Dynamic factors in life cycle assessment: systematic review and categorization

Globally, the building sector accounts for about one third of global CO2 emissions. In this context, life cycle assessment (LCA) serves as a strong instrument to quantify the environmental impacts throughout the complete life cycle of a building. Nevertheless, current LCA methods can be further refined from certain perspectives. For instance, the static calculation in the current LCA methods does not take temporal variations into account. Unlike analyses of life cycle cost and operational energy consumption, the dynamic changes of LCA results are rarely considered, which makes equivalent comparisons difficult. To deal with this problem, many studies were carried out in the last decade to discuss the dynamic LCA (DLCA) methodologies. Current studies on DLCA can be broadly divided into two main groups: dynamic life cycle inventory (DLCI) and dynamic life cycle impact assessment (DLCIA). However, this division is not sufficiently intuitive for LCA practitioners, complicating the integration and proper categorization of other various dynamic factors in practice. This leads to the lack of wider understanding about the concept of DLCA, especially within the building industry, which indirectly slows down the practical implementation of DLCA approaches in the building industry.

In this research, we conduct a comprehensive review of dynamic factors that can potentially impact LCA results in the building industry and categorize the investigated dynamic factors into two types: background and foreground. This categorization directly addresses the difference of background and foreground databases. In addition, we introduce methods for modeling these dynamic factors. To demonstrate their practical applicability, we applied the categorized dynamic factors to one background database, ecoinvent, and one local foreground database, Ökobaudat. The results show that our new categorization of dynamic factors simplifies the modeling of dynamic factors and serves as a foundation for the further expansion of dynamic factors and for further development in DLCA methodologies.