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How do I know that the data is right?
How do I know that the data is right?

How we make our calculations verifiable and transparant

Tess Heeremans avatar
Written by Tess Heeremans
Updated over a week ago

It's important to recognize that achieving accuracy in LCA is a challenging task. Even the most advanced LCA methods have an accuracy that typically does not exceed 30%. This is because conducting a calculation involves analyzing millions of data points, many of which are unavailable and subject to various dependencies and assumptions. For instance, factors such as weather conditions and route changes for transport can impact the percentage of green electricity on the grid.

As a result, the environmental impact is often estimated based on secondary data or averages, and the level of accuracy varies depending on the specific case being analyzed.

While adding depth to LCA can enhance the analysis, it can also increase the cost and complexity of collecting and maintaining data. Therefore, the key lies in describing the scope of the analysis accurately and transparently, outlining what is and isn't included, and indicating the sources used to support the claims made.

The Pickler methodology is an approach that helps to ensure that the data used in LCA is well-substantiated and supported. For example, it requires that input data be backed up by proof from suppliers. By using this approach, the data can be more confidently relied upon to make credible claims about the environmental impact of a product or service.

Most importantly, the transparency of the Pickler methodology not only makes it possible to assess the credibility of a claim, but also enables users to evaluate the evidence (LCA data) themselves.

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