Europe’s Livestock Footprint Models Are Stuck Studying Small Fixes, Review Finds

When European policymakers want to know whether a new manure treatment, a redesigned cattle diet, or a sweeping agricultural reform will actually help the environment, they increasingly turn to a modelling technique called consequential life cycle assessment, or CLCA. Unlike the more common attributional form of life cycle assessment, which simply tallies the environmental burdens of a product system as it exists today, consequential LCA attempts to answer a harder and more politically charged question: what will happen to the world if we change something? It tries to trace the ripple effects of a decision through feed markets, land use, energy systems, and the fate of by-products, capturing the indirect and market-mediated consequences that conventional accounting leaves invisible. A new systematic review published in the Journal of Industrial Ecology suggests that despite this ambition, the method is being applied far too timidly in the very sector where it is needed most: European livestock production.

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