# LCA Practices of Plastics and Their Recycling: A Critical Review
## 2 The Methodological Foundations and Advances in Life Cycle Assessment
### Types of LCA
#### 2.2.1 Attributional vs. Consequential LCA  
The differences between attributional and consequential LCA approaches in modelling become clear in the life cycle inventory analysis phase, where, in case of an attributional LCA (aLCA), modelers attribute inputs and outputs to the functional unit of the product within a chosen temporal window by linking the unit processes of the system according to a normative rule (e.g., based on physical processes or geographical boundaries [16]).  
When a modeler uses consequential LCA (cLCA), the analysis is geared towards describing how changes and decisions in the product system relate to the (environmental)flows of interest [17; 18; 19]. In essence, an aLCA assumes there is no interaction between decisions made within the chosen system and the outside world, whereas cLCA does [20; 21]. As a result, cLCA tends to yield more complex models which investigate impacts over a wider range of temporal and spatial horizons, with models that are not necessarily static and linear, as is the case with aLCA [20; 22], although it should be noted that such models are still far from being dynamic.  
Another point of differentiation in LCA approaches refers to how the study looks at the time component of the data and decisions of the product system. In some cases, the terms 'prospective' and'retrospective' are used to respectively refer to studies that are concerned with future situations or situations that occurred in the past [19]. It is important to note that these terms are sometimes used loosely. For instance, [23] used the term 'prospective' in a broader sense and used it to reflect an LCA that assesses consequences of decisions made in the past, but evaluated at a later point in time. At the same time, prospective LCAs are also coined 'ex-ante' or 'anticipatory' by others [24]; such applications will be discussed later on.  
The choice for a prospective or retrospective study usually has implications for the system boundaries. Typically, these are expanded when a prospective LCA is performed to include activities and corresponding emissions resulting from substitution due to the new product system. In addition, the difference in the goal of the study is also reflected in the methodological choices made in the LCI, as this phase is concerned with quantifying the relevant flows. In particular, compiling the data can become problematic as the static nature of the model requires an estimate of the data to be used within the chosen time horizon. Questions might arise whether or not to average data within a time frame, or to use marginal data. In case of a retrospective LCA, the latter is not used [23; 25]. This might be somewhat confusing, as the case of marginal vs. average data is typically also a main difference between cLCA and aLCA [26]; cLCA tends to use marginal data in order to reflect on what happens when changes are made within the product system. Others [23] have discussed the notion that retrospective LCAs are useful to learn about life cycles and subsystems and avoid undesirable impacts, while prospective LCAs are suitable for decision-making by stating that both methods can be used for decision-making and learning purposes.  
There are some strong limitations to these types of LCA. For instance, [27] extensively discussed inherent weaknesses in data quality, lack of consideration of temporal and spatial effects, the problem with allocating flows to product systems by means of Input-Output (I/O) tables, and uncertainty.