procedure. Separately, Langkau and colleagues introduced the SIMPL approach, a stepwise framework for scenario-based inventory modeling in prospective LCA. But the SIMPL approach explicitly excludes the uncertainties tied to multifunctionality decisions, and reviews have found that the identification and handling of multifunctional processes in pLCA studies is often inconsistent, incomplete, or missing altogether. The new guidance is designed to fill precisely that gap by weaving the four-step multifunctionality approach into the three iterative steps of SIMPL.

The resulting framework is built around sub-steps attached to the first and fourth steps of the Guinée approach. In the identification phase, practitioners are guided to identify the external key factors—political, economic, social, technological, environmental, and legal influences—that could change the type of each flow or the number of functional flows in the future. They then develop explicit future assumptions about these changes and integrate them into consistent future scenarios. In the handling phase, the same logic is applied to the parameters that govern how multifunctionality is resolved: the substituted product, the substitution ratio, and the allocation factors. For each handling procedure—system expansion, substitution, physical allocation, or economic allocation—the guidance provides tailored recommendations for identifying what could change and building those changes into scenarios.

Each procedure carries its own distinctive vulnerabilities to the future. System expansion, which redefines the system boundaries to include all functions of the process, is comparatively insensitive to future variability in a non-comparative study, though the authors note that analysts should still verify that goals and scopes align with future scenarios. Substitution, by contrast, is highly sensitive: the substitution ratio, defined as the functionality of the coproduct divided by the functionality of the substituted product, can shift as coproduct quality, input composition, and production technology evolve. Physical allocation depends on the physical relationships between functional flows, which may change through process upscaling, learning curves, and economies of scale. Economic allocation is arguably the most volatile of all, because it rests on relative revenues that fluctuate with commodity prices, patents, technology diffusion, and inflation—yet many published pLCAs apply static revenue ratios that freeze today’s market conditions into a model of tomorrow.

To demonstrate the framework in action, the researchers applied it to a novel process for valorizing incinerator bottom slag, the residual ash left after municipal waste combustion in Germany. Today, most of this slag ends up in landfill construction, but it contains minerals and metals that could be recovered as high-quality secondary raw materials. The process under study uses selective milling followed by magnetic, eddy current, and density separation to yield three fractions: minerals, iron, and non-ferrous metals. The recovered minerals can substitute natural resources in cement production, with the pre-calcinated calcium oxide content reducing carbon dioxide emissions by replacing calcium carbonate. Because the process has only been tested discontinuously at laboratory scale and is unlikely to reach industrial operation before 2030, it is a textbook candidate for prospective assessment.

The team convened a scenario workshop with seven industry representatives spanning waste incineration, slag processing, plant engineering, and cement production to apply the guidance. The workshop revealed how regulatory and quality considerations could flip flow classifications: a minerals stream currently classified as waste under German construction regulations could become a product if metal separation improves or regulations change, while an iron-rich stream’s status depends on whether it is landfilled or used as copper ore. The researchers ultimately built ten foreground scenarios—covering system expansion, three substitution variants, three physical allocation variants, and three economic allocation variants—and combined each with three background scenarios for 2030 generated with the premise tool based on ecoinvent data and the REMIND integrated assessment model, yielding thirty scenario combinations evaluated with the superstructure approach.

The results carry a message that should unsettle anyone who assumes background energy scenarios are the dominant source of uncertainty in future-facing assessments. Within a single foreground scenario, switching between background datasets changed the climate impact by at most 12 kilograms of carbon dioxide equivalents per metric ton of treated slag. But varying the foreground assumptions within the same handling procedure produced far larger swings: 109 kilograms for substitution and 30 kilograms for economic allocation. Most strikingly, in the substitution case, the choice of foreground scenario could reverse the sign of the result entirely, flipping the process from a net carbon sink to a net emitter. In other words, the methodological assumptions about how multifunctionality is handled can matter more than the choice of future energy system.

The authors are careful to note that this finding comes from a single case study and should not be generalized, since sensitivity patterns depend on system characteristics and temporal scope. They also acknowledge practical limitations: the framework depends on expert and stakeholder participation, generates a combinatorial explosion of potential scenarios that requires subjective judgment to prune, and currently keeps the multifunctionality handling procedure fixed across background scenarios. Yet the core contribution stands. By making the invisible choices visible—documenting which assumptions about flow types, substitution ratios, and allocation factors underlie a result—the guidance supports more transparent and reproducible pLCA, particularly for dynamic systems such as energy-intensive electrification projects. For decision-makers weighing investments in emerging green technologies, the study is a reminder that the environmental verdict on the future depends not only on what the world will look like, but on the small, consequential choices analysts make when modeling it.

