From static to dynamic: Reviewing the application and potential of dynamic LCA to bio-based systems

Purpose
Static Life Cycle Assessment (LCA) meets its limit at capturing critical aspects in the climate impact of bio-based products (BBPs) and bio-energy, particularly biogenic carbon accounting and land-use change impacts. These issues depend strongly on spatial and temporal dynamics of feedstock systems. Dynamic LCA (DLCA) has been proposed as a potential improvement over conventional LCAs, however no comprehensive overview exists of its application to bio-based systems, including BBPs or bio-energy.

Methods
We conducted a systematic literature review of 44 DLCA articles including a bio-related term (e.g., “biobased”, “bio-based”), yielding 83 case studies. Articles were screened by sector, feedstock, product types and dynamic inventory and impact assessment. We analysed how dynamics in biogenic carbon and land-use change (LUC) were addressed. We specifically reviewed parameters critical to biogenic carbon dynamics: sequestration models, time horizon, inventory period and modelling, and storage period. We compared SLCA and DLCA results, reflecting on the added insights of DLCA for different feedstocks (short- vs. long-rotation) and product types (short- vs. long-lived).We further examined whether and how LUC was modelled in the reviewed articles.

Results
Most DLCA case studies assess long-lived BBPs from forest biomass. Dynamic modelling is primarily used to represent biogenic carbon flows rather than time-varying foreground or background processes. Carbon uptake is modelled using approaches ranging from distribution functions and parametric models to detailed forest growth simulations, reflecting limited methodological consensus. Differences between static LCA and DLCA are not consistently reported and, when available, vary widely; DLCA often yields lower climate impacts, although results are context-dependent. Several interrelated choices contribute to this variability and the complexity of DLCA: assessment period and method, time horizon, growth vs. regrowth perspective and assumed carbon storage duration. Spatial impacts (e.g., soil organic carbon and biodiversity from LUC) are rarely included, although this reflects broader challenges in LCA.

Conclusion
Integrating temporal (biogenic carbon) and spatial (land-use change) dynamics into LCA remains challenging due to its static structure. DLCA improves the representation of carbon sequestration and delayed emissions, but approaches vary widely. Its added value is context-dependent and introduces significant methodological complexity and choices. This study highlights key modelling choices influencing DLCA results and provides recommendations for their application, underscoring the importance of transparency to ensure results are interpretable and comparable.