Normalization is a procedure used to convert absolute values of a system, generally expressed in different measurement scales, into normalized values, thus enabling comparison, ranking, and aggregation of attribute values. In the context of the Life Cycle Assessment (LCA), normalized results can be obtained using internal and external approaches. The latter requires normalization factors gathered within a precise spatial context (e.g., a country), and this data usually originates from environmentally aware nations. However, several countries, such as Brazil, lack this sort of data; therefore, it is more difficult to apply representative external normalization factors. Alternatively, one may apply an internal normalization approach since the analysis of the data is specific to individual assessments, thus simplifying LCA in “non-normalized” countries. Since there are many internal procedures and the literature lacks discussions on how they perform in LCA contexts, it might be challenging for decision-makers to select and apply them as Multiple Attribute Decision Making (MADM) methods. In order to fill this research gap, we performed exploratory research aiming to compare eight procedures of internal normalization through a Monte Carlo Simulation using artificial data. Results indicate that procedures of internal normalization generally present a good performance since they influence the choice of the preferable alternative in < 30% of the simulations. Additionally, only two internal normalization approaches have reduced ranking performance. On the other hand, the least influential procedures on the final ranking of alternatives were Vector Normalization and Simple Normalization using the maximum value as a reference.

归一化是用于将通常以不同度量标准表示的系统的绝对值​​转换为归一化值的过程，从而可以对属性值进行比较，排序和汇总。在生命周期评估（LCA）的背景下，可以使用内部和外部方法获得标准化结果。后者需要在精确的空间环境（例如一个国家）内收集归一化因子，并且该数据通常来自具有环保意识的国家。但是，一些国家（例如巴西）缺乏此类数据。因此，应用代表性的外部归一化因子更加困难。另外，由于数据分析是针对个别评估的，因此可以采用内部归一化方法，从而简化了“非标准化”国家/地区的LCA。由于存在许多内部程序，并且文献缺乏关于它们在LCA上下文中如何执行的讨论，因此对于决策者来说，选择和应用它们作为多属性决策（MADM）方法可能具有挑战性。为了填补这一研究空白，我们进行了探索性研究，旨在通过使用人工数据的蒙特卡洛模拟比较内部归一化的八个过程。结果表明，内部归一化的过程通常表现出良好的性能，因为它们会影响<30％的模拟中首选替代方案的选择。此外，只有两种内部规范化方法降低了排名性能。另一方面，