Talking to Life Cycle Models: AI Tool Turns Plain English Into Sustainability Scenarios

Life cycle assessment, the accounting method behind virtually every credible claim about the environmental footprint of a product, has long been a specialist’s game. Building a model of an electric vehicle charger, an algae biodiesel plant or a carbon-negative power station requires months of inventory work, database wrangling and methodological judgement. And once the model exists, exploring alternative scenarios, different material mixes, transport distances, capture rates, has traditionally meant clicking through software menus again and again, or writing scripts that only programmers can produce. Now researchers report a framework that lets practitioners simply ask for those scenarios in plain language, while keeping the actual calculations firmly in the hands of trusted software.

The study, published in the Journal of Industrial Ecology by Sadiq Abubakar of South Bank Colleges and Coventry University’s Centre for Future Transport and Cities, together with Fernando Cepero Mejias, describes a natural-language interface bolted onto OpenLCA, one of the most widely used open-source life cycle assessment platforms. The key architectural decision is deliberately conservative: the large language model behind the interface never computes environmental impacts itself. Instead, it acts as a translation layer, converting a user’s prose request into structured parameter-edit proposals that are checked by a deterministic validator before anything is executed in the LCA engine.

That separation matters because of a well-known weakness of language models: hallucination, the confident generation of plausible but wrong information. In life cycle assessment, where results feed into carbon footprints, policy decisions and environmental claims, a hallucinated number is not merely embarrassing, it is potentially misleading at scale. The researchers therefore treat every output of the language model as an untrusted artefact. Proposed edits are parsed as JSON, checked against the editable parameters actually present in the loaded model, and rejected outright if they touch the model’s structural elements, processes, flows, exchanges or datasets, or refer to parameters that do not exist in the correct scope.

Under the hood, the workflow runs in four stages. A practitioner first builds and parameterises a base product system in OpenLCA, defining its goal, scope and system boundary. The framework then imports this model through OpenLCA’s inter-process communication interface, exposing to the language model only a reduced representation: parameter names, process identifiers and functional-unit information, but never the full biosphere inventory. A fixed system prompt, which end users cannot modify, restricts the model to supported tools and schema-compliant responses. When validation succeeds, the accepted parameter changes are applied as a scenario and executed deterministically in OpenLCA, producing a complete audit bundle with prompts, diagnostics, results and SHA-256 checksums.

The interface ships with a toolkit of analytically standard operations. One-at-a-time sensitivity analysis perturbs single parameters while holding the rest constant. A simplex-lattice tool generates systematic mixtures of components, a design long used in mixture experiments. A PDF-extraction tool lets the model pull parameter values from uploaded documents, with the backend scoring candidate tables by lexical overlap before returning them. An uncertainty-propagation tool accepts explicitly stated triangular distributions and runs Latin hypercube or Monte Carlo sampling in Python, never letting the model invent distributions of its own. Finally, a bounded goal-seeking tool locates parameter values satisfying environmental constraints, using interval scanning and bisection for one-dimensional thresholds and simplicial homology global optimisation for harder multi-parameter problems.

The framework was tested on three real parameterised models. The first, an electric vehicle charger model, was asked to explore interior concrete footing mixtures of cement, sand and gravel summing to 145 kilograms using a simplex lattice design, and to label each scenario with a compressive strength computed from Abrams’ law. The system correctly generated all six feasible interior mixtures, and the resulting life cycle impact results matched manual OpenLCA reproductions to within rounding. The same model also faced a multilingual test: a single prompt mixing Italian, Hindi, Chinese and Arabic instructions, asking respectively for a steel increase, a copper tripling, a transport-distance change and a specific aluminium mass. All four edits were extracted and executed correctly in the flagship demonstration.

The second case used the NETL carbon dioxide utilisation algae biodiesel model with a functional unit of one megajoule of combusted diesel equivalent. Asked to propagate uncertainty through four parameters, electricity input, carbon dioxide pumping demand, pond utilisation efficiency and recycle rate, the framework sampled 250 parameter sets with a fixed random seed and delivered percentile ranges for global warming, acidification and water-consumption indicators, all consistent with the specified distributions. A follow-up test uploaded a PDF containing four predefined scenarios; the system extracted the relevant tables, generated 22 parameter edits across the scenarios and executed all four calculations successfully.

Perhaps the most striking demonstration came from a carbon-negative BECCS power plant model. Asked in plain English to find the minimum carbon dioxide capture rate at which the plant remains carbon-negative per kilowatt-hour, the system completed 54 model evaluations and identified a threshold of roughly 0.887, which manual recalculation in OpenLCA confirmed with a result of essentially zero emissions. A harder multi-constraint task asked the system to maximise the allowable biomass transport distance while holding climate change in a narrow negative band and capping five other impact categories. The optimiser ran 880 evaluations and nearly tripled the feasible transport distance from the 193-mile baseline to about 553 miles, although manual verification showed three constraints were very slightly exceeded, leading the authors to describe the solution as near-boundary rather than strictly feasible.

The honesty of the evaluation is what sets this work apart from breathless AI announcements. In 175 scored diagnostic runs across five language models and seven prompts, reliability varied substantially. A single-parameter threshold task succeeded in all 25 runs across every model tested, and uncertainty-propagation requests were nearly as stable. But arithmetic labelling proved fragile: without a deterministic calculator tool, only four of fourteen accepted simplex-lattice runs produced strength labels within tolerance, and even with the calculator enabled, some models passed incorrect arguments, such as a water-to-cement ratio ten times too small, producing absurd strength values. Multilingual extraction stumbled on the Arabic aluminium instruction, and PDF-driven scenarios sometimes mapped values to the wrong parameter. Crucially, the authors distinguish schema validity, execution success and task correctness, showing that a payload can pass validation and execute perfectly while still representing the wrong scenario.

The researchers are careful about what their framework is and is not. It is a complement to scripting, not a replacement: for fully specified, repeated workflows, direct Python or IPC scripts remain more transparent and efficient. It cannot build product systems, choose allocation rules or judge whether the underlying model is methodologically sound, responsibilities that remain with the practitioner. Nor does the audit trail prevent selective interpretation of results. Yet the contribution is nonetheless significant: a single natural-language doorway to parameter-level scenario analysis, with deterministic validation, tool calling and cryptographic audit records making every accepted and rejected proposal inspectable. As language models and structured-output reliability improve, and as future work adds semantic retrieval and automated correctness checks, the vision of sustainability analysts interrogating their own models by conversation, without surrendering reproducibility, comes considerably closer to routine practice.

Subject of Research: Natural-language interfaces for parameter-level scenario analysis in life cycle assessment models

Article Title: A natural-language interface for parameter-level scenario analysis in parameterised life cycle assessment models

Article References: A natural-language interface for parameter-level scenario analysis in parameterised life cycle assessment models. (n.d.). https://doi.org/10.1007/s44498-026-00163-4

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00163-4

Keywords: life cycle assessment, large language models, OpenLCA, natural-language interface, parameter scenario analysis, uncertainty propagation, sensitivity analysis, BECCS, tool calling, hallucination, deterministic validation, industrial ecology