posted on 2021-10-15, 14:04authored byHui Xu, Gregory Latta, Uisung Lee, Jan Lewandrowski, Michael Wang
This study presents a cradle-to-grave
life cycle analysis (LCA)
of the greenhouse gas (GHG) emissions of the electricity generated
from forest biomass in different regions of the United States (U.S.),
taking into consideration regional variations in biomass availabilities
and logistics. The regional biomass supply for a 20 MW bioelectricity
facility is estimated using the Land Use and Resource Allocation (LURA)
model. Results from LURA and data on regional forest management, harvesting,
and processing are incorporated into the GHGs, Regulated Emissions,
and Energy Use in Technologies (GREET) model for LCA. The results
suggest that GHG emissions of mill residues-based pathways can be
15–52% lower than those of pulpwood-based pathways, with logging
residues falling in between. Nonetheless, our analysis suggests that
screening bioenergy projects on specific feedstock types alone is
not sufficient because GHG emissions of a pulpwood-based pathway in
one state can be lower than those of a mill residue-based pathway
in another state. Furthermore, the available biomass supply often
consists of several woody feedstocks, and its composition is region-dependent.
Forest biomass-derived electricity is associated with 86–93%
lower life-cycle GHG emissions than the emissions of the average grid
electricity in the U.S. Key factors driving bioelectricity GHG emissions
include electricity generation efficiency, transportation distance,
and energy use for biomass harvesting and processing.