What is a life cycle assessment?

A method for measuring the environmental impacts of a product across its entire life — extraction, manufacture, distribution, use and disposal — rather than at one stage, which is where intuitions about environmental choices usually go wrong.

The stages assessed:

Raw material extraction, including mining, farming and forestry.

Manufacturing and processing.

Distribution and packaging.

Use phase, including energy and consumables over the product's lifetime.

End of life — disposal, recycling or reuse.

The boundary matters enormously. Cradle to gate covers extraction to the factory door; cradle to grave adds use and disposal; cradle to cradle accounts for materials re-entering production. Comparing assessments with different boundaries is meaningless, and it is done constantly.

What it measures. Not only carbon: water use, land use, acidification, eutrophication, ozone depletion, human toxicity and resource depletion. A product better on one measure is frequently worse on another, and there is no objective way to combine them into a single score — weighting is a value judgement.

Why it overturns intuitions:

The use phase dominates for many products. For appliances and vehicles, energy consumed in use exceeds manufacturing impact, which is why replacing a working but inefficient appliance can be justified — and why replacing an efficient one rarely is.

Manufacturing dominates for others, notably electronics and clothing, where extending life is far more effective than any disposal choice.

Reusable items require a break-even number of uses. A cotton bag must be reused many times before it outperforms a plastic one, because its production impact is far higher. The number of uses is the whole question, and it is frequently ignored by people confident they have made the better choice.

Transport is frequently minor relative to production, which is why locally produced food is not automatically lower impact than food shipped from a more suitable growing climate.

Its limitations: results depend heavily on assumptions, data quality varies, and studies funded by interested parties reach predictable conclusions. Check the boundary, the functional unit, the assumptions and the funder before accepting any comparison.