Bias in normalization: Causes, consequences, detection and remedies
- Reinout HeijungsAffiliated withInstitute of Environmental Sciences (CML), Leiden University Email author
- , Jeroen GuinéeAffiliated withInstitute of Environmental Sciences (CML), Leiden University
- , René KleijnAffiliated withInstitute of Environmental Sciences (CML), Leiden University
- , Vera RoversAffiliated withInstitute of Environmental Sciences (CML), Leiden University
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
Normalization is an optional step in LCIA that is used to better understand the relative importance and magnitude of the impact category indicator results. It is used for error checking, as a first step in weighting, and for standalone presentation of results. A normalized score for a certain impact category is obtained by determining the ratio of the category indicator result of the product and that of a reference system, such as the world in a certain year or the population of a specific area in a certain year.
In determining these two quantities, the numerator, the denominator, or both can suffer from incompleteness due to a lack of emission data and/or characterisation factors. This leads to what we call a biased normalization. As a consequence. the normalized category indicator result can be too low or too high. Some examples from hypothetical and real case studies demonstrate this.
Consequences of Biased Normalization
Especially when for some impact categories the normalized category indicator result is right, for others too low, and for others too high, severe problems in using normalized scores can show up. It is shown how this may affect the three types of usage of normalized results: error checking, weighting and standalone presentation.
Detection and Remedies of Biased Normalization
Some easy checks are proposed that at least alert the LCA practitioner of the possibility of a biased result. These checks are illustrated for an example system on hydrogen production. A number of remedies of this problem is possible. These are discussed. In particular, casedependent normalization is shown to solve some problems, but on the expense of creating other problems.
It appears that there is only one good solution: databases and tables of characterisation factors must be made more completely, so that the risk of detrimental bias is reduced. On the other hand, the use of the previously introduced checks should become a standard element in LCA practice, and should be facilitated with LCA software.
KeywordsData gaps hydrogen production LCIA normalization
- Bias in normalization: Causes, consequences, detection and remedies
The International Journal of Life Cycle Assessment
Volume 12, Issue 4 , pp 211-216
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- Data gaps
- hydrogen production
- Industry Sectors