# LCA Practices of Plastics and Their Recycling: A Critical Review
## 2 The Methodological Foundations and Advances in Life Cycle Assessment
### Types of LCA
#### 2.2.3 Dynamic LCA  
In the early years of this century, the concept of DLCA emerged. This was done in response to the methodological limitations of traditional LCA methods which, amongst others, do not consider factors that exhibit time varying behavior. Ever since, various authors have contributed to the methodological discussion surrounding LCA and DLCA, as well as numerous examples of applications of DLCA. A recent PhD thesis [36] pointed out that it is essential to model temporal characteristics in order to capture the transient behavior of complex processes and supply chains. This is also due to the fact that the impacts of anthropogenic emissions are time dependent due to absorptive capacities which depend on the concentration profiles temporally and spatially. To introduce dynamism, the author created a tool to import the technology matrix \(\mathbf{A}\) (see Equation (1)) and include time dependency, which can be found in other works as well [37,38]. Since the inventory step in the LCA describes production systems and such systems are dynamic, it is likely that their dynamics are influential in the outcome of LCA studies [27,36]. Other authors [39] argue that especially for products with long life cycles, such as, in their case, buildings, their performance might be affected significantly by economic, environmental and societal changes. The timing of emissions matters in such cases. This can be seen in another study [40], where the author performed a DLCA on renewable energy and suggests that future developments and context is typically not considered in regular LCA approaches, thereby \"severely distorting the analysis of the environmental characteristics of future energy systems\". This does not only play a role in production systems or energy markets but has been observed by other authors as well in agricultural systems, where dynamic factors can have a strong influence on the outcome of LCA studies [41,42]. Other authors [37] performed a sensitivity analysis of temporal parameters in a DLCA framework and showed that it is important to study the emissions' profile during the lifetime of the system, as well as several decades afterwards in order to understand accumulation effects. In a recent review paper on DLCA [7], which aimed to assess the state of the art in DLCA applications and the various types of DLCA that were involved, the authors distinguished between three types of LCA dynamics:  
1. dynamic process inventory: Potential future developments are accounted for in unit-processes;
2. dynamic systems inventory: Potential future changes in components of the system under study are modelled by switching between unit processes or behaviors;
3. dynamic characterization: adjusting factors to account for temporality in emissions of for example, CO\({}_{2}\) equivalency (future versus present day). The vast majority (93%) of the papers they analyzed did not develop and use a fully dynamic model, but only partially accounted for dynamism. They proceeded to assert that there are ample cases where the use of a partially dynamic LCA would provide better decision support than the conventional static LCA, but at the same time also caution that standards should be set as to how one should implement and report on (partially) dynamic LCAs to improve consistency and avoid potentially skewed results, which could result from arbitrary choices and assumptions in dynamism. Indeed, it seems the literature regarding DLCA is still in its infancy, considering that very few articles include fully dynamic models. As outlined above, the timing of emissions and the efficiency and response of background systems play an important role. Here, background systems are defined as being part of the supply chain, but outside the sphere of influence of the decision-makers [43].