The reduction of GHG emission from the pyrolysis process as
compared to AD was attributed to production of higher amount
of stabilized carbon in the pyrolysis process as compared to AD.
Land application of residual biomass from AD (scenario 1 and 3)
stabilized 10% of the applied carbon after 100 years [65]. In this
study, 53% biochar was produced from pyrolysis, and biochar contains
50% carbon. If 75% of the biochar becomes stabilize after
100 years, 20% of the carbon in the sludge being stabilized through
the pyrolysis process. However, variability in the greenhouse gas
emissions from the pyrolysis incorporated scenarios can be produced
from several factors such as: (i) there is variability in the
carbon content, in the biochar, depending on the feedstock
[66,67], (ii) several studies also highlighted high degrees of variability
in nitrous oxide and methane emission from biochar
applied on the land [68]. These studies also show high variability
in the greenhouse gas emission from biochar amended soil.
Except above mentioned variability in GHG emission, variability
in GHG emission can also be produced from the processing of
waste for bioenergy production. For example, the residence time
of waste in an AD varies from 10 days to 1 month [25,38].
Whereas maturation time of algae can vary from few days to
weeks. With the increase in residence time, greenhouse gas emission
and energy required for agitation were also increased. There is
a considerable scope to optimize energy consumption per unit of
algal biomass production in the raceway ponds [69]. Some recent
studies reported time dependent greenhouse gas emission from
the algal pond [52,53]. Time dependent greenhouse gas emission
from AD was not available in the literature. Hence, there is a considerable
scope to develop a comprehensive greenhouse gas emission
budget incorporating above mentioned variability. We also
observed that incorporating nutrient values of organic residues
does not change the NRPER of the produced bioenergy, however,
increases the GHG emission marginally (Case A scenarios).
Incorporating nutrient values in the calculations can increase the
variability of the results.