Subject of Research: A stepwise methodological framework for handling multifunctionality and its uncertainties in prospective life cycle assessment

Article Title: Stepwise guidance for tackling multifunctionality challenges in prospective life cycle assessment

Article References: Zacharopoulos, L., Thonemann, N., Steubing, B., Guinée, J., & Geldermann, J. (2026). Stepwise guidance for tackling multifunctionality challenges in prospective life cycle assessment. Journal of Industrial Ecology, 30(4), 1885-1901. https://doi.org/10.1007/s44498-026-00128-7

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00128-7

Keywords: prospective life cycle assessment, multifunctionality, life cycle assessment, uncertainty analysis, scenario development, substitution, allocation, waste incineration, slag valorization, industrial ecology, circular economy, integrated assessment models

The review, led by Dengsheng Sun of the Swedish University of Agricultural Sciences together with an international team spanning Denmark, Portugal, the United Kingdom, France, Italy, the Netherlands, Switzerland, and Denmark’s Aalborg University, followed the PRISMA 2020 framework for systematic reviews. The researchers searched Web of Science and Scopus using a three-level combination of terms covering geography, livestock systems, and methodology, screening studies published between January 2012 and December 2024. To be included, studies had to be peer-reviewed, written in English, focused on the European context, address at least one of the major livestock groups, and apply life cycle assessment. Crucially, studies were classified as consequential only if they explicitly described their assessment as such or applied recognised consequential modelling principles, such as system expansion, substitution modelling, or the identification of marginal suppliers. Studies relying purely on attributional allocation were excluded. Twenty-three studies made the cut, covering dairy and beef cattle, pigs, poultry, and sheep across intensive production countries including Belgium, Denmark, France, Germany, Italy, the Netherlands, Spain, and the United Kingdom.

The thematic picture that emerged is strikingly narrow. Most of the reviewed studies concentrated on manure treatment and handling or on alternative feeding strategies, both of which target specific sub-systems within existing production models. A smaller set examined incremental improvements to current farms, such as changing bedding materials, integrating on-farm biogas, or replacing fossil energy inputs. Only a handful tested genuinely new production systems, such as alternative land use or farm conversion pathways, and fewer still analysed explicit policy changes or human dietary shifts. This matters because feed production alone accounts for an estimated 65 to 95 percent of environmental impacts in monogastric systems such as pig and poultry production, and because contemporary European policy debates increasingly concern profound transformations, including large reductions in livestock numbers, dietary change, and wholesale land reallocation, rather than marginal adjustments at the farm gate.

The review’s analysis of scenario development reveals why transformative questions are being dodged. The researchers inductively identified six scenario approaches in the literature. Technical-based approaches, the most common, model changes to specific technologies or processes, such as slurry treatment or biogas production for combined heat and power. Management-based approaches, also frequent, represent changes in farm-level decision-making, such as altered feeding regimes or manure handling, while leaving the overall production structure untouched. Factorial approaches, which coordinate simultaneous changes across land use, production orientation, inputs, and outputs, appeared less often, as did policy-based approaches driven by anticipated interventions and optimisation approaches generated through formal mathematical models. Some studies combined approaches, such as work evaluating slurry acidification across high-density pig-producing regions in Denmark, Limburg, and Catalonia. The pattern is clear: technical and management approaches cluster around incremental change, while factorial, policy, and optimisation approaches, though better suited to systemic questions, remain rare.

Inventory construction, the painstaking process of assembling the data behind each model, tells a similar story of pragmatic compromise. Most studies leaned on the Ecoinvent database as their primary source for identifying marginal products and suppliers, supplemented when necessary by experimental datasets, expert elicitation, survey data, or outputs from economic models. In one notable example, researchers used the MATSIM-LUCA economic model to simulate agricultural market conditions in 2030 under different policy scenarios, feeding those outputs directly into the consequential inventory. But the experts consulted in the review’s companion workshop, held in October 2025 with twelve specialists from academia, research organisations, and consultancy, flagged serious gaps. Market classifications in general databases may not reflect regional realities, particularly for constrained markets such as animal manure, and agriculture-focused databases like Agri-footprint and AGRIBALYSE, though rich in livestock detail, were built for attributional purposes and lack the marginal suppliers and substitution effects that consequential modelling demands. Regionally specific inventory data for marginal processes and by-product flows are especially scarce.

