Carbon Footprint: Concept, Methodology and Calculation
13
countries due to the significant increase of land intended for agricultural use. Maciel
et al. [45] highlighted the importance of dLUC inclusion in the CF analysis of soybean
cultivation in Brazil, showing that the GHG emissions increases up to 205% when
the contribution of carbon emissions from the transformation of 15.4% of land from
grassland to farming is considered. Papong et al. [62] evaluated the GHG emissions of
bioethanol production from cassava and molasses in Thailand, excluding or including
dLUC effect according to two different scenarios (land transformation from perennial
crop to annual crop and from rice field area to annual crop). Results showed that
dLUC emissions can increase the CF of bioethanol from 10 to 73% depends on the
considered scenario.
The assessment of iLUC emissions is an even more challenging mission due to the
influence of the employed model, input data, spatial coverage and scenario assumptions. Among the different approaches, the one elaborated by Schmidt et al. [73] is
one of the most interesting which is based on the assumption of perfect elasticity
in the markets for products dependent on land use and it avoids the amortization of
the GHG emissions by using discounted Global Warming Potentials (GWPs). The
iLUC issue is mainly felt in the biofuels sector due to the expansion of energy crops
in response to increased biofuel demand. At this regard, the European Commission
has included the iLUC emissions in the RED [18], by defining a single factor by
type of crop. However, this approach is too simplified as suggested by Garrain et al.
[24] who proposed origin-dependent iLUC factors. In particular, they analysed the
iLUC impacts caused by an additional demand of biofuel in Spain, showing different
values of GHG emissions of biodiesel and bioethanol from iLUC depend on where
the biofuel is produced.
4.4 Impact of Temporal Dimension
4.4.1 Time Horizon
It is well-known that the results of a CF study are significantly influenced by the
choice of time horizon in the GWP. Typically, in literature the most used time horizon
of the GWP is 100-year, maybe because it was the middle value of the three time
horizons (20, 100, and 500 years) analyzed in the IPCC First Assessment Report
[46]. Time horizon is the time over which the radiative forcing have to be integrated,
therefore a 20-year and 500-year time frames are used to evaluate short and long
term environmental effects, respectively. However there is no scientific reason to
choose 100 year time scale than the other two. Lueddeckens et al. [44] reviewed
that the definition of the time horizon is a subjective decision and it depends on
the goal and scope of the analysis and the interests of stakeholders of the CF study.
De Rosa et al. [70] analyzed the influence of time horizon on the GHG emissions
from the production of sawn spruce timber in Sweden, demonstrating that 20-year
time frame causes an increase in GHG emissions from 502 kg CO 2 e/m
3 of structural
spruce timber (100-year time frame) to 3220 kg CO 2 e/m
3 . For this reason, Ocko
13
countries due to the significant increase of land intended for agricultural use. Maciel
et al. [45] highlighted the importance of dLUC inclusion in the CF analysis of soybean
cultivation in Brazil, showing that the GHG emissions increases up to 205% when
the contribution of carbon emissions from the transformation of 15.4% of land from
grassland to farming is considered. Papong et al. [62] evaluated the GHG emissions of
bioethanol production from cassava and molasses in Thailand, excluding or including
dLUC effect according to two different scenarios (land transformation from perennial
crop to annual crop and from rice field area to annual crop). Results showed that
dLUC emissions can increase the CF of bioethanol from 10 to 73% depends on the
considered scenario.
The assessment of iLUC emissions is an even more challenging mission due to the
influence of the employed model, input data, spatial coverage and scenario assumptions. Among the different approaches, the one elaborated by Schmidt et al. [73] is
one of the most interesting which is based on the assumption of perfect elasticity
in the markets for products dependent on land use and it avoids the amortization of
the GHG emissions by using discounted Global Warming Potentials (GWPs). The
iLUC issue is mainly felt in the biofuels sector due to the expansion of energy crops
in response to increased biofuel demand. At this regard, the European Commission
has included the iLUC emissions in the RED [18], by defining a single factor by
type of crop. However, this approach is too simplified as suggested by Garrain et al.
[24] who proposed origin-dependent iLUC factors. In particular, they analysed the
iLUC impacts caused by an additional demand of biofuel in Spain, showing different
values of GHG emissions of biodiesel and bioethanol from iLUC depend on where
the biofuel is produced.
4.4 Impact of Temporal Dimension
4.4.1 Time Horizon
It is well-known that the results of a CF study are significantly influenced by the
choice of time horizon in the GWP. Typically, in literature the most used time horizon
of the GWP is 100-year, maybe because it was the middle value of the three time
horizons (20, 100, and 500 years) analyzed in the IPCC First Assessment Report
[46]. Time horizon is the time over which the radiative forcing have to be integrated,
therefore a 20-year and 500-year time frames are used to evaluate short and long
term environmental effects, respectively. However there is no scientific reason to
choose 100 year time scale than the other two. Lueddeckens et al. [44] reviewed
that the definition of the time horizon is a subjective decision and it depends on
the goal and scope of the analysis and the interests of stakeholders of the CF study.
De Rosa et al. [70] analyzed the influence of time horizon on the GHG emissions
from the production of sawn spruce timber in Sweden, demonstrating that 20-year
time frame causes an increase in GHG emissions from 502 kg CO 2 e/m
3 of structural
spruce timber (100-year time frame) to 3220 kg CO 2 e/m
3 . For this reason, Ocko