Biomass & Bioenergy, Vol.107, 384-397, 2017
Life cycle assessment of biomass densification systems
Several recent life cycle assessments (LCA) of biomass densification have been carried out. This paper reviews data from 19 sources with 48 case scenarios to assess the current status of LCA of biomass densification. It describes the specific units in a reference "gate-to-gate" LCA in relation to the existing studies, and summarises key differences between them. Finally, it provides a qualitative analysis of the associated sources of uncertainty. Existing LCA studies of biomass densification were found to provide insufficient and inconsistent information for full transparency and comparability, due to different choices in system boundary, functional unit, allocation procedure, densification technology and biomass residues. Most of the reviewed studies attributed most of the energy use and greenhouse gas (GHG) emissions to transportation, drying and densification. The energy and GHG emissions of the gate-to-gate densification system were highly sensitive to the technology, feed material used in densification and scale of production. Apart from one study with zero energy consumption as a result of the use of manual operations, the normalised values of energy consumption for the reviewed studies ranged from 20 to 900 kJ MJ(-1). Neglecting three outlier values, GHG emissions as mass of CO2-eq for the reviewed studies ranged from 600 t MJ(-1) to 50 g MJ(-1). Similar variations in result and outlier cases have beesn reported for other bioenergy processes, by other authors. Assuming that the biggest impact of densification processes is on transport fuel use, and based on 5 studies that reported densification ratios, the net energy and GHG emissions savings resulting from densification ranged from 200 to 1000 kJ MJ(-1) and 9 to 50 CO2-eq (g MJ(-1)), respectively. On this basis, it can be concluded that biomass densification is a worthwhile addition to the biomass energy conversion system. There is a need for more transparent reporting and analysis of uncertainty in the modelling, to better understand the wide variation in outcomes.