On impact assessment, the reviewed studies overwhelmingly stopped at midpoint indicators, which quantify environmental mechanisms such as greenhouse gas emissions, acidification, or eutrophication without translating them into damages. Global warming potential dominated, followed by acidification and land use, with twelve studies including at least one eutrophication indicator. A recurring core set emerged across topics: climate change, acidification, eutrophication, land use, and resource or energy use. Only six studies combined midpoint and endpoint indicators, which aggregate impacts into damage categories such as human health, ecosystem quality, and resource scarcity, and just one relied exclusively on endpoints. The authors attribute this caution to the lower methodological maturity, greater uncertainty, and weaker regional specificity of endpoint characterisation factors. Yet they argue that a well-justified set of midpoint indicators, selectively supplemented by endpoint modelling when its assumptions are clearly articulated, offers the best balance between transparency and decision relevance, particularly for livestock systems where land competition, nutrient cycling, biodiversity, and ecotoxicity are often underrepresented.

Uncertainty analysis, thankfully, was common: eighteen of the reviewed cases reported it, and none relied solely on crude screening-level approaches. Monte Carlo simulation was the workhorse for propagating parameter uncertainty, while sensitivity analysis, the most frequently applied advanced method, tested alternative marginal technologies, substitution options, and background processes. Some studies went further, with one generating thirty-four combinations of feed substitution pathways, electricity sources, fertilisation rates, crop yields, and displaced pasture. But the review identifies a blind spot: uncertainty about model structure, meaning the representation of markets, substitution mechanisms, and behavioural responses, was rarely explored. In consequential modelling this structural uncertainty is arguably the most consequential of all, because the entire result hinges on which activities are judged unconstrained and therefore affected by a decision. The experts recommended borrowing methodologies from economics to better characterise market-mediated substitution effects, and treating sensitivity analysis as a tool for testing alternative system hypotheses rather than merely perturbing parameters around a single assumed pathway.

The workshop discussions added a forward-looking agenda. Participants argued that CLCA is best suited to analysing the consequences of decisions, including changes in production volume, technology adoption, and policy, but only when aligned with a clearly articulated research question. They called for collaborative, co-designed scenario development that begins with jointly defined narrative storylines and progresses toward quantitative representation of market responses, ideally with economists at the table. They also urged closer collaboration with social scientists to capture rebound effects, behavioural change, and non-economic drivers such as social norms and political dynamics. Future applications, they suggested, should extend to organic and mixed crop-livestock systems, integrated crop-livestock-bioenergy configurations, agroecological and precision livestock farming, nutrient cycling, carbon dynamics, water footprints, and temporal dynamics, all domains where interactions among land use, biodiversity, and resource use extend far beyond individual production processes.

The review’s limitations are candidly acknowledged: it covered only terrestrial livestock and peer-reviewed literature, excluded grey literature such as EU project reports, identified no eligible goat studies, and closed its search in December 2024. Its classification of scenario approaches is admittedly interpretive. Yet the synthesis lands on a conclusion with real urgency. Europe’s Green Deal, Common Agricultural Policy, and climate and biodiversity strategies increasingly demand ex ante assessment of system-wide, cross-sectoral impacts, and normative foresight scenarios such as the EAT-Lancet Commission, Afterres 2050, and the TYFA agroecological Europe pathway have so far been evaluated mainly with attributional or hybrid methods that cannot capture market-mediated responses. The authors’ verdict is that the constraint lies not in the consequential method itself but in how research questions are framed: a field trained on tractable, incremental interventions has under-asked the transformative questions its own tool was built to answer. Open-source computational frameworks such as Brightway, which facilitate scenario analysis, regionalisation, and integration with complementary models, may help close the gap. What is needed, the review concludes, is a deliberate shift in emphasis from refining existing systems toward systematically exploring alternative livestock configurations, guided by transparent assumptions, honest uncertainty treatment, and a willingness to model the consequences of genuine change.

Subject of Research: Consequential life cycle assessment methods applied to European livestock production systems

Article Title: Consequential life cycle assessment of European livestock systems: current practices, limitations, and priorities

Article References: Sun, D., Knudsen, M. T., Ponsioen, T., Teixeira, R., Chervinska, A., Raposo, M., Lucić, R., Gravell, M., Davison, N., Cameron, L., Westaway, S., Wilfart, A., Goglio, P., Wang, Y., Hashemi, F., Diogo, V., Weidema, B. P., & Smith, L. G. (2026). Consequential life cycle assessment of European livestock systems: current practices, limitations, and priorities. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00154-5

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00154-5

Keywords: consequential life cycle assessment, livestock systems, life cycle assessment, scenario development, inventory modelling, marginal suppliers, uncertainty analysis, sustainability transitions, European agriculture, manure management, feed strategies, environmental policy