Normalization and weighting: Normalisation is commonly used to generate a single numerical score by weighting each impact category and totaling them. Environmental impact scores in LCA are displayed in physical units representing the potential for environmental impact. The results of different impact categories cannot be compared or combined. One method of Normalization is dividing your scores by the scores of a reference situation (e.g., total CO2 emissions from transportation in a particular country or industry). Normalization means relating the data of your lCIA results to one tone or 1 kg of activity or production.
Many midpoint categories and some damage categories have non-intuitive reference units, making it difficult to interpret the meanings of the resulting impacts. To better understand the magnitude of the damage, the normalization step expresses a given impact per functional unit about the total impact in that category. Thus, it compares the respective contribution of the considered product or service to the total impact for a given category on a global, continental, or regional level. The impact characterization results are reported concerning these complete reference or normalization values.
Normalization: express the impact potentials concerning a reference situation to place a study on an understandable standard scale. Weighting: weighting is the next step after doing Normalization. Weighting ranks or scores an impact category’s contribution to the overall impact. For example, how much the Global Warming Potential of your modeled system is essential compared to other 10 impacts (e.g., accidification, human toxicity, etc.). You can understand weighting as ranking or scoring the impacts. At the same time, Normalization standardizes numerical values with a reference unit. Weighting means measuring how important every impact category is compared to others.
Finally, we can only use weighting by normalizing the results first. The process is Normalizing our LCIA results first with normalization factor (reference units) and then weighting them with weighting factors.