Weighting is the evaluation of the relative importance of impacts, according to (i) specific value choices of individuals, groups, populations, or organizations and/or (ii) the statistical structure of the input dataset (i.e., data-driven).

They are parameters used to define the relative importance of impacts. Two main groups of weighting methods can be distinguished:

• Those providing weights as unit-specific compensation (conversion) rates
• Those providing weights as dimensionless numbers, either reflecting compensation rates or importance coefficients

There are several methods that can be used to calculate weights. We distinguish four main groups:

1. Distance to target: Define the importance of impacts depending on the distance between the existing impact level and a target level
2. Multiple Criteria Decision Analysis: Define the importance of the impacts according to the preferences of a group of stakeholders or a set of constraints
3. Monetary: Define the importance of impacts in monetary terms
4. Data-driven: Uses the statistical structure of the input dataset to calculate the importance of the impacts

Compensation rates indicate the weight of one unit in the respective impact category indicator result (e.g., per 1 kg of CO₂ equivalents or 1 kg of SO₂ equivalents). If W_x is the weight of 1 unit in impact category indicator x and W_y is the weight of 1 unit in impact category indicator y, then 1 unit of x is worth the same as (W_x/_Wy) units in y. This type of weighting factor is used by methods where being better in one impact category indicator result can compensate for being worse in another impact category indicator. For example, W could be USD/CO_2-eq for climate change or €/DALY for respiratory inorganics. Other examples are the W of climate change expressed in kg of CO_2-eq with a weight of 1 and the W of acidification per kg of SO_2-eq with a weight of 2. This expression means that the improvement of 2 units (i.e., 2 kg of CO_2-eq) of climate change compensates for the worsening of 1 unit (i.e., 1 kg of SO_2-eq) of acidification.

Importance coefficients indicate the intrinsic importance of the impact indicator itself when comparing two or more alternatives. If an impact category indicator is given more weight than another one, then it is more important to be better in this impact category indicator than in another. This type of weighting factor is used by methods where a better result in one impact category may or may not fully compensate for a worse result in another impact category. For example, W for climate change could be 35%, W for ozone depletion could be 15% and W for eutrophication could be 20%, etc., with the meaning that being better in terms of climate change (alone) is as important as being better in terms of ozone depletion and eutrophication (simultaneously).

The foreseen uses of the GLAM weights include (i) impact contribution analysis and (ii) aggregation.

It is the type of decision support that the decision maker would like to receive when using the weights.

• Scoring = Assign a score to each alternative according to their performance
  
• Ranking = Order the alternatives from the most to the least preferred
  
• Sorting = Assign the alternatives to pre-defined preference-ordered decision classes
  
• Choice = Select the most preferred subset of alternatives
  
• Clustering = Divide alternatives into groups according to some similarity measure or preference relation)