LCA and emerging technologies: how to move from the laboratory to a credible industrial-scale assessment

Emilie Guilvert

The development of emerging technologies is a key driver in the transition toward carbon neutrality. However, their environmental assessment remains a major challenge during the early stages of development.

At low technology readiness levels (TRL 1 to 6), processes are poorly representative of industrial implementation. Life cycle assessments conducted at this stage often significantly overestimate environmental impacts, making them difficult to compare with those of mature, high-TRL processes, as illustrated in the figure below.

In this context, a central question arises: how can a life cycle assessment based on a laboratory-scale process be used to estimate the impacts of future industrial production?

Pitfalls of Laboratory-Scale Processes in LCA

Laboratory-scale processes are non-optimized and produce small quantities. LCAs of laboratory-scale processes exhibit specific hotspots that can significantly bias results. The table below summarizes the main pitfalls encountered and explains why they are not directly representative of an industrial process.

General

Consumables

Electricity

Laboratory scale

– Small production volume

– Little or no internal recycling

– Non-optimized laboratory equipment

– Excess quantities

– Specific solvents

– Additional consumables ( filters)

– Higher quality gases and reagents

– Equipment used for small-scale production

– High electricity-to-reagents ratio

Industry scale

– Large production volume

– Routine internal recycling

– Optimized equipment for large-scale production

– Optimized quantities

– Standard/technical solvents

– Industrial-grade quality (different nature)

– Large-scale production = allocation

– Optimized ratios

Typical example: a chemical synthesis performed in the laboratory produces 10 g of product while consuming 2 kWh of electricity and 200 g of solvents. If these data are used directly in an LCA, the impact per kilogram can be 100 to 1,000 times higher than that of an optimized industrial-scale production.

Prospective LCA for More Reliable Laboratory-Scale LCAs

LCA has historically been designed to assess the environmental impacts of existing or already industrialized systems. However, in the context of the energy transition and the accelerated development of low-carbon technologies, environmental assessment must take place from the earliest stages of innovation. It is within this framework that prospective LCA (pLCA) is applied.

Prospective LCA does not aim to precisely predict the future impact of a technology, but rather to reduce decision-making uncertainty in order to inform research, investment, and public policy choices. It notably allows:

to identify environmental hotspots from the R&D phases,
to compare different technological pathways using scenario analysis,
to assess the compatibility of an innovation with long-term climate goals,
to direct optimization efforts toward the most influential parameters.

Prospective LCA aims to estimate the future environmental performance of emerging technologies before their large-scale deployment. pLCA is based on assumptions regarding:

the evolution of technical performance,
industrial scale-up
future yields,
the evolution of energy mixes,
Technological learning dynamics.

It generally applies to technologies at low to intermediate technology readiness levels (TRL 1 to 7), for which the available data mainly comes from laboratory experiments or pilot-scale phases.

Different Scale-Up Techniques in Prospective Life Cycle Assessment (LCA)

Studies show that scaling up relies on several approaches:

Prospective modeling of the foreground
Systemic scenario modeling of the background

Prospective modeling of the foreground

The most robust method, to be used when the process is well-characterized in the laboratory or has already been tested at pilot scale.

Based on engineering laws, thermal balances, and material balances.
Widely used to scale a batch laboratory process to a continuous industrial process.
Piccinno et al. (2016) provides the reference methodology for this type of scale-up.

Advantage: Physical consistency and granularity of the life cycle inventory (LCI).
Limitation: Requires process expertise.

Used when the process is sufficiently described to be simulated.

Enables the generation of quantitative data for a hypothetical industrial process.
Useful for estimating consumption, losses, heat integration, and internal recycling.

Advantage: realistic projections for equipment, utilities, and losses.
Limitation: dependence on assumptions and requires tools such as Aspen, ProSim, SuperPro…

Lightweight method: uses data from a similar industrial process as a proxy.

Widely used when data are scarce (poorly documented lab-scale process).
An approach frequently reported in previous studies.

Advantage: quick and pragmatic.
Limitation: high uncertainty.

Proportional adjustments: yield ×X, losses ×Y
Often combined with other methods.

Advantage: useful as a quick preliminary analysis.
Limitation: nonlinearity ignored, therefore a risk of bias.

Techniques derived from economics and industrial engineering:

Learning curves
Regressions on equipment size / production ramp-up.
Power laws to extrapolate energy and material.

Advantage: captures real scale effects
Limitation: requires historical data, making this technique uncertain for very new technologies.

Systemic scenario modeling of the background

For a prospective LCA to be credible, the future process (foreground) must be evaluated within a coherent future environment (background). Otherwise, the results are methodologically flawed.

Key points to remember:

1. Align the time horizons

If the process is assessed in 2035, the electricity mix, upstream processes, and materials must also be projected for 2035.

4. Make assumptions explicit

Decarbonization rates, future yields, material substitutions: transparency is essential.

2. Use existing scenarios

Rely on energy/climate scenarios, industrial outlooks, or projected LCA databases, rather than creating a “home-made” scenario that is too subjective.

5. Test multiple future scenarios

A single scenario gives a false sense of certainty.
Always compare at least: low, medium, high.

3. Developing what really changes

Electric mix, process efficiency, recycling rates, supply chain: these are the parameters that will transform future impact.

Compare to a conventional process: LCA recommendations

Best practices	Poor practices
Compare technologies at the same future TRL.	Comparing a laboratory process (TRL 3–4) with an optimized process
Scale up foreground data using a robust method.	Use raw laboratory data
Project background data using future scenarios.	Use the current mix for a future technology
Document and justify all assumptions.	Leave yield or loss assumptions implicit
Explore ranges/scenarios and perform sensitivity analyses.	Generate only a single, fragile scenario

A comparison that is finally useful for decision-making

Building a credible LCA from laboratory data requires clear and deliberate methodological choices. A well-constructed comparison does not aim to predict the exact future industrial impact, but rather to guide the technology:

Where are the critical points to address?

Which improvement levers should be prioritized?

Which technological trajectory is the most credible?

Under what conditions will the technology become competitive?

This approach enables transforming a laboratory-scale LCA into a genuine decision-support tool.

Prospective LCA is currently the most suitable method for supporting certain research and innovation projects in funding applications (Innovation Fund, national calls, R&I programs). It allows justifying realistic scale-up trajectories, exploring multiple possible futures, and demonstrating the environmental maturity of the process from the early stages.

At WeLOOP, we have supported several funded projects and projects seeking funding by applying this approach:

Development of scale-up scenarios consistent with the technology readiness level (TRL),

Sensitivity analyses of key parameters,

Future scenario modeling of the background,

Direct support for decision-making and for justifying assumptions to funders.

We have developed specific expertise in LCA scale-up for emerging processes, transforming your R&D data into comparable, credible analyses that are genuinely useful for decision-